mirror of
https://github.com/chanzuckerberg/cellxgene.git
synced 2026-09-23 10:28:12 +08:00
Merge branch 'csweaver/addbackend'
This commit is contained in:
Charlotte Weaver
+30
@@ -14,3 +14,33 @@ npm-debug.log
|
||||
.vscode
|
||||
|
||||
data
|
||||
|
||||
|
||||
*.idea*
|
||||
|
||||
__pycache__
|
||||
*.DS_Store*
|
||||
|
||||
# Elastic Beanstalk Files
|
||||
.elasticbeanstalk/*
|
||||
!.elasticbeanstalk/*.cfg.yml
|
||||
!.elasticbeanstalk/*.global.yml
|
||||
|
||||
GBM
|
||||
venv
|
||||
extesting
|
||||
|
||||
Dockerfile-*
|
||||
*-data/*
|
||||
data/*
|
||||
runServer.py
|
||||
templates/favicon.png
|
||||
templates/index.html
|
||||
templates/service-worker.js
|
||||
templates/static/*
|
||||
Graph.dot*
|
||||
|
||||
server/app/web/static/css/
|
||||
server/app/web/static/img/
|
||||
server/app/web/static/js/
|
||||
server/app/web/templates/index\.html
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||||
|
||||
@@ -3,7 +3,7 @@
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||||
A React + Redux web application for exploring large scale single cell RNA sequence data.
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||||
|
||||
##### Quickstart:
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||||
|
||||
* `cd client`
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||||
* `npm install`
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||||
* `npm start`
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||||
* `localhost:3000`
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||||
* `localhost:3000`
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||||
@@ -0,0 +1,184 @@
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||||
// jshint esversion: 6
|
||||
|
||||
const BitArray = require("../../src/util/typedCrossfilter/bitArray");
|
||||
const defaultTestLength = 8;
|
||||
|
||||
describe("default select state", () => {
|
||||
test("newly created Bitarray should be deselected", () => {
|
||||
const ba = new BitArray(defaultTestLength);
|
||||
expect(ba).toBeDefined();
|
||||
|
||||
for (let i = 0; i < defaultTestLength; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(false);
|
||||
}
|
||||
|
||||
const dim = ba.allocDimension();
|
||||
expect(dim).toBeDefined();
|
||||
for (let i = 0; i < defaultTestLength; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(false);
|
||||
}
|
||||
|
||||
ba.freeDimension(dim);
|
||||
for (let i = 0; i < defaultTestLength; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(false);
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
describe("select and deselect", () => {
|
||||
test("selectAll and deselectAll", () => {
|
||||
const ba = new BitArray(defaultTestLength);
|
||||
expect(ba).toBeDefined();
|
||||
const dim1 = ba.allocDimension();
|
||||
expect(dim1).toBeDefined();
|
||||
ba.selectAll(dim1);
|
||||
|
||||
for (let i = 0; i < defaultTestLength; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(true);
|
||||
}
|
||||
|
||||
const dim2 = ba.allocDimension();
|
||||
expect(dim2).toBeDefined();
|
||||
for (let i = 0; i < defaultTestLength; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(false);
|
||||
}
|
||||
|
||||
ba.selectAll(dim2);
|
||||
for (let i = 0; i < defaultTestLength; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(true);
|
||||
}
|
||||
|
||||
ba.deselectAll(dim1);
|
||||
for (let i = 0; i < defaultTestLength; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(false);
|
||||
}
|
||||
|
||||
ba.deselectAll(dim2);
|
||||
for (let i = 0; i < defaultTestLength; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(false);
|
||||
}
|
||||
|
||||
ba.selectAll(dim1);
|
||||
ba.selectAll(dim2);
|
||||
for (let i = 0; i < defaultTestLength; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(true);
|
||||
}
|
||||
|
||||
ba.freeDimension(dim1);
|
||||
for (let i = 0; i < defaultTestLength; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(true);
|
||||
}
|
||||
|
||||
ba.freeDimension(dim2);
|
||||
for (let i = 0; i < defaultTestLength; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(false);
|
||||
}
|
||||
});
|
||||
|
||||
test("selectOne and deselectOne", () => {
|
||||
const ba = new BitArray(defaultTestLength);
|
||||
expect(ba).toBeDefined();
|
||||
const dim = ba.allocDimension();
|
||||
expect(dim).toBeDefined();
|
||||
|
||||
ba.selectOne(dim, 0);
|
||||
expect(ba.isSelected(0)).toEqual(true);
|
||||
for (let i = 1; i < defaultTestLength; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(false);
|
||||
}
|
||||
|
||||
ba.deselectOne(dim, 0);
|
||||
for (let i = 0; i < defaultTestLength; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(false);
|
||||
}
|
||||
|
||||
ba.selectOne(dim, 1);
|
||||
expect(ba.isSelected(1)).toEqual(true);
|
||||
expect(ba.isSelected(0)).toEqual(false);
|
||||
for (let i = 2; i < defaultTestLength; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(false);
|
||||
}
|
||||
|
||||
ba.selectAll(dim);
|
||||
ba.deselectOne(dim, defaultTestLength - 1);
|
||||
expect(ba.isSelected(defaultTestLength - 1)).toEqual(false);
|
||||
for (let i = 0; i < defaultTestLength - 1; i++) {
|
||||
expect(ba.isSelected(i)).toEqual(true);
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
describe("selectionCount", () => {
|
||||
test("simple", () => {
|
||||
const ba = new BitArray(defaultTestLength);
|
||||
expect(ba).toBeDefined();
|
||||
const dim1 = ba.allocDimension();
|
||||
expect(dim1).toBeDefined();
|
||||
const dim2 = ba.allocDimension();
|
||||
expect(dim2).toBeDefined();
|
||||
|
||||
expect(ba.selectionCount).toEqual(0);
|
||||
ba.selectAll(dim1);
|
||||
expect(ba.selectionCount).toEqual(0);
|
||||
ba.selectAll(dim2);
|
||||
expect(ba.selectionCount).toEqual(defaultTestLength);
|
||||
|
||||
for (let i = 0; i < defaultTestLength; i++) {
|
||||
ba.deselectOne(dim1, i);
|
||||
expect(ba.selectionCount).toEqual(defaultTestLength - i - 1);
|
||||
expect(ba.selectionCount).toEqual(ba.countAllOnes());
|
||||
}
|
||||
|
||||
ba.freeDimension(dim1);
|
||||
ba.freeDimension(dim2);
|
||||
});
|
||||
});
|
||||
|
||||
describe("fillBySelection", () => {
|
||||
test("sets values correctly", () => {
|
||||
const ba = new BitArray(defaultTestLength);
|
||||
expect(ba).toBeDefined();
|
||||
const dim = ba.allocDimension();
|
||||
expect(dim).toBeDefined();
|
||||
|
||||
const arr = new Int32Array(defaultTestLength);
|
||||
arr.fill(0);
|
||||
const truth = new Int32Array(defaultTestLength);
|
||||
truth.fill(0);
|
||||
|
||||
// initial state should be deselected
|
||||
ba.fillBySelection(arr, 1, 0);
|
||||
expect(arr).toEqual(expect.not.arrayContaining([1]));
|
||||
|
||||
// selectAll
|
||||
ba.selectAll(dim);
|
||||
ba.fillBySelection(arr, 1, 0);
|
||||
expect(arr).toEqual(expect.not.arrayContaining([0]));
|
||||
|
||||
// deselectOne
|
||||
ba.deselectOne(dim, 3);
|
||||
ba.fillBySelection(arr, 1, 0);
|
||||
truth.fill(1);
|
||||
truth[3] = 0;
|
||||
expect(arr).toEqual(truth);
|
||||
|
||||
// deselectAll
|
||||
ba.deselectAll(dim);
|
||||
ba.fillBySelection(arr, 1, 0);
|
||||
truth.fill(0);
|
||||
expect(arr).toEqual(truth);
|
||||
|
||||
// selectOne
|
||||
ba.selectOne(dim, 5);
|
||||
ba.fillBySelection(arr, 6, 1);
|
||||
truth.fill(1);
|
||||
truth[5] = 6;
|
||||
expect(arr).toEqual(truth);
|
||||
|
||||
// should be deselected after dimension disposal
|
||||
ba.freeDimension(dim);
|
||||
ba.fillBySelection(arr, 3, 9);
|
||||
truth.fill(9);
|
||||
expect(arr).toEqual(truth);
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,140 @@
|
||||
// jshint esversion: 6
|
||||
|
||||
const PositiveIntervals = require("../../src/util/typedCrossfilter/positiveIntervals");
|
||||
|
||||
describe("canonicalize", () => {
|
||||
test("empty", () => {
|
||||
expect(PositiveIntervals.canonicalize([])).toEqual([]);
|
||||
});
|
||||
|
||||
test("simple, already correct", () => {
|
||||
expect(PositiveIntervals.canonicalize([[0, 1]])).toEqual([[0, 1]]);
|
||||
expect(PositiveIntervals.canonicalize([[0, 1], [2, 3]])).toEqual([
|
||||
[0, 1],
|
||||
[2, 3]
|
||||
]);
|
||||
});
|
||||
|
||||
test("non-canonical, need to be canonicalized", () => {
|
||||
expect(PositiveIntervals.canonicalize([[0, 1], [1, 2]])).toEqual([[0, 2]]);
|
||||
expect(PositiveIntervals.canonicalize([[1, 2], [2, 3]])).toEqual([[1, 3]]);
|
||||
});
|
||||
});
|
||||
|
||||
describe("union", () => {
|
||||
test("empty range", () => {
|
||||
expect(PositiveIntervals.union([], [])).toEqual([]);
|
||||
expect(PositiveIntervals.union([], [[1, 2]])).toEqual([[1, 2]]);
|
||||
expect(PositiveIntervals.union([], [[1, 2], [3, 4]])).toEqual([
|
||||
[1, 2],
|
||||
[3, 4]
|
||||
]);
|
||||
expect(PositiveIntervals.union([[3, 4]], [])).toEqual([[3, 4]]);
|
||||
expect(PositiveIntervals.union([[1, 2], [3, 4]], [])).toEqual([
|
||||
[1, 2],
|
||||
[3, 4]
|
||||
]);
|
||||
expect(PositiveIntervals.union([[3, 3]], [])).toEqual([[3, 3]]);
|
||||
expect(PositiveIntervals.union([], [[3, 3]])).toEqual([[3, 3]]);
|
||||
});
|
||||
|
||||
test("simple ranges", () => {
|
||||
expect(PositiveIntervals.union([[1, 2]], [[2, 3]])).toEqual([[1, 3]]);
|
||||
expect(PositiveIntervals.union([[2, 3]], [[1, 2]])).toEqual([[1, 3]]);
|
||||
expect(PositiveIntervals.union([[1, 2]], [[3, 4]])).toEqual([
|
||||
[1, 2],
|
||||
[3, 4]
|
||||
]);
|
||||
expect(
|
||||
PositiveIntervals.union([[1, 2], [3, 4]], [[6, 7], [19, 40]])
|
||||
).toEqual([[1, 2], [3, 4], [6, 7], [19, 40]]);
|
||||
expect(PositiveIntervals.union([[1, 4]], [[1, 1], [3, 4]])).toEqual([
|
||||
[1, 4]
|
||||
]);
|
||||
expect(PositiveIntervals.union([[3, 3]], [[4, 4]])).toEqual([
|
||||
[3, 3],
|
||||
[4, 4]
|
||||
]);
|
||||
});
|
||||
});
|
||||
|
||||
describe("intersection", () => {
|
||||
test("empty range", () => {
|
||||
expect(PositiveIntervals.intersection([], [])).toEqual([]);
|
||||
expect(PositiveIntervals.intersection([], [[1, 2]])).toEqual([]);
|
||||
expect(PositiveIntervals.intersection([[1, 2]], [])).toEqual([]);
|
||||
});
|
||||
|
||||
test("simple", () => {
|
||||
expect(PositiveIntervals.intersection([[1, 2]], [[2, 3]])).toEqual([]);
|
||||
expect(PositiveIntervals.intersection([[2, 3]], [[1, 2]])).toEqual([]);
|
||||
expect(PositiveIntervals.intersection([[1, 10]], [[1, 10]])).toEqual([
|
||||
[1, 10]
|
||||
]);
|
||||
expect(PositiveIntervals.intersection([[1, 10]], [[2, 8]])).toEqual([
|
||||
[2, 8]
|
||||
]);
|
||||
expect(PositiveIntervals.intersection([[2, 8]], [[1, 10]])).toEqual([
|
||||
[2, 8]
|
||||
]);
|
||||
expect(PositiveIntervals.intersection([[1, 10]], [[2, 12]])).toEqual([
|
||||
[2, 10]
|
||||
]);
|
||||
expect(PositiveIntervals.intersection([[2, 12]], [[1, 10]])).toEqual([
|
||||
[2, 10]
|
||||
]);
|
||||
expect(PositiveIntervals.intersection([[1, 10]], [[1, 8]])).toEqual([
|
||||
[1, 8]
|
||||
]);
|
||||
expect(PositiveIntervals.intersection([[1, 8]], [[1, 10]])).toEqual([
|
||||
[1, 8]
|
||||
]);
|
||||
expect(PositiveIntervals.intersection([[1, 10]], [[1, 2], [6, 9]])).toEqual(
|
||||
[[1, 2], [6, 9]]
|
||||
);
|
||||
expect(PositiveIntervals.intersection([[0, 2638]], [[1363, 2638]])).toEqual(
|
||||
[[1363, 2638]]
|
||||
);
|
||||
expect(PositiveIntervals.intersection([[1, 2]], [[1, 2]])).toEqual([
|
||||
[1, 2]
|
||||
]);
|
||||
});
|
||||
});
|
||||
|
||||
describe("difference", () => {
|
||||
test("empty", () => {
|
||||
expect(PositiveIntervals.difference([], [])).toEqual([]);
|
||||
expect(PositiveIntervals.difference([], [[1, 10]])).toEqual([]);
|
||||
expect(PositiveIntervals.difference([[1, 10]], [])).toEqual([[1, 10]]);
|
||||
});
|
||||
|
||||
test("simple", () => {
|
||||
expect(PositiveIntervals.difference([[1, 2], [3, 4]], [])).toEqual([
|
||||
[1, 2],
|
||||
[3, 4]
|
||||
]);
|
||||
expect(PositiveIntervals.difference([[1, 2], [3, 10]], [[5, 10]])).toEqual([
|
||||
[1, 2],
|
||||
[3, 5]
|
||||
]);
|
||||
expect(PositiveIntervals.difference([[1, 2], [3, 10]], [[0, 5]])).toEqual([
|
||||
[5, 10]
|
||||
]);
|
||||
expect(
|
||||
PositiveIntervals.difference([[0, 2638]], [[0, 1363], [2055, 2638]])
|
||||
).toEqual([[1363, 2055]]);
|
||||
expect(
|
||||
PositiveIntervals.difference([[0, 1363], [2055, 2638]], [[0, 2638]])
|
||||
).toEqual([]);
|
||||
expect(PositiveIntervals.difference([[0, 10]], [[0, 1]])).toEqual([
|
||||
[1, 10]
|
||||
]);
|
||||
expect(PositiveIntervals.difference([[0, 10]], [[1, 2]])).toEqual([
|
||||
[0, 1],
|
||||
[2, 10]
|
||||
]);
|
||||
expect(PositiveIntervals.difference([[0, 10]], [[9, 10]])).toEqual([
|
||||
[0, 9]
|
||||
]);
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,276 @@
|
||||
// jshint esversion: 6
|
||||
const _ = require("lodash");
|
||||
const crossfilter = require("../../src/util/typedCrossfilter");
|
||||
|
||||
const someData = [
|
||||
{
|
||||
date: "2011-11-14T16:17:54Z",
|
||||
quantity: 2,
|
||||
total: 190,
|
||||
tip: 100,
|
||||
type: "tab",
|
||||
productIDs: ["001"]
|
||||
},
|
||||
{
|
||||
date: "2011-11-14T16:20:19Z",
|
||||
quantity: 2,
|
||||
total: 190,
|
||||
tip: 100,
|
||||
type: "tab",
|
||||
productIDs: ["001", "005"]
|
||||
},
|
||||
{
|
||||
date: "2011-11-14T16:28:54Z",
|
||||
quantity: 1,
|
||||
total: 300,
|
||||
tip: 200,
|
||||
type: "visa",
|
||||
productIDs: ["004", "005"]
|
||||
},
|
||||
{
|
||||
date: "2011-11-14T16:30:43Z",
|
||||
quantity: 2,
|
||||
total: 90,
|
||||
tip: 0,
|
||||
type: "tab",
|
||||
productIDs: ["001", "002"]
|
||||
},
|
||||
{
|
||||
date: "2011-11-14T16:48:46Z",
|
||||
quantity: 2,
|
||||
total: 90,
|
||||
tip: 0,
|
||||
type: "tab",
|
||||
productIDs: ["005"]
|
||||
},
|
||||
{
|
||||
date: "2011-11-14T16:53:41Z",
|
||||
quantity: 2,
|
||||
total: 90,
|
||||
tip: 0,
|
||||
type: "tab",
|
||||
productIDs: ["001", "004", "005"]
|
||||
},
|
||||
{
|
||||
date: "2011-11-14T16:54:06Z",
|
||||
quantity: 1,
|
||||
total: 100,
|
||||
tip: 0,
|
||||
type: "cash",
|
||||
productIDs: ["001", "002", "003", "004", "005"]
|
||||
},
|
||||
{
|
||||
date: "2011-11-14T16:58:03Z",
|
||||
quantity: 2,
|
||||
total: 90,
|
||||
tip: 0,
|
||||
type: "tab",
|
||||
productIDs: ["001"]
|
||||
},
|
||||
{
|
||||
date: "2011-11-14T17:07:21Z",
|
||||
quantity: 2,
|
||||
total: 90,
|
||||
tip: 0,
|
||||
type: "tab",
|
||||
productIDs: ["004", "005"]
|
||||
},
|
||||
{
|
||||
date: "2011-11-14T17:22:59Z",
|
||||
quantity: 2,
|
||||
total: 90,
|
||||
tip: 0,
|
||||
type: "tab",
|
||||
productIDs: ["001", "002", "004", "005"]
|
||||
},
|
||||
{
|
||||
date: "2011-11-14T17:25:45Z",
|
||||
quantity: 2,
|
||||
total: 200,
|
||||
tip: 0,
|
||||
type: "cash",
|
||||
productIDs: ["002"]
|
||||
},
|
||||
{
|
||||
date: "2011-11-14T17:29:52Z",
|
||||
quantity: 1,
|
||||
total: 200,
|
||||
tip: 100,
|
||||
type: "visa",
|
||||
productIDs: ["004"]
|
||||
}
|
||||
];
|
||||
|
||||
var payments = null;
|
||||
beforeEach(() => {
|
||||
payments = crossfilter(someData);
|
||||
});
|
||||
|
||||
describe("typedCrossfilter", () => {
|
||||
test("alloc and free", () => {
|
||||
expect(payments).toBeDefined();
|
||||
expect(payments.size()).toEqual(someData.length);
|
||||
expect(payments.all()).toEqual(someData);
|
||||
|
||||
const quantity = payments.dimension(r => r.quantity, Int32Array);
|
||||
expect(quantity).toBeDefined();
|
||||
expect(quantity.id()).toBeDefined();
|
||||
|
||||
quantity.dispose();
|
||||
expect(payments.size()).toEqual(someData.length);
|
||||
expect(payments.all()).toEqual(someData);
|
||||
});
|
||||
|
||||
test("filterAll and filterNone", () => {
|
||||
expect(payments).toBeDefined();
|
||||
const quantity = payments.dimension(r => r.quantity, Int32Array);
|
||||
const tip = payments.dimension(r => r.tip, Float32Array);
|
||||
const total = payments.dimension(r => r.total, Float32Array);
|
||||
const type = payments.dimension(r => r.type, "enum");
|
||||
|
||||
expect(quantity).toBeDefined();
|
||||
expect(tip).toBeDefined();
|
||||
expect(total).toBeDefined();
|
||||
expect(type).toBeDefined();
|
||||
|
||||
// initially, all should be filtered
|
||||
expect(payments.allFiltered().length).toEqual(payments.size());
|
||||
expect(payments.allFiltered()).toEqual(payments.all());
|
||||
expect(payments.countFiltered()).toEqual(someData.length);
|
||||
|
||||
// filterAll
|
||||
tip.filterAll(); // should change nothing
|
||||
expect(payments.allFiltered()).toEqual(payments.all());
|
||||
expect(payments.countFiltered()).toEqual(someData.length);
|
||||
|
||||
// ditto
|
||||
total.filterAll();
|
||||
expect(payments.allFiltered()).toEqual(payments.all());
|
||||
expect(payments.countFiltered()).toEqual(someData.length);
|
||||
|
||||
// filterNone
|
||||
type.filterNone();
|
||||
expect(payments.allFiltered()).toEqual([]);
|
||||
expect(payments.countFiltered()).toEqual(0);
|
||||
|
||||
quantity.filterNone();
|
||||
expect(payments.allFiltered()).toEqual([]);
|
||||
expect(payments.countFiltered()).toEqual(0);
|
||||
|
||||
// invert the first none; should have no effect because type is
|
||||
// still not filtered
|
||||
quantity.filterAll();
|
||||
expect(payments.allFiltered()).toEqual([]);
|
||||
expect(payments.countFiltered()).toEqual(0);
|
||||
|
||||
// filter all of type; should select all
|
||||
type.filterAll();
|
||||
expect(payments.allFiltered()).toEqual(payments.all());
|
||||
expect(payments.countFiltered()).toEqual(payments.size());
|
||||
});
|
||||
|
||||
test("filterExact", () => {
|
||||
expect(payments).toBeDefined();
|
||||
const quantity = payments.dimension(r => r.quantity, Int32Array);
|
||||
const tip = payments.dimension(r => r.tip, Float32Array);
|
||||
const total = payments.dimension(r => r.total, Float32Array);
|
||||
const type = payments.dimension(r => r.type, "enum");
|
||||
|
||||
quantity.filterExact(1);
|
||||
expect(payments.countFiltered()).toEqual(
|
||||
_.countBy(someData, "quantity")[1]
|
||||
);
|
||||
expect(payments.allFiltered()).toEqual(_.filter(someData, { quantity: 1 }));
|
||||
|
||||
tip.filterExact(0);
|
||||
expect(payments.allFiltered()).toEqual(
|
||||
_.filter(someData, { tip: 0, quantity: 1 })
|
||||
);
|
||||
|
||||
type.filterExact("cash");
|
||||
expect(payments.allFiltered()).toEqual(
|
||||
_.filter(someData, { tip: 0, quantity: 1, type: "cash" })
|
||||
);
|
||||
});
|
||||
|
||||
test("filterRange", () => {
|
||||
expect(payments).toBeDefined();
|
||||
const quantity = payments.dimension(r => r.quantity, Int32Array);
|
||||
const tip = payments.dimension(r => r.tip, Float32Array);
|
||||
const total = payments.dimension(r => r.total, Float32Array);
|
||||
const type = payments.dimension(r => r.type, "enum");
|
||||
|
||||
tip.filterRange([0, 91]);
|
||||
expect(payments.allFiltered()).toEqual(
|
||||
_(someData)
|
||||
.filter(r => r.tip >= 0 && r.tip < 91)
|
||||
.value()
|
||||
);
|
||||
|
||||
tip.filterRange([0, 90]);
|
||||
expect(payments.allFiltered()).toEqual(
|
||||
_(someData)
|
||||
.filter(r => r.tip >= 0 && r.tip < 90)
|
||||
.value()
|
||||
);
|
||||
|
||||
tip.filterRange([1, 90]);
|
||||
expect(payments.allFiltered()).toEqual(
|
||||
_(someData)
|
||||
.filter(r => r.tip >= 1 && r.tip < 91)
|
||||
.value()
|
||||
);
|
||||
});
|
||||
|
||||
test("filterEnum", () => {
|
||||
expect(payments).toBeDefined();
|
||||
const quantity = payments.dimension(r => r.quantity, Int32Array);
|
||||
const tip = payments.dimension(r => r.tip, Float32Array);
|
||||
const total = payments.dimension(r => r.total, Float32Array);
|
||||
const type = payments.dimension(r => r.type, "enum");
|
||||
|
||||
type.filterEnum(["tab", "cash"]);
|
||||
expect(payments.allFiltered()).toEqual(
|
||||
_(someData)
|
||||
.filter(r => r.type === "cash" || r.type === "tab")
|
||||
.value()
|
||||
);
|
||||
|
||||
tip.filterEnum([0, 100]);
|
||||
expect(payments.allFiltered()).toEqual(
|
||||
_(someData)
|
||||
.filter(r => r.type === "cash" || r.type === "tab")
|
||||
.filter(r => r.tip === 0 || r.tip === 100)
|
||||
.value()
|
||||
);
|
||||
});
|
||||
|
||||
test("more than 32 dimensions", () => {
|
||||
expect(payments).toBeDefined();
|
||||
const quantity = payments.dimension(r => r.quantity, Int32Array);
|
||||
const tip = payments.dimension(r => r.tip, Float32Array);
|
||||
const total = payments.dimension(r => r.total, Float32Array);
|
||||
const type = payments.dimension(r => r.type, "enum");
|
||||
|
||||
// Create a bunch of fake dimensions to ensure we can handle > 32
|
||||
let dimMap = {};
|
||||
for (let i = 0; i < 65; i++) {
|
||||
dimMap[i] = payments.dimension(r => Math.random(), Float32Array);
|
||||
expect(dimMap[i]).toBeDefined();
|
||||
expect(dimMap[i].id()).toBeDefined();
|
||||
}
|
||||
|
||||
// everything should start as selected/filtered
|
||||
expect(payments.countFiltered()).toEqual(someData.length);
|
||||
|
||||
dimMap[0].filterAll();
|
||||
dimMap[64].filterAll();
|
||||
expect(payments.countFiltered()).toEqual(someData.length);
|
||||
|
||||
dimMap[33].filterNone();
|
||||
expect(payments.allFiltered()).toEqual([]);
|
||||
|
||||
dimMap[33].filterAll();
|
||||
expect(payments.allFiltered()).toEqual(someData);
|
||||
});
|
||||
});
|
||||
|
Before Width: | Height: | Size: 43 KiB After Width: | Height: | Size: 43 KiB |
@@ -115,6 +115,8 @@
|
||||
"whatwg-fetch": "^2.0.1"
|
||||
},
|
||||
"jest": {
|
||||
"testMatch": ["**/__tests__/**/?(*.)(spec|test).js?(x)"]
|
||||
"testMatch": [
|
||||
"**/__tests__/**/?(*.)(spec|test).js?(x)"
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -2,7 +2,7 @@
|
||||
import _ from "lodash";
|
||||
import { parseRGB } from "../util/parseRGB";
|
||||
import { createSchemaByDataSniffing } from "../util/schema";
|
||||
var crossfilter = require("../util/typedCrossfilter");
|
||||
import crossfilter from "../util/typedCrossfilter";
|
||||
|
||||
// Deduce the correct crossfilter dimension type from a metadata
|
||||
// schema description.
|
||||
+1
-1
@@ -226,4 +226,4 @@ class BitArray {
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = BitArray;
|
||||
export default BitArray;
|
||||
@@ -29,9 +29,9 @@ more complex API. In a few cases, elements of that API were incorporated.
|
||||
|
||||
*/
|
||||
|
||||
var PositiveIntervals = require("./positiveIntervals");
|
||||
var BitArray = require("./bitArray");
|
||||
var Util = require("./util");
|
||||
import PositiveIntervals from "./positiveIntervals";
|
||||
import BitArray from "./bitArray";
|
||||
import {fillRange, lowerBound, lowerBoundIndirect, upperBound, upperBoundIndirect} from "./util";
|
||||
|
||||
class TypedCrossfilter {
|
||||
constructor(data) {
|
||||
@@ -118,7 +118,7 @@ class ScalarDimension {
|
||||
this.value = array;
|
||||
|
||||
// create sort index
|
||||
this.index = Util.fillRange(new Uint32Array(this.crossfilter.data.length));
|
||||
this.index = fillRange(new Uint32Array(this.crossfilter.data.length));
|
||||
this.index.sort((a, b) => array[a] - array[b]);
|
||||
}
|
||||
|
||||
@@ -179,14 +179,14 @@ class ScalarDimension {
|
||||
// filter by value - exact match
|
||||
filterExact(value) {
|
||||
const newFilter = [
|
||||
Util.lowerBoundIndirect(
|
||||
lowerBoundIndirect(
|
||||
this.value,
|
||||
this.index,
|
||||
value,
|
||||
0,
|
||||
this.value.length
|
||||
),
|
||||
Util.upperBoundIndirect(
|
||||
upperBoundIndirect(
|
||||
this.value,
|
||||
this.index,
|
||||
value,
|
||||
@@ -207,14 +207,14 @@ class ScalarDimension {
|
||||
const newFilter = [];
|
||||
for (let v = 0, len = values.length; v < len; v++) {
|
||||
const intv = [
|
||||
Util.lowerBoundIndirect(
|
||||
lowerBoundIndirect(
|
||||
this.value,
|
||||
this.index,
|
||||
values[v],
|
||||
0,
|
||||
this.value.length
|
||||
),
|
||||
Util.upperBoundIndirect(
|
||||
upperBoundIndirect(
|
||||
this.value,
|
||||
this.index,
|
||||
values[v],
|
||||
@@ -233,14 +233,14 @@ class ScalarDimension {
|
||||
filterRange(range) {
|
||||
const newFilter = [];
|
||||
const intv = [
|
||||
Util.lowerBoundIndirect(
|
||||
lowerBoundIndirect(
|
||||
this.value,
|
||||
this.index,
|
||||
range[0],
|
||||
0,
|
||||
this.value.length
|
||||
),
|
||||
Util.upperBoundIndirect(
|
||||
upperBoundIndirect(
|
||||
this.value,
|
||||
this.index,
|
||||
range[1],
|
||||
@@ -350,7 +350,7 @@ class EnumDimension extends ScalarDimension {
|
||||
const enumLen = this.enumIndex.length;
|
||||
for (let i = 0; i < len; i++) {
|
||||
const v = value(data[i]);
|
||||
const e = Util.lowerBound(this.enumIndex, v, 0, enumLen);
|
||||
const e = lowerBound(this.enumIndex, v, 0, enumLen);
|
||||
array[i] = e;
|
||||
}
|
||||
return array;
|
||||
@@ -358,14 +358,14 @@ class EnumDimension extends ScalarDimension {
|
||||
|
||||
filterExact(value) {
|
||||
return super.filterExact(
|
||||
Util.lowerBound(this.enumIndex, value, 0, this.enumIndex.length)
|
||||
lowerBound(this.enumIndex, value, 0, this.enumIndex.length)
|
||||
);
|
||||
}
|
||||
|
||||
filterEnum(values) {
|
||||
return super.filterEnum(
|
||||
values.map(v =>
|
||||
Util.lowerBound(this.enumIndex, v, 0, this.enumIndex.length)
|
||||
lowerBound(this.enumIndex, v, 0, this.enumIndex.length)
|
||||
)
|
||||
);
|
||||
}
|
||||
@@ -373,7 +373,7 @@ class EnumDimension extends ScalarDimension {
|
||||
filterRange(range) {
|
||||
return super.filterEnum(
|
||||
range.map(v =>
|
||||
Util.lowerBound(this.enumIndex, v, 0, this.enumIndex.length)
|
||||
lowerBound(this.enumIndex, v, 0, this.enumIndex.length)
|
||||
)
|
||||
);
|
||||
}
|
||||
@@ -391,4 +391,4 @@ crossfilter.TypedCrossfilter = TypedCrossfilter;
|
||||
crossfilter.ScalarDimension = ScalarDimension;
|
||||
crossfilter.EnumDimension = EnumDimension;
|
||||
|
||||
module.exports = crossfilter;
|
||||
export default crossfilter;
|
||||
+1
-1
@@ -129,4 +129,4 @@ class PositiveIntervals {
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = PositiveIntervals;
|
||||
export default PositiveIntervals;
|
||||
@@ -8,7 +8,7 @@
|
||||
// fill an array or typedarray with a sequential range of numbers,
|
||||
// starting with `start`
|
||||
//
|
||||
function fillRange(arr, start = 0) {
|
||||
export function fillRange(arr, start = 0) {
|
||||
for (let i = 0, len = arr.length; i < len; i++) {
|
||||
arr[i] = i + start;
|
||||
}
|
||||
@@ -30,7 +30,7 @@ function fillRange(arr, start = 0) {
|
||||
// a factory version of lowerBound that takes an accessor (rather than having
|
||||
// a special-cased version for lining the indirection).
|
||||
//
|
||||
function lowerBound(valueArray, value, first, last) {
|
||||
export function lowerBound(valueArray, value, first, last) {
|
||||
// this is just a binary search
|
||||
while (first < last) {
|
||||
const middle = (first + last) >>> 1;
|
||||
@@ -45,7 +45,7 @@ function lowerBound(valueArray, value, first, last) {
|
||||
|
||||
// Inlined performance optimization - used to indirect through a sort map.
|
||||
//
|
||||
function lowerBoundIndirect(valueArray, indexArray, value, first, last) {
|
||||
export function lowerBoundIndirect(valueArray, indexArray, value, first, last) {
|
||||
// this is just a binary search
|
||||
while (first < last) {
|
||||
const middle = (first + last) >>> 1;
|
||||
@@ -69,7 +69,7 @@ function lowerBoundIndirect(valueArray, indexArray, value, first, last) {
|
||||
// C++: upper_bound()
|
||||
// Python: bisect.bisect_right()
|
||||
//
|
||||
function upperBound(valueArray, value, first, last) {
|
||||
export function upperBound(valueArray, value, first, last) {
|
||||
// this is just a binary search
|
||||
while (first < last) {
|
||||
const middle = (first + last) >>> 1;
|
||||
@@ -84,7 +84,7 @@ function upperBound(valueArray, value, first, last) {
|
||||
|
||||
// Inline performance optimization
|
||||
//
|
||||
function upperBoundIndirect(valueArray, indexArray, value, first, last) {
|
||||
export function upperBoundIndirect(valueArray, indexArray, value, first, last) {
|
||||
// this is just a binary search
|
||||
while (first < last) {
|
||||
const middle = (first + last) >>> 1;
|
||||
@@ -96,11 +96,3 @@ function upperBoundIndirect(valueArray, indexArray, value, first, last) {
|
||||
}
|
||||
return first;
|
||||
}
|
||||
|
||||
module.exports = {
|
||||
fillRange,
|
||||
lowerBound,
|
||||
lowerBoundIndirect,
|
||||
upperBound,
|
||||
upperBoundIndirect
|
||||
};
|
||||
@@ -0,0 +1,55 @@
|
||||
import os
|
||||
|
||||
from flask import Flask
|
||||
from flask_compress import Compress
|
||||
from flask_cors import CORS
|
||||
from flask_restful_swagger_2 import get_swagger_blueprint
|
||||
|
||||
from .web import webapp
|
||||
from .rest_api.rest import get_api_resources
|
||||
|
||||
app = Flask(__name__)
|
||||
Compress(app)
|
||||
CORS(app)
|
||||
|
||||
# Config
|
||||
CXG_DIR = os.environ.get("CXG_DIRECTORY", default="/Users/charlotteweaver/Documents/Git/cxg-v2/data/")
|
||||
SECRET_KEY = os.environ.get("CXG_SECRET_KEY", default="SparkleAndShine")
|
||||
ENGINE = os.environ.get("CXG_ENGINE", default="scanpy")
|
||||
TITLE = os.environ.get("DATASET_TITLE", default="PBMC 3K")
|
||||
# TODO remove the 2 when this is prod
|
||||
CXG_API_BASE = os.environ.get("CXG_API_BASE2", default="http://0.0.0.0:5005/api/")
|
||||
|
||||
app.config.update(
|
||||
SECRET_KEY=SECRET_KEY,
|
||||
CXG_API_BASE=CXG_API_BASE,
|
||||
ENGINE=ENGINE,
|
||||
DATA=CXG_DIR,
|
||||
DATASET_TITLE=TITLE
|
||||
)
|
||||
|
||||
app.config["PROFILE"] = True
|
||||
# app.wsgi_app = ProfilerMiddleware(app.wsgi_app, restrictions=[15])
|
||||
|
||||
# Application Data
|
||||
data = None
|
||||
if app.config["ENGINE"] == "scanpy":
|
||||
from .scanpy_engine.scanpy_engine import ScanpyEngine
|
||||
data = ScanpyEngine(app.config["DATA"], schema="data_schema.json")
|
||||
|
||||
REACTIVE_LIMIT = 1_000_000
|
||||
|
||||
# A list of swagger document objects
|
||||
docs = []
|
||||
resources = get_api_resources()
|
||||
docs.append(resources.get_swagger_doc())
|
||||
|
||||
|
||||
app.register_blueprint(webapp.bp)
|
||||
app.register_blueprint(resources.blueprint)
|
||||
app.register_blueprint(
|
||||
get_swagger_blueprint(docs, "/api/swagger", produces=["application/json"], title="cellxgene rest api",
|
||||
description="An API connecting ExpressionMatrix2 clustering algorithm to cellxgene"))
|
||||
|
||||
|
||||
app.add_url_rule("/", endpoint="index")
|
||||
@@ -0,0 +1,81 @@
|
||||
from abc import ABCMeta, abstractmethod
|
||||
|
||||
|
||||
class CXGDriver(metaclass=ABCMeta):
|
||||
def __init__(self, data, schema=None, graph_method=None, diffexp_method=None):
|
||||
self.data = self._load_data(data)
|
||||
|
||||
@staticmethod
|
||||
@abstractmethod
|
||||
def _load_data(data):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def _load_or_infer_schema(data):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def cells(self):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def genes(self):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def filter_cells(self, filter):
|
||||
"""
|
||||
Filter cells from data and return a subset of the data
|
||||
A filter is a dictionary where the key is a metadatata category
|
||||
Value is dictionary
|
||||
value_type: int, float, string
|
||||
variable_type: continuous, categorical
|
||||
query: filter value, for categorical [val1, val2], for continuous {min: x, max:y}
|
||||
Filters are combined with the and operator
|
||||
:param filter:
|
||||
:return: filtered dataframe
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def metadata(self, df, fields=None):
|
||||
"""
|
||||
Gets metadata key:value for each cells
|
||||
|
||||
:param df: from filter_cells, dataframe
|
||||
:param fields: list of keys for metadata to return, returns all metadata values if not set.
|
||||
:return: list of metadata values
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def create_graph(self, df):
|
||||
"""
|
||||
Computes a n-d layout for cells through dimensionality reduction.
|
||||
:param df: from filter_cells, dataframe
|
||||
:return: [cellid, x, y]
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def diffexp(self, df1, df2):
|
||||
"""
|
||||
Computes the top differentially expressed genes between two clusters
|
||||
:param df1: from filter_cells, dataframe containing first set of cells
|
||||
:param df2: from filter_cells, dataframe containing second set of cells
|
||||
:return: top genes, stats and expression values for top genes
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def expression(self, df):
|
||||
"""
|
||||
Retrieves expression for each gene for cells in data frame
|
||||
:param df:
|
||||
:return: {
|
||||
"genes": list of genes,
|
||||
"cells": list of cells and expression list,
|
||||
"nonzero_gene_count": number of nonzero genes
|
||||
}
|
||||
"""
|
||||
pass
|
||||
@@ -0,0 +1,439 @@
|
||||
from flask import (
|
||||
Blueprint, request
|
||||
)
|
||||
from flask_restful_swagger_2 import Api, swagger, Resource
|
||||
|
||||
from ..util.utils import make_payload
|
||||
from ..util.filter import parse_filter
|
||||
|
||||
|
||||
class InitializeAPI(Resource):
|
||||
@swagger.doc({
|
||||
"summary": "get metadata schema, ranges for values, and cell count to initialize cellxgene app",
|
||||
"tags": ["initialize"],
|
||||
"parameters": [],
|
||||
"responses": {
|
||||
"200": {
|
||||
"description": "initialization data for UI",
|
||||
"examples": {
|
||||
"application/json": {
|
||||
"data": {
|
||||
"cellcount": 3589,
|
||||
"options": {
|
||||
"Sample.type": {
|
||||
"options": {
|
||||
"Glioblastoma": 3589
|
||||
}
|
||||
},
|
||||
"Selection": {
|
||||
"options": {
|
||||
"Astrocytes(HEPACAM)": 714,
|
||||
"Endothelial(BSC)": 123,
|
||||
"Microglia(CD45)": 1108,
|
||||
"Neurons(Thy1)": 685,
|
||||
"Oligodendrocytes(GC)": 294,
|
||||
"Unpanned": 665
|
||||
}
|
||||
},
|
||||
"Splice_sites_AT.AC": {
|
||||
"range": {
|
||||
"max": 1025,
|
||||
"min": 152
|
||||
}
|
||||
},
|
||||
"Splice_sites_Annotated": {
|
||||
"range": {
|
||||
"max": 1075869,
|
||||
"min": 26
|
||||
}
|
||||
}
|
||||
},
|
||||
"schema": {
|
||||
"CellName": {
|
||||
"displayname": "Name",
|
||||
"type": "string",
|
||||
"variabletype": "categorical"
|
||||
},
|
||||
"Class": {
|
||||
"displayname": "Class",
|
||||
"type": "string",
|
||||
"variabletype": "categorical"
|
||||
},
|
||||
"ERCC_reads": {
|
||||
"displayname": "ERCC Reads",
|
||||
"type": "int",
|
||||
"variabletype": "continuous"
|
||||
},
|
||||
"ERCC_to_non_ERCC": {
|
||||
"displayname": "ERCC:Non-ERCC",
|
||||
"type": "float",
|
||||
"variabletype": "continuous"
|
||||
},
|
||||
"Genes_detected": {
|
||||
"displayname": "Genes Detected",
|
||||
"type": "int",
|
||||
"variabletype": "continuous"
|
||||
}
|
||||
},
|
||||
"genes": ["1/2-SBSRNA4", "A1BG", "A1BG-AS1"]
|
||||
|
||||
},
|
||||
"status": {
|
||||
"error": False,
|
||||
"errormessage": ""
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
})
|
||||
def get(self):
|
||||
from app import data, REACTIVE_LIMIT
|
||||
return make_payload({
|
||||
"schema": data.schema,
|
||||
"cellcount": data.cell_count,
|
||||
"reactivelimit": REACTIVE_LIMIT,
|
||||
"genes": data.genes(),
|
||||
"ranges": data.metadata_ranges(),
|
||||
|
||||
})
|
||||
|
||||
|
||||
class CellsAPI(Resource):
|
||||
@swagger.doc({
|
||||
"summary": "filter based on metadata fields to get a subset cells, expression data, and metadata",
|
||||
"tags": ["cells"],
|
||||
"description": "Cells takes query parameters defined in the schema retrieved from the /initialize enpoint. "
|
||||
"<br>For categorical metadata keys filter based on `key=value` <br>"
|
||||
" For continuous metadata keys filter by `key=min,max`<br> Either value "
|
||||
"can be replaced by a \*. To have only a minimum value `key=min,\*` To have only a maximum "
|
||||
"value `key=\*,max` <br>Graph data (if retrieved) is normalized"
|
||||
" To only retrieve cells that don't have a value for the key filter by `key`",
|
||||
"parameters": [],
|
||||
|
||||
"responses": {
|
||||
"200": {
|
||||
"description": "initialization data for UI",
|
||||
"examples": {
|
||||
"application/json": {
|
||||
"data": {
|
||||
"badmetadatacount": 0,
|
||||
"cellcount": 0,
|
||||
"cellids": ["..."],
|
||||
"metadata": [
|
||||
{
|
||||
"CellName": "1001000173.G8",
|
||||
"Class": "Neoplastic",
|
||||
"Cluster_2d": "11",
|
||||
"Cluster_2d_color": "#8C564B",
|
||||
"Cluster_CNV": "1",
|
||||
"Cluster_CNV_color": "#1F77B4",
|
||||
"ERCC_reads": "152104",
|
||||
"ERCC_to_non_ERCC": "0.562454470489481",
|
||||
"Genes_detected": "1962",
|
||||
"Location": "Tumor",
|
||||
"Location.color": "#FF7F0E",
|
||||
"Multimapping_reads_percent": "2.67",
|
||||
"Neoplastic": "Neoplastic",
|
||||
"Non_ERCC_reads": "270429",
|
||||
"Sample.name": "BT_S2",
|
||||
"Sample.name.color": "#AEC7E8",
|
||||
"Sample.type": "Glioblastoma",
|
||||
"Sample.type.color": "#1F77B4",
|
||||
"Selection": "Unpanned",
|
||||
"Selection.color": "#98DF8A",
|
||||
"Splice_sites_AT.AC": "102",
|
||||
"Splice_sites_Annotated": "122397",
|
||||
"Splice_sites_GC.AG": "761",
|
||||
"Splice_sites_GT.AG": "125741",
|
||||
"Splice_sites_non_canonical": "56",
|
||||
"Splice_sites_total": "126660",
|
||||
"Total_reads": "1741039",
|
||||
"Unique_reads": "1400382",
|
||||
"Unique_reads_percent": "80.43",
|
||||
"Unmapped_mismatch": "2.15",
|
||||
"Unmapped_other": "0.18",
|
||||
"Unmapped_short": "14.56",
|
||||
"housekeeping_cluster": "2",
|
||||
"housekeeping_cluster_color": "#AEC7E8",
|
||||
"recluster_myeloid": "NA",
|
||||
"recluster_myeloid_color": "NA"
|
||||
},
|
||||
],
|
||||
"reactive": True,
|
||||
"graph": [
|
||||
[
|
||||
"1001000173.G8",
|
||||
0.93836,
|
||||
0.28623
|
||||
],
|
||||
|
||||
[
|
||||
"1001000173.D4",
|
||||
0.1662,
|
||||
0.79438
|
||||
]
|
||||
|
||||
],
|
||||
"status": {
|
||||
"error": False,
|
||||
"errormessage": ""
|
||||
}
|
||||
|
||||
},
|
||||
}
|
||||
},
|
||||
},
|
||||
|
||||
"400": {
|
||||
"description": "bad query params",
|
||||
}
|
||||
}
|
||||
})
|
||||
def get(self):
|
||||
from app import data
|
||||
payload = {
|
||||
"metadata": [],
|
||||
"cellcount": 0,
|
||||
"graph": [],
|
||||
"ranges": {},
|
||||
}
|
||||
# get query params
|
||||
filter = parse_filter(request.args, data.schema)
|
||||
filtered_data = data.filter_cells(filter)
|
||||
payload["metadata"] = data.metadata(filtered_data)
|
||||
payload["ranges"] = data.metadata_ranges(filtered_data)
|
||||
payload["graph"] = data.create_graph(filtered_data)
|
||||
payload["cellcount"] = data.cell_count
|
||||
return make_payload(payload)
|
||||
|
||||
|
||||
class ExpressionAPI(Resource):
|
||||
@swagger.doc({
|
||||
"summary": "Json with gene list and expression data by cell, limited to first 40 cells",
|
||||
"tags": ["expression"],
|
||||
"parameters": [
|
||||
{
|
||||
"name": "include_unexpressed_genes",
|
||||
"description": "Include genes that have 0 expression across all cells in set",
|
||||
"in": "path",
|
||||
"type": "bool",
|
||||
}
|
||||
],
|
||||
"responses": {
|
||||
"200": {
|
||||
"description": "Json for heatmap",
|
||||
"examples": {
|
||||
"application/json": {
|
||||
"data": {
|
||||
"cells": [
|
||||
{
|
||||
"cellname": "1/2-SBSRNA4",
|
||||
"e": [0, 0, 214, 0, 0]
|
||||
},
|
||||
],
|
||||
"genes": [
|
||||
"1001000173.G8",
|
||||
"1001000173.D4",
|
||||
"1001000173.B4",
|
||||
"1001000173.A2",
|
||||
"1001000173.E2"
|
||||
],
|
||||
"nonzero_gene_count": 2857
|
||||
},
|
||||
"status": {
|
||||
"error": False,
|
||||
"errormessage": ""
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
})
|
||||
def get(self):
|
||||
from app import data
|
||||
expression_data = data.expression()
|
||||
return make_payload(expression_data)
|
||||
|
||||
@swagger.doc({
|
||||
"summary": "Json with gene list and expression data by cell",
|
||||
"tags": ["expression"],
|
||||
"parameters": [
|
||||
{
|
||||
"name": "body",
|
||||
"in": "body",
|
||||
"schema": {
|
||||
"example": {
|
||||
"celllist": ["1001000173.G8", "1001000173.D4"],
|
||||
"genelist": ["1/2-SBSRNA4", "A1BG", "A1BG-AS1", "A1CF", "A2LD1", "A2M", "A2ML1", "A2MP1",
|
||||
"A4GALT"],
|
||||
"include_unexpressed_genes": True,
|
||||
}
|
||||
|
||||
}
|
||||
},
|
||||
],
|
||||
"responses": {
|
||||
"200": {
|
||||
"description": "Json for expressiondata",
|
||||
"examples": {
|
||||
"application/json": {
|
||||
"data": {
|
||||
"cells": [
|
||||
{
|
||||
"cellname": "1001000173.D4",
|
||||
"e": [0, 0]
|
||||
},
|
||||
{
|
||||
"cellname": "1001000173.G8",
|
||||
"e": [0, 0]
|
||||
}
|
||||
],
|
||||
"genes": [
|
||||
"ABCD4",
|
||||
"ZWINT"
|
||||
],
|
||||
"nonzero_gene_count": 2857
|
||||
},
|
||||
"status": {
|
||||
"error": False,
|
||||
"errormessage": ""
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
},
|
||||
"400": {
|
||||
"description": "Required parameter missing/incorrect",
|
||||
}
|
||||
}
|
||||
})
|
||||
def post(self):
|
||||
from app import data
|
||||
args = request.get_json()
|
||||
cell_list = args.get("celllist", [])
|
||||
gene_list = args.get("genelist", [])
|
||||
if not cell_list and not gene_list:
|
||||
return make_payload([], "must include celllist and/or genelist parameter", 400)
|
||||
|
||||
expression_data = data.expression(cell_list, gene_list)
|
||||
|
||||
if cell_list and len(expression_data["cells"]) < len(cell_list):
|
||||
return make_payload([], "Some cell ids not available", 400)
|
||||
if gene_list and len(expression_data["genes"]) < len(gene_list):
|
||||
return make_payload([], "Some genes not available", 400)
|
||||
|
||||
return make_payload(expression_data)
|
||||
|
||||
|
||||
class DifferentialExpressionAPI(Resource):
|
||||
@swagger.doc({
|
||||
"summary": "Get the top expressed genes for two cell sets. Calculated using t-test",
|
||||
"tags": ["expression"],
|
||||
"parameters": [
|
||||
{
|
||||
"name": "body",
|
||||
"in": "body",
|
||||
"schema": {
|
||||
"example": {
|
||||
"celllist1": ["1001000176.C12", "1001000176.C7", "1001000177.F11"],
|
||||
"celllist2": ["1001000012.D2", "1001000017.F10", "1001000033.C3", "1001000229.D4"],
|
||||
"num_genes": 5,
|
||||
"pval": 0.000001,
|
||||
},
|
||||
}
|
||||
}
|
||||
],
|
||||
"responses": {
|
||||
"200": {
|
||||
"description": "top expressed genes for cellset1, cellset2",
|
||||
"examples": {
|
||||
"application/json": {
|
||||
"data": {
|
||||
"celllist1": {
|
||||
"ave_diff": [
|
||||
432.0132935431362,
|
||||
12470.5623982637,
|
||||
957.0246880086814
|
||||
],
|
||||
"mean_expression_cellset1": [
|
||||
438.6185567010309,
|
||||
13315.536082474227,
|
||||
1076.5773195876288
|
||||
],
|
||||
"mean_expression_cellset2": [
|
||||
6.605263157894737,
|
||||
844.9736842105264,
|
||||
119.55263157894737
|
||||
],
|
||||
"pval": [
|
||||
3.8906598089944563e-35,
|
||||
1.9086226376018916e-25,
|
||||
7.847480544069826e-21
|
||||
],
|
||||
"topgenes": [
|
||||
"TMSB10",
|
||||
"FTL",
|
||||
"TMSB4X"
|
||||
]
|
||||
},
|
||||
"celllist2": {
|
||||
"ave_diff": [
|
||||
-6860.599158979924,
|
||||
-519.1314432989691,
|
||||
-10278.328269126423
|
||||
],
|
||||
"mean_expression_cellset1": [
|
||||
2.8350515463917527,
|
||||
0.6185567010309279,
|
||||
23.09278350515464
|
||||
],
|
||||
"mean_expression_cellset2": [
|
||||
6863.434210526316,
|
||||
519.75,
|
||||
10301.421052631578
|
||||
],
|
||||
"pval": [
|
||||
4.662891833748732e-44,
|
||||
3.6278087029927103e-37,
|
||||
8.396825170618402e-35
|
||||
],
|
||||
"topgenes": [
|
||||
"SPARCL1",
|
||||
"C1orf61",
|
||||
"CLU"
|
||||
]
|
||||
}
|
||||
},
|
||||
"status": {
|
||||
"error": False,
|
||||
"errormessage": ""
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
})
|
||||
def post(self):
|
||||
from app import data
|
||||
args = request.get_json()
|
||||
cell_list_1 = args.get("celllist1", [])
|
||||
cell_list_2 = args.get("celllist2", [])
|
||||
num_genes = args.get("num_genes", 7)
|
||||
pval = args.get("pval", 0.5)
|
||||
if not (cell_list_1 and cell_list_2):
|
||||
return make_payload([],
|
||||
"must include celllist1 and celllist2 parameters",
|
||||
400)
|
||||
data = data.diffexp(cell_list_1, cell_list_2, pval, num_genes)
|
||||
return make_payload(data)
|
||||
|
||||
|
||||
def get_api_resources():
|
||||
bp = Blueprint("api", __name__, url_prefix="/api/v0.1")
|
||||
api = Api(bp, add_api_spec_resource=False)
|
||||
api.add_resource(InitializeAPI, "/initialize")
|
||||
api.add_resource(CellsAPI, "/cells")
|
||||
api.add_resource(ExpressionAPI, "/expression")
|
||||
api.add_resource(DifferentialExpressionAPI, "/diffexpression")
|
||||
return api
|
||||
@@ -0,0 +1,197 @@
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
import scanpy.api as sc
|
||||
from scipy import stats
|
||||
|
||||
from ..util.schema_parse import parse_schema
|
||||
from ..driver.driver import CXGDriver
|
||||
|
||||
|
||||
class ScanpyEngine(CXGDriver):
|
||||
|
||||
def __init__(self, data, schema=None, graph_method="umap", diffexp_method="ttest"):
|
||||
self.data = self._load_data(data)
|
||||
self.schema = self._load_or_infer_schema(data, schema)
|
||||
self._set_cell_names()
|
||||
self.cell_count = self.data.shape[0]
|
||||
self.gene_count = self.data.shape[1]
|
||||
self.graph_method = graph_method
|
||||
self.diffexp_method = diffexp_method
|
||||
|
||||
def _set_cell_names(self):
|
||||
self.data.obs["cell_name"] = list(self.data.obs.index)
|
||||
|
||||
@staticmethod
|
||||
def _load_data(data):
|
||||
return sc.read(os.path.join(data, "data.h5ad"))
|
||||
|
||||
@staticmethod
|
||||
def _load_or_infer_schema(data, schema):
|
||||
data_schema = None
|
||||
if not schema:
|
||||
pass
|
||||
else:
|
||||
data_schema = parse_schema(os.path.join(data, schema))
|
||||
return data_schema
|
||||
|
||||
def cells(self):
|
||||
return list(self.data.obs.index)
|
||||
|
||||
def genes(self):
|
||||
return self.data.var.index.tolist()
|
||||
|
||||
def filter_cells(self, filter):
|
||||
"""
|
||||
Filter cells from data and return a subset of the data
|
||||
A filter is a dictionary where the key is a metadatata category
|
||||
Value is dictionary
|
||||
value_type: int, float, string
|
||||
variable_type: continuous, categorical
|
||||
query: filter value, for categorical [val1, val2], for continuous {min: x, max:y}
|
||||
Filters are combined with the and operator
|
||||
:param filter:
|
||||
:return: filtered dataframe
|
||||
"""
|
||||
cell_idx = np.ones((self.cell_count,), dtype=bool)
|
||||
for key, value in filter.items():
|
||||
if value["variable_type"] == "categorical":
|
||||
key_idx = np.in1d(getattr(self.data.obs, key), value["query"])
|
||||
cell_idx = np.logical_and(cell_idx, key_idx)
|
||||
else:
|
||||
min_ = value["query"]["min"]
|
||||
max_ = value["query"]["max"]
|
||||
if min_:
|
||||
key_idx = np.array((getattr(self.data.obs, key) >= min_).data)
|
||||
cell_idx = np.logical_and(cell_idx, key_idx)
|
||||
if max_:
|
||||
key_idx = np.array((getattr(self.data.obs, key) <= min_).data)
|
||||
cell_idx = np.logical_and(cell_idx, key_idx)
|
||||
return self.data[cell_idx, :]
|
||||
|
||||
def metadata_ranges(self, df=None):
|
||||
metadata_ranges = {}
|
||||
if not df:
|
||||
df = self.data
|
||||
for field in self.schema:
|
||||
if self.schema[field]["variabletype"] == "categorical":
|
||||
group_by = field
|
||||
if group_by == "CellName":
|
||||
group_by = "cell_name"
|
||||
metadata_ranges[field] = {"options": df.obs.groupby(group_by).size().to_dict()}
|
||||
else:
|
||||
metadata_ranges[field] = {
|
||||
"range": {
|
||||
"min": df.obs[field].min(),
|
||||
"max": df.obs[field].max()
|
||||
}
|
||||
}
|
||||
return metadata_ranges
|
||||
|
||||
def metadata(self, df, fields=None):
|
||||
"""
|
||||
Gets metadata key:value for each cells
|
||||
|
||||
:param df: from filter_cells, dataframe
|
||||
:param fields: list of keys for metadata to return, returns all metadata values if not set.
|
||||
:return: list of metadata values
|
||||
"""
|
||||
metadata = df.obs.to_dict(orient="records")
|
||||
for idx in range(len(metadata)):
|
||||
metadata[idx]["CellName"] = metadata[idx].pop("cell_name", None)
|
||||
return metadata
|
||||
|
||||
def create_graph(self, df):
|
||||
"""
|
||||
Computes a n-d layout for cells through dimensionality reduction.
|
||||
:param df: from filter_cells, dataframe
|
||||
:return: [cellid, x, y]
|
||||
"""
|
||||
getattr(sc.tl, self.graph_method)(df)
|
||||
graph = df.obsm["X_{graph_method}".format(graph_method=self.graph_method)]
|
||||
normalized_graph = (graph - graph.min()) / (graph.max() - graph.min())
|
||||
return np.hstack((df.obs["cell_name"].values.reshape(len(df.obs.index), 1), normalized_graph)).tolist()
|
||||
|
||||
def diffexp(self, cell_list_1, cell_list_2, pval, num_genes):
|
||||
"""
|
||||
Computes the top differentially expressed genes between two clusters
|
||||
:param df1: from filter_cells, dataframe containing first set of cells
|
||||
:param df2: from filter_cells, dataframe containing second set of cells
|
||||
:return: top genes, stats and expression values for top genes
|
||||
"""
|
||||
cells_idx_1 = np.in1d(self.data.obs["cell_name"], cell_list_1)
|
||||
cells_idx_2 = np.in1d(self.data.obs["cell_name"], cell_list_2)
|
||||
expression_1 = self.data.X[cells_idx_1, :]
|
||||
expression_2 = self.data.X[cells_idx_2, :]
|
||||
diff_exp = stats.ttest_ind(expression_1, expression_2)
|
||||
set1 = np.logical_and(diff_exp.pvalue < pval, diff_exp.statistic > 0)
|
||||
set2 = np.logical_and(diff_exp.pvalue < pval, diff_exp.statistic < 0)
|
||||
stat1 = diff_exp.statistic[set1]
|
||||
stat2 = diff_exp.statistic[set2]
|
||||
sort_set1 = np.argsort(stat1)[::-1]
|
||||
sort_set2 = np.argsort(stat2)
|
||||
pval1 = diff_exp.pvalue[set1][sort_set1]
|
||||
pval2 = diff_exp.pvalue[set2][sort_set2]
|
||||
mean_ex1_set1 = np.mean(expression_1[:, set1], axis=0)[sort_set1]
|
||||
mean_ex2_set1 = np.mean(expression_2[:, set1], axis=0)[sort_set1]
|
||||
mean_ex1_set2 = np.mean(expression_1[:, set2], axis=0)[sort_set2]
|
||||
mean_ex2_set2 = np.mean(expression_2[:, set2], axis=0)[sort_set2]
|
||||
mean_diff1 = mean_ex1_set1 - mean_ex2_set1
|
||||
mean_diff2 = mean_ex1_set2 - mean_ex2_set2
|
||||
genes_cellset_1 = self.data.var_names[set1][sort_set1]
|
||||
genes_cellset_2 = self.data.var_names[set2][sort_set2]
|
||||
return {
|
||||
"celllist1": {
|
||||
"topgenes": genes_cellset_1.tolist()[:num_genes],
|
||||
"mean_expression_cellset1": mean_ex1_set1.tolist()[:num_genes],
|
||||
"mean_expression_cellset2": mean_ex2_set1.tolist()[:num_genes],
|
||||
"pval": pval1.tolist()[:num_genes],
|
||||
"ave_diff": mean_diff1.tolist()[:num_genes]
|
||||
},
|
||||
"celllist2": {
|
||||
"topgenes": genes_cellset_2.tolist()[:num_genes],
|
||||
"mean_expression_cellset1": mean_ex1_set2.tolist()[:num_genes],
|
||||
"mean_expression_cellset2": mean_ex2_set2.tolist()[:num_genes],
|
||||
"pval": pval2.tolist()[:num_genes],
|
||||
"ave_diff": mean_diff2.tolist()[:num_genes]
|
||||
},
|
||||
}
|
||||
|
||||
def expression(self, cells=None, genes=None):
|
||||
"""
|
||||
Retrieves expression for each gene for cells in data frame
|
||||
:param df:
|
||||
:return: {
|
||||
"genes": list of genes,
|
||||
"cells": list of cells and expression list,
|
||||
"nonzero_gene_count": number of nonzero genes
|
||||
}
|
||||
"""
|
||||
if cells:
|
||||
cells_idx = np.in1d(self.data.obs["cell_name"], cells)
|
||||
else:
|
||||
cells_idx = np.ones((self.cell_count,), dtype=bool)
|
||||
if genes:
|
||||
genes_idx = np.in1d(self.data.var_names, genes)
|
||||
else:
|
||||
genes_idx = np.ones((self.gene_count,), dtype=bool)
|
||||
index = np.ix_(cells_idx, genes_idx)
|
||||
expression = self.data.X[index]
|
||||
|
||||
if not genes:
|
||||
genes = self.data.var.index.tolist()
|
||||
if not cells:
|
||||
cells = self.data.obs["cell_name"].tolist()
|
||||
|
||||
cell_data = []
|
||||
for idx, cell in enumerate(cells):
|
||||
cell_data.append({
|
||||
"cellname": cell,
|
||||
"e": list(expression[idx]),
|
||||
})
|
||||
|
||||
return {
|
||||
"genes": genes,
|
||||
"cells": cell_data,
|
||||
"nonzero_gene_count": int(np.sum(expression.any(axis=0)))
|
||||
}
|
||||
@@ -0,0 +1,70 @@
|
||||
class QueryStringError(Exception):
|
||||
pass
|
||||
|
||||
|
||||
def _convert_variable(datatype, variable):
|
||||
"""
|
||||
Convert variable to number (float/int)
|
||||
Used for dataset metadata and for query string
|
||||
:param datatype: type to convert to
|
||||
:param variable (string or None): value of variable
|
||||
:return: converted variable
|
||||
:raises: ValueError
|
||||
"""
|
||||
try:
|
||||
if variable is None:
|
||||
return variable
|
||||
if datatype == "int":
|
||||
variable = int(variable)
|
||||
elif datatype == "float":
|
||||
variable = float(variable)
|
||||
return variable
|
||||
except ValueError:
|
||||
raise
|
||||
|
||||
|
||||
def parse_filter(filter, schema):
|
||||
"""
|
||||
The filter comes in as arguments from a GET/POST request
|
||||
For categorical metadata keys filter based on key=value
|
||||
For continuous metadata keys filter by key=min,max
|
||||
Either value can be replaced by a * To have only a minimum value key=min, To have only a maximum value key=*,max
|
||||
|
||||
They combine via AND so a cell's metadata would have to match every filter
|
||||
|
||||
The results is a matrix with the cells the pass the filter and at this point all the genes
|
||||
:param filter: flask's request.args
|
||||
:param schema: dictionary schema
|
||||
:return:
|
||||
"""
|
||||
query = {}
|
||||
for key in filter:
|
||||
value = filter.getlist(key)
|
||||
if key not in schema:
|
||||
raise QueryStringError("Error: key {} not in metadata schema".format(key))
|
||||
query[key] = {
|
||||
"variable_type": schema[key]["variabletype"],
|
||||
"value_type": schema[key]["type"]
|
||||
}
|
||||
if query[key]["variable_type"] == "categorical":
|
||||
query[key]["query"] = [_convert_variable(query[key]["value_type"], v) for v in value]
|
||||
elif query[key]["variable_type"] == "continuous":
|
||||
value = value[0]
|
||||
try:
|
||||
min, max = value.split(",")
|
||||
except ValueError:
|
||||
raise QueryStringError("Error: min,max format required for range for key {}, got {}".format(key, value))
|
||||
if min == "*":
|
||||
min = None
|
||||
if max == "*":
|
||||
max = None
|
||||
try:
|
||||
query[key]["query"] = {
|
||||
"min": _convert_variable(query[key]["value_type"], min),
|
||||
"max": _convert_variable(query[key]["value_type"], max)
|
||||
}
|
||||
except ValueError:
|
||||
raise QueryStringError(
|
||||
"Error: expected type {} for key {}, got {}".format(query[key]["type"], key, value)
|
||||
)
|
||||
return query
|
||||
@@ -0,0 +1,7 @@
|
||||
import json
|
||||
|
||||
|
||||
def parse_schema(filename):
|
||||
with open(filename) as fh:
|
||||
schema = json.load(fh)
|
||||
return schema
|
||||
@@ -0,0 +1,40 @@
|
||||
import json
|
||||
|
||||
from numpy import float32, integer
|
||||
from flask import make_response, jsonify, Response
|
||||
|
||||
|
||||
class Float32JSONEncoder(json.JSONEncoder):
|
||||
def default(self, obj):
|
||||
if isinstance(obj, float32):
|
||||
return float(obj)
|
||||
elif isinstance(obj, integer):
|
||||
return int(obj)
|
||||
return json.JSONEncoder.default(self, obj)
|
||||
|
||||
|
||||
def make_payload(data, errormessage="", errorcode=200):
|
||||
"""
|
||||
Creates JSON respons for requests
|
||||
:param data: json data
|
||||
:param errormessage: error message
|
||||
:param errorcode: http error code
|
||||
:return: flask json repsonse
|
||||
"""
|
||||
error = False
|
||||
if errormessage:
|
||||
error = True
|
||||
# Questionable
|
||||
data = json.loads(json.dumps(data, cls=Float32JSONEncoder))
|
||||
return make_response(jsonify({
|
||||
"data": data,
|
||||
"status": {
|
||||
"error": error,
|
||||
"errormessage": errormessage,
|
||||
}
|
||||
}), errorcode)
|
||||
|
||||
|
||||
def make_streaming_response(data_generator, errorcode=200, content_type="application/json"):
|
||||
# TODO headers
|
||||
return Response(data_generator, status=errorcode, content_type=content_type)
|
||||
@@ -0,0 +1,97 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<title>CellxGene REST API - Swagger definition</title>
|
||||
<link href="https://fonts.googleapis.com/css?family=Open+Sans:400,700|Source+Code+Pro:300,600|Titillium+Web:400,600,700"
|
||||
rel="stylesheet">
|
||||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/swagger-ui/3.2.1/swagger-ui.css"
|
||||
crossorigin="anonymous"/>
|
||||
<style>
|
||||
html {
|
||||
box-sizing: border-box;
|
||||
overflow: -moz-scrollbars-vertical;
|
||||
overflow-y: scroll;
|
||||
}
|
||||
|
||||
*,
|
||||
*:before,
|
||||
*:after {
|
||||
box-sizing: inherit;
|
||||
}
|
||||
|
||||
body {
|
||||
margin: 0;
|
||||
background: #fafafa;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
|
||||
<body>
|
||||
|
||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"
|
||||
style="position:absolute;width:0;height:0">
|
||||
<defs>
|
||||
<symbol viewBox="0 0 20 20" id="unlocked">
|
||||
<path d="M15.8 8H14V5.6C14 2.703 12.665 1 10 1 7.334 1 6 2.703 6 5.6V6h2v-.801C8 3.754 8.797 3 10 3c1.203 0 2 .754 2 2.199V8H4c-.553 0-1 .646-1 1.199V17c0 .549.428 1.139.951 1.307l1.197.387C5.672 18.861 6.55 19 7.1 19h5.8c.549 0 1.428-.139 1.951-.307l1.196-.387c.524-.167.953-.757.953-1.306V9.199C17 8.646 16.352 8 15.8 8z"></path>
|
||||
</symbol>
|
||||
|
||||
<symbol viewBox="0 0 20 20" id="locked">
|
||||
<path d="M15.8 8H14V5.6C14 2.703 12.665 1 10 1 7.334 1 6 2.703 6 5.6V8H4c-.553 0-1 .646-1 1.199V17c0 .549.428 1.139.951 1.307l1.197.387C5.672 18.861 6.55 19 7.1 19h5.8c.549 0 1.428-.139 1.951-.307l1.196-.387c.524-.167.953-.757.953-1.306V9.199C17 8.646 16.352 8 15.8 8zM12 8H8V5.199C8 3.754 8.797 3 10 3c1.203 0 2 .754 2 2.199V8z"/>
|
||||
</symbol>
|
||||
|
||||
<symbol viewBox="0 0 20 20" id="close">
|
||||
<path d="M14.348 14.849c-.469.469-1.229.469-1.697 0L10 11.819l-2.651 3.029c-.469.469-1.229.469-1.697 0-.469-.469-.469-1.229 0-1.697l2.758-3.15-2.759-3.152c-.469-.469-.469-1.228 0-1.697.469-.469 1.228-.469 1.697 0L10 8.183l2.651-3.031c.469-.469 1.228-.469 1.697 0 .469.469.469 1.229 0 1.697l-2.758 3.152 2.758 3.15c.469.469.469 1.229 0 1.698z"/>
|
||||
</symbol>
|
||||
|
||||
<symbol viewBox="0 0 20 20" id="large-arrow">
|
||||
<path d="M13.25 10L6.109 2.58c-.268-.27-.268-.707 0-.979.268-.27.701-.27.969 0l7.83 7.908c.268.271.268.709 0 .979l-7.83 7.908c-.268.271-.701.27-.969 0-.268-.269-.268-.707 0-.979L13.25 10z"/>
|
||||
</symbol>
|
||||
|
||||
<symbol viewBox="0 0 20 20" id="large-arrow-down">
|
||||
<path d="M17.418 6.109c.272-.268.709-.268.979 0s.271.701 0 .969l-7.908 7.83c-.27.268-.707.268-.979 0l-7.908-7.83c-.27-.268-.27-.701 0-.969.271-.268.709-.268.979 0L10 13.25l7.418-7.141z"/>
|
||||
</symbol>
|
||||
|
||||
|
||||
<symbol viewBox="0 0 24 24" id="jump-to">
|
||||
<path d="M19 7v4H5.83l3.58-3.59L8 6l-6 6 6 6 1.41-1.41L5.83 13H21V7z"/>
|
||||
</symbol>
|
||||
|
||||
<symbol viewBox="0 0 24 24" id="expand">
|
||||
<path d="M10 18h4v-2h-4v2zM3 6v2h18V6H3zm3 7h12v-2H6v2z"/>
|
||||
</symbol>
|
||||
|
||||
</defs>
|
||||
</svg>
|
||||
|
||||
<div id="swagger-ui"></div>
|
||||
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/swagger-ui/3.2.1/swagger-ui-bundle.js"
|
||||
crossorigin="anonymous"></script>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/swagger-ui/3.2.1/swagger-ui-standalone-preset.js"
|
||||
crossorigin="anonymous"></script>
|
||||
<script>
|
||||
window.onload = function () {
|
||||
const ui = SwaggerUIBundle({
|
||||
url: window.location.origin + "/api/swagger.json",
|
||||
dom_id: '#swagger-ui',
|
||||
deepLinking: true,
|
||||
presets: [
|
||||
SwaggerUIBundle.presets.apis,
|
||||
SwaggerUIStandalonePreset
|
||||
],
|
||||
plugins: [
|
||||
SwaggerUIBundle.plugins.DownloadUrl
|
||||
],
|
||||
layout: "StandaloneLayout"
|
||||
});
|
||||
|
||||
window.ui = ui
|
||||
}
|
||||
</script>
|
||||
</body>
|
||||
|
||||
</html>
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,24 @@
|
||||
from flask import (
|
||||
Blueprint, render_template, url_for, current_app
|
||||
)
|
||||
|
||||
bp = Blueprint("webapp", __name__, template_folder="templates")
|
||||
|
||||
|
||||
@bp.route("/")
|
||||
def index():
|
||||
url_base = current_app.config["CXG_API_BASE"]
|
||||
dataset_title = current_app.config["DATASET_TITLE"]
|
||||
return render_template("index.html", prefix=url_base, datasetTitle=dataset_title)
|
||||
|
||||
|
||||
# renders swagger documentation
|
||||
@bp.route("/swagger")
|
||||
def swag():
|
||||
return render_template("swagger.html")
|
||||
|
||||
|
||||
# renders swagger documentation
|
||||
@bp.route("/favicon.png")
|
||||
def favicon():
|
||||
return url_for("static", filename="img/favicon.png")
|
||||
@@ -0,0 +1,41 @@
|
||||
aniso8601==3.0.2
|
||||
anndata==0.6.1
|
||||
certifi==2018.4.16
|
||||
chardet==3.0.4
|
||||
click==6.7
|
||||
cycler==0.10.0
|
||||
decorator==4.3.0
|
||||
Flask==0.12.4
|
||||
Flask-Compress==1.4.0
|
||||
Flask-Cors==3.0.6
|
||||
Flask-RESTful==0.3.6
|
||||
flask-restful-swagger-2==0.35
|
||||
h5py==2.8.0
|
||||
idna==2.7
|
||||
itsdangerous==0.24
|
||||
Jinja2==2.10
|
||||
joblib==0.12.0
|
||||
kiwisolver==1.0.1
|
||||
llvmlite==0.23.2
|
||||
MarkupSafe==1.0
|
||||
matplotlib==2.2.2
|
||||
natsort==5.3.2
|
||||
networkx==2.1
|
||||
numba==0.38.1
|
||||
numexpr==2.6.5
|
||||
numpy==1.14.5
|
||||
pandas==0.23.1
|
||||
patsy==0.5.0
|
||||
pyparsing==2.2.0
|
||||
python-dateutil==2.7.3
|
||||
pytz==2018.4
|
||||
requests==2.19.1
|
||||
scanpy==1.2.2
|
||||
scikit-learn==0.19.1
|
||||
scipy==1.1.0
|
||||
seaborn==0.8.1
|
||||
six==1.11.0
|
||||
statsmodels==0.9.0
|
||||
tables==3.4.4
|
||||
urllib3==1.23
|
||||
Werkzeug==0.14.1
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user