@jupyterlab/galata
Advanced tools
Galata*
Galata is a set of helpers and fixtures for JupyterLab UI Testing using Playwright Test Runner that provides:
Add Galata to your project:
jlpm add -D @jupyterlab/galata
# Install playwright supported browser
jlpm playwright install
Create a Playwright configuration file playwright.config.js containing:
module.exports = require('@jupyterlab/galata/lib/playwright-config');
Create ui-tests/foo.spec.ts to define your test.
import { test } from '@jupyterlab/galata';
import { expect } from '@playwright/test';
test.describe('Notebook Tests', () => {
test('Create New Notebook', async ({ page, tmpPath }) => {
const fileName = 'create_test.ipynb';
await page.notebook.createNew(fileName);
expect(
await page.waitForSelector(`[role="main"] >> text=${fileName}`)
).toBeTruthy();
expect(await page.contents.fileExists(`${tmpPath}/${fileName}`)).toEqual(
true
);
});
});
This will create a notebook, open it and check it exists.
Before running the test, you will need to launch the JupyterLab server with some specific options.
Create jupyter_server_test_config.py with the following content.
from tempfile import mkdtemp
c.ServerApp.port = 8888
c.ServerApp.open_browser = False
c.LabApp.dev_mode = True
c.ServerApp.root_dir = mkdtemp(prefix='galata-test-')
c.ServerApp.token = ""
c.ServerApp.password = ""
c.ServerApp.disable_check_xsrf = True
c.LabApp.expose_app_in_browser = True
Then start the server with:
jupyter lab --config jupyter_server_test_config.py
jlpm playwright test
Galata should generate console output similar to following
Using config at .../playwright.config.js
Running 1 test using 1 worker
✓ ui-tests/foo.spec.ts:5:3 › Notebook Tests Create New Notebook (13s)
1 passed (15s)
Playwright Test just ran a test using Chromium browser, in a headless manner. You can use headed browser to see what is going on during the test:
jlpm playwright test --headed
Test assets (including test videos) will be saved in a test-results folder and by default a HTML
report will be created in playwright-report folder. That report can be see by running:
http-server ./playwright-report -a localhost -o
To create tests, the easiest way is to use the code generator tool of playwright:
jupyter lab --config jupyter_server_test_config.py &
jlpm playwright codegen localhost:8888
To debug tests, a good way is to use the inspector tool of playwright:
jupyter lab --config jupyter_server_test_config.py &
PWDEBUG=1 jlpm playwright test
Here are the new test fixture introduced by Galata on top of Playwright fixtures.
Application URL path fragment; default "/lab"
Whether to go to JupyterLab page within the fixture or not; default true.
If set to false, it allows you to add route mock before loading JupyterLab.
Example:
test.use({ autoGoto: false });
test('Open language menu', async ({ page }) => {
await page.route(/.*\/api\/translation.*/, (route, request) => {
if (request.method() === 'GET') {
return route.fulfill({
status: 200,
body:
'{"data": {"en": {"displayName": "English", "nativeName": "English"}}, "message": ""}'
});
} else {
return route.continue();
}
});
await page.goto();
// ...
});
Galata can keep the uploaded and created files in tmpPath on
the server root for debugging purpose. By default the files are kept
on failure.
tmpPath is deleted after each teststmpPath is never deletedtmpPath is deleted except if a test failed or timed out.Mock JupyterLab state in-memory or not. Possible values are:
Example:
test.use({
mockState: {
'layout-restorer:data': {
main: {
dock: {
type: 'tab-area',
currentIndex: 0,
widgets: []
}
},
down: {
size: 0,
widgets: []
},
left: {
collapsed: false,
visible: true,
current: 'running-sessions',
widgets: [
'filebrowser',
'jp-property-inspector',
'running-sessions',
'@jupyterlab/toc:plugin',
'debugger-sidebar',
'extensionmanager.main-view'
]
},
right: {
collapsed: true,
visible: true,
widgets: []
},
relativeSizes: [0.4, 0.6, 0]
}
} as any
});
test('should return the mocked state', async ({ page }) => {
expect(
await page.waitForSelector(
'[aria-label="Running Sessions section"] >> text=Open Tabs'
)
).toBeTruthy();
});
Mock JupyterLab settings in-memory or not. Possible values are:
true: JupyterLab settings will be mocked on a per test basis
false: JupyterLab settings won't be mocked (Be careful it will read & write settings local files)
Record<string, unknown>: Mapping {pluginId: settings} that will be default user settings
The default value is galata.DEFAULT_SETTINGS
By default the settings are stored in-memory. However the they are still initialized with the hard drive values.
Example:
test.use({
mockSettings: {
...galata.DEFAULT_SETTINGS,
'@jupyterlab/apputils-extension:themes': {
theme: 'JupyterLab Dark'
}
}
});
test('should return mocked settings', async ({ page }) => {
expect(await page.theme.getTheme()).toEqual('JupyterLab Dark');
});
Sessions created during the test. Possible values are:
Example:
test('should return the active sessions', async ({ page, sessions }) => {
await page.notebook.createNew();
// Wait for the poll to tick
await page.waitForResponse(
async response =>
response.url().includes('api/sessions') &&
response.request().method() === 'GET' &&
((await response.json()) as any[]).length === 1
);
expect(sessions.size).toEqual(1);
// You can introspect the sessions.values()[0] if needed
});
Terminals created during the test. Possible values are:
Example:
test('should return the active terminals', async ({ page, terminals }) => {
await Promise.all([
page.waitForResponse(
response =>
response.request().method() === 'POST' &&
response.url().includes('api/terminals')
),
page.menu.clickMenuItem('File>New>Terminal')
]);
// Wait for the poll to tick
await page.waitForResponse(
async response =>
response.url().includes('api/terminals') &&
response.request().method() === 'GET' &&
((await response.json()) as any[]).length === 1
);
expect(terminals.size).toEqual(1);
// You can introspect the [...terminals.values()][0] if needed
});
Unique test temporary path created on the server.
Note: if you override this string, you will need to take care of creating the folder and cleaning it.
Example:
test.use({ tmpPath: 'test-toc' });
test.describe.serial('Table of Contents', () => {
test.beforeAll(async ({ baseURL, tmpPath }) => {
const contents = galata.newContentsHelper(baseURL);
await contents.uploadFile(
path.resolve(__dirname, `./notebooks/${fileName}`),
`${tmpPath}/${fileName}`
);
});
test.afterAll(async ({ baseURL, tmpPath }) => {
const contents = galata.newContentsHelper(baseURL);
await contents.deleteDirectory(tmpPath);
});
});
Benchmark of JupyterLab is done automatically using Playwright. The actions measured are:
Two files are tested: a notebook with many code cells and another with many markdown cells.
The tests are located in the subfolder test/benchmark. And they can be executed with the following command:
jlpm run test:benchmark
A special report will be generated in the folder benchmark-results that will contain 4 files:
lab-benchmark.json: The execution time of the tests and some metadata.lab-benchmark.md: A report in Markdownlab-benchmark.png: A comparison of execution time distributionlab-benchmark.vl.json: The Vega-Lite description used to produce the PNG file.The reference, tagged expected, is stored in lab-benchmark-expected.json. It can be
updated using the -u option of Playwright; i.e. jlpm run test:benchmark -u.
The benchmark can be customized using the following environment variables:
BENCHMARK_NUMBER_SAMPLES: Number of samples to compute the execution time distribution; default 20.BENCHMARK_OUTPUTFILE: Benchmark result output file; default benchmark.json. It is overridden in the playwright-benchmark.config.js.BENCHMARK_REFERENCE: Reference name of the data; default is actual for current data and expected for the reference.Install dependencies and build
cd galata
jlpm
jlpm run build
For tests to be run, a JupyterLab instance must be up and running. Launch it without credentials. Tests expect to connect JupyterLab from localhost:8888 by default. If a different URL is to be used, it can be specified by defining TARGET_URL environment variable or setting the Playwright baseURL fixture.
jlpm run start
The JupyterLab root directory is randomly generated in the temporary folder (prefixed with galata-test-).
Tests are grouped in two projects: galata and jupyterlab. The first one is testing Galata helpers and fixtures when the other one is running all tests for Jupyterlab.
By default, both projects will be executed when running jlpm run test. But you can select one project with the CLI option --project <project-id>.
Galata can be configured by using command line arguments or using playwright.config.js file. Full list of config options can be accessed using jlpm playwright test --help.
By default, Galata will generate a text report in the form of markdown table and a Vega-Lite graph of execution time distribution. Users can customize these reports in two ways:
playwright.config.js file: in reporter section, users can supply two functions vegaLiteConfigFactory and textReportFactory to the reporter's constructor options. These functions will be used to create Vega-Lite configuration (vegaLiteConfigFactory) or to create a text report (textReportFactory) from test records. // An example of `playwright.config.js` with customized builder
reporter: [
...,
[
'@jupyterlab/galata/lib/benchmarkReporter',
{ outputFile: 'lab-benchmark.json',
vegaLiteConfigFactory: (
allData: Array<IReportRecord>, // All test records
comparison: 'snapshot' | 'project'// Logic of test comparisons:'snapshot' or 'project'.
) => {
// Return a Vega-Lite graph configuration object
return {};
}
textReportFactory: (
allData: Array<IReportRecord> // All test records
) => {
// Return a promise of with the tuple [report content, file extension]
return Promise.resolve(['My report content', 'md']);
}
}
],
...
]
defaultTextReportFactory) and Vega-Lite graph config factory (defaultVegaLiteConfigFactory) of BenchmarkReporter class in a sub-class and then use it as a reporter in playwright.config.js file.Reference image are saved next to test files in <test-file-name>-snapshots folders. If a reference screenshots does not exist, it will be generated at the first execution
of a test. You can also update them by running jlpm playwright test --update-snapshots.
Galata framework is named after Galata Tower in Istanbul. Centuries ago, Galata Tower was used to spot fires in the city. Tower was also used as astronomical observatory in the past.
Development of this project began under Bloomberg organization by Mehmet Bektas, then it was transferred to JupyterLab organization. We gratefully acknowledge Bloomberg for the generous contribution and supporting open-source software community.
4.0.0 - Highlights
Below are the major highlights in JupyterLab 4.0.0.
CodeMirror, the text editor used for cells and file editors, has been updated to CodeMirror 6. This brings important accessibility and performance improvements as well as better customization capabilities. We have also improved the editor settings. Previously, users had to customize settings separately for each type of cell, the file editor, and the console editor. Now, you can change your settings in one place. It is now easier to use the default settings for all editors and to change some settings for specific cases. For example, you can now hide line numbers only for markdown cells.
Developers can now provide editor extensions, like themes and programming language parsers, through new application registries.
Starting with JupyterLab 3, extensions can be installed via Python packages (or other providers of prebuilt extensions).
In JupyterLab 4, building on this feature, the Extension Manager now includes extensions from pypi.org. This removes the build step from installation of extension when using Extension Manager.
Developers can provide an alternative package repository to display their own set of extensions.
The Search and Replace functionality has been improved with new features when searching in a notebook:
Some new elements have been added or changed in the UI:
JupyterLab is not yet fully accessible. Currently, we are focused on making Notebook 7 accessible. A big part of the code is shared, though, and the following accessibility improvements are in JupyterLab 4:
JupyterLab is now faster, thanks to the following improvements:
Notebook windowing might add side effects for example if some cell outputs are displaying iframes. Therefore it is not yet the default value. But we recommend user to switch to it and report bugs to help us polish it. To test it, you
need to set the user setting Notebook > Windowing mode to full. If you have issues with notebook rendering, try changing back to defer or none. (none should be used as a last resort, because it disables all optimizations.)
JupyterLab 3.6 already made significant improvements to the Real Time Collaboration (RTC) feature. The feature is now in a separate repository: jupyter_collaboration. The rationale is to limit the dependencies for users who don't need RTC. Separating RTC also helps organizations using JupyterLab that do not meet the specific requirements regarding file content management.
To enable RTC, install the jupyter-collaboration package with either pip or conda.
pip install "jupyter-collaboration>=1.0.0a0"RTC highlights in the standalone jupyter-collboration package, version 1.0.0, include:
Here are the main tool updates that will benefit extension authors and developers:
We recommend using Node.js v18 or newer, because older versions will reach end of life in 2023 or earlier (see Node release schedule).
To ease code migration to JupyterLab 4, developers should review the migration guide. A few existing extensions have already been migrated and can be used as examples:
<!-- <START NEW CHANGELOG ENTRY> -->FAQs
JupyterLab UI Testing Framework
The npm package @jupyterlab/galata receives a total of 2,416 weekly downloads. As such, @jupyterlab/galata popularity was classified as popular.
We found that @jupyterlab/galata demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 0 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Node.js will be enforcing stricter semver-major PR policies a month before major releases to enhance stability and ensure reliable release candidates.
Security News
Research
Socket's threat research team has detected five malicious npm packages targeting Roblox developers, deploying malware to steal credentials and personal data.
Security News
vlt introduced its new package manager and a serverless registry this week, innovating in a space where npm has stagnated.