I hereby claim:
- I am gnestor on github.
- I am senornestor (https://keybase.io/senornestor) on keybase.
- I have a public key ASAIOpv0jj6kqyQ-7xezUwnwiSKbSk-ypzL-VVUgfzyrbQo
To claim this, I am signing this object:
def build_path | |
"build/iPhoneOS-7.0-Release/" | |
end | |
def ipa_name | |
"APP_FILE_NAME.ipa" | |
end | |
def dsym_name | |
"APP_FILE_NAME.app.dSYM" |
// Welcome! require() some modules from npm (like you were using browserify) | |
// and then hit Run Code to run your code on the right side. | |
// Modules get downloaded from browserify-cdn and bundled in your browser. | |
const embed = require('vega-embed'); | |
const data = { | |
"width": 400, | |
"height": 200, | |
"padding": 5, |
I hereby claim:
To claim this, I am signing this object:
{"lastUpload":"2020-07-16T06:53:05.772Z","extensionVersion":"v3.4.3"} |
The best practice for pinning a Python library (or any kernel-side library) to a specific JupyterLab extension is to use a versioned MIME type or media type. A good example of this is the Altair Python library which supports multiple versions of Vega and Vega-lite and vega4-extension, vega3-extension, and vega2-extension which render Altair output.
In Jupyter notebooks, cell output areas contain one or more outputs of a given MIME type. The MIME type describes the data type of the output. The default MIME type is text/plain
. JupyterLab provides a set of mimerender extensions that render common MIME types such as text/plain
or application/json
. There are many third-part
vdom |