README.md

Generate Dash.jl artifacts

Dash.jl uses Julia Artifacts to load front-end resources that Dash.jl shares with the python version of dash.

The Artifacts.toml file lists the location of the publicly-available tarball containing all the required resources.

The tarballs are hosted on the DashCoreResources repo, under Releases. They are generated and deployed using the generate.jl script in this directory.

How to run generate.jl ?

Step 0: get push rights to DashCoreResources

Step 1: get GitHub personal access token

See GitHub docs for more info.

If using a fine-grained token, make sure to enable Read and Write access to code.

Step 2: expose your token into your shell

# for example:
export GITHUB_TOKEN="<your GitHub personal access token>"

Step 3: run generate.jl

cd Dash.jl/gen_resources

# install `generate.jl` deps
julia --project -e 'import Pkg; Pkg.instantiate()'

# generate `gen_resources/build/deploy/` content,
# but do not deploy!
julia --project generate.jl

# if everything looks fine,
# generate `gen_resources/build/deploy/` content (again) and
# deploy to the `DashCoreResource` releases with:
julia --project generate.jl --deploy

If generate.jl errors

with a PyError / PyImport error

that is an error like:

ERROR: LoadError: PyError (PyImport_ImportModule

The Python package dash could not be imported by pyimport. Usually this means
that you did not install dash in the Python version being used by PyCall.

PyCall is currently configured to use the Python version at:

/usr/bin/python3

try

using PyCall
ENV["PYTHON"] = joinpath(homedir(), ".julia/conda/3/x86_64/bin")
import Pkg
Pkg.build("PyCall")
# check that it matches with
PyCall.pyprogramname

and then re-run generate.jl.

Step 4: Commit the changes to Artifacts.toml

and push to plotly/Dash.jl (preferably on a new branch) to get a CI test run started.