Add vector rendering

[davidbrochart]

Same as #69, but from my fork of xarray-leaflet.

[davidbrochart]

@jfoster17 I made some optimizations that should hopefully fix the issues you reported. I also changed the API so that you can choose to make a vector map dynamic, and pass an external rasterize function.

[jfoster17]

Thanks @davidbrochart -- as we discussed offline, I'm having trouble getting a new vector layer to serve through voila on a remote server. You suggested the following:

Pass get_base_url to df.leaflet.plot with:

def get_base_url(window_url):
    return window_url

and launching the application as:

jupyter server --ServerApp.default_url="voila/render/examples/vector.ipynb"

I haven't been able to get this working on my server, which I configured following the directions here. Specifically, since your instructions suggest launching voila via jupyter server, I'm sure there is some extra steps to make this work. I don't really understand the distinction...

I did try updating the nginx configuration for the proxy_pass to http://localhost:8888 since that is the port jupyter server was trying to serve to, and that got me as a login page for jupyer server, but then a 404 for the actual page.

Is there not a way to get this running with the voila command? Or is there some documentation on how to serve voila via jupyter server?

[jfoster17]

I also get this odd behavior when I am running this locally on my Mac versus (remotely) on Ubuntu. Serving the notebook notebook from Linux I get the expected behavior, but locally I get some sort of jumbled tiles where the color scales of buildings changes radically on zoom/pan and sometimes I can see obvious tile boundaries.

https://user-images.githubusercontent.com/3639698/191332677-dc5dd0f2-3840-4431-955a-7327151971d1.mov

[raybellwaves]

Anything I can do to help review this?

[davidbrochart]

Thanks for the help @raybellwaves! The code is a bit of a mess, I'll probably refactor later, but it would be great if you could try this PR and give your feedback. @jfoster17 I'll answer your comments tomorrow, it's late here :)

[davidbrochart]

I did try updating the nginx configuration for the proxy_pass to http://localhost:8888/ since that is the port jupyter server was trying to serve to, and that got me as a login page for jupyer server, but then a 404 for the actual page.

@jfoster17 You can pass --port=8866 or whatever port number you want. If you land on a login page, then you need to enter the token jupyter-server showed in the terminal. Or you can disable authentication with --ServerApp.token='' --ServerApp.password=''. Also, if your server has a base URL, e.g. https://yourdomain.com/my/base/url, then you need:

def get_base_url(window_url):
    return "https://yourdomain.com/my/base/url"

[jfoster17]

Another request -- it would be nice to be able to fiddle with the size of point-like data. Currently, points end up very small and can be hard to see. Obviously one option is to transform points back into larger polygon. Another easy fix might be to pass additional kwargs into make_geocube so that one could specify resolution to that method (at least I think that would work).

[davidbrochart]

I get some sort of jumbled tiles where the color scales of buildings changes radically on zoom/pan and sometimes I can see obvious tile boundaries.

@jfoster17 I was finally able to track down the issue. It is a regression that has been reported here. For now, a workaround is to install pandas<1.5, e.g. pip install pandas==1.4.

[davidbrochart]

it would be nice to be able to fiddle with the size of point-like data

@jfoster17 I think it would be nice to be able to apply transformations to the data, as with the raster data flow. For instance, the rivers in this notebook could not be revealed without "thickening", because rivers are represented with one-pixel wide streams in the original array. This is achieved by applying scipy.ndimage.maximum_filter(array, footprint=circle) on each tile. I'm going to try and support transformation functions in the vector data flow.

[davidbrochart]

I added an example notebook in examples/vector_points.ipynb to show how points can be made bigger with a transform function. The issue is that it runs independently on every tile, so you can see boundary effects (points being cut).

[raybellwaves]

Making some notes for reviewing this (mostly for myself).

git clone https://github.com/xarray-contrib/xarray_leaflet.git
cd xarray_leaflet
git fetch origin pull/75/head:vector
git checkout vector
mamba create -n xarray_leaflet python=3.10 --y
conda activate xarray_leaflet
mkdir -p ~/miniforge3/envs/xarray_leaflet/etc/jupyter/jupyter_notebook_config.d
mkdir -p ~/miniforge3/envs/xarray_leaflet/etc/jupyter/jupyter_server_config.d
cp etc/jupyter/jupyter_notebook_config.d/* ~/miniforge3/envs/xarray_leaflet/etc/jupyter/jupyter_notebook_config.d/
cp etc/jupyter/jupyter_server_config.d/* ~/miniforge3/envs/xarray_leaflet/etc/jupyter/jupyter_server_config.d/
mamba install jupyterlab pyarrow
pip install -e .
jupyter lab
# run examples/vector.ipynb and vector_points.ipynb

Notebooks work great.

TODO try it on a month of the NYC taxi cab data inspired by https://examples.pyviz.org/nyc_taxi/nyc_taxi.html as an example of big point data.

!wget https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2022-01.parquet
!wget https://d37ci6vzurychx.cloudfront.net/misc/taxi_zones.zip

[davidbrochart]

Thanks @raybellwaves. Note that if you install JupyterLab before, then you don't need the mkdir, and you only need:

cp etc/jupyter/jupyter_server_config.d/* ~/miniforge3/envs/xarray_leaflet/etc/jupyter/jupyter_server_config.d/

[jfoster17]

Hey @davidbrochart -- here are the issues I was talking about offline.

1) Fails to deal with no data in the viewport

If the user moves the map outside of the region where there is data, xarray-leaflet hangs. For instance, starting the map in vector.ipynb so that NYC is not in the viewport:

m = Map(center=(45.74427755695474, -73.96981238541562), zoom=11, basemap=basemaps.CartoDB.DarkMatter, interpolation='nearest')

will cause the spinner to spin endlessly and nothing will ever display, even if you zoom over to New York City. You can get the same behavior moving away from a region where data has previously been rendered (perhaps a more likely scenario for most users).

[jfoster17]

2. Fails to deal with E/W longitude swap

Again, on the vector.ipynb notebook if you specify the starting map location as positive longitude

m = Map(center=(40.74427755695474, 286), zoom=11, basemap=basemaps.CartoDB.DarkMatter, interpolation='nearest')

the map will be center on NYC but xarray-leaflet will fail to plot any data, probably because the data is in negative longitude (i.e. -73) and thus not in the range of the viewport (again, leading to infinite spinning). So this is really two problems, lack of wrapping longitude leading to the above-specified problem.

[davidbrochart]

Thanks Jonathan for reporting these issues.

• issue 1: I cannot reproduce it, maybe my recent commits fix it and you are not up to date?
• issue 2: I fixed it in https://github.com/xarray-contrib/xarray_leaflet/pull/75/commits/9ee6b34e44559060f69feba48c475bc9e5385117, but it also needs a fix in ipyleaflet.

[davidbrochart]

I released ipyleaflet v0.17.2 with the fix and require that version in xarray-leaflet.

[jfoster17]

Thanks for your quick response David -- indeed Issue 1 goes away in a new up-to-date env, and Issue 2 goes away for me too with the new ipyleaflet version!