Multiband support and MPL colormaps

[banesullivan]

Resolve #22

This all feels a bit hacky, but it works. @giswqs, would you please review/test.

Here is an example

from localtileserver import get_leaflet_tile_layer, TileClient
from ipyleaflet import Map

# First, create a tile server from local raster file
tile_client = TileClient('./landsat.tif')

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client, band=[4, 3, 2])

# Create ipyleaflet map, add tile layer, and display
m = Map(center=tile_client.center())
m.add_layer(t)
m

or like the screenshots in #22:

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client, band=[5, 4, 3])

# Create ipyleaflet map, add tile layer, and display
m = Map(center=tile_client.center())
m.add_layer(t)
m

---

To do:

• Update type hints and documentation
• Add example to README
• User test

[codecov-commenter]

Codecov Report

Merging #28 (36be185) into main (c9890cc) will increase coverage by 1.10%.
The diff coverage is 85.38%.

@@            Coverage Diff             @@
##             main      #28      +/-   ##
==========================================
+ Coverage   76.51%   77.61%   +1.10%     
==========================================
  Files          12       14       +2     
  Lines         545      630      +85     
==========================================
+ Hits          417      489      +72     
- Misses        128      141      +13     

[banesullivan]

Also, @giswqs, what Landsat data is this? 7? 8? I'd like to use it for a demo and know which bands are what per something like: http://gsp.humboldt.edu/olm/Courses/GSP_216/lessons/composites.html

So that I can make a false color image, etc.

edit: this looks like LS8 to me

[banesullivan]

This example was added to the README:

from localtileserver import get_leaflet_tile_layer, TileClient
from ipyleaflet import Map, ScaleControl, FullScreenControl, SplitMapControl

# First, create a tile server from local raster file
tile_client = TileClient('landsat.tif')

# Create 2 tile layers from same raster viewing different bands
l = get_leaflet_tile_layer(tile_client, band=[7, 5, 4])
r = get_leaflet_tile_layer(tile_client, band=[5, 3, 2])

# Make the ipyleaflet map
m = Map(center=tile_client.center(), zoom=12)
control = SplitMapControl(left_layer=l, right_layer=r)
m.add_control(control)
m.add_control(ScaleControl(position='bottomleft'))
m.add_control(FullScreenControl())
m

[giswqs]

This is AWESOME! I can confirm that it works for both local raster datasets and remote pre-tiled COG. The sample dataset I provided is Landsat 7 image.

from localtileserver import get_leaflet_tile_layer, TileClient
from ipyleaflet import Map

# First, create a tile server from local raster file
# tile_client = TileClient('./landsat7.tif')
url = 'https://opendata.digitalglobe.com/events/california-fire-2020/pre-event/2018-02-16/pine-gulch-fire20/1030010076004E00.tif'

tile_client = TileClient(url)

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client, band=[3, 2, 1])

# Create ipyleaflet map, add tile layer, and display
m = Map(center=tile_client.center())
m.add_layer(t)
m

[giswqs]

By default, if band is not provided, the image looks like this. Maybe there is a nodata issue?

[giswqs]

Here is the dataset I used for the example above.
https://drive.google.com/file/d/16ji8ze8gqSR1kJssf7vqdhR3d58ZBYT0/view?usp=sharing

[banesullivan]

I can reproduce that visual effect with the shared file on my end. I suspect there is an issue with the alpha channel on that particular image rather than a nodata issue or an issue with the tile serving