SlideRule Interactive Feature Find

SlideRule is an on-demand science data processing service that runs in on Amazon Web Services and responds to REST API calls to process and return science results. SlideRule was designed to enable researchers and other data systems to have low-latency access to custom-generated, high-level, analysis-ready data products using processing parameters supplied at the time of the request.

Documentation for using SlideRule is available from the project website

Learning goals

• Finding interesting features with ICESat-2!

from sliderule import icesat2, ipysliderule, sliderule, io
import ipywidgets as widgets
import warnings
# turn off warnings for demo
warnings.filterwarnings('ignore')

Paste Show Code contents from Web Demo

icesat2.init("slideruleearth.io")
parms = {
    "srt": 0,
    "len": 40,
    "res": 20,
    "pass_invalid": True,
    "cnf": -2,
    "atl08_class": [
        "atl08_noise",
        "atl08_ground",
        "atl08_canopy",
        "atl08_top_of_canopy",
        "atl08_unclassified"
    ],
    "yapc": {
        "score": 0
    },
    "ats": 20.0,
    "cnt": 10,
    "poly": [
        {
            "lon": 13.648796,
            "lat": 52.007611
        },
        {
            "lon": 13.847923,
            "lat": 52.007611
        },
        {
            "lon": 13.847923,
            "lat": 52.075609
        },
        {
            "lon": 13.648796,
            "lat": 52.075609
        },
        {
            "lon": 13.648796,
            "lat": 52.007611
        }
    ],
    "rgt": 84,
    "cycle": 7,
    "track": 1,
    "asset": "icesat2"
}
gdf = icesat2.atl03sp(parms, asset="icesat2")

Review GeoDataFrame output

Can inspect the columns, number of returns and returns at the top of the GeoDataFrame.

See the ICESat-2 documentation for descriptions of each column

print(f'Returned {gdf.shape[0]} records')
gdf.head()

Returned 39631 records

	cycle	sc_orient	background_rate	track	segment_id	rgt	segment_dist	solar_elevation	height	atl03_cnf	landcover	atl08_class	quality_ph	distance	yapc_score	snowcover	relief	pair	geometry	spot
time
2020-04-01 01:18:14.833129216	7	1	0.0	1	712496	84	1.427102e+07	-26.952822	96.497551	4	255	1	0	-18.253809	98	255	0.0	0	POINT (13.75534 52.07558)	6
2020-04-01 01:18:14.833328896	7	1	0.0	1	712496	84	1.427102e+07	-26.952822	96.632645	4	255	1	0	-16.830337	146	255	0.0	0	POINT (13.75534 52.07557)	6
2020-04-01 01:18:14.833928960	7	1	0.0	1	712496	84	1.427102e+07	-26.952822	108.836823	4	255	2	0	-12.584853	67	255	0.0	0	POINT (13.75533 52.07553)	6
2020-04-01 01:18:14.833928960	7	1	0.0	1	712496	84	1.427102e+07	-26.952822	96.426636	4	255	1	0	-12.560813	244	255	0.0	0	POINT (13.75533 52.07553)	6
2020-04-01 01:18:14.834029056	7	1	0.0	1	712496	84	1.427102e+07	-26.952822	108.848206	4	255	2	0	-11.873586	67	255	0.0	0	POINT (13.75533 52.07552)	6

Interactive Mapping with Leaflet

Global (Web Mercator, EPSG:3857)	North (Alaska Polar Stereographic, EPSG:5936)	South (Antarctic Polar Stereographic, EPSG:3031)
USGS 3DEP Hillshade ASTER GDEM Hillshade ESRI Imagery Global Land Ice Measurements from Space (GLIMS) Randolph Glacier Inventory (RGI)	ESRI Imagery ArcticDEM	Landsat Image Mosaic of Antarctica (LIMA) MODIS Mosaic of Antarctica (MOA) Radarsat Antarctic Mapping Project (RAMP) Reference Elevation Model of Antarctica (REMA)

# display widgets for setting SlideRule parameters
SRwidgets = ipysliderule.widgets()
SRwidgets.set_atl03_defaults()
widgets.VBox([
    SRwidgets.projection,
    SRwidgets.layers,
    SRwidgets.raster_functions
])

# center map on polygon centroid
lon, lat = io.from_region(parms['poly'])
cx, cy = io.centroid(lon, lat)
# create ipyleaflet map in specified projection
m = ipysliderule.leaflet(SRwidgets.projection.value,
    center=(cy, cx), prefer_canvas=True)
# install click handler callback
m.add_selected_callback(SRwidgets.atl03_click_handler)
# add polygon from web demo code
m.add_region([parms['poly']])
# show map
m.map

# add any selected base layers
m.add_layer(
    layers=SRwidgets.layers.value,
    rendering_rule=SRwidgets.rendering_rule
)

Add GeoDataFrame to map

For stability of the leaflet map, SlideRule will as a default limit the plot to have up to 10000 points from the GeoDataFrame

GeoDataFrames can be plotted in any available matplotlib colormap

widgets.VBox([
    SRwidgets.variable,
    SRwidgets.cmap,
    SRwidgets.reverse,
])

%matplotlib inline
# ATL03 fields for hover tooltip
fields = m.default_atl03_fields()
m.GeoData(gdf, column_name=SRwidgets.variable.value, cmap=SRwidgets.colormap,
    max_plot_points=10000, tooltip=True, colorbar=True, fields=fields)

Visualize photons as an along-track plot

widgets.VBox([
    SRwidgets.plot_classification,
    SRwidgets.rgt,
    SRwidgets.ground_track,
    SRwidgets.cycle,
])

%matplotlib inline
SRwidgets.plot(atl03=gdf, kind='scatter', title='Photon Cloud',
    cmap=SRwidgets.colormap, legend=True, legend_frameon=True,
    classification=SRwidgets.plot_classification.value, 
    segments=False)