Cubes Visualization with Lexcube¶
This tutorial shows how to visualize different cubes using Lexcube:
[1]:
importcuboimportlexcube
If you are in Google Colab you have to run the following cell:
[ ]:
# from google.colab import output# output.enable_custom_widget_manager()
Sentinel-2¶
An example over crop fields in Colombia.
[2]:
s2=cubo.create(lat=4.31,lon=-76.2,collection="sentinel-2-l2a",bands=["B02","B03","B04"],start_date="2019-01-01",end_date="2021-12-31",edge_size=64,resolution=10,query={"eo:cloud_cover":{"lt":40}})ws2=lexcube.Cube3DWidget(s2.sel(band="B04"),cmap="viridis",vmin=0,vmax=2000)ws2
/net/home/dmontero/.conda/envs/cube/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument. times = pd.to_datetime(
[2]:
Sentinel-1¶
A C-band cube over the Panama Canal.
[5]:
s1=cubo.create(lat=8.93548,lon=-79.56005,collection="sentinel-1-rtc",bands=["vv","vh"],start_date="2021-01-01",end_date="2021-12-31",edge_size=256,resolution=10,)ws1=lexcube.Cube3DWidget(s1.sel(band="vv"),cmap="Greys_r",vmin=0,vmax=0.6)ws1
/net/home/dmontero/.conda/envs/cube/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument. times = pd.to_datetime(
[5]:
MODIS Gross Primary Productivity (GPP)¶
GPP over the Hainich National Park in Germany.
[6]:
gpp=cubo.create(lat=51.0825008,lon=10.437455,collection="modis-17A2H-061",bands=["Gpp_500m"],start_date="2022-01-01",end_date="2022-12-31",edge_size=64,resolution=500,).where(lambdax:x["platform"]=="terra",drop=True)wgpp=lexcube.Cube3DWidget(gpp.sel(band="Gpp_500m"),cmap="YlGn",vmin=0,vmax=0.1)wgpp
/net/home/dmontero/.conda/envs/cube/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument. times = pd.to_datetime(
[6]:
MODIS Land Surface Temperature (LST)¶
LST over Budapest.
[12]:
lst=cubo.create(lat=47.49744,lon=19.048363,collection="modis-11A2-061",bands=["LST_Day_1km"],start_date="2022-01-01",end_date="2022-12-31",edge_size=128,resolution=500,).where(lambdax:x["platform"]=="terra",drop=True)wlst=lexcube.Cube3DWidget(lst.sel(band="LST_Day_1km"),cmap="inferno",vmin=270,vmax=310)wlst
/net/home/dmontero/.conda/envs/cube/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument. times = pd.to_datetime(
[12]: