How easy to use is easysnowdata?¶
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import easysnowdata
import easysnowdata
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bbox = (-122.0, 46.7, -121.5, 47.0)
snow_classification_da = easysnowdata.remote_sensing.get_esa_worldcover(bbox)
#snow_classification_da
bbox = (-122.0, 46.7, -121.5, 47.0)
snow_classification_da = easysnowdata.remote_sensing.get_esa_worldcover(bbox)
#snow_classification_da
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f,ax = snow_classification_da.attrs['example_plot'](snow_classification_da)
f,ax = snow_classification_da.attrs['example_plot'](snow_classification_da)
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s2 = easysnowdata.remote_sensing.Sentinel2(
bbox_input=bbox,
start_date="2022-07-21",
end_date="2022-07-31",
resolution=80,
)
#s2.data
s2 = easysnowdata.remote_sensing.Sentinel2(
bbox_input=bbox,
start_date="2022-07-21",
end_date="2022-07-31",
resolution=80,
)
#s2.data
Data searched. Access the returned seach with the .search attribute. Data retrieved. Access with the .data attribute. Data CRS: WGS 84 / UTM zone 10N. Nodata values removed from the data. In doing so, all bands converted to float32. To turn this behavior off, set remove_nodata=False. Data acquired after January 25th, 2022 harmonized to old baseline. To override this behavior, set harmonize_to_old=False. Data scaled to float reflectance. To turn this behavior off, set scale_data=False. Metadata retrieved. Access with the .metadata attribute.
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s2.get_rgb()
s2.get_rgb()
RGB data retrieved. Access with the following attributes: .rgb for raw RGB, .rgba for RGBA, .rgb_percentile for percentile RGB, .rgb_clahe for CLAHE RGB. You can pass in percentile_kwargs and clahe_kwargs to adjust RGB calculations, check documentation for options.
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s2.rgb_clahe.plot.imshow(col='time',col_wrap=3)
s2.rgb_clahe.plot.imshow(col='time',col_wrap=3)
/home/eric/miniconda3/envs/easysnowdata/lib/python3.10/site-packages/rasterio/warp.py:344: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned. _reproject(
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<xarray.plot.facetgrid.FacetGrid at 0x7f68575bdd50>
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