Testing examples from topography¶
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#!mamba env create -f '../../environment.yml'
#!mamba env update -f '../../environment.yml' --prune
#!mamba env create -f '../../environment.yml'
#!mamba env update -f '../../environment.yml' --prune
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# this block is for developing the module, comment out when using the module, and uncomment import easysnowdata
%load_ext autoreload
%autoreload 2
%aimport easysnowdata
# this block is for developing the module, comment out when using the module, and uncomment import easysnowdata
%load_ext autoreload
%autoreload 2
%aimport easysnowdata
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import geopandas as gpd
import rioxarray as rxr
import xarray as xr
import shapely
import dask
import pystac_client
import planetary_computer
import odc.stac
import matplotlib.pyplot as plt
import matplotlib.colors
import datetime
import geopandas as gpd
import rioxarray as rxr
import xarray as xr
import shapely
import dask
import pystac_client
import planetary_computer
import odc.stac
import matplotlib.pyplot as plt
import matplotlib.colors
import datetime
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bbox_gdf = gpd.read_file('https://github.com/egagli/sar_snowmelt_timing/raw/main/input/shapefiles/mt_rainier.geojson')
bbox_gdf = gpd.read_file('https://github.com/egagli/sar_snowmelt_timing/raw/main/input/shapefiles/mt_rainier.geojson')
Copernicus DEM¶
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%%time
cop30_dem = easysnowdata.topography.get_copernicus_dem(bbox_gdf)
cop30_dem
%%time
cop30_dem = easysnowdata.topography.get_copernicus_dem(bbox_gdf)
cop30_dem
CPU times: user 19.3 ms, sys: 9.33 ms, total: 28.6 ms Wall time: 1.9 s
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<xarray.DataArray 'data' (latitude: 563, longitude: 828)> Size: 2MB
dask.array<getitem, shape=(563, 828), dtype=float32, chunksize=(563, 828), chunktype=numpy.ndarray>
Coordinates:
* latitude (latitude) float64 5kB 46.95 46.95 46.95 ... 46.79 46.79 46.79
* longitude (longitude) float64 7kB -121.9 -121.9 -121.9 ... -121.6 -121.6
spatial_ref int32 4B 4326
time datetime64[ns] 8B 2021-04-22In [5]:
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%%time
cop90_dem = easysnowdata.topography.get_copernicus_dem(bbox_gdf, resolution=90)
cop90_dem
%%time
cop90_dem = easysnowdata.topography.get_copernicus_dem(bbox_gdf, resolution=90)
cop90_dem
CPU times: user 13.1 ms, sys: 9.52 ms, total: 22.6 ms Wall time: 1.17 s
Out[5]:
<xarray.DataArray 'data' (latitude: 188, longitude: 276)> Size: 208kB
dask.array<getitem, shape=(188, 276), dtype=float32, chunksize=(188, 276), chunktype=numpy.ndarray>
Coordinates:
* latitude (latitude) float64 2kB 46.95 46.95 46.95 ... 46.8 46.79 46.79
* longitude (longitude) float64 2kB -121.9 -121.9 -121.9 ... -121.6 -121.6
spatial_ref int32 4B 4326
time datetime64[ns] 8B 2021-04-22In [7]:
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f,ax=plt.subplots(1,2,figsize=(12,5))
cop30_dem.plot(ax=ax[0],cmap='terrain')
cop90_dem.plot(ax=ax[1],cmap='terrain')
ax[0].set_title('Copernicus 30m DEM')
ax[1].set_title('Copernicus 90m DEM')
f,ax=plt.subplots(1,2,figsize=(12,5))
cop30_dem.plot(ax=ax[0],cmap='terrain')
cop90_dem.plot(ax=ax[1],cmap='terrain')
ax[0].set_title('Copernicus 30m DEM')
ax[1].set_title('Copernicus 90m DEM')
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Text(0.5, 1.0, 'Copernicus 90m DEM')
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# %%time
# dsm_3dep = easysnowdata.topography.get_3dep_dem(bbox_gdf,dem_type='DTM')
# dsm_3dep.item_collection()
# %%time
# dsm_3dep = easysnowdata.topography.get_3dep_dem(bbox_gdf,dem_type='DTM')
# dsm_3dep.item_collection()
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# !pip install -q py3dep
# import py3dep
# !pip install -q py3dep
# import py3dep
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# py3dep.get_map('DEM',geometry=bbox_gdf.geometry[0],resolution=10).plot()
# py3dep.get_map('DEM',geometry=bbox_gdf.geometry[0],resolution=10).plot()
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# tuple(bbox_gdf.bounds.values[0])
# tuple(bbox_gdf.bounds.values[0])
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# py3dep.query_3dep_sources(tuple(bbox_gdf.bounds.values[0]))
# py3dep.query_3dep_sources(tuple(bbox_gdf.bounds.values[0]))
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# py3dep.check_3dep_availability(tuple(bbox_gdf.bounds.values[0]))
# py3dep.check_3dep_availability(tuple(bbox_gdf.bounds.values[0]))
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