Xarray + rio-tiler
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import xarray
import matplotlib.pyplot as plt
from rio_tiler.io.xarray import XarrayReader
import xarray
import matplotlib.pyplot as plt
from rio_tiler.io.xarray import XarrayReader
daymet¶
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ds = xarray.open_dataset(
"https://pangeo.blob.core.windows.net/pangeo-public/daymet-rio-tiler/na-wgs84.zarr/",
engine="zarr",
decode_coords="all",
consolidated=True,
)
ds
ds = xarray.open_dataset(
"https://pangeo.blob.core.windows.net/pangeo-public/daymet-rio-tiler/na-wgs84.zarr/",
engine="zarr",
decode_coords="all",
consolidated=True,
)
ds
Out[2]:
<xarray.Dataset>
Dimensions: (time: 1, y: 3728, x: 17268)
Coordinates:
lambert_conformal_conic int64 0
* time (time) datetime64[ns] 1980-07-01T12:00:00
* x (x) float64 -180.0 -180.0 -179.9 ... 180.0 180.0
* y (y) float64 83.78 83.76 83.74 ... 6.126 6.105 6.084
Data variables:
tmax (time, y, x) float32 ...
Attributes:
Conventions: CF-1.6
Version_data: Daymet Data Version 4.0
Version_software: Daymet Software Version 4.0
citation: Please see http://daymet.ornl.gov/ for current Daymet ...
references: Please see http://daymet.ornl.gov/ for current informa...
source: Daymet Software Version 4.0
start_year: 1980In [3]:
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da = ds["tmax"]
da
da = ds["tmax"]
da
Out[3]:
<xarray.DataArray 'tmax' (time: 1, y: 3728, x: 17268)>
[64375104 values with dtype=float32]
Coordinates:
lambert_conformal_conic int64 0
* time (time) datetime64[ns] 1980-07-01T12:00:00
* x (x) float64 -180.0 -180.0 -179.9 ... 180.0 180.0
* y (y) float64 83.78 83.76 83.74 ... 6.126 6.105 6.084
Attributes:
cell_methods: area: mean time: maximum within days time: mean over days
coordinates: lon lat
long_name: annual average of daily maximum temperature
units: degrees CIn [4]:
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da = ds["tmax"]
with XarrayReader(da) as dst:
print(dst.info())
da = ds["tmax"]
with XarrayReader(da) as dst:
print(dst.info())
bounds=BoundingBox(left=-179.99998449579846, bottom=6.073484821356791, right=179.98170598363066, top=83.79467217916716) minzoom=1 maxzoom=6 band_metadata=[('b1', {'long_name': '24-hour day based on local time', 'standard_name': 'time'})] band_descriptions=[('b1', '1980-07-01T12:00:00.000000000')] dtype='float32' nodata_type='Nodata' colorinterp=None scale=None offset=None colormap=None count=1 width=17268 attrs={'cell_methods': 'area: mean time: maximum within days time: mean over days', 'coordinates': 'lon lat', 'long_name': 'annual average of daily maximum temperature', 'units': 'degrees C'} name='tmax' height=3728
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with XarrayReader(da) as dst:
img = dst.tile(1, 1, 2)
plt.imshow(img.data_as_image());
with XarrayReader(da) as dst:
img = dst.tile(1, 1, 2)
plt.imshow(img.data_as_image());
noaa-coastwatch-geopolar-sst¶
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with xarray.open_dataset(
"https://ncsa.osn.xsede.org/Pangeo/pangeo-forge/noaa-coastwatch-geopolar-sst-feedstock/noaa-coastwatch-geopolar-sst.zarr",
engine="zarr",
decode_coords="all"
) as src:
ds = src["analysed_sst"][:1]
# the SST dataset do not have a CRS info
# so we need to add it to `virtualy` within the Xarray DataArray
ds.rio.write_crs("epsg:4326", inplace=True)
with XarrayReader(ds) as dst:
print(dst.info())
img = dst.tile(1, 1, 2)
plt.imshow(img.data_as_image())
with xarray.open_dataset(
"https://ncsa.osn.xsede.org/Pangeo/pangeo-forge/noaa-coastwatch-geopolar-sst-feedstock/noaa-coastwatch-geopolar-sst.zarr",
engine="zarr",
decode_coords="all"
) as src:
ds = src["analysed_sst"][:1]
# the SST dataset do not have a CRS info
# so we need to add it to `virtualy` within the Xarray DataArray
ds.rio.write_crs("epsg:4326", inplace=True)
with XarrayReader(ds) as dst:
print(dst.info())
img = dst.tile(1, 1, 2)
plt.imshow(img.data_as_image())
bounds=BoundingBox(left=-180.00000610436345, bottom=-89.99999847369712, right=180.00000610436345, top=89.99999847369712) minzoom=0 maxzoom=2 band_metadata=[('b1', {'axis': 'T', 'comment': 'Nominal time of Level 4 analysis', 'long_name': 'reference time of sst field', 'standard_name': 'time'})] band_descriptions=[('b1', '2002-09-01T12:00:00.000000000')] dtype='float32' nodata_type='Nodata' colorinterp=None scale=None offset=None colormap=None count=1 width=7200 attrs={'comment': 'Analysed SST for each ocean grid point', 'long_name': 'analysed sea surface temperature', 'reference': 'Fieguth,P.W. et al. "Mapping Mediterranean altimeter data with a multiresolution optimal interpolation algorithm", J. Atmos. Ocean Tech, 15 (2): 535-546, 1998. Fieguth, P. Multiply-Rooted Multiscale Models for Large-Scale Estimation, IEEE Image Processing, 10(11), 1676-1686, 2001. Khellah, F., P.W. Fieguth, M.J. Murray and M.R. Allen, "Statistical Processing of Large Image Sequences", IEEE Transactions on Geoscience and Remote Sensing, 12 (1), 80-93, 2005. Maturi, E., A. Harris, J. Mittaz, J. Sapper, G. Wick, X. Zhu, P. Dash, P. Koner, "A New High-Resolution Sea Surface Temperature Blended Analysis", Bulleting of the American Meteorological Society, 98 (5), 1015-1026, 2017.', 'source': 'STAR-ACSPO_GAC, STAR-ACSPO_H-8, STAR-Geo_SST, UKMO-OSTIA', 'standard_name': 'sea_surface_foundation_temperature', 'units': 'kelvin', 'valid_max': 4000, 'valid_min': -200} name='analysed_sst' height=3600
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