def check_open_with_dc(index):
from datacube.api.core import Datacube
dc = Datacube(index=index)
data_array = dc.load(product='ls5_nbar_albers',
variables=['blue'],
stack='variable')
assert data_array.shape
data_array = dc.load(product='ls5_nbar_albers',
latitude=(-34, -35),
longitude=(149, 150),
stack='variable')
assert data_array.shape
dataset = dc.load(product='ls5_nbar_albers', variables=['blue'])
assert dataset['blue'].size
dataset = dc.load(product='ls5_nbar_albers',
latitude=(-35.2, -35.3),
longitude=(149.1, 149.2))
assert dataset['blue'].size
data_array = dc.load(product='ls5_nbar_albers',
latitude=(-34, -35),
longitude=(149, 150),
variables=['blue'],
group_by='solar_day')
products_df = dc.list_products()
assert len(products_df)
assert len(products_df[products_df['name'].isin(['ls5_nbar_albers'])])
assert len(products_df[products_df['name'].isin(['ls5_pq_albers'])])
assert len(dc.list_measurements())
def check_open_with_dc(index):
from datacube.api.core import Datacube
dc = Datacube(index=index)
data_array = dc.load(product='ls5_nbar_albers', variables=['blue'], stack='variable')
assert data_array.shape
data_array = dc.load(product='ls5_nbar_albers', latitude=(-34, -35), longitude=(149, 150), stack='variable')
assert data_array.shape
dataset = dc.load(product='ls5_nbar_albers', variables=['blue'])
assert dataset['blue'].size
dataset = dc.load(product='ls5_nbar_albers', latitude=(-35.2, -35.3), longitude=(149.1, 149.2))
assert dataset['blue'].size
data_array = dc.load(product='ls5_nbar_albers',
latitude=(-34, -35), longitude=(149, 150),
variables=['blue'], group_by='solar_day')
products_df = dc.list_products()
assert len(products_df)
assert len(products_df[products_df['name'].isin(['ls5_nbar_albers'])])
assert len(products_df[products_df['name'].isin(['ls5_pq_albers'])])
assert len(dc.list_measurements())
def check_open_with_dc(index):
from datacube.api.core import Datacube
dc = Datacube(index=index)
data_array = dc.load(product='ls5_nbar_albers', measurements=['blue'], stack='variable')
assert data_array.shape
assert (data_array != -999).any()
data_array = dc.load(product='ls5_nbar_albers', measurements=['blue'], time='1992-03-23T23:14:25.500000')
assert data_array['blue'].shape[0] == 1
assert (data_array.blue != -999).any()
data_array = dc.load(product='ls5_nbar_albers', measurements=['blue'], latitude=-35.3, longitude=149.1)
assert data_array['blue'].shape[1:] == (1, 1)
assert (data_array.blue != -999).any()
data_array = dc.load(product='ls5_nbar_albers', latitude=(-35, -36), longitude=(149, 150), stack='variable')
assert data_array.ndim == 4
assert 'variable' in data_array.dims
assert (data_array != -999).any()
with rasterio.Env():
lazy_data_array = dc.load(product='ls5_nbar_albers', latitude=(-35, -36), longitude=(149, 150),
stack='variable', dask_chunks={'time': 1, 'x': 1000, 'y': 1000})
assert lazy_data_array.data.dask
assert lazy_data_array.ndim == data_array.ndim
assert 'variable' in lazy_data_array.dims
assert lazy_data_array[1, :2, 950:1050, 950:1050].equals(data_array[1, :2, 950:1050, 950:1050])
dataset = dc.load(product='ls5_nbar_albers', measurements=['blue'])
assert dataset['blue'].size
dataset = dc.load(product='ls5_nbar_albers', latitude=(-35.2, -35.3), longitude=(149.1, 149.2))
assert dataset['blue'].size
with rasterio.Env():
lazy_dataset = dc.load(product='ls5_nbar_albers', latitude=(-35.2, -35.3), longitude=(149.1, 149.2),
dask_chunks={'time': 1})
assert lazy_dataset['blue'].data.dask
assert lazy_dataset.blue[:2, :100, :100].equals(dataset.blue[:2, :100, :100])
assert lazy_dataset.isel(time=slice(0, 2), x=slice(950, 1050), y=slice(950, 1050)).equals(
dataset.isel(time=slice(0, 2), x=slice(950, 1050), y=slice(950, 1050)))
dataset_like = dc.load(product='ls5_nbar_albers', measurements=['blue'], like=dataset)
assert (dataset.blue == dataset_like.blue).all()
data_array = dc.load(product='ls5_nbar_albers',
latitude=(-35, -36), longitude=(149, 150),
measurements=['blue'], group_by='solar_day')
dataset = dc.load(product='ls5_nbar_albers', latitude=(-35.2, -35.3), longitude=(149.1, 149.2), align=(5, 20))
assert dataset.geobox.affine.f % abs(dataset.geobox.affine.e) == 5
assert dataset.geobox.affine.c % abs(dataset.geobox.affine.a) == 20
dataset_like = dc.load(product='ls5_nbar_albers', measurements=['blue'], like=dataset)
assert (dataset.blue == dataset_like.blue).all()
products_df = dc.list_products()
assert len(products_df)
assert len(products_df[products_df['name'].isin(['ls5_nbar_albers'])])
assert len(products_df[products_df['name'].isin(['ls5_pq_albers'])])
assert len(dc.list_measurements())
resamp = ['nearest', 'cubic', 'bilinear', 'cubic_spline', 'lanczos', 'average']
results = {}
# WTF
def calc_max_change(da):
midline = int(da.shape[0] * 0.5)
a = int(abs(da[midline, :-1].data - da[midline, 1:].data).max())
centerline = int(da.shape[1] * 0.5)
b = int(abs(da[:-1, centerline].data - da[1:, centerline].data).max())
return a + b
for resamp_meth in resamp:
dataset = dc.load(product='ls5_nbar_albers', measurements=['blue'],
latitude=(-35.28, -35.285), longitude=(149.15, 149.155),
output_crs='EPSG:4326', resolution=(-0.0000125, 0.0000125), resampling=resamp_meth)
results[resamp_meth] = calc_max_change(dataset.blue.isel(time=0))
assert results['cubic_spline'] < results['nearest']
assert results['lanczos'] < results['average']
