# xcube_sh imports
from xcube_sh.cube import open_cube
from xcube_sh.config import CubeConfig
from xcube_sh.sentinelhub import SentinelHub
Sentinel-1#
Issues:
How to query by track number
bands ‘localIncidenceAngle’ and ‘shadowMask’ return an error
how to set the value for ‘orthorectify’ in the Processing Options (https://docs.sentinel-hub.com/api/latest/data/sentinel-1-grd/#processing-options)?
SentinelHub().band_names('S1GRD')
['VV', 'HH', 'VH', 'localIncidenceAngle', 'scatteringArea', 'shadowMask', 'HV']
cube_config_s1 = CubeConfig(
dataset_name='S1GRD',
band_names=['VV', 'VH', 'localIncidenceAngle', 'shadowMask'],
bbox=[11.02, 46.65, 11.36, 46.95],
spatial_res=0.0018, # = 100 meters in degree>
time_range=['2018-02-01', '2018-06-30'],
time_period='6D'
)
cube_s1 = open_cube(cube_config_s1)
cube_s1
<xarray.Dataset>
Dimensions: (time: 25, lat: 167, lon: 189, bnds: 2)
Coordinates:
* lat (lat) float64 46.95 46.95 46.95 ... 46.65 46.65 46.65
* lon (lon) float64 11.02 11.02 11.02 ... 11.36 11.36 11.36
* time (time) datetime64[ns] 2018-02-04 ... 2018-06-28
time_bnds (time, bnds) datetime64[ns] dask.array<chunksize=(25, 2), meta=np.ndarray>
Dimensions without coordinates: bnds
Data variables:
VH (time, lat, lon) float32 dask.array<chunksize=(1, 167, 189), meta=np.ndarray>
VV (time, lat, lon) float32 dask.array<chunksize=(1, 167, 189), meta=np.ndarray>
localIncidenceAngle (time, lat, lon) float32 dask.array<chunksize=(1, 167, 189), meta=np.ndarray>
shadowMask (time, lat, lon) float32 dask.array<chunksize=(1, 167, 189), meta=np.ndarray>
Attributes: (12/13)
Conventions: CF-1.7
title: S1GRD Data Cube Subset
history: [{'program': 'xcube_sh.chunkstore.SentinelHubC...
date_created: 2023-03-07T06:54:16.049322
time_coverage_start: 2018-02-01T00:00:00+00:00
time_coverage_end: 2018-07-01T00:00:00+00:00
... ...
time_coverage_resolution: P6DT0H0M0S
geospatial_lon_min: 11.02
geospatial_lat_min: 46.65
geospatial_lon_max: 11.360199999999999
geospatial_lat_max: 46.9506
processing_level: L1B cube_s1.VV.sel(time='2018-05-10', method='nearest').plot.imshow(vmin=0, vmax=1)
<matplotlib.image.AxesImage at 0x7fcfae7c2970>
cube_s1.VH.sel(time='2018-05-10', method='nearest').plot.imshow(vmin=0, vmax=1)
<matplotlib.image.AxesImage at 0x7fcfac8551f0>
cube_s1.localIncidenceAngle.sel(time='2018-05-10', method='nearest').plot.imshow(vmin=0, vmax=1)
Failed to fetch data from Sentinel Hub after 44.90783905982971 seconds and 200 retries
HTTP status code was 400
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/storage.py:2434, in LRUStoreCache.__getitem__(self, key)
2433 with self._mutex:
-> 2434 value = self._values_cache[key]
2435 # cache hit if no KeyError is raised
KeyError: 'localIncidenceAngle/16.0.0'
During handling of the above exception, another exception occurred:
HTTPError Traceback (most recent call last)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/sentinelhub.py:645, in SentinelHubError.maybe_raise_for_response(cls, response)
644 try:
--> 645 response.raise_for_status()
646 except requests.HTTPError as e:
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/requests/models.py:1021, in Response.raise_for_status(self)
1020 if http_error_msg:
-> 1021 raise HTTPError(http_error_msg, response=self)
HTTPError: 400 Client Error: Bad Request for url: https://services.sentinel-hub.com/api/v1/process
The above exception was the direct cause of the following exception:
SentinelHubError Traceback (most recent call last)
Cell In [7], line 1
----> 1 cube_s1.localIncidenceAngle.sel(time='2018-05-10', method='nearest').plot.imshow(vmin=0, vmax=1)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/plot/plot.py:1311, in _plot2d.<locals>.plotmethod(_PlotMethods_obj, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, colors, center, robust, extend, levels, infer_intervals, subplot_kws, cbar_ax, cbar_kwargs, xscale, yscale, xticks, yticks, xlim, ylim, norm, **kwargs)
1309 for arg in ["_PlotMethods_obj", "newplotfunc", "kwargs"]:
1310 del allargs[arg]
-> 1311 return newplotfunc(**allargs)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/plot/plot.py:1175, in _plot2d.<locals>.newplotfunc(darray, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, center, robust, extend, levels, infer_intervals, colors, subplot_kws, cbar_ax, cbar_kwargs, xscale, yscale, xticks, yticks, xlim, ylim, norm, **kwargs)
1172 yval = yval.to_numpy()
1174 # Pass the data as a masked ndarray too
-> 1175 zval = darray.to_masked_array(copy=False)
1177 # Replace pd.Intervals if contained in xval or yval.
1178 xplt, xlab_extra = _resolve_intervals_2dplot(xval, plotfunc.__name__)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/dataarray.py:3574, in DataArray.to_masked_array(self, copy)
3560 def to_masked_array(self, copy: bool = True) -> np.ma.MaskedArray:
3561 """Convert this array into a numpy.ma.MaskedArray
3562
3563 Parameters
(...)
3572 Masked where invalid values (nan or inf) occur.
3573 """
-> 3574 values = self.to_numpy() # only compute lazy arrays once
3575 isnull = pd.isnull(values)
3576 return np.ma.MaskedArray(data=values, mask=isnull, copy=copy)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/dataarray.py:740, in DataArray.to_numpy(self)
729 def to_numpy(self) -> np.ndarray:
730 """
731 Coerces wrapped data to numpy and returns a numpy.ndarray.
732
(...)
738 DataArray.data
739 """
--> 740 return self.variable.to_numpy()
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/variable.py:1178, in Variable.to_numpy(self)
1176 # TODO first attempt to call .to_numpy() once some libraries implement it
1177 if hasattr(data, "chunks"):
-> 1178 data = data.compute()
1179 if isinstance(data, cupy_array_type):
1180 data = data.get()
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/base.py:315, in DaskMethodsMixin.compute(self, **kwargs)
291 def compute(self, **kwargs):
292 """Compute this dask collection
293
294 This turns a lazy Dask collection into its in-memory equivalent.
(...)
313 dask.base.compute
314 """
--> 315 (result,) = compute(self, traverse=False, **kwargs)
316 return result
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/base.py:600, in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
597 keys.append(x.__dask_keys__())
598 postcomputes.append(x.__dask_postcompute__())
--> 600 results = schedule(dsk, keys, **kwargs)
601 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/threaded.py:89, in get(dsk, keys, cache, num_workers, pool, **kwargs)
86 elif isinstance(pool, multiprocessing.pool.Pool):
87 pool = MultiprocessingPoolExecutor(pool)
---> 89 results = get_async(
90 pool.submit,
91 pool._max_workers,
92 dsk,
93 keys,
94 cache=cache,
95 get_id=_thread_get_id,
96 pack_exception=pack_exception,
97 **kwargs,
98 )
100 # Cleanup pools associated to dead threads
101 with pools_lock:
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/local.py:511, in get_async(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs)
509 _execute_task(task, data) # Re-execute locally
510 else:
--> 511 raise_exception(exc, tb)
512 res, worker_id = loads(res_info)
513 state["cache"][key] = res
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/local.py:319, in reraise(exc, tb)
317 if exc.__traceback__ is not tb:
318 raise exc.with_traceback(tb)
--> 319 raise exc
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/local.py:224, in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
222 try:
223 task, data = loads(task_info)
--> 224 result = _execute_task(task, data)
225 id = get_id()
226 result = dumps((result, id))
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/core.py:119, in _execute_task(arg, cache, dsk)
115 func, args = arg[0], arg[1:]
116 # Note: Don't assign the subtask results to a variable. numpy detects
117 # temporaries by their reference count and can execute certain
118 # operations in-place.
--> 119 return func(*(_execute_task(a, cache) for a in args))
120 elif not ishashable(arg):
121 return arg
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/array/core.py:127, in getter(a, b, asarray, lock)
122 # Below we special-case `np.matrix` to force a conversion to
123 # `np.ndarray` and preserve original Dask behavior for `getter`,
124 # as for all purposes `np.matrix` is array-like and thus
125 # `is_arraylike` evaluates to `True` in that case.
126 if asarray and (not is_arraylike(c) or isinstance(c, np.matrix)):
--> 127 c = np.asarray(c)
128 finally:
129 if lock:
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/indexing.py:459, in ImplicitToExplicitIndexingAdapter.__array__(self, dtype)
458 def __array__(self, dtype=None):
--> 459 return np.asarray(self.array, dtype=dtype)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/indexing.py:623, in CopyOnWriteArray.__array__(self, dtype)
622 def __array__(self, dtype=None):
--> 623 return np.asarray(self.array, dtype=dtype)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/indexing.py:524, in LazilyIndexedArray.__array__(self, dtype)
522 def __array__(self, dtype=None):
523 array = as_indexable(self.array)
--> 524 return np.asarray(array[self.key], dtype=None)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/backends/zarr.py:76, in ZarrArrayWrapper.__getitem__(self, key)
74 array = self.get_array()
75 if isinstance(key, indexing.BasicIndexer):
---> 76 return array[key.tuple]
77 elif isinstance(key, indexing.VectorizedIndexer):
78 return array.vindex[
79 indexing._arrayize_vectorized_indexer(key, self.shape).tuple
80 ]
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:807, in Array.__getitem__(self, selection)
805 result = self.vindex[selection]
806 else:
--> 807 result = self.get_basic_selection(pure_selection, fields=fields)
808 return result
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:933, in Array.get_basic_selection(self, selection, out, fields)
930 return self._get_basic_selection_zd(selection=selection, out=out,
931 fields=fields)
932 else:
--> 933 return self._get_basic_selection_nd(selection=selection, out=out,
934 fields=fields)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:976, in Array._get_basic_selection_nd(self, selection, out, fields)
970 def _get_basic_selection_nd(self, selection, out=None, fields=None):
971 # implementation of basic selection for array with at least one dimension
972
973 # setup indexer
974 indexer = BasicIndexer(selection, self)
--> 976 return self._get_selection(indexer=indexer, out=out, fields=fields)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:1267, in Array._get_selection(self, indexer, out, fields)
1261 if not hasattr(self.chunk_store, "getitems") or \
1262 any(map(lambda x: x == 0, self.shape)):
1263 # sequentially get one key at a time from storage
1264 for chunk_coords, chunk_selection, out_selection in indexer:
1265
1266 # load chunk selection into output array
-> 1267 self._chunk_getitem(chunk_coords, chunk_selection, out, out_selection,
1268 drop_axes=indexer.drop_axes, fields=fields)
1269 else:
1270 # allow storage to get multiple items at once
1271 lchunk_coords, lchunk_selection, lout_selection = zip(*indexer)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:1966, in Array._chunk_getitem(self, chunk_coords, chunk_selection, out, out_selection, drop_axes, fields)
1962 ckey = self._chunk_key(chunk_coords)
1964 try:
1965 # obtain compressed data for chunk
-> 1966 cdata = self.chunk_store[ckey]
1968 except KeyError:
1969 # chunk not initialized
1970 if self._fill_value is not None:
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/storage.py:2442, in LRUStoreCache.__getitem__(self, key)
2438 self._values_cache.move_to_end(key)
2440 except KeyError:
2441 # cache miss, retrieve value from the store
-> 2442 value = self._store[key]
2443 with self._mutex:
2444 self.misses += 1
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/storage.py:724, in KVStore.__getitem__(self, key)
723 def __getitem__(self, key):
--> 724 return self._mutable_mapping[key]
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:508, in RemoteStore.__getitem__(self, key)
506 value = self._vfs[key]
507 if isinstance(value, tuple):
--> 508 return self._fetch_chunk(key, *value)
509 return value
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:419, in RemoteStore._fetch_chunk(self, key, band_name, chunk_index)
411 observer(band_name=band_name,
412 chunk_index=chunk_index,
413 bbox=request_bbox,
414 time_range=request_time_range,
415 duration=duration,
416 exception=exception)
418 if exception:
--> 419 raise exception
421 return chunk_data
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:400, in RemoteStore._fetch_chunk(self, key, band_name, chunk_index)
398 try:
399 exception = None
--> 400 chunk_data = self.fetch_chunk(key,
401 band_name,
402 chunk_index,
403 bbox=request_bbox,
404 time_range=request_time_range)
405 except Exception as e:
406 exception = e
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:729, in SentinelHubChunkStore.fetch_chunk(self, key, band_name, chunk_index, bbox, time_range)
712 band_sample_types = band_sample_types[index]
714 request = SentinelHub.new_data_request(
715 self.cube_config.dataset_name,
716 band_names,
(...)
726 band_units=self.cube_config.band_units
727 )
--> 729 response = self._sentinel_hub.get_data(
730 request,
731 mime_type='application/octet-stream'
732 )
734 if response is None or not response.ok:
735 message = (f'{key}: cannot fetch chunk for variable'
736 f' {band_name!r}, bbox {bbox!r}, and'
737 f' time_range {time_range!r}')
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/sentinelhub.py:432, in SentinelHub.get_data(self, request, mime_type)
430 raise response_error
431 elif response is not None:
--> 432 SentinelHubError.maybe_raise_for_response(response)
433 elif self.error_policy == 'warn' and self.enable_warnings:
434 if response_error:
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/sentinelhub.py:655, in SentinelHubError.maybe_raise_for_response(cls, response)
653 except Exception:
654 pass
--> 655 raise SentinelHubError(f'{e}: {detail}' if detail else f'{e}',
656 response=response) from e
SentinelHubError: 400 Client Error: Bad Request for url: https://services.sentinel-hub.com/api/v1/process
cube_s1.shadowMask.sel(time='2018-05-10', method='nearest').plot.imshow(vmin=0, vmax=1)
Failed to fetch data from Sentinel Hub after 39.48806810379028 seconds and 200 retries
HTTP status code was 400
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/storage.py:2434, in LRUStoreCache.__getitem__(self, key)
2433 with self._mutex:
-> 2434 value = self._values_cache[key]
2435 # cache hit if no KeyError is raised
KeyError: 'shadowMask/16.0.0'
During handling of the above exception, another exception occurred:
HTTPError Traceback (most recent call last)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/sentinelhub.py:645, in SentinelHubError.maybe_raise_for_response(cls, response)
644 try:
--> 645 response.raise_for_status()
646 except requests.HTTPError as e:
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/requests/models.py:1021, in Response.raise_for_status(self)
1020 if http_error_msg:
-> 1021 raise HTTPError(http_error_msg, response=self)
HTTPError: 400 Client Error: Bad Request for url: https://services.sentinel-hub.com/api/v1/process
The above exception was the direct cause of the following exception:
SentinelHubError Traceback (most recent call last)
Cell In [8], line 1
----> 1 cube_s1.shadowMask.sel(time='2018-05-10', method='nearest').plot.imshow(vmin=0, vmax=1)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/plot/plot.py:1311, in _plot2d.<locals>.plotmethod(_PlotMethods_obj, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, colors, center, robust, extend, levels, infer_intervals, subplot_kws, cbar_ax, cbar_kwargs, xscale, yscale, xticks, yticks, xlim, ylim, norm, **kwargs)
1309 for arg in ["_PlotMethods_obj", "newplotfunc", "kwargs"]:
1310 del allargs[arg]
-> 1311 return newplotfunc(**allargs)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/plot/plot.py:1175, in _plot2d.<locals>.newplotfunc(darray, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, center, robust, extend, levels, infer_intervals, colors, subplot_kws, cbar_ax, cbar_kwargs, xscale, yscale, xticks, yticks, xlim, ylim, norm, **kwargs)
1172 yval = yval.to_numpy()
1174 # Pass the data as a masked ndarray too
-> 1175 zval = darray.to_masked_array(copy=False)
1177 # Replace pd.Intervals if contained in xval or yval.
1178 xplt, xlab_extra = _resolve_intervals_2dplot(xval, plotfunc.__name__)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/dataarray.py:3574, in DataArray.to_masked_array(self, copy)
3560 def to_masked_array(self, copy: bool = True) -> np.ma.MaskedArray:
3561 """Convert this array into a numpy.ma.MaskedArray
3562
3563 Parameters
(...)
3572 Masked where invalid values (nan or inf) occur.
3573 """
-> 3574 values = self.to_numpy() # only compute lazy arrays once
3575 isnull = pd.isnull(values)
3576 return np.ma.MaskedArray(data=values, mask=isnull, copy=copy)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/dataarray.py:740, in DataArray.to_numpy(self)
729 def to_numpy(self) -> np.ndarray:
730 """
731 Coerces wrapped data to numpy and returns a numpy.ndarray.
732
(...)
738 DataArray.data
739 """
--> 740 return self.variable.to_numpy()
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/variable.py:1178, in Variable.to_numpy(self)
1176 # TODO first attempt to call .to_numpy() once some libraries implement it
1177 if hasattr(data, "chunks"):
-> 1178 data = data.compute()
1179 if isinstance(data, cupy_array_type):
1180 data = data.get()
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/base.py:315, in DaskMethodsMixin.compute(self, **kwargs)
291 def compute(self, **kwargs):
292 """Compute this dask collection
293
294 This turns a lazy Dask collection into its in-memory equivalent.
(...)
313 dask.base.compute
314 """
--> 315 (result,) = compute(self, traverse=False, **kwargs)
316 return result
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/base.py:600, in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
597 keys.append(x.__dask_keys__())
598 postcomputes.append(x.__dask_postcompute__())
--> 600 results = schedule(dsk, keys, **kwargs)
601 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/threaded.py:89, in get(dsk, keys, cache, num_workers, pool, **kwargs)
86 elif isinstance(pool, multiprocessing.pool.Pool):
87 pool = MultiprocessingPoolExecutor(pool)
---> 89 results = get_async(
90 pool.submit,
91 pool._max_workers,
92 dsk,
93 keys,
94 cache=cache,
95 get_id=_thread_get_id,
96 pack_exception=pack_exception,
97 **kwargs,
98 )
100 # Cleanup pools associated to dead threads
101 with pools_lock:
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/local.py:511, in get_async(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs)
509 _execute_task(task, data) # Re-execute locally
510 else:
--> 511 raise_exception(exc, tb)
512 res, worker_id = loads(res_info)
513 state["cache"][key] = res
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/local.py:319, in reraise(exc, tb)
317 if exc.__traceback__ is not tb:
318 raise exc.with_traceback(tb)
--> 319 raise exc
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/local.py:224, in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
222 try:
223 task, data = loads(task_info)
--> 224 result = _execute_task(task, data)
225 id = get_id()
226 result = dumps((result, id))
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/core.py:119, in _execute_task(arg, cache, dsk)
115 func, args = arg[0], arg[1:]
116 # Note: Don't assign the subtask results to a variable. numpy detects
117 # temporaries by their reference count and can execute certain
118 # operations in-place.
--> 119 return func(*(_execute_task(a, cache) for a in args))
120 elif not ishashable(arg):
121 return arg
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/dask/array/core.py:127, in getter(a, b, asarray, lock)
122 # Below we special-case `np.matrix` to force a conversion to
123 # `np.ndarray` and preserve original Dask behavior for `getter`,
124 # as for all purposes `np.matrix` is array-like and thus
125 # `is_arraylike` evaluates to `True` in that case.
126 if asarray and (not is_arraylike(c) or isinstance(c, np.matrix)):
--> 127 c = np.asarray(c)
128 finally:
129 if lock:
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/indexing.py:459, in ImplicitToExplicitIndexingAdapter.__array__(self, dtype)
458 def __array__(self, dtype=None):
--> 459 return np.asarray(self.array, dtype=dtype)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/indexing.py:623, in CopyOnWriteArray.__array__(self, dtype)
622 def __array__(self, dtype=None):
--> 623 return np.asarray(self.array, dtype=dtype)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/core/indexing.py:524, in LazilyIndexedArray.__array__(self, dtype)
522 def __array__(self, dtype=None):
523 array = as_indexable(self.array)
--> 524 return np.asarray(array[self.key], dtype=None)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xarray/backends/zarr.py:76, in ZarrArrayWrapper.__getitem__(self, key)
74 array = self.get_array()
75 if isinstance(key, indexing.BasicIndexer):
---> 76 return array[key.tuple]
77 elif isinstance(key, indexing.VectorizedIndexer):
78 return array.vindex[
79 indexing._arrayize_vectorized_indexer(key, self.shape).tuple
80 ]
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:807, in Array.__getitem__(self, selection)
805 result = self.vindex[selection]
806 else:
--> 807 result = self.get_basic_selection(pure_selection, fields=fields)
808 return result
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:933, in Array.get_basic_selection(self, selection, out, fields)
930 return self._get_basic_selection_zd(selection=selection, out=out,
931 fields=fields)
932 else:
--> 933 return self._get_basic_selection_nd(selection=selection, out=out,
934 fields=fields)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:976, in Array._get_basic_selection_nd(self, selection, out, fields)
970 def _get_basic_selection_nd(self, selection, out=None, fields=None):
971 # implementation of basic selection for array with at least one dimension
972
973 # setup indexer
974 indexer = BasicIndexer(selection, self)
--> 976 return self._get_selection(indexer=indexer, out=out, fields=fields)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:1267, in Array._get_selection(self, indexer, out, fields)
1261 if not hasattr(self.chunk_store, "getitems") or \
1262 any(map(lambda x: x == 0, self.shape)):
1263 # sequentially get one key at a time from storage
1264 for chunk_coords, chunk_selection, out_selection in indexer:
1265
1266 # load chunk selection into output array
-> 1267 self._chunk_getitem(chunk_coords, chunk_selection, out, out_selection,
1268 drop_axes=indexer.drop_axes, fields=fields)
1269 else:
1270 # allow storage to get multiple items at once
1271 lchunk_coords, lchunk_selection, lout_selection = zip(*indexer)
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/core.py:1966, in Array._chunk_getitem(self, chunk_coords, chunk_selection, out, out_selection, drop_axes, fields)
1962 ckey = self._chunk_key(chunk_coords)
1964 try:
1965 # obtain compressed data for chunk
-> 1966 cdata = self.chunk_store[ckey]
1968 except KeyError:
1969 # chunk not initialized
1970 if self._fill_value is not None:
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/storage.py:2442, in LRUStoreCache.__getitem__(self, key)
2438 self._values_cache.move_to_end(key)
2440 except KeyError:
2441 # cache miss, retrieve value from the store
-> 2442 value = self._store[key]
2443 with self._mutex:
2444 self.misses += 1
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/zarr/storage.py:724, in KVStore.__getitem__(self, key)
723 def __getitem__(self, key):
--> 724 return self._mutable_mapping[key]
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:508, in RemoteStore.__getitem__(self, key)
506 value = self._vfs[key]
507 if isinstance(value, tuple):
--> 508 return self._fetch_chunk(key, *value)
509 return value
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:419, in RemoteStore._fetch_chunk(self, key, band_name, chunk_index)
411 observer(band_name=band_name,
412 chunk_index=chunk_index,
413 bbox=request_bbox,
414 time_range=request_time_range,
415 duration=duration,
416 exception=exception)
418 if exception:
--> 419 raise exception
421 return chunk_data
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:400, in RemoteStore._fetch_chunk(self, key, band_name, chunk_index)
398 try:
399 exception = None
--> 400 chunk_data = self.fetch_chunk(key,
401 band_name,
402 chunk_index,
403 bbox=request_bbox,
404 time_range=request_time_range)
405 except Exception as e:
406 exception = e
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/chunkstore.py:729, in SentinelHubChunkStore.fetch_chunk(self, key, band_name, chunk_index, bbox, time_range)
712 band_sample_types = band_sample_types[index]
714 request = SentinelHub.new_data_request(
715 self.cube_config.dataset_name,
716 band_names,
(...)
726 band_units=self.cube_config.band_units
727 )
--> 729 response = self._sentinel_hub.get_data(
730 request,
731 mime_type='application/octet-stream'
732 )
734 if response is None or not response.ok:
735 message = (f'{key}: cannot fetch chunk for variable'
736 f' {band_name!r}, bbox {bbox!r}, and'
737 f' time_range {time_range!r}')
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/sentinelhub.py:432, in SentinelHub.get_data(self, request, mime_type)
430 raise response_error
431 elif response is not None:
--> 432 SentinelHubError.maybe_raise_for_response(response)
433 elif self.error_policy == 'warn' and self.enable_warnings:
434 if response_error:
File /opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/sentinelhub.py:655, in SentinelHubError.maybe_raise_for_response(cls, response)
653 except Exception:
654 pass
--> 655 raise SentinelHubError(f'{e}: {detail}' if detail else f'{e}',
656 response=response) from e
SentinelHubError: 400 Client Error: Bad Request for url: https://services.sentinel-hub.com/api/v1/process
Sentinel-3 SLSTR#
Issues:
S1 band have only values of 0 and 1
SentinelHub().band_names('S3SLSTR')
['S1',
'S2',
'S3',
'S4',
'S4_A',
'S4_B',
'S5',
'S5_A',
'S5_B',
'S6',
'S6_A',
'S6_B',
'S7',
'S8',
'S9',
'F1',
'F2',
'CLOUD_FRACTION',
'SEA_ICE_FRACTION',
'SEA_SURFACE_TEMPERATURE',
'DEW_POINT',
'SKIN_TEMPERATURE',
'SNOW_ALBEDO',
'SNOW_DEPTH',
'SOIL_WETNESS',
'TEMPERATURE',
'TOTAL_COLUMN_OZONE',
'TOTAL_COLUMN_WATER_VAPOR']
cube_config_s3 = CubeConfig(
dataset_name='S3SLSTR',
band_names=['S1'],
bbox=[11.02, 46.65, 11.36, 46.95],
spatial_res=0.009, # = 500 meters in degree>
upsampling='BILINEAR',
downsampling='BILINEAR',
time_range=['2018-02-01', '2018-06-30']
)
/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/config.py:248: FutureWarning: Units 'M', 'Y' and 'y' do not represent unambiguous timedelta values and will be removed in a future version.
time_tolerance = pd.to_timedelta(time_tolerance)
cube_s3 = open_cube(cube_config_s3, api_url="https://creodias.sentinel-hub.com")
cube_s3
<xarray.Dataset>
Dimensions: (time: 230, lat: 33, lon: 38, bnds: 2)
Coordinates:
* lat (lat) float64 46.94 46.93 46.92 46.92 ... 46.68 46.67 46.66 46.65
* lon (lon) float64 11.02 11.03 11.04 11.05 ... 11.33 11.34 11.35 11.36
* time (time) datetime64[ns] 2018-02-01T20:13:30 ... 2018-06-29T21:16:59
time_bnds (time, bnds) datetime64[ns] dask.array<chunksize=(230, 2), meta=np.ndarray>
Dimensions without coordinates: bnds
Data variables:
S1 (time, lat, lon) float32 dask.array<chunksize=(1, 33, 38), meta=np.ndarray>
Attributes:
Conventions: CF-1.7
title: S3SLSTR Data Cube Subset
history: [{'program': 'xcube_sh.chunkstore.SentinelHubChu...
date_created: 2023-03-07T06:52:28.574972
time_coverage_start: 2018-02-01T20:13:30.178000+00:00
time_coverage_end: 2018-06-29T21:17:14.559000+00:00
time_coverage_duration: P148DT1H3M44.381S
geospatial_lon_min: 11.02
geospatial_lat_min: 46.65
geospatial_lon_max: 11.362
geospatial_lat_max: 46.946999999999996
processing_level: L1Bcube_s3.S1.sel(time='2018-06-25', method='nearest').plot.imshow()
<matplotlib.image.AxesImage at 0x7f3be5b837c0>
