Test error on i386

[avalentino]

Two tests of the test suite seems to fail on i386 architectures (see the salsa CI log).
The problem has been spotted on one of the Debian CI systems.
Both issues seems to be linked to the size of the image and the limited resources of the VM used in CI (I do not know details but the amount of memory seems to be quite limited).

Does it make sense to try reduce the size of the image used for the test? ... is it something that can be done easily?
I could try to prepare a patch in the case ...

============================ test session starts ==============================
platform linux -- Python 3.11.1, pytest-7.2.0, pluggy-1.0.0+repack
rootdir: /builds/debian-gis-team/sarsen/debian/output/source_dir/.pybuild/cpython3_3.11/build
collected 30 items
tests/test_10_chunking.py ...                                            [ 10%]
tests/test_10_datamodel.py .                                             [ 13%]
tests/test_10_orbit.py ..                                                [ 20%]
tests/test_10_scene.py ...                                               [ 30%]
tests/test_20_geocoding.py ...                                           [ 40%]
tests/test_20_sentinel1.py FF.                                           [ 50%]
tests/test_30_radiometry.py .                                            [ 53%]
tests/test_50_apps.py ssssssssssss                                       [ 93%]
tests/test_60_main.py .                                                  [ 96%]
tests/test_90_sarsen.py .                                                [100%]
=================================== FAILURES ===================================
__________________ test_Sentinel1SarProduct[data_path0-IW/VV] __________________
data_path = PosixPath('/builds/debian-gis-team/sarsen/debian/output/source_dir/.pybuild/cpython3_3.11/build/tests/data/S1B_IW_GRDH_1SDV_20211223T051122_20211223T051147_030148_039993_5371.SAFE')
group = 'IW/VV'
    @pytest.mark.parametrize("data_path,group", zip(DATA_PATHS, GROUPS))
    def test_Sentinel1SarProduct(data_path: str, group: str) -> None:
        res = sentinel1.Sentinel1SarProduct(data_path, group)
    
        assert isinstance(res.measurement, xr.Dataset)
        assert isinstance(res.orbit, xr.Dataset)
        assert isinstance(res.calibration, xr.Dataset)
    
        if res.product_type == "GRD":
            assert isinstance(res.coordinate_conversion, xr.Dataset)
            assert res.azimuth_fm_rate is None
            assert res.dc_estimate is None
        else:
            assert res.coordinate_conversion is None
            assert isinstance(res.azimuth_fm_rate, xr.Dataset)
            assert isinstance(res.dc_estimate, xr.Dataset)
    
        assert res.product_type in {"SLC", "GRD"}
>       assert isinstance(res.beta_nought(), xr.DataArray)
tests/test_20_sentinel1.py:39: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
sarsen/sentinel1.py:156: in beta_nought
    return calibrate_measurement(measurement, self.calibration.betaNought)
sarsen/sentinel1.py:44: in calibrate_measurement
    beta_nought = xarray_sentinel.calibrate_intensity(measurement, beta_nought_lut)
/usr/lib/python3/dist-packages/xarray_sentinel/sentinel1.py:842: in calibrate_intensity
    amplitude = calibrate_amplitude(digital_number, calibration_lut, **kwargs)
/usr/lib/python3/dist-packages/xarray_sentinel/sentinel1.py:816: in calibrate_amplitude
    amplitude = digital_number / calibration
/usr/lib/python3/dist-packages/xarray/core/_typed_ops.py:218: in __truediv__
    return self._binary_op(other, operator.truediv)
/usr/lib/python3/dist-packages/xarray/core/dataarray.py:4358: in _binary_op
    f(self.variable, other_variable)
/usr/lib/python3/dist-packages/xarray/core/_typed_ops.py:408: in __truediv__
    return self._binary_op(other, operator.truediv)
/usr/lib/python3/dist-packages/xarray/core/variable.py:2634: in _binary_op
    self_data, other_data, dims = _broadcast_compat_data(self, other)
/usr/lib/python3/dist-packages/xarray/core/variable.py:3123: in _broadcast_compat_data
    self_data = new_self.data
/usr/lib/python3/dist-packages/xarray/core/variable.py:435: in data
    return self.values
/usr/lib/python3/dist-packages/xarray/core/variable.py:608: in values
    return _as_array_or_item(self._data)
/usr/lib/python3/dist-packages/xarray/core/variable.py:314: in _as_array_or_item
    data = np.asarray(data)
/usr/lib/python3/dist-packages/xarray/core/indexing.py:653: in __array__
    self._ensure_cached()
/usr/lib/python3/dist-packages/xarray/core/indexing.py:650: in _ensure_cached
    self.array = NumpyIndexingAdapter(np.asarray(self.array))
/usr/lib/python3/dist-packages/xarray/core/indexing.py:623: in __array__
    return np.asarray(self.array, dtype=dtype)
/usr/lib/python3/dist-packages/xarray/core/indexing.py:653: in __array__
    self._ensure_cached()
/usr/lib/python3/dist-packages/xarray/core/indexing.py:650: in _ensure_cached
    self.array = NumpyIndexingAdapter(np.asarray(self.array))
/usr/lib/python3/dist-packages/xarray/core/indexing.py:623: in __array__
    return np.asarray(self.array, dtype=dtype)
/usr/lib/python3/dist-packages/xarray/core/indexing.py:653: in __array__
    self._ensure_cached()
/usr/lib/python3/dist-packages/xarray/core/indexing.py:650: in _ensure_cached
    self.array = NumpyIndexingAdapter(np.asarray(self.array))
/usr/lib/python3/dist-packages/xarray/core/indexing.py:623: in __array__
    return np.asarray(self.array, dtype=dtype)
/usr/lib/python3/dist-packages/xarray/core/indexing.py:524: in __array__
    return np.asarray(array[self.key], dtype=None)
/usr/lib/python3/dist-packages/rioxarray/_io.py:423: in __getitem__
    return indexing.explicit_indexing_adapter(
/usr/lib/python3/dist-packages/xarray/core/indexing.py:815: in explicit_indexing_adapter
    result = raw_indexing_method(raw_key.tuple)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <rioxarray._io.RasterioArrayWrapper object at 0xe665e230>
key = (0, slice(None, None, None), slice(None, None, None))
    def _getitem(self, key):
        band_key, window, squeeze_axis, np_inds = self._get_indexer(key)
        if not band_key or any(start == stop for (start, stop) in window):
            # no need to do IO
            shape = (len(band_key),) + tuple(stop - start for (start, stop) in window)
            out = np.zeros(shape, dtype=self.dtype)
        else:
            with self.lock:
                riods = self.manager.acquire(needs_lock=False)
                if self.vrt_params is not None:
                    riods = WarpedVRT(riods, **self.vrt_params)
                out = riods.read(band_key, window=window, masked=self.masked)
                if self._unsigned_dtype is not None:
                    out = out.astype(self._unsigned_dtype)
                if self.masked:
>                   out = np.ma.filled(out.astype(self.dtype), self.fill_value)
E                   numpy.core._exceptions._ArrayMemoryError: Unable to allocate 1.62 GiB for an array with shape (16705, 26102) and data type float32
/usr/lib/python3/dist-packages/rioxarray/_io.py:404: MemoryError
_________________ test_Sentinel1SarProduct[data_path1-IW1/VV] __________________
data_path = PosixPath('/builds/debian-gis-team/sarsen/debian/output/source_dir/.pybuild/cpython3_3.11/build/tests/data/S1A_IW_SLC__1SDV_20220104T170557_20220104T170624_041314_04E951_F1F1.SAFE')
group = 'IW1/VV'
    @pytest.mark.parametrize("data_path,group", zip(DATA_PATHS, GROUPS))
    def test_Sentinel1SarProduct(data_path: str, group: str) -> None:
        res = sentinel1.Sentinel1SarProduct(data_path, group)
    
        assert isinstance(res.measurement, xr.Dataset)
        assert isinstance(res.orbit, xr.Dataset)
        assert isinstance(res.calibration, xr.Dataset)
    
        if res.product_type == "GRD":
            assert isinstance(res.coordinate_conversion, xr.Dataset)
            assert res.azimuth_fm_rate is None
            assert res.dc_estimate is None
        else:
            assert res.coordinate_conversion is None
            assert isinstance(res.azimuth_fm_rate, xr.Dataset)
            assert isinstance(res.dc_estimate, xr.Dataset)
    
        assert res.product_type in {"SLC", "GRD"}
>       assert isinstance(res.beta_nought(), xr.DataArray)
tests/test_20_sentinel1.py:39: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
sarsen/sentinel1.py:156: in beta_nought
    return calibrate_measurement(measurement, self.calibration.betaNought)
sarsen/sentinel1.py:42: in calibrate_measurement
    measurement = xarray_sentinel.mosaic_slc_iw(measurement)
/usr/lib/python3/dist-packages/xarray_sentinel/sentinel1.py:791: in mosaic_slc_iw
    return xr.concat(bursts, dim="azimuth_time")
/usr/lib/python3/dist-packages/xarray/core/concat.py:231: in concat
    return _dataarray_concat(
/usr/lib/python3/dist-packages/xarray/core/concat.py:657: in _dataarray_concat
    ds = _dataset_concat(
/usr/lib/python3/dist-packages/xarray/core/concat.py:584: in _dataset_concat
    combined_var = concat_vars(
/usr/lib/python3/dist-packages/xarray/core/variable.py:3186: in concat
    return Variable.concat(variables, dim, positions, shortcut, combine_attrs)
/usr/lib/python3/dist-packages/xarray/core/variable.py:2047: in concat
    data = duck_array_ops.concatenate(arrays, axis=axis)
/usr/lib/python3/dist-packages/xarray/core/duck_array_ops.py:314: in concatenate
    return _concatenate(as_shared_dtype(arrays), axis=axis)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
args = ([array([[0.+0.j, 0.+0.j, 0.+0.j, ..., 0.+0.j, 0.+0.j, 0.+0.j],
       [0.+0.j, 0.+0.j, 0.+0.j, ..., 0.+0.j, 0.+0.j, 0... 0.+0.j, 0.+0.j, 0.+0.j],
       [0.+0.j, 0.+0.j, 0.+0.j, ..., 0.+0.j, 0.+0.j, 0.+0.j]],
      dtype=complex64), ...],)
kwargs = {'axis': 0}
relevant_args = [array([[0.+0.j, 0.+0.j, 0.+0.j, ..., 0.+0.j, 0.+0.j, 0.+0.j],
       [0.+0.j, 0.+0.j, 0.+0.j, ..., 0.+0.j, 0.+0.j, 0....., 0.+0.j, 0.+0.j, 0.+0.j],
       [0.+0.j, 0.+0.j, 0.+0.j, ..., 0.+0.j, 0.+0.j, 0.+0.j]],
      dtype=complex64), ...]
>   ???
E   ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.
<__array_function__ internals>:180: ValueError
=============================== warnings summary ===============================
tests/test_20_geocoding.py::test_secant_method
  /builds/debian-gis-team/sarsen/debian/output/source_dir/.pybuild/cpython3_3.11/build/sarsen/geocoding.py:46: RuntimeWarning: invalid value encountered in divide
    t_prev, t_curr = t_curr, t_curr - np.where(q != 0, p / q, 0)  # type: ignore
tests/test_20_sentinel1.py::test_Sentinel1SarProduct[data_path0-IW/VV]
tests/test_20_sentinel1.py::test_Sentinel1SarProduct[data_path0-IW/VV]
tests/test_20_sentinel1.py::test_Sentinel1SarProduct[data_path0-IW/VV]
tests/test_20_sentinel1.py::test_Sentinel1SarProduct[data_path0-IW/VV]
tests/test_20_sentinel1.py::test_Sentinel1SarProduct[data_path0-IW/VV]
tests/test_20_sentinel1.py::test_Sentinel1SarProduct[data_path0-IW/VV]
tests/test_20_sentinel1.py::test_Sentinel1SarProduct[data_path0-IW/VV]
  /usr/lib/python3/dist-packages/rioxarray/_io.py:1111: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.
    warnings.warn(str(rio_warning.message), type(rio_warning.message))  # type: ignore
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
FAILED tests/test_20_sentinel1.py::test_Sentinel1SarProduct[data_path0-IW/VV] - numpy.core._exceptions._ArrayMemoryError: Unable to allocate 1.62 GiB for an array with shape (16705, 26102) and data type float32
FAILED tests/test_20_sentinel1.py::test_Sentinel1SarProduct[data_path1-IW1/VV] - ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.
============ 2 failed, 16 passed, 12 skipped, 8 warnings in 29.65s =============