Testraster failures without numba: 'dia_matrix' object is not subscriptable #420
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I am updating the Fedora package to 4.5.1, but seeing issues with 3 tests:
libpysal/weights/tests/test_raster.py::Testraster::test_da2Wlibpysal/weights/tests/test_raster.py::Testraster::test_da2WSPlibpysal/weights/tests/test_raster.py::Testraster::test_wsp2daAll fail in the fallback to non-
numbacode at:Perhaps this is related to #419, based on the test names.
posix linux posix.uname_result(sysname='Linux', release='5.12.11-300.fc34.x86_64', version='#1 SMP Wed Jun 16 15:47:58 UTC 2021', machine='x86_64')3.10.0b3 (default, Jun 17 2021, 00:00:00) [GCC 11.1.1 20210617 (Red Hat 11.1.1-5)]Full pytest traceback
_____________________________ Testraster.test_da2W _____________________________ da = <xarray.DataArray (band: 1, y: 4, x: 4)> array([[[-32768, -32768, -32768, 129], [-32768, -32768, -32768, -3...float64 90.0 30.0 -30.0 -90.0 * x (x) float64 -180.0 -60.0 60.0 180.0 Attributes: nodatavals: (-32768.0,) criterion = 'queen', z_value = None coords_labels = {'x_label': 'x', 'y_label': 'y', 'z_label': 'band'}, k = 2 include_nodata = False, n_jobs = -1 def da2WSP( da, criterion="queen", z_value=None, coords_labels={}, k=1, include_nodata=False, n_jobs=1, ): """ Create a WSP object from xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex. Parameters ---------- da : xarray.DataArray Input 2D or 3D DataArray with shape=(z, y, x) criterion : {"rook", "queen"} Type of contiguity. Default is queen. z_value : int/string/float Select the z_value of 3D DataArray with multiple layers. coords_labels : dictionary Pass dimension labels for coordinates and layers if they do not belong to default dimensions, which are (band/time, y/lat, x/lon) e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"} Default is {} empty dictionary. k : int Order of contiguity, this will select all neighbors upto kth order. Default is 1. include_nodata : boolean If True, missing values will be assumed as non-missing when selecting higher_order neighbors, Default is False n_jobs : int Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Default is 1. Returns ------- wsp : libpysal.weights.WSP instance of spatial weights class WSP with an index attribute Notes ----- 1. Lower order contiguities are also selected. 2. Returned object contains `index` attribute that includes a `Pandas.MultiIndex` object from the DataArray. Examples -------- >>> from libpysal.weights.raster import da2WSP, testDataArray >>> da = testDataArray().rename( {'band': 'layer', 'x': 'longitude', 'y': 'latitude'}) >>> da.dims ('layer', 'latitude', 'longitude') >>> da.shape (3, 4, 4) >>> da.coords Coordinates: * layer (layer) int64 1 2 3 * latitude (latitude) float64 90.0 30.0 -30.0 -90.0 * longitude (longitude) float64 -180.0 -60.0 60.0 180.0 >>> da.attrs {'nodatavals': (-32768.0,)} >>> coords_labels = { "z_label": "layer", "y_label": "latitude", "x_label": "longitude" } >>> wsp = da2WSP(da, z_value=2, coords_labels=coords_labels) >>> wsp.n 10 >>> pct_sp = wsp.sparse.nnz *1. / wsp.n**2 >>> "%.3f"%pct_sp '0.300' >>> print(wsp.sparse[4].todense()) [[0 0 1 0 0 1 1 1 0 0]] >>> wsp.index[:2] MultiIndex([(2, 90.0, 60.0), (2, 90.0, 180.0)], names=['layer', 'latitude', 'longitude']) See Also -------- :class:`libpysal.weights.weights.WSP` """ z_id, coords_labels = _da_checker(da, z_value, coords_labels) shape = da.shape if z_id: slice_dict = {} slice_dict[coords_labels["z_label"]] = 0 shape = da[slice_dict].shape slice_dict[coords_labels["z_label"]] = slice(z_id - 1, z_id) da = da[slice_dict] ser = da.to_series() dtype = np.int32 if (shape[0] * shape[1]) < 46340 ** 2 else np.int64 if "nodatavals" in da.attrs and da.attrs["nodatavals"]: mask = (ser != da.attrs["nodatavals"][0]).to_numpy() ids = np.where(mask)[0] id_map = _idmap(ids, mask, dtype) ser = ser[ser != da.attrs["nodatavals"][0]] else: ids = np.arange(len(ser), dtype=dtype) id_map = ids.copy() n = len(ids) try: > import numba E ModuleNotFoundError: No module named 'numba' libpysal/weights/raster.py:241: ModuleNotFoundError During handling of the above exception, another exception occurred: self = <libpysal.weights.tests.test_raster.Testraster testMethod=test_da2W> def test_da2W(self): > w1 = raster.da2W(self.da1, "queen", k=2, n_jobs=-1) libpysal/weights/tests/test_raster.py:20: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ libpysal/weights/raster.py:124: in da2W wsp = da2WSP(da, criterion, z_value, coords_labels, k, include_nodata, n_jobs) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ da = <xarray.DataArray (band: 1, y: 4, x: 4)> array([[[-32768, -32768, -32768, 129], [-32768, -32768, -32768, -3...float64 90.0 30.0 -30.0 -90.0 * x (x) float64 -180.0 -60.0 60.0 180.0 Attributes: nodatavals: (-32768.0,) criterion = 'queen', z_value = None coords_labels = {'x_label': 'x', 'y_label': 'y', 'z_label': 'band'}, k = 2 include_nodata = False, n_jobs = -1 def da2WSP( da, criterion="queen", z_value=None, coords_labels={}, k=1, include_nodata=False, n_jobs=1, ): """ Create a WSP object from xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex. Parameters ---------- da : xarray.DataArray Input 2D or 3D DataArray with shape=(z, y, x) criterion : {"rook", "queen"} Type of contiguity. Default is queen. z_value : int/string/float Select the z_value of 3D DataArray with multiple layers. coords_labels : dictionary Pass dimension labels for coordinates and layers if they do not belong to default dimensions, which are (band/time, y/lat, x/lon) e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"} Default is {} empty dictionary. k : int Order of contiguity, this will select all neighbors upto kth order. Default is 1. include_nodata : boolean If True, missing values will be assumed as non-missing when selecting higher_order neighbors, Default is False n_jobs : int Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Default is 1. Returns ------- wsp : libpysal.weights.WSP instance of spatial weights class WSP with an index attribute Notes ----- 1. Lower order contiguities are also selected. 2. Returned object contains `index` attribute that includes a `Pandas.MultiIndex` object from the DataArray. Examples -------- >>> from libpysal.weights.raster import da2WSP, testDataArray >>> da = testDataArray().rename( {'band': 'layer', 'x': 'longitude', 'y': 'latitude'}) >>> da.dims ('layer', 'latitude', 'longitude') >>> da.shape (3, 4, 4) >>> da.coords Coordinates: * layer (layer) int64 1 2 3 * latitude (latitude) float64 90.0 30.0 -30.0 -90.0 * longitude (longitude) float64 -180.0 -60.0 60.0 180.0 >>> da.attrs {'nodatavals': (-32768.0,)} >>> coords_labels = { "z_label": "layer", "y_label": "latitude", "x_label": "longitude" } >>> wsp = da2WSP(da, z_value=2, coords_labels=coords_labels) >>> wsp.n 10 >>> pct_sp = wsp.sparse.nnz *1. / wsp.n**2 >>> "%.3f"%pct_sp '0.300' >>> print(wsp.sparse[4].todense()) [[0 0 1 0 0 1 1 1 0 0]] >>> wsp.index[:2] MultiIndex([(2, 90.0, 60.0), (2, 90.0, 180.0)], names=['layer', 'latitude', 'longitude']) See Also -------- :class:`libpysal.weights.weights.WSP` """ z_id, coords_labels = _da_checker(da, z_value, coords_labels) shape = da.shape if z_id: slice_dict = {} slice_dict[coords_labels["z_label"]] = 0 shape = da[slice_dict].shape slice_dict[coords_labels["z_label"]] = slice(z_id - 1, z_id) da = da[slice_dict] ser = da.to_series() dtype = np.int32 if (shape[0] * shape[1]) < 46340 ** 2 else np.int64 if "nodatavals" in da.attrs and da.attrs["nodatavals"]: mask = (ser != da.attrs["nodatavals"][0]).to_numpy() ids = np.where(mask)[0] id_map = _idmap(ids, mask, dtype) ser = ser[ser != da.attrs["nodatavals"][0]] else: ids = np.arange(len(ser), dtype=dtype) id_map = ids.copy() n = len(ids) try: import numba except (ModuleNotFoundError, ImportError): warn( "numba cannot be imported, parallel processing " "and include_nodata functionality will be disabled. " "falling back to slower method" ) include_nodata = False # Fallback method to build sparse matrix sw = lat2SW(*shape, criterion) if "nodatavals" in da.attrs and da.attrs["nodatavals"]: > sw = sw[mask] E TypeError: 'dia_matrix' object is not subscriptable libpysal/weights/raster.py:252: TypeError ____________________________ Testraster.test_da2WSP ____________________________ da = <xarray.DataArray (band: 1, y: 4, x: 4)> array([[[-32768, -32768, -32768, 129], [-32768, -32768, -32768, -3...float64 90.0 30.0 -30.0 -90.0 * x (x) float64 -180.0 -60.0 60.0 180.0 Attributes: nodatavals: (-32768.0,) criterion = 'rook', z_value = None coords_labels = {'x_label': 'x', 'y_label': 'y', 'z_label': 'band'}, k = 1 include_nodata = False, n_jobs = -1 def da2WSP( da, criterion="queen", z_value=None, coords_labels={}, k=1, include_nodata=False, n_jobs=1, ): """ Create a WSP object from xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex. Parameters ---------- da : xarray.DataArray Input 2D or 3D DataArray with shape=(z, y, x) criterion : {"rook", "queen"} Type of contiguity. Default is queen. z_value : int/string/float Select the z_value of 3D DataArray with multiple layers. coords_labels : dictionary Pass dimension labels for coordinates and layers if they do not belong to default dimensions, which are (band/time, y/lat, x/lon) e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"} Default is {} empty dictionary. k : int Order of contiguity, this will select all neighbors upto kth order. Default is 1. include_nodata : boolean If True, missing values will be assumed as non-missing when selecting higher_order neighbors, Default is False n_jobs : int Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Default is 1. Returns ------- wsp : libpysal.weights.WSP instance of spatial weights class WSP with an index attribute Notes ----- 1. Lower order contiguities are also selected. 2. Returned object contains `index` attribute that includes a `Pandas.MultiIndex` object from the DataArray. Examples -------- >>> from libpysal.weights.raster import da2WSP, testDataArray >>> da = testDataArray().rename( {'band': 'layer', 'x': 'longitude', 'y': 'latitude'}) >>> da.dims ('layer', 'latitude', 'longitude') >>> da.shape (3, 4, 4) >>> da.coords Coordinates: * layer (layer) int64 1 2 3 * latitude (latitude) float64 90.0 30.0 -30.0 -90.0 * longitude (longitude) float64 -180.0 -60.0 60.0 180.0 >>> da.attrs {'nodatavals': (-32768.0,)} >>> coords_labels = { "z_label": "layer", "y_label": "latitude", "x_label": "longitude" } >>> wsp = da2WSP(da, z_value=2, coords_labels=coords_labels) >>> wsp.n 10 >>> pct_sp = wsp.sparse.nnz *1. / wsp.n**2 >>> "%.3f"%pct_sp '0.300' >>> print(wsp.sparse[4].todense()) [[0 0 1 0 0 1 1 1 0 0]] >>> wsp.index[:2] MultiIndex([(2, 90.0, 60.0), (2, 90.0, 180.0)], names=['layer', 'latitude', 'longitude']) See Also -------- :class:`libpysal.weights.weights.WSP` """ z_id, coords_labels = _da_checker(da, z_value, coords_labels) shape = da.shape if z_id: slice_dict = {} slice_dict[coords_labels["z_label"]] = 0 shape = da[slice_dict].shape slice_dict[coords_labels["z_label"]] = slice(z_id - 1, z_id) da = da[slice_dict] ser = da.to_series() dtype = np.int32 if (shape[0] * shape[1]) < 46340 ** 2 else np.int64 if "nodatavals" in da.attrs and da.attrs["nodatavals"]: mask = (ser != da.attrs["nodatavals"][0]).to_numpy() ids = np.where(mask)[0] id_map = _idmap(ids, mask, dtype) ser = ser[ser != da.attrs["nodatavals"][0]] else: ids = np.arange(len(ser), dtype=dtype) id_map = ids.copy() n = len(ids) try: > import numba E ModuleNotFoundError: No module named 'numba' libpysal/weights/raster.py:241: ModuleNotFoundError During handling of the above exception, another exception occurred: self = <libpysal.weights.tests.test_raster.Testraster testMethod=test_da2WSP> def test_da2WSP(self): > w1 = raster.da2WSP(self.da1, "rook", n_jobs=-1) libpysal/weights/tests/test_raster.py:54: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ da = <xarray.DataArray (band: 1, y: 4, x: 4)> array([[[-32768, -32768, -32768, 129], [-32768, -32768, -32768, -3...float64 90.0 30.0 -30.0 -90.0 * x (x) float64 -180.0 -60.0 60.0 180.0 Attributes: nodatavals: (-32768.0,) criterion = 'rook', z_value = None coords_labels = {'x_label': 'x', 'y_label': 'y', 'z_label': 'band'}, k = 1 include_nodata = False, n_jobs = -1 def da2WSP( da, criterion="queen", z_value=None, coords_labels={}, k=1, include_nodata=False, n_jobs=1, ): """ Create a WSP object from xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex. Parameters ---------- da : xarray.DataArray Input 2D or 3D DataArray with shape=(z, y, x) criterion : {"rook", "queen"} Type of contiguity. Default is queen. z_value : int/string/float Select the z_value of 3D DataArray with multiple layers. coords_labels : dictionary Pass dimension labels for coordinates and layers if they do not belong to default dimensions, which are (band/time, y/lat, x/lon) e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"} Default is {} empty dictionary. k : int Order of contiguity, this will select all neighbors upto kth order. Default is 1. include_nodata : boolean If True, missing values will be assumed as non-missing when selecting higher_order neighbors, Default is False n_jobs : int Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Default is 1. Returns ------- wsp : libpysal.weights.WSP instance of spatial weights class WSP with an index attribute Notes ----- 1. Lower order contiguities are also selected. 2. Returned object contains `index` attribute that includes a `Pandas.MultiIndex` object from the DataArray. Examples -------- >>> from libpysal.weights.raster import da2WSP, testDataArray >>> da = testDataArray().rename( {'band': 'layer', 'x': 'longitude', 'y': 'latitude'}) >>> da.dims ('layer', 'latitude', 'longitude') >>> da.shape (3, 4, 4) >>> da.coords Coordinates: * layer (layer) int64 1 2 3 * latitude (latitude) float64 90.0 30.0 -30.0 -90.0 * longitude (longitude) float64 -180.0 -60.0 60.0 180.0 >>> da.attrs {'nodatavals': (-32768.0,)} >>> coords_labels = { "z_label": "layer", "y_label": "latitude", "x_label": "longitude" } >>> wsp = da2WSP(da, z_value=2, coords_labels=coords_labels) >>> wsp.n 10 >>> pct_sp = wsp.sparse.nnz *1. / wsp.n**2 >>> "%.3f"%pct_sp '0.300' >>> print(wsp.sparse[4].todense()) [[0 0 1 0 0 1 1 1 0 0]] >>> wsp.index[:2] MultiIndex([(2, 90.0, 60.0), (2, 90.0, 180.0)], names=['layer', 'latitude', 'longitude']) See Also -------- :class:`libpysal.weights.weights.WSP` """ z_id, coords_labels = _da_checker(da, z_value, coords_labels) shape = da.shape if z_id: slice_dict = {} slice_dict[coords_labels["z_label"]] = 0 shape = da[slice_dict].shape slice_dict[coords_labels["z_label"]] = slice(z_id - 1, z_id) da = da[slice_dict] ser = da.to_series() dtype = np.int32 if (shape[0] * shape[1]) < 46340 ** 2 else np.int64 if "nodatavals" in da.attrs and da.attrs["nodatavals"]: mask = (ser != da.attrs["nodatavals"][0]).to_numpy() ids = np.where(mask)[0] id_map = _idmap(ids, mask, dtype) ser = ser[ser != da.attrs["nodatavals"][0]] else: ids = np.arange(len(ser), dtype=dtype) id_map = ids.copy() n = len(ids) try: import numba except (ModuleNotFoundError, ImportError): warn( "numba cannot be imported, parallel processing " "and include_nodata functionality will be disabled. " "falling back to slower method" ) include_nodata = False # Fallback method to build sparse matrix sw = lat2SW(*shape, criterion) if "nodatavals" in da.attrs and da.attrs["nodatavals"]: > sw = sw[mask] E TypeError: 'dia_matrix' object is not subscriptable libpysal/weights/raster.py:252: TypeError ____________________________ Testraster.test_wsp2da ____________________________ da = <xarray.DataArray (band: 1, y: 4, x: 4)> array([[[-32768, -32768, -32768, 129], [-32768, -32768, -32768, -3...float64 90.0 30.0 -30.0 -90.0 * x (x) float64 -180.0 -60.0 60.0 180.0 Attributes: nodatavals: (-32768.0,) criterion = 'queen', z_value = None coords_labels = {'x_label': 'x', 'y_label': 'y', 'z_label': 'band'}, k = 1 include_nodata = False, n_jobs = 1 def da2WSP( da, criterion="queen", z_value=None, coords_labels={}, k=1, include_nodata=False, n_jobs=1, ): """ Create a WSP object from xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex. Parameters ---------- da : xarray.DataArray Input 2D or 3D DataArray with shape=(z, y, x) criterion : {"rook", "queen"} Type of contiguity. Default is queen. z_value : int/string/float Select the z_value of 3D DataArray with multiple layers. coords_labels : dictionary Pass dimension labels for coordinates and layers if they do not belong to default dimensions, which are (band/time, y/lat, x/lon) e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"} Default is {} empty dictionary. k : int Order of contiguity, this will select all neighbors upto kth order. Default is 1. include_nodata : boolean If True, missing values will be assumed as non-missing when selecting higher_order neighbors, Default is False n_jobs : int Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Default is 1. Returns ------- wsp : libpysal.weights.WSP instance of spatial weights class WSP with an index attribute Notes ----- 1. Lower order contiguities are also selected. 2. Returned object contains `index` attribute that includes a `Pandas.MultiIndex` object from the DataArray. Examples -------- >>> from libpysal.weights.raster import da2WSP, testDataArray >>> da = testDataArray().rename( {'band': 'layer', 'x': 'longitude', 'y': 'latitude'}) >>> da.dims ('layer', 'latitude', 'longitude') >>> da.shape (3, 4, 4) >>> da.coords Coordinates: * layer (layer) int64 1 2 3 * latitude (latitude) float64 90.0 30.0 -30.0 -90.0 * longitude (longitude) float64 -180.0 -60.0 60.0 180.0 >>> da.attrs {'nodatavals': (-32768.0,)} >>> coords_labels = { "z_label": "layer", "y_label": "latitude", "x_label": "longitude" } >>> wsp = da2WSP(da, z_value=2, coords_labels=coords_labels) >>> wsp.n 10 >>> pct_sp = wsp.sparse.nnz *1. / wsp.n**2 >>> "%.3f"%pct_sp '0.300' >>> print(wsp.sparse[4].todense()) [[0 0 1 0 0 1 1 1 0 0]] >>> wsp.index[:2] MultiIndex([(2, 90.0, 60.0), (2, 90.0, 180.0)], names=['layer', 'latitude', 'longitude']) See Also -------- :class:`libpysal.weights.weights.WSP` """ z_id, coords_labels = _da_checker(da, z_value, coords_labels) shape = da.shape if z_id: slice_dict = {} slice_dict[coords_labels["z_label"]] = 0 shape = da[slice_dict].shape slice_dict[coords_labels["z_label"]] = slice(z_id - 1, z_id) da = da[slice_dict] ser = da.to_series() dtype = np.int32 if (shape[0] * shape[1]) < 46340 ** 2 else np.int64 if "nodatavals" in da.attrs and da.attrs["nodatavals"]: mask = (ser != da.attrs["nodatavals"][0]).to_numpy() ids = np.where(mask)[0] id_map = _idmap(ids, mask, dtype) ser = ser[ser != da.attrs["nodatavals"][0]] else: ids = np.arange(len(ser), dtype=dtype) id_map = ids.copy() n = len(ids) try: > import numba E ModuleNotFoundError: No module named 'numba' libpysal/weights/raster.py:241: ModuleNotFoundError During handling of the above exception, another exception occurred: self = <libpysal.weights.tests.test_raster.Testraster testMethod=test_wsp2da> def test_wsp2da(self): > wsp1 = raster.da2WSP(self.da1, "queen") libpysal/weights/tests/test_raster.py:89: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ da = <xarray.DataArray (band: 1, y: 4, x: 4)> array([[[-32768, -32768, -32768, 129], [-32768, -32768, -32768, -3...float64 90.0 30.0 -30.0 -90.0 * x (x) float64 -180.0 -60.0 60.0 180.0 Attributes: nodatavals: (-32768.0,) criterion = 'queen', z_value = None coords_labels = {'x_label': 'x', 'y_label': 'y', 'z_label': 'band'}, k = 1 include_nodata = False, n_jobs = 1 def da2WSP( da, criterion="queen", z_value=None, coords_labels={}, k=1, include_nodata=False, n_jobs=1, ): """ Create a WSP object from xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex. Parameters ---------- da : xarray.DataArray Input 2D or 3D DataArray with shape=(z, y, x) criterion : {"rook", "queen"} Type of contiguity. Default is queen. z_value : int/string/float Select the z_value of 3D DataArray with multiple layers. coords_labels : dictionary Pass dimension labels for coordinates and layers if they do not belong to default dimensions, which are (band/time, y/lat, x/lon) e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"} Default is {} empty dictionary. k : int Order of contiguity, this will select all neighbors upto kth order. Default is 1. include_nodata : boolean If True, missing values will be assumed as non-missing when selecting higher_order neighbors, Default is False n_jobs : int Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Default is 1. Returns ------- wsp : libpysal.weights.WSP instance of spatial weights class WSP with an index attribute Notes ----- 1. Lower order contiguities are also selected. 2. Returned object contains `index` attribute that includes a `Pandas.MultiIndex` object from the DataArray. Examples -------- >>> from libpysal.weights.raster import da2WSP, testDataArray >>> da = testDataArray().rename( {'band': 'layer', 'x': 'longitude', 'y': 'latitude'}) >>> da.dims ('layer', 'latitude', 'longitude') >>> da.shape (3, 4, 4) >>> da.coords Coordinates: * layer (layer) int64 1 2 3 * latitude (latitude) float64 90.0 30.0 -30.0 -90.0 * longitude (longitude) float64 -180.0 -60.0 60.0 180.0 >>> da.attrs {'nodatavals': (-32768.0,)} >>> coords_labels = { "z_label": "layer", "y_label": "latitude", "x_label": "longitude" } >>> wsp = da2WSP(da, z_value=2, coords_labels=coords_labels) >>> wsp.n 10 >>> pct_sp = wsp.sparse.nnz *1. / wsp.n**2 >>> "%.3f"%pct_sp '0.300' >>> print(wsp.sparse[4].todense()) [[0 0 1 0 0 1 1 1 0 0]] >>> wsp.index[:2] MultiIndex([(2, 90.0, 60.0), (2, 90.0, 180.0)], names=['layer', 'latitude', 'longitude']) See Also -------- :class:`libpysal.weights.weights.WSP` """ z_id, coords_labels = _da_checker(da, z_value, coords_labels) shape = da.shape if z_id: slice_dict = {} slice_dict[coords_labels["z_label"]] = 0 shape = da[slice_dict].shape slice_dict[coords_labels["z_label"]] = slice(z_id - 1, z_id) da = da[slice_dict] ser = da.to_series() dtype = np.int32 if (shape[0] * shape[1]) < 46340 ** 2 else np.int64 if "nodatavals" in da.attrs and da.attrs["nodatavals"]: mask = (ser != da.attrs["nodatavals"][0]).to_numpy() ids = np.where(mask)[0] id_map = _idmap(ids, mask, dtype) ser = ser[ser != da.attrs["nodatavals"][0]] else: ids = np.arange(len(ser), dtype=dtype) id_map = ids.copy() n = len(ids) try: import numba except (ModuleNotFoundError, ImportError): warn( "numba cannot be imported, parallel processing " "and include_nodata functionality will be disabled. " "falling back to slower method" ) include_nodata = False # Fallback method to build sparse matrix sw = lat2SW(*shape, criterion) if "nodatavals" in da.attrs and da.attrs["nodatavals"]: > sw = sw[mask] E TypeError: 'dia_matrix' object is not subscriptable libpysal/weights/raster.py:252: TypeErrorThe text was updated successfully, but these errors were encountered: