def test_encode_decode_array_datetime_timedelta(): # some variations for k in ['m8[s]', 'M8[s]']: compressor = Blosc(cname='lz4', clevel=3, shuffle=2) dtype = np.dtype(k) fill_value = dtype.type("NaT") meta = dict( shape=(100, 100), chunks=(10, 10), dtype=dtype, compressor=compressor.get_config(), fill_value=fill_value, order=dtype.char, filters=[] ) meta_json = '''{ "chunks": [10, 10], "compressor": { "id": "blosc", "clevel": 3, "cname": "lz4", "shuffle": 2, "blocksize": 0 }, "dtype": "%s", "fill_value": -9223372036854775808, "filters": [], "order": "%s", "shape": [100, 100], "zarr_format": %s }''' % (dtype.str, dtype.char, ZARR_FORMAT) # test encoding meta_enc = encode_array_metadata(meta) assert_json_equal(meta_json, meta_enc) # test decoding meta_dec = decode_array_metadata(meta_enc) assert ZARR_FORMAT == meta_dec['zarr_format'] assert meta['shape'] == meta_dec['shape'] assert meta['chunks'] == meta_dec['chunks'] assert meta['dtype'] == meta_dec['dtype'] assert meta['compressor'] == meta_dec['compressor'] assert meta['order'] == meta_dec['order'] # Based off of this SO answer: https://stackoverflow.com/a/49972198 assert np.all( fill_value.view((np.uint8, fill_value.itemsize)) == meta_dec['fill_value'].view((np.uint8, meta_dec['fill_value'].itemsize)) ) assert [] == meta_dec['filters']
def test_encode_decode_array_2(): # some variations df = Delta(astype='u2', dtype='V14') compressor = Blosc(cname='lz4', clevel=3, shuffle=2) dtype = np.dtype([('a', 'i4'), ('b', 'S10')]) fill_value = np.zeros((), dtype=dtype)[()] meta = dict( shape=(100, 100), chunks=(10, 10), dtype=dtype, compressor=compressor.get_config(), fill_value=fill_value, order='F', filters=[df.get_config()] ) meta_json = '''{ "chunks": [10, 10], "compressor": { "id": "blosc", "clevel": 3, "cname": "lz4", "shuffle": 2, "blocksize": 0 }, "dtype": [["a", "<i4"], ["b", "|S10"]], "fill_value": "AAAAAAAAAAAAAAAAAAA=", "filters": [ {"id": "delta", "astype": "<u2", "dtype": "|V14"} ], "order": "F", "shape": [100, 100], "zarr_format": %s }''' % ZARR_FORMAT # test encoding meta_enc = encode_array_metadata(meta) assert_json_equal(meta_json, meta_enc) # test decoding meta_dec = decode_array_metadata(meta_enc) assert ZARR_FORMAT == meta_dec['zarr_format'] assert meta['shape'] == meta_dec['shape'] assert meta['chunks'] == meta_dec['chunks'] assert meta['dtype'] == meta_dec['dtype'] assert meta['compressor'] == meta_dec['compressor'] assert meta['order'] == meta_dec['order'] assert fill_value == meta_dec['fill_value'] assert [df.get_config()] == meta_dec['filters']
def test_encode_decode_array_2(): # some variations df = Delta(astype='u2', dtype='V14') compressor = Blosc(cname='lz4', clevel=3, shuffle=2) dtype = np.dtype([('a', 'i4'), ('b', 'S10')]) fill_value = np.zeros((), dtype=dtype)[()] meta = dict( shape=(100, 100), chunks=(10, 10), dtype=dtype, compressor=compressor.get_config(), fill_value=fill_value, order='F', filters=[df.get_config()] ) meta_json = '''{ "chunks": [10, 10], "compressor": { "id": "blosc", "clevel": 3, "cname": "lz4", "shuffle": 2, "blocksize": 0 }, "dtype": [["a", "<i4"], ["b", "|S10"]], "fill_value": "AAAAAAAAAAAAAAAAAAA=", "filters": [ {"id": "delta", "astype": "<u2", "dtype": "|V14"} ], "order": "F", "shape": [100, 100], "zarr_format": %s }''' % ZARR_FORMAT # test encoding meta_enc = encode_array_metadata(meta) assert_json_equal(meta_json, meta_enc) # test decoding meta_dec = decode_array_metadata(meta_enc) assert ZARR_FORMAT == meta_dec['zarr_format'] assert meta['shape'] == meta_dec['shape'] assert meta['chunks'] == meta_dec['chunks'] assert meta['dtype'] == meta_dec['dtype'] assert meta['compressor'] == meta_dec['compressor'] assert meta['order'] == meta_dec['order'] assert fill_value == meta_dec['fill_value'] assert [df.get_config()] == meta_dec['filters']