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']