def decode(cls, encoding, spec, df):
return cls(df,
spec['expression'],
encoding.decode('dtype', spec['dtype']),
encoding.decode('dtype', spec['dtype_item']),
flatten=spec['flatten'],
unique_limit=spec['unique_limit'],
selection=spec['selection'])
def decode(cls, encoding, spec, df, nthreads):
return cls(df,
spec['expression'],
encoding.decode('dtype', spec['dtype']),
encoding.decode('dtype', spec['dtype_item']),
flatten=spec['flatten'],
unique_limit=spec['unique_limit'],
selection=spec['selection'],
return_inverse=spec['return_inverse'],
nthreads=nthreads)
def decode(cls, encoding, spec, df):
spec = spec.copy()
spec['op'] = encoding.decode('_op', spec['op'])
spec['dtype'] = encoding.decode('dtype', spec['dtype'])
selection_waslist = spec.pop('selection_waslist')
if selection_waslist:
spec['selection'] = spec.pop('selections')
else:
spec['selection'] = spec.pop('selections')[0]
spec['limits'] = list(zip(spec.pop('minima'), spec.pop('maxima')))
return cls(df, **spec)
def decode(cls, encoding, spec, df):
grid = encoding.decode('grid', spec['grid'])
task = cls(df, grid)
aggs = encoding.decode_list('aggregation', spec['aggregations'])
for agg in aggs:
agg._prepare_types(df)
task.add_aggregation_operation(agg)
return task
def test_encoding():
encoding = vaex.encoding.Encoding()
data = encoding.encode('blobtest', {'someblob': b'1234'})
wiredata = vaex.encoding.serialize(data, encoding)
encoding = vaex.encoding.Encoding()
data = vaex.encoding.deserialize(wiredata, encoding)
values = encoding.decode('blobtest', data)
assert values['someblob'] == b'1234'
def decode(cls, encoding, spec, df):
# aggs = [vaex.agg._from_spec(agg_spec) for agg_spec in spec['aggregations']]
aggs = encoding.decode_list('aggregation', spec['aggregations'])
dtypes = encoding.decode_dict('dtype', spec['dtypes'])
grid = encoding.decode('grid', spec['grid'])
# dtypes = {expr: _deserialize_type(type_spec) for expr, type_spec in spec['dtypes'].items()}
for agg in aggs:
agg._prepare_types(df)
return cls(df, grid, aggs, dtypes)
def rebuild_dataframe_vaex(df):
# encoding and decode
encoding = vaex.encoding.Encoding()
data = encoding.encode('dataframe', df)
blob = vaex.encoding.serialize(data, encoding)
encoding = vaex.encoding.Encoding()
data = vaex.encoding.deserialize(blob, encoding)
return encoding.decode('dataframe', data)
def rebuild_dataset_vaex(ds):
# encoding and decode
encoding = vaex.encoding.Encoding()
data = encoding.encode('dataset', ds)
blob = vaex.encoding.serialize(data, encoding)
encoding = vaex.encoding.Encoding()
data = vaex.encoding.deserialize(blob, encoding)
return encoding.decode('dataset', data)
def test_encoding_numpy():
x = np.arange(10, dtype='>f4')
encoding = vaex.encoding.Encoding()
data = encoding.encode('ndarray', x)
wiredata = vaex.encoding.serialize(data, encoding)
encoding = vaex.encoding.Encoding()
data = vaex.encoding.deserialize(wiredata, encoding)
value = encoding.decode('ndarray', data)
assert np.all(value == x)
def test_encoding_numpy_datetime():
x = np.arange('2001', '2005', dtype='M')
encoding = vaex.encoding.Encoding()
data = encoding.encode('ndarray', x)
wiredata = vaex.encoding.serialize(data, encoding)
encoding = vaex.encoding.Encoding()
data = vaex.encoding.deserialize(wiredata, encoding)
value = encoding.decode('ndarray', data)
assert np.all(value == x)
def test_encoding_numpy_string_objects():
x = np.array(['vaex', 'is', None, 'fast'])
encoding = vaex.encoding.Encoding()
data = encoding.encode('ndarray', x)
wiredata = vaex.encoding.serialize(data, encoding)
encoding = vaex.encoding.Encoding()
data = vaex.encoding.deserialize(wiredata, encoding)
value = encoding.decode('ndarray', data)
assert np.all(value == x)
def test_encoding_arrow(array_factory_arrow):
x = array_factory_arrow(np.arange(10, dtype='f4'))
encoding = vaex.encoding.Encoding()
data = encoding.encode('arrow-array', x)
wiredata = vaex.encoding.serialize(data, encoding)
encoding = vaex.encoding.Encoding()
data = vaex.encoding.deserialize(wiredata, encoding)
value = encoding.decode('arrow-array', data)
assert value.to_pylist() == x.to_pylist()
def test_encoding_dtype():
dtype = np.dtype('>f8')
encoding = vaex.encoding.Encoding()
data = encoding.encode('dtype', dtype)
wiredata = vaex.encoding.serialize(data, encoding)
encoding = vaex.encoding.Encoding()
data = vaex.encoding.deserialize(wiredata, encoding)
print(data)
value = encoding.decode('dtype', data)
assert value == dtype
assert value.is_numpy
def decode(encoding, binner_spec, nthreads):
type = binner_spec['binner-type']
dtype = encoding.decode('dtype', binner_spec['dtype'])
if type == 'ordinal':
cls = vaex.utils.find_type_from_dtype(vaex.superagg, "BinnerOrdinal_", dtype)
return cls(nthreads, binner_spec['expression'], binner_spec['count'], binner_spec['minimum'], False)
elif type == 'scalar':
cls = vaex.utils.find_type_from_dtype(vaex.superagg, "BinnerScalar_", dtype)
return cls(nthreads, binner_spec["expression"], binner_spec["minimum"], binner_spec["maximum"], binner_spec["count"])
elif type == "hash":
cls = vaex.utils.find_type_from_dtype(vaex.superagg, "BinnerHash_", dtype)
hash_map_unique_id = binner_spec["hash_map_unique"]
if encoding.has_object(hash_map_unique_id):
hash_map_unique = encoding.get_object(hash_map_unique_id)
else:
hash_map_unique_spec = encoding.get_object_spec(hash_map_unique_id)
hash_map_unique = encoding.decode("hash-map-unique", hash_map_unique_spec)
encoding.set_object(hash_map_unique_id, hash_map_unique)
return cls(binner_spec["expression"], hash_map_unique._internal)
else:
raise ValueError('Cannot deserialize: %r' % binner_spec)
def create_task_part():
nonlocal memory_usage
task_part = encoding.decode('task-part-cpu',
spec,
df=task.df,
nthreads=nthreads)
memory_usage += task_part.memory_usage()
for task_checker in task_checkers:
task_checker.add_task(task)
if task.requires_fingerprint:
task_part.fingerprint = task.fingerprint()
return task_part
def test_encoding_numpy_masked():
x = np.arange(10, dtype='>f4')
mask = x > 4
x = np.ma.array(x, mask=mask)
encoding = vaex.encoding.Encoding()
data = encoding.encode('ndarray', x)
wiredata = vaex.encoding.serialize(data, encoding)
encoding = vaex.encoding.Encoding()
data = vaex.encoding.deserialize(wiredata, encoding)
value = encoding.decode('ndarray', data)
assert np.all(value == x)
assert np.all(value.mask == x.mask)
def rebuild_with_skip(ds, skip):
repr(ds) # for coverage
# encoding and decode
encoding = vaex.encoding.Encoding()
encoding.set_object_spec(skip.id, None) # this will cause it to skip serialization
data = encoding.encode('dataset', ds)
assert encoding._object_specs[skip.id] is None
del encoding._object_specs[skip.id]
blob = vaex.encoding.serialize(data, encoding)
encoding = vaex.encoding.Encoding()
encoding.set_object(skip.id, skip)
data = vaex.encoding.deserialize(blob, encoding)
return encoding.decode('dataset', data)
def decode(encoding, binner_spec):
type = binner_spec['binner-type']
dtype = encoding.decode('dtype', binner_spec['dtype'])
if type == 'ordinal':
cls = vaex.utils.find_type_from_dtype(vaex.superagg,
"BinnerOrdinal_", dtype)
return cls(binner_spec['expression'], binner_spec['count'],
binner_spec['minimum'])
elif type == 'scalar':
cls = vaex.utils.find_type_from_dtype(vaex.superagg,
"BinnerScalar_", dtype)
return cls(binner_spec['expression'], binner_spec['minimum'],
binner_spec['maximum'], binner_spec['count'])
else:
raise ValueError('Cannot deserialize: %r' % binner_spec)
def decode(cls, encoding, spec, df):
binners = tuple(encoding.decode_list('binner', spec['binners']))
agg = encoding.decode('aggregation', spec['aggregation'])
task = cls(df, binners, agg)
return task
def decode(cls, encoding, spec, df, nthreads):
spec = spec.copy()
spec['op'] = encoding.decode('_op', spec['op'])
spec['dtype'] = encoding.decode('dtype', spec['dtype'])
return cls(df, **spec)
def decode(cls, encoding, spec, df):
spec = spec.copy()
del spec['task']
spec['op'] = encoding.decode('_op', spec['op'])
spec['dtype'] = encoding.decode('dtype', spec['dtype'])
return cls(df, **spec)
def create_task_part():
return encoding.decode('task-part-cpu',
spec,
df=run.df)
