def _op_msg_no_header(flags, command, identifier, docs, opts):
"""Get a OP_MSG message.
Note: this method handles multiple documents in a type one payload but
it does not perform batch splitting and the total message size is
only checked *after* generating the entire message.
"""
# Encode the command document in payload 0 without checking keys.
encoded = _dict_to_bson(command, False, opts)
flags_type = _pack_op_msg_flags_type(flags, 0)
total_size = len(encoded)
max_doc_size = 0
if identifier:
type_one = _pack_byte(1)
cstring = _make_c_string(identifier)
encoded_docs = [_dict_to_bson(doc, False, opts) for doc in docs]
size = len(cstring) + sum(len(doc) for doc in encoded_docs) + 4
encoded_size = _pack_int(size)
total_size += size
max_doc_size = max(len(doc) for doc in encoded_docs)
data = [flags_type, encoded, type_one, encoded_size, cstring
] + encoded_docs
else:
data = [flags_type, encoded]
return b"".join(data), total_size, max_doc_size
def test_incorrect_sub_dtype4(self):
# Sub document not a document
bad_doc = bson.SON([("x", bson.SON([("y", 0), ("z", 0)])), ("q", 10)])
bad_raw_docs = self.raw_docs[:9]
bad_raw_docs.append(
bson._dict_to_bson(bad_doc, False, bson.DEFAULT_CODEC_OPTIONS))
with self.assertRaisesPattern(
bsonnumpy.error,
"invalid document: expected subdoc from dtype,"
" got other type"):
bsonnumpy.sequence_to_ndarray(bad_raw_docs, self.dtype_sub, 10)
bad_doc = bson.SON([("x", bson.SON([("y", 0), ("z", 0)])),
("q", [10, 11, 12])])
bad_raw_docs = self.raw_docs[:9]
bad_raw_docs.append(
bson._dict_to_bson(bad_doc, False, bson.DEFAULT_CODEC_OPTIONS))
with self.assertRaisesPattern(
bsonnumpy.error,
"invalid document: expected subdoc from dtype,"
" got other type"):
bsonnumpy.sequence_to_ndarray(bad_raw_docs, self.dtype_sub, 10)
def _batched_op_msg_impl(operation, command, docs, ack, opts, ctx, buf):
"""Create a batched OP_MSG write."""
max_bson_size = ctx.max_bson_size
max_write_batch_size = ctx.max_write_batch_size
max_message_size = ctx.max_message_size
flags = b"\x00\x00\x00\x00" if ack else b"\x02\x00\x00\x00"
# Flags
buf.write(flags)
# Type 0 Section
buf.write(b"\x00")
buf.write(_dict_to_bson(command, False, opts))
# Type 1 Section
buf.write(b"\x01")
size_location = buf.tell()
# Save space for size
buf.write(b"\x00\x00\x00\x00")
try:
buf.write(_OP_MSG_MAP[operation])
except KeyError:
raise InvalidOperation("Unknown command")
to_send = []
idx = 0
for doc in docs:
# Encode the current operation
value = _dict_to_bson(doc, False, opts)
doc_length = len(value)
new_message_size = buf.tell() + doc_length
# Does first document exceed max_message_size?
doc_too_large = idx == 0 and (new_message_size > max_message_size)
# When OP_MSG is used unacknowleged we have to check
# document size client side or applications won't be notified.
# Otherwise we let the server deal with documents that are too large
# since ordered=False causes those documents to be skipped instead of
# halting the bulk write operation.
unacked_doc_too_large = not ack and (doc_length > max_bson_size)
if doc_too_large or unacked_doc_too_large:
write_op = list(_FIELD_MAP.keys())[operation]
_raise_document_too_large(write_op, len(value), max_bson_size)
# We have enough data, return this batch.
if new_message_size > max_message_size:
break
buf.write(value)
to_send.append(doc)
idx += 1
# We have enough documents, return this batch.
if idx == max_write_batch_size:
break
# Write type 1 section size
length = buf.tell()
buf.seek(size_location)
buf.write(_pack_int(length - size_location))
return to_send, length
def _query_impl(options, collection_name, num_to_skip, num_to_return, query,
field_selector, opts, check_keys):
"""Get an OP_QUERY message."""
encoded = _dict_to_bson(query, check_keys, opts)
if field_selector:
efs = _dict_to_bson(field_selector, False, opts)
else:
efs = b""
max_bson_size = max(len(encoded), len(efs))
return b"".join([
_pack_int(options),
_make_c_string(collection_name),
_pack_int(num_to_skip),
_pack_int(num_to_return), encoded, efs
]), max_bson_size
def test_array_scalar_load2(self):
# Test sub arrays with documents as elements
son_docs = [
bson.SON(
[('x', [
[
bson.SON([('a', i), ('b', i)]),
bson.SON([('a', -i), ('b', -i)])
],
[
bson.SON([('c', i), ('d', i)]),
bson.SON([('c', -i), ('d', -i)])
],
])]) for i in range(2, 4)]
raw_docs = [bson._dict_to_bson(
doc, False, bson.DEFAULT_CODEC_OPTIONS) for doc in son_docs]
sub_sub_dtype = np.dtype(([('a', 'int32'), ('b', 'int32')], 2))
sub_dtype = np.dtype((sub_sub_dtype, 2))
dtype = np.dtype([('x', sub_dtype)])
ndarray = np.array(
[[([(i, i), (-i, -i)],),
([(i, i), (-i, -i)],)] for i in range(2, 4)], dtype)
# Correct dtype
with self.assertRaisesPattern(bsonnumpy.error,
r'unsupported BSON type: unknown'):
bsonnumpy.sequence_to_ndarray(raw_docs, dtype, 2)
def test_null(self):
data = bson._dict_to_bson({"x": None}, True,
bson.DEFAULT_CODEC_OPTIONS)
with self.assertRaisesPattern(bsonnumpy.error,
r'unsupported BSON type: Null'):
bsonnumpy.sequence_to_ndarray(iter([data]),
np.dtype([('x', '<V10')]), 1)
def test_array_scalar_load4(self):
# Test documents with multiple levels of sub documents
son_docs = [
bson.SON(
[('x', [
[
bson.SON([('a', i), ('b', i)]),
bson.SON([('a', -i), ('b', -i)])
],
[
bson.SON([('c', i), ('d', i)]),
bson.SON([('c', -i), ('d', -i)])
],
])]) for i in range(10)]
raw_docs = [bson._dict_to_bson(
doc, False, bson.DEFAULT_CODEC_OPTIONS) for doc in son_docs]
sub_sub_sub_dtype = np.dtype([('q', 'int32')])
sub_sub_dtype = np.dtype(
([('a', sub_sub_sub_dtype), ('b', sub_sub_sub_dtype)], 2))
sub_dtype = np.dtype((sub_sub_dtype, 2))
dtype = np.dtype([('x', sub_dtype)])
# Correct dtype
with self.assertRaisesPattern(bsonnumpy.error,
r'unsupported BSON type: unknown'):
bsonnumpy.sequence_to_ndarray(raw_docs, dtype, 4)
def encode(self):
"""
Returns the encoded representation of this message.
"""
bodybytes = _dict_to_bson(self, False)
self.hdr.length = len(bodybytes)
return self.hdr.encode() + bodybytes
def encrypt(self, database, cmd, check_keys, codec_options):
"""Encrypt a MongoDB command.
:Parameters:
- `services`: The services for this command.
- `cmd`: A command document.
- `check_keys`: If True, check `cmd` for invalid keys.
- `codec_options`: The CodecOptions to use while encoding `cmd`.
:Returns:
The encrypted command to execute.
"""
self._check_closed()
# Workaround for $clusterTime which is incompatible with
# check_keys.
cluster_time = check_keys and cmd.pop('$clusterTime', None)
encoded_cmd = _dict_to_bson(cmd, check_keys, codec_options)
max_cmd_size = _MAX_ENC_BSON_SIZE + _COMMAND_OVERHEAD
if len(encoded_cmd) > max_cmd_size:
raise _raise_document_too_large(
next(iter(cmd)), len(encoded_cmd), max_cmd_size)
with _wrap_encryption_errors():
encrypted_cmd = self._auto_encrypter.encrypt(database, encoded_cmd)
# TODO: PYTHON-1922 avoid decoding the encrypted_cmd.
encrypt_cmd = _inflate_bson(
encrypted_cmd, DEFAULT_RAW_BSON_OPTIONS)
if cluster_time:
encrypt_cmd['$clusterTime'] = cluster_time
return encrypt_cmd
def encrypt(self, database, cmd, check_keys, codec_options):
"""Encrypt a MongoDB command.
:Parameters:
- `database`: The database for this command.
- `cmd`: A command document.
- `check_keys`: If True, check `cmd` for invalid keys.
- `codec_options`: The CodecOptions to use while encoding `cmd`.
:Returns:
The encrypted command to execute.
"""
self._check_closed()
# Workaround for $clusterTime which is incompatible with
# check_keys.
cluster_time = check_keys and cmd.pop('$clusterTime', None)
encoded_cmd = _dict_to_bson(cmd, check_keys, codec_options)
with _wrap_encryption_errors():
encrypted_cmd = self._auto_encrypter.encrypt(database, encoded_cmd)
# TODO: PYTHON-1922 avoid decoding the encrypted_cmd.
encrypt_cmd = _inflate_bson(encrypted_cmd,
DEFAULT_RAW_BSON_OPTIONS)
if cluster_time:
encrypt_cmd['$clusterTime'] = cluster_time
return encrypt_cmd
def test_deeply_nested_array(self):
# arrays of length 1 are maintained when they are within another array
dtype = np.dtype([("a", "(3,2,1)int32"), ("b", "(3,2,1)int32")])
doc = bson.SON([("a", [[[9], [9]], [[8], [8]], [[7], [7]]]),
("b", [[[6], [6]], [[5], [5]], [[4], [4]]])])
utf8 = bson._dict_to_bson(doc, False, bson.DEFAULT_CODEC_OPTIONS)
result = bsonnumpy.sequence_to_ndarray([utf8], dtype, 1)
self.assertEqual(dtype, result.dtype)
self.assertTrue(
np.array_equal(
result,
np.array([([[[9], [9]], [[8], [8]], [[7], [7]]
], [[[6], [6]], [[5], [5]], [[4], [4]]])], dtype)))
dtype = np.dtype([("a", "(3,1)int32"), ("b", "(3,1)int32"),
("c", "(3,1)int32")])
doc = bson.SON([("a", [[9], [8], [7]]), ("b", [[6], [5], [4]]),
("c", [[3], [2], [1]])])
utf8 = bson._dict_to_bson(doc, False, bson.DEFAULT_CODEC_OPTIONS)
result = bsonnumpy.sequence_to_ndarray([utf8], dtype, 1)
self.assertEqual(dtype, result.dtype)
self.assertTrue(
np.array_equal(
result,
np.array([([[9], [8], [7]], [[6], [5], [4]], [[3], [2], [1]])],
dtype)))
dtype = np.dtype([("a", "2int32")])
doc = bson.SON([("a", [7, 7])])
utf8 = bson._dict_to_bson(doc, False, bson.DEFAULT_CODEC_OPTIONS)
result = bsonnumpy.sequence_to_ndarray([utf8], dtype, 1)
self.assertEqual(dtype, result.dtype)
self.assertTrue(np.array_equal(result, np.array([([7, 7], )], dtype)))
dtype = np.dtype([("a", "(2,1,1,1)int32")])
doc = bson.SON([("a", [[[[99]]], [[[88]]]])])
utf8 = bson._dict_to_bson(doc, False, bson.DEFAULT_CODEC_OPTIONS)
result = bsonnumpy.sequence_to_ndarray([utf8], dtype, 1)
self.assertEqual(dtype, result.dtype)
self.assertTrue(
np.array_equal(result, np.array([([[[[99]]], [[[88]]]], )],
dtype)))
def _write(self, obj):
if isinstance(obj, dict):
self._validate_write(obj)
self.fh.write(_dict_to_bson(obj, False))
else:
#raise Exception("Can only write a Dict. No support for direct BSON Serialization of '%s'" % type(obj))
#print >> sys.stderr, "Bare (or non-dict) output value '%s' found. Wrapping in a BSON object 'value' field." % obj
self._write({'value': obj})
def _delete(collection_name, spec, opts, flags):
"""Get an OP_DELETE message."""
encoded = _dict_to_bson(spec, False, opts) # Uses extensions.
return b"".join([
_ZERO_32,
_make_c_string(collection_name),
_pack_int(flags),
encoded]), len(encoded)
def encode(self):
"""
Returns a string.
"""
if self._body is not None:
self._bodybytes = _dict_to_bson(self._body, False)
self.hdr.length = len(self._bodybytes)
return self.hdr.encode() + self._bodybytes
def compare_seq_to_ndarray_result(self, np_type, document):
data = bson._dict_to_bson(document, False, bson.DEFAULT_CODEC_OPTIONS)
dtype = np.dtype(np_type)
result = bsonnumpy.sequence_to_ndarray([data], dtype, 1)
self.assertEqual(result.dtype, dtype)
for key in document:
self.assertEqual(result[0][key], document[key],
"Comparison failed for type %s: %s != %s" % (
dtype, result[0][key], document[key]))
def _test_error(self, value, bson_type_name, codes):
data = bson._dict_to_bson({'x': value}, True,
bson.DEFAULT_CODEC_OPTIONS)
for code in codes:
dtype = np.dtype([('x', code)])
expected = "cannot convert %s to dtype" % bson_type_name
with self.assertRaisesPattern(bsonnumpy.error, expected):
bsonnumpy.sequence_to_ndarray(iter([data]), dtype, 1)
def test(self):
data = bson._dict_to_bson(
{"a": value},
True, # check_keys
bson.DEFAULT_CODEC_OPTIONS)
with self.assertRaises(bsonnumpy.error) as context:
bsonnumpy.sequence_to_ndarray([data], np.dtype([("a", dtype)]), 1)
self.assertIn("unsupported BSON type: %s" % type_name,
str(context.exception))
def test_incorrect_sub_dtype_array7(self):
# Sub array too short
bad_doc = bson.SON([("x", [['d' * 1, 'd' * 2], ['d' * 4, 'd' * 5]]),
("y", [['d' * 7, 'd' * 8], ['d' * 10, 'd' * 11]])])
bad_raw_docs = self.raw_docs[:3]
bad_raw_docs.append(
bson._dict_to_bson(bad_doc, False, bson.DEFAULT_CODEC_OPTIONS))
with self.assertRaisesPattern(
bsonnumpy.error,
"invalid document: array is of incorrect length"):
bsonnumpy.sequence_to_ndarray(bad_raw_docs, self.dtype, 4)
def test_incorrect_sub_dtype_array1(self):
# Top document missing key
bad_doc = bson.SON([("x", [['d' * 1, 'd' * 2, 'd' * 3],
['d' * 4, 'd' * 5, 'd' * 6]]),
("bad_key", [['d' * 7, 'd' * 7, 'd' * 9],
['d' * 10, 'd' * 11, 'd' * 12]])])
bad_raw_docs = self.raw_docs[:3]
bad_raw_docs.append(
bson._dict_to_bson(bad_doc, False, bson.DEFAULT_CODEC_OPTIONS))
with self.assertRaisesPattern(bsonnumpy.error,
"document does not match dtype"):
bsonnumpy.sequence_to_ndarray(bad_raw_docs, self.dtype, 4)
def test_incorrect_sub_dtype3(self):
# Sub document missing key
bad_doc = bson.SON([("x", bson.SON([("bad", 0), ("z", 0)])),
("q", bson.SON([("y", 0), ("z", 0)]))])
bad_raw_docs = self.raw_docs[:9]
bad_raw_docs.append(
bson._dict_to_bson(bad_doc, False, bson.DEFAULT_CODEC_OPTIONS))
with self.assertRaisesPattern(bsonnumpy.error,
"document does not match dtype"):
bsonnumpy.sequence_to_ndarray(bad_raw_docs, self.dtype_sub, 10)
def test_incorrect_sub_dtype_array2(self):
# Top-level array not array
bad_doc = bson.SON([("x", [['d' * 1, 'd' * 2, 'd' * 3],
['d' * 4, 'd' * 5, 'd' * 6]]),
("y", 'not an array')])
bad_raw_docs = self.raw_docs[:3]
bad_raw_docs.append(
bson._dict_to_bson(bad_doc, False, bson.DEFAULT_CODEC_OPTIONS))
with self.assertRaisesPattern(
bsonnumpy.error,
"invalid document: expected list from dtype, got other type"):
bsonnumpy.sequence_to_ndarray(bad_raw_docs, self.dtype, 4)
def encode_dict_list(products, output_bson_path, total=None, overwrite=False):
if os.path.exists(output_bson_path) and overwrite is False:
logging.info('already exists ({})'.format(output_bson_path))
return
logging.info('write {} products to {}'.format(total, output_bson_path))
with open(output_bson_path, 'wb') as writer:
for i, prod in tqdm(enumerate(products),
unit='products',
total=total or len(products),
ascii=True):
obj = bson._dict_to_bson(prod, False, bson.DEFAULT_CODEC_OPTIONS)
writer.write(obj)
def __init__(self, client, opts):
"""Create a _Encrypter for a client.
:Parameters:
- `client`: The encrypted MongoClient.
- `opts`: The encrypted client's :class:`AutoEncryptionOpts`.
"""
if opts._schema_map is None:
schema_map = None
else:
schema_map = _dict_to_bson(opts._schema_map, False, _DATA_KEY_OPTS)
self._bypass_auto_encryption = opts._bypass_auto_encryption
self._internal_client = None
def _get_internal_client(encrypter, mongo_client):
if mongo_client.max_pool_size is None:
# Unlimited pool size, use the same client.
return mongo_client
# Else - limited pool size, use an internal client.
if encrypter._internal_client is not None:
return encrypter._internal_client
internal_client = mongo_client._duplicate(
minPoolSize=0, auto_encryption_opts=None)
encrypter._internal_client = internal_client
return internal_client
if opts._key_vault_client is not None:
key_vault_client = opts._key_vault_client
else:
key_vault_client = _get_internal_client(self, client)
if opts._bypass_auto_encryption:
metadata_client = None
else:
metadata_client = _get_internal_client(self, client)
db, coll = opts._key_vault_namespace.split('.', 1)
key_vault_coll = key_vault_client[db][coll]
mongocryptd_client = MongoClient(
opts._mongocryptd_uri,
connect=False,
serverSelectionTimeoutMS=_MONGOCRYPTD_TIMEOUT_MS)
io_callbacks = _EncryptionIO(metadata_client, key_vault_coll,
mongocryptd_client, opts)
self._auto_encrypter = AutoEncrypter(
io_callbacks, MongoCryptOptions(opts._kms_providers, schema_map))
self._closed = False
def test_array_scalar_load00(self):
# Test arrays with documents as elements
son_docs = [
bson.SON([('x', [i, i, i, i])]) for i in range(2, 4)]
raw_docs = [bson._dict_to_bson(
doc, False, bson.DEFAULT_CODEC_OPTIONS) for doc in son_docs]
dtype = np.dtype([('x', '4int32')])
ndarray = np.array([([i, i, i, i],) for i in range(2, 4)], dtype)
# Correct dtype
res = bsonnumpy.sequence_to_ndarray(raw_docs, dtype, 2)
self.assertTrue(np.array_equal(ndarray, res))
def test_incorrect_sub_dtype2(self):
# Top document has extra key
data = bson._dict_to_bson({
"x": 12,
"y": 13
}, True, bson.DEFAULT_CODEC_OPTIONS)
ndarray = bsonnumpy.sequence_to_ndarray([data],
np.dtype([("y", np.int)]), 1)
self.assertEqual(1, len(ndarray))
self.assertEqual(13, ndarray[0]["y"])
with self.assertRaises(ValueError):
ndarray[0]["x"]
def __init__(self, io_callbacks, opts):
"""Encrypts and decrypts MongoDB commands.
This class is used to support automatic encryption and decryption of
MongoDB commands.
:Parameters:
- `io_callbacks`: A :class:`MongoCryptCallback`.
- `opts`: The encrypted client's :class:`AutoEncryptionOpts`.
"""
if opts._schema_map is None:
schema_map = None
else:
schema_map = _dict_to_bson(opts._schema_map, False, _DATA_KEY_OPTS)
self._auto_encrypter = AutoEncrypter(
io_callbacks, MongoCryptOptions(opts._kms_providers, schema_map))
self._bypass_auto_encryption = opts._bypass_auto_encryption
def collection_info(self, database, filter):
"""Get the collection info for a namespace.
The returned collection info is passed to libmongocrypt which reads
the JSON schema.
:Parameters:
- `database`: The database on which to run listCollections.
- `filter`: The filter to pass to listCollections.
:Returns:
The first document from the listCollections command response as BSON.
"""
with self.client_ref()[database].list_collections(
filter=RawBSONDocument(filter)) as cursor:
for doc in cursor:
return _dict_to_bson(doc, False, _DATA_KEY_OPTS)
def encrypt(self, database, cmd, codec_options):
"""Encrypt a MongoDB command.
:Parameters:
- `database`: The database for this command.
- `cmd`: A command document.
- `codec_options`: The CodecOptions to use while encoding `cmd`.
:Returns:
The encrypted command to execute.
"""
self._check_closed()
encoded_cmd = _dict_to_bson(cmd, False, codec_options)
with _wrap_encryption_errors():
encrypted_cmd = self._auto_encrypter.encrypt(database, encoded_cmd)
# TODO: PYTHON-1922 avoid decoding the encrypted_cmd.
encrypt_cmd = _inflate_bson(encrypted_cmd,
DEFAULT_RAW_BSON_OPTIONS)
return encrypt_cmd
def test_string_length(self):
data = bson._dict_to_bson({"x": "abc"}, True,
bson.DEFAULT_CODEC_OPTIONS)
ndarray = bsonnumpy.sequence_to_ndarray(iter([data]),
np.dtype([("x", "V1")]), 1)
self.assertEqual(ndarray[0]["x"].tobytes(), b"a")
ndarray = bsonnumpy.sequence_to_ndarray(iter([data]),
np.dtype([("x", "V2")]), 1)
self.assertEqual(ndarray[0]["x"].tobytes(), b"ab")
ndarray = bsonnumpy.sequence_to_ndarray(iter([data]),
np.dtype([("x", "V3")]), 1)
self.assertEqual(ndarray[0]["x"].tobytes(), b"abc")
ndarray = bsonnumpy.sequence_to_ndarray(iter([data]),
np.dtype([("x", "V4")]), 1)
self.assertEqual(ndarray[0]["x"].tobytes(), b"abc\0")
def test_array_scalar_load3(self):
# Test sub arrays with documents that have arrays
son_docs = [
bson.SON(
[('x', [
bson.SON([('a', [i, i, i, i]),
('b', [i, i, i, i])]),
bson.SON([('a', [-i, -i, -i, -i]),
('b', [-i, -i, -i, -i])])
])]) for i in range(10)]
raw_docs = [bson._dict_to_bson(
doc, False, bson.DEFAULT_CODEC_OPTIONS) for doc in son_docs]
sub_dtype = np.dtype(([('a', '4int32'), ('b', '4int32')], 2))
dtype = np.dtype([('x', sub_dtype)])
# Correct dtype
with self.assertRaisesPattern(bsonnumpy.error,
r'unsupported BSON type: Sub-document'):
bsonnumpy.sequence_to_ndarray(raw_docs, dtype, 4)
def _element_to_bson(key, value, check_keys, uuid_subtype):
objtype = type(value)
if isinstance(objtype,object): #objtype == types.InstanceType:
objtype = value.__class__
encoder = Extension.getpickler(objtype)
if not encoder and hasattr(value, '__getstate__'):
for _type in type(value).__mro__:
encoder = Extension.getpickler(_type)
if encoder:
break
if encoder:
code, value = encoder(value)
if isinstance(value, dict):
assert '__customtype__' not in value
value = value.copy()
value['__customtype__'] = code
if isinstance(value, (list, tuple)):
name = bson._make_c_string(key)
as_dict = OrderedDict((str(k), v) for (k, v) in enumerate(value))
return bson.BSONARR + name + bson._dict_to_bson(as_dict, check_keys,
uuid_subtype, False)
return _bson_element_to_bson(key, value, check_keys, uuid_subtype)
def test__dict_to_bson(self):
document = {'average': Decimal('56.47')}
rawbytes = BSON.encode(document, codec_options=self.codecopts)
encoded_document = _dict_to_bson(document, False, self.codecopts)
self.assertEqual(encoded_document, rawbytes)
class TestNdarrayFlat(TestToNdarray):
dtype = np.dtype([('x', np.int32), ('y', np.int32)])
bson_docs = [
bson._dict_to_bson(bson.SON([("x", i), ("y", -i)]), False, DEFAULT)
for i in range(10)
]
ndarray = np.array([(i, -i) for i in range(10)], dtype=dtype)
if hasattr(unittest.TestCase, 'assertRaisesRegex'):
assertRaisesPattern = unittest.TestCase.assertRaisesRegex
else:
assertRaisesPattern = unittest.TestCase.assertRaisesRegexp
def test_incorrect_arguments(self):
with self.assertRaisesPattern(
bsonnumpy.error, r'sequence_to_ndarray requires a numpy\.'
r'ndarray'):
bsonnumpy.ndarray_to_sequence(None)
with self.assertRaisesPattern(
bsonnumpy.error, r'sequence_to_ndarray requires a numpy\.'
r'ndarray'):
bsonnumpy.ndarray_to_sequence([])
with self.assertRaisesPattern(
bsonnumpy.error, r'sequence_to_ndarray requires a numpy\.'
r'ndarray'):
bsonnumpy.ndarray_to_sequence(1)
with self.assertRaises(TypeError):
bsonnumpy.ndarray_to_sequence(10, 10)
def test_empty(self):
dtype = np.dtype([('x', np.int32), ('y', np.float)])
batch = b''.join([
bson.BSON.encode({
"x": 1,
"y": 1.1
}),
bson.BSON.encode({}),
bson.BSON.encode({
"x": 3,
"y": 1.3
}),
])
with self.assertRaisesPattern(bsonnumpy.error,
r'document does not match dtype'):
bsonnumpy.sequence_to_ndarray([batch], dtype, 3)
def test_raw_batch(self):
dtype = np.dtype([('x', np.int32), ('y', np.float)])
# A variety of lengths.
batch = b''.join([
bson.BSON.encode({
"x": 1,
"y": 1.1
}),
bson.BSON.encode({
"x": 2,
"y": 1.2,
"extra key": "foobar"
}),
bson.BSON.encode({
"x": 3,
"y": 1.3
}),
])
result = bsonnumpy.sequence_to_ndarray([batch], dtype, 3)
ndarray = np.array([(1, 1.1), (2, 1.2), (3, 1.3)], dtype)
np.testing.assert_array_equal(result, ndarray)
dtype = np.dtype([('x', np.int32), ('y', np.float), ('z', np.int32)])
# A variety of orders.
batch = b''.join([
bson.BSON.encode(SON([("x", 1), ("y", 1.1), ("z", 4)])),
bson.BSON.encode(SON([("x", 2), ("z", 5), ("y", 1.2)])),
bson.BSON.encode(SON([("z", 6), ("x", 3), ("y", 1.3)]))
])
result = bsonnumpy.sequence_to_ndarray([batch], dtype, 3)
ndarray = np.array([(1, 1.1, 4), (2, 1.2, 5), (3, 1.3, 6)], dtype)
np.testing.assert_array_equal(result, ndarray)
def test_dimensions_limit(self):
# Make a deeply-nested dtype([('x', dtype([('x', dtype([('x', ...
dtype = np.dtype([('y', np.int32)])
for _ in range(31):
dtype = np.dtype([('x', dtype)])
# No error.
bsonnumpy.sequence_to_ndarray([], dtype, 0)
# One more level.
dtype = np.dtype([('x', dtype)])
with self.assertRaisesPattern(bsonnumpy.error, r'exceeds 32 levels'):
bsonnumpy.sequence_to_ndarray([], dtype, 0)
def test__dict_to_bson(self):
document = {'average': Decimal('56.47')}
rawbytes = encode(document, codec_options=self.codecopts)
encoded_document = _dict_to_bson(document, False, self.codecopts)
self.assertEqual(encoded_document, rawbytes)
