body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
def get_standard_ids_by_id(self, _id):
'Get chebi_id, pubmed_id, and kegg_id from\n database specific id.\n \n Args:\n _id (:obj:`str`): Database specific ID.\n\n Return:\n (:obj:`dict`): Dictionary containing the information.\n '
if (self.collection_str ... | 4,700,694,575,084,467,000 | Get chebi_id, pubmed_id, and kegg_id from
database specific id.
Args:
_id (:obj:`str`): Database specific ID.
Return:
(:obj:`dict`): Dictionary containing the information. | datanator_query_python/query/query_xmdb.py | get_standard_ids_by_id | KarrLab/datanator_query_python | python | def get_standard_ids_by_id(self, _id):
'Get chebi_id, pubmed_id, and kegg_id from\n database specific id.\n \n Args:\n _id (:obj:`str`): Database specific ID.\n\n Return:\n (:obj:`dict`): Dictionary containing the information.\n '
if (self.collection_str ... |
@configurable
def __init__(self, is_train: bool, *, augmentations: List[Union[(T.Augmentation, T.Transform)]], image_format: str, use_instance_mask: bool=False, use_keypoint: bool=False, instance_mask_format: str='polygon', keypoint_hflip_indices: Optional[np.ndarray]=None, precomputed_proposal_topk: Optional[int]=None... | -208,818,319,591,433,820 | NOTE: this interface is experimental.
Args:
is_train: whether it's used in training or inference
augmentations: a list of augmentations or deterministic transforms to apply
image_format: an image format supported by :func:`detection_utils.read_image`.
use_instance_mask: whether to process instance segm... | detectron2/data/dataset_mapper.py | __init__ | Jerrypiglet/detectron2 | python | @configurable
def __init__(self, is_train: bool, *, augmentations: List[Union[(T.Augmentation, T.Transform)]], image_format: str, use_instance_mask: bool=False, use_keypoint: bool=False, instance_mask_format: str='polygon', keypoint_hflip_indices: Optional[np.ndarray]=None, precomputed_proposal_topk: Optional[int]=None... |
def __call__(self, dataset_dict):
'\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n '
dataset_dict = copy.deepcopy(dataset_dict)
image = utils.read_image(da... | 689,083,837,638,239,400 | Args:
dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
Returns:
dict: a format that builtin models in detectron2 accept | detectron2/data/dataset_mapper.py | __call__ | Jerrypiglet/detectron2 | python | def __call__(self, dataset_dict):
'\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n '
dataset_dict = copy.deepcopy(dataset_dict)
image = utils.read_image(da... |
@contextlib.contextmanager
def _open_archive(archive, directory):
'Manages a directory in which an existing SDK is laid out.'
if directory:
(yield directory)
elif archive:
temp_dir = tempfile.mkdtemp(prefix='fuchsia-merger')
with tarfile.open(archive) as archive_file:
arc... | 3,065,005,589,519,523,300 | Manages a directory in which an existing SDK is laid out. | scripts/sdk/merger/merge.py | _open_archive | allansrc/fuchsia | python | @contextlib.contextmanager
def _open_archive(archive, directory):
if directory:
(yield directory)
elif archive:
temp_dir = tempfile.mkdtemp(prefix='fuchsia-merger')
with tarfile.open(archive) as archive_file:
archive_file.extractall(temp_dir)
try:
(yi... |
@contextlib.contextmanager
def _open_output(archive, directory):
'Manages the output of this script.'
if directory:
shutil.rmtree(directory, ignore_errors=True)
(yield directory)
elif archive:
temp_dir = tempfile.mkdtemp(prefix='fuchsia-merger')
try:
(yield temp_d... | 138,120,787,399,427,680 | Manages the output of this script. | scripts/sdk/merger/merge.py | _open_output | allansrc/fuchsia | python | @contextlib.contextmanager
def _open_output(archive, directory):
if directory:
shutil.rmtree(directory, ignore_errors=True)
(yield directory)
elif archive:
temp_dir = tempfile.mkdtemp(prefix='fuchsia-merger')
try:
(yield temp_dir)
with tarfile.open(ar... |
def _get_manifest(sdk_dir):
'Returns the set of elements in the given SDK.'
with open(os.path.join(sdk_dir, 'meta', 'manifest.json'), 'r') as manifest:
return json.load(manifest) | -6,938,343,977,296,843,000 | Returns the set of elements in the given SDK. | scripts/sdk/merger/merge.py | _get_manifest | allansrc/fuchsia | python | def _get_manifest(sdk_dir):
with open(os.path.join(sdk_dir, 'meta', 'manifest.json'), 'r') as manifest:
return json.load(manifest) |
def _get_meta(element, sdk_dir):
"Returns the contents of the given element's manifest in a given SDK."
with open(os.path.join(sdk_dir, element), 'r') as meta:
return json.load(meta) | -4,476,401,921,661,051,400 | Returns the contents of the given element's manifest in a given SDK. | scripts/sdk/merger/merge.py | _get_meta | allansrc/fuchsia | python | def _get_meta(element, sdk_dir):
with open(os.path.join(sdk_dir, element), 'r') as meta:
return json.load(meta) |
def _get_type(element):
'Returns the SDK element type.'
if ('schema_id' in element):
return element['data']['type']
return element['type'] | 55,824,938,872,102,580 | Returns the SDK element type. | scripts/sdk/merger/merge.py | _get_type | allansrc/fuchsia | python | def _get_type(element):
if ('schema_id' in element):
return element['data']['type']
return element['type'] |
def _get_files(element_meta):
'Extracts the files associated with the given element.\n Returns a 2-tuple containing:\n - the set of arch-independent files;\n - the sets of arch-dependent files, indexed by architecture.\n '
type = _get_type(element_meta)
common_files = set()
arch_files = {}... | 7,918,897,328,551,751,000 | Extracts the files associated with the given element.
Returns a 2-tuple containing:
- the set of arch-independent files;
- the sets of arch-dependent files, indexed by architecture. | scripts/sdk/merger/merge.py | _get_files | allansrc/fuchsia | python | def _get_files(element_meta):
'Extracts the files associated with the given element.\n Returns a 2-tuple containing:\n - the set of arch-independent files;\n - the sets of arch-dependent files, indexed by architecture.\n '
type = _get_type(element_meta)
common_files = set()
arch_files = {}... |
def _ensure_directory(path):
'Ensures that the directory hierarchy of the given path exists.'
target_dir = os.path.dirname(path)
try:
os.makedirs(target_dir)
except OSError as exception:
if ((exception.errno == errno.EEXIST) and os.path.isdir(target_dir)):
pass
else:
... | -2,222,556,865,119,095,600 | Ensures that the directory hierarchy of the given path exists. | scripts/sdk/merger/merge.py | _ensure_directory | allansrc/fuchsia | python | def _ensure_directory(path):
target_dir = os.path.dirname(path)
try:
os.makedirs(target_dir)
except OSError as exception:
if ((exception.errno == errno.EEXIST) and os.path.isdir(target_dir)):
pass
else:
raise |
def _copy_file(file, source_dir, dest_dir):
'Copies a file to a given path, taking care of creating directories if\n needed.\n '
source = os.path.join(source_dir, file)
destination = os.path.join(dest_dir, file)
_ensure_directory(destination)
shutil.copy2(source, destination) | -1,162,556,876,817,004,300 | Copies a file to a given path, taking care of creating directories if
needed. | scripts/sdk/merger/merge.py | _copy_file | allansrc/fuchsia | python | def _copy_file(file, source_dir, dest_dir):
'Copies a file to a given path, taking care of creating directories if\n needed.\n '
source = os.path.join(source_dir, file)
destination = os.path.join(dest_dir, file)
_ensure_directory(destination)
shutil.copy2(source, destination) |
def _copy_files(files, source_dir, dest_dir):
'Copies a set of files to a given directory.'
for file in files:
_copy_file(file, source_dir, dest_dir) | 679,370,016,772,844,700 | Copies a set of files to a given directory. | scripts/sdk/merger/merge.py | _copy_files | allansrc/fuchsia | python | def _copy_files(files, source_dir, dest_dir):
for file in files:
_copy_file(file, source_dir, dest_dir) |
def _copy_identical_files(set_one, source_dir_one, set_two, source_dir_two, dest_dir):
'Verifies that two sets of files are absolutely identical and then copies\n them to the output directory.\n '
if (set_one != set_two):
return False
_copy_files(set_one, source_dir_one, dest_dir)
return T... | -6,738,155,008,769,796,000 | Verifies that two sets of files are absolutely identical and then copies
them to the output directory. | scripts/sdk/merger/merge.py | _copy_identical_files | allansrc/fuchsia | python | def _copy_identical_files(set_one, source_dir_one, set_two, source_dir_two, dest_dir):
'Verifies that two sets of files are absolutely identical and then copies\n them to the output directory.\n '
if (set_one != set_two):
return False
_copy_files(set_one, source_dir_one, dest_dir)
return T... |
def _copy_element(element, source_dir, dest_dir):
'Copy an entire SDK element to a given directory.'
meta = _get_meta(element, source_dir)
(common_files, arch_files) = _get_files(meta)
files = common_files
for more_files in arch_files.values():
files.update(more_files)
_copy_files(files,... | 8,808,345,156,117,892,000 | Copy an entire SDK element to a given directory. | scripts/sdk/merger/merge.py | _copy_element | allansrc/fuchsia | python | def _copy_element(element, source_dir, dest_dir):
meta = _get_meta(element, source_dir)
(common_files, arch_files) = _get_files(meta)
files = common_files
for more_files in arch_files.values():
files.update(more_files)
_copy_files(files, source_dir, dest_dir)
_copy_file(element, sou... |
def _write_meta(element, source_dir_one, source_dir_two, dest_dir):
'Writes a meta file for the given element, resulting from the merge of the\n meta files for that element in the two given SDK directories.\n '
meta_one = _get_meta(element, source_dir_one)
meta_two = _get_meta(element, source_dir_two)... | 4,619,572,242,825,215,000 | Writes a meta file for the given element, resulting from the merge of the
meta files for that element in the two given SDK directories. | scripts/sdk/merger/merge.py | _write_meta | allansrc/fuchsia | python | def _write_meta(element, source_dir_one, source_dir_two, dest_dir):
'Writes a meta file for the given element, resulting from the merge of the\n meta files for that element in the two given SDK directories.\n '
meta_one = _get_meta(element, source_dir_one)
meta_two = _get_meta(element, source_dir_two)... |
def _has_host_content(parts):
'Returns true if the given list of SDK parts contains an element with\n content built for a host.\n '
return ('host_tool' in [part.type for part in parts]) | 8,791,001,200,716,074,000 | Returns true if the given list of SDK parts contains an element with
content built for a host. | scripts/sdk/merger/merge.py | _has_host_content | allansrc/fuchsia | python | def _has_host_content(parts):
'Returns true if the given list of SDK parts contains an element with\n content built for a host.\n '
return ('host_tool' in [part.type for part in parts]) |
def _write_manifest(source_dir_one, source_dir_two, dest_dir):
'Writes a manifest file resulting from the merge of the manifest files for\n the two given SDK directories.\n '
manifest_one = _get_manifest(source_dir_one)
manifest_two = _get_manifest(source_dir_two)
parts_one = set([Part(p) for p in... | 6,378,681,545,367,784,000 | Writes a manifest file resulting from the merge of the manifest files for
the two given SDK directories. | scripts/sdk/merger/merge.py | _write_manifest | allansrc/fuchsia | python | def _write_manifest(source_dir_one, source_dir_two, dest_dir):
'Writes a manifest file resulting from the merge of the manifest files for\n the two given SDK directories.\n '
manifest_one = _get_manifest(source_dir_one)
manifest_two = _get_manifest(source_dir_two)
parts_one = set([Part(p) for p in... |
def testDAGCollectionAllOf(self):
'Test DAGCollectionAllOf'
pass | 2,568,053,484,597,592,000 | Test DAGCollectionAllOf | airflow_client/test/test_dag_collection_all_of.py | testDAGCollectionAllOf | sptsakcg/airflow-client-python | python | def testDAGCollectionAllOf(self):
pass |
@pytest.mark.usefixtures('init_blockchain')
def test_check_script(rpconn, piece_hashes, spool_regtest, transactions):
'\n Test :staticmethod:`check_script`.\n\n Args;\n alice (str): bitcoin address of alice, the sender\n bob (str): bitcoin address of bob, the receiver\n rpconn (AuthServic... | 7,597,036,693,908,579,000 | Test :staticmethod:`check_script`.
Args;
alice (str): bitcoin address of alice, the sender
bob (str): bitcoin address of bob, the receiver
rpconn (AuthServiceProxy): JSON-RPC connection
(:class:`AuthServiceProxy` instance) to bitcoin regtest
transactions (Transactions): :class:`Transactions` in... | tests/test_spoolex.py | test_check_script | ascribe/pyspool | python | @pytest.mark.usefixtures('init_blockchain')
def test_check_script(rpconn, piece_hashes, spool_regtest, transactions):
'\n Test :staticmethod:`check_script`.\n\n Args;\n alice (str): bitcoin address of alice, the sender\n bob (str): bitcoin address of bob, the receiver\n rpconn (AuthServic... |
@pytest.mark.usefixtures('init_blockchain')
def test_check_script_with_invalid_tx(eve, wendy, rpconn, transactions):
'\n An invalid transaction in this context is one that does not contain a\n ``vout`` for which the ``hex`` is a valid ``Spool`` verb.\n\n Args;\n eve (str): bitcoin address of eve, th... | -3,958,756,659,847,866,000 | An invalid transaction in this context is one that does not contain a
``vout`` for which the ``hex`` is a valid ``Spool`` verb.
Args;
eve (str): bitcoin address of eve, the sender
wendy (str): bitcoin address of wendy, the receiver
rpconn (AuthServiceProxy): JSON-RPC connection
(:class:`AuthService... | tests/test_spoolex.py | test_check_script_with_invalid_tx | ascribe/pyspool | python | @pytest.mark.usefixtures('init_blockchain')
def test_check_script_with_invalid_tx(eve, wendy, rpconn, transactions):
'\n An invalid transaction in this context is one that does not contain a\n ``vout`` for which the ``hex`` is a valid ``Spool`` verb.\n\n Args;\n eve (str): bitcoin address of eve, th... |
@pytest.mark.usefixtures('init_blockchain')
def test_get_addresses_with_invalid_tx(eve, wendy, rpconn, transactions):
'\n An invalid transaction in this context is one that has inputs from\n different addresses.\n\n Args;\n eve (str): bitcoin address of eve, the sender\n wendy (str): bitcoin ... | -5,346,686,385,827,037,000 | An invalid transaction in this context is one that has inputs from
different addresses.
Args;
eve (str): bitcoin address of eve, the sender
wendy (str): bitcoin address of wendy, the receiver
rpconn (AuthServiceProxy): JSON-RPC connection
(:class:`AuthServiceProxy` instance) a local bitcoin regtest... | tests/test_spoolex.py | test_get_addresses_with_invalid_tx | ascribe/pyspool | python | @pytest.mark.usefixtures('init_blockchain')
def test_get_addresses_with_invalid_tx(eve, wendy, rpconn, transactions):
'\n An invalid transaction in this context is one that has inputs from\n different addresses.\n\n Args;\n eve (str): bitcoin address of eve, the sender\n wendy (str): bitcoin ... |
@contextlib.contextmanager
def syslog(ctx, config):
'\n start syslog / stop syslog on exit.\n '
if (ctx.archive is None):
(yield)
return
log.info('Starting syslog monitoring...')
archive_dir = misc.get_archive_dir(ctx)
log_dir = '{adir}/syslog'.format(adir=archive_dir)
run.... | -8,120,165,371,422,887,000 | start syslog / stop syslog on exit. | teuthology/task/internal/syslog.py | syslog | dzedro/teuthology | python | @contextlib.contextmanager
def syslog(ctx, config):
'\n \n '
if (ctx.archive is None):
(yield)
return
log.info('Starting syslog monitoring...')
archive_dir = misc.get_archive_dir(ctx)
log_dir = '{adir}/syslog'.format(adir=archive_dir)
run.wait(ctx.cluster.run(args=['mkdir',... |
def define_graph(inputs, labels, is_training, batch_size, replicas_to_aggregate):
'\n Define graph for synchronized training.\n '
model = Cifar10Model(resnet_size=20, data_format='channels_last', resnet_version=2, dtype=tf.float32)
logits = model(inputs, is_training)
loss = softmax_cross_entropy_w... | -1,640,052,700,166,835,500 | Define graph for synchronized training. | tensorflow/imagerecognition/openmpi-cifar10-resnet20-all-reduce/main.py | define_graph | mlbench/mlbench-benchmarks | python | def define_graph(inputs, labels, is_training, batch_size, replicas_to_aggregate):
'\n \n '
model = Cifar10Model(resnet_size=20, data_format='channels_last', resnet_version=2, dtype=tf.float32)
logits = model(inputs, is_training)
loss = softmax_cross_entropy_with_logits_v2_l2_regularized(logits=log... |
def shift_decoder(decoder, shift_constant):
' Shifts the indices of a decoder by a constant.\n\n Args:\n decoder (iterable): list of BinaryPolynomial; the decoder\n shift_constant (int): the qubit index that corresponds to the offset.\n\n Returns (list): list of BinaryPolynomial shifted decoder\... | 1,393,594,874,258,132,700 | Shifts the indices of a decoder by a constant.
Args:
decoder (iterable): list of BinaryPolynomial; the decoder
shift_constant (int): the qubit index that corresponds to the offset.
Returns (list): list of BinaryPolynomial shifted decoder | src/openfermion/ops/_binary_code.py | shift_decoder | 0tt3r/OpenFermion | python | def shift_decoder(decoder, shift_constant):
' Shifts the indices of a decoder by a constant.\n\n Args:\n decoder (iterable): list of BinaryPolynomial; the decoder\n shift_constant (int): the qubit index that corresponds to the offset.\n\n Returns (list): list of BinaryPolynomial shifted decoder\... |
def double_decoding(decoder_1, decoder_2):
' Concatenates two decodings\n\n Args:\n decoder_1 (iterable): list of BinaryPolynomial\n decoding of the outer code layer\n decoder_2 (iterable): list of BinaryPolynomial\n decoding of the inner code layer\n\n Returns (list): list... | -7,527,210,314,016,534,000 | Concatenates two decodings
Args:
decoder_1 (iterable): list of BinaryPolynomial
decoding of the outer code layer
decoder_2 (iterable): list of BinaryPolynomial
decoding of the inner code layer
Returns (list): list of BinaryPolynomial the decoding defined by
w -> decoder_1( decoder_2(w) ) | src/openfermion/ops/_binary_code.py | double_decoding | 0tt3r/OpenFermion | python | def double_decoding(decoder_1, decoder_2):
' Concatenates two decodings\n\n Args:\n decoder_1 (iterable): list of BinaryPolynomial\n decoding of the outer code layer\n decoder_2 (iterable): list of BinaryPolynomial\n decoding of the inner code layer\n\n Returns (list): list... |
def __init__(self, encoding, decoding):
' Initialization of a binary code.\n\n Args:\n encoding (np.ndarray or list): nested lists or binary 2D-array\n decoding (array or list): list of BinaryPolynomial (list or str).\n\n Raises:\n TypeError: non-list, array like encod... | -5,366,171,525,332,897,000 | Initialization of a binary code.
Args:
encoding (np.ndarray or list): nested lists or binary 2D-array
decoding (array or list): list of BinaryPolynomial (list or str).
Raises:
TypeError: non-list, array like encoding or decoding, unsuitable
BinaryPolynomial generators,
BinaryCodeError: in case... | src/openfermion/ops/_binary_code.py | __init__ | 0tt3r/OpenFermion | python | def __init__(self, encoding, decoding):
' Initialization of a binary code.\n\n Args:\n encoding (np.ndarray or list): nested lists or binary 2D-array\n decoding (array or list): list of BinaryPolynomial (list or str).\n\n Raises:\n TypeError: non-list, array like encod... |
def __iadd__(self, appendix):
' In-place appending a binary code with +=.\n\n Args:\n appendix (BinaryCode): The code to append to the present one.\n\n Returns (BinaryCode): A global binary code with size\n (n_modes1 + n_modes2), (n_qubits1,n_qubits2)\n\n Raises:\n ... | -2,979,734,494,670,934,000 | In-place appending a binary code with +=.
Args:
appendix (BinaryCode): The code to append to the present one.
Returns (BinaryCode): A global binary code with size
(n_modes1 + n_modes2), (n_qubits1,n_qubits2)
Raises:
TypeError: Appendix must be a BinaryCode. | src/openfermion/ops/_binary_code.py | __iadd__ | 0tt3r/OpenFermion | python | def __iadd__(self, appendix):
' In-place appending a binary code with +=.\n\n Args:\n appendix (BinaryCode): The code to append to the present one.\n\n Returns (BinaryCode): A global binary code with size\n (n_modes1 + n_modes2), (n_qubits1,n_qubits2)\n\n Raises:\n ... |
def __add__(self, appendix):
'Appends two binary codes via addition +.\n\n Args:\n appendix (BinaryCode): The code to append to the present one.\n\n Returns (BinaryCode): global binary code\n '
twin = copy.deepcopy(self)
twin += appendix
return twin | 6,067,573,203,762,698,000 | Appends two binary codes via addition +.
Args:
appendix (BinaryCode): The code to append to the present one.
Returns (BinaryCode): global binary code | src/openfermion/ops/_binary_code.py | __add__ | 0tt3r/OpenFermion | python | def __add__(self, appendix):
'Appends two binary codes via addition +.\n\n Args:\n appendix (BinaryCode): The code to append to the present one.\n\n Returns (BinaryCode): global binary code\n '
twin = copy.deepcopy(self)
twin += appendix
return twin |
def __imul__(self, factor):
'In-place code concatenation or appendage via *= .\n Multiplication with integer will yield appendage, otherwise\n concatenation.\n\n Args:\n factor (int or BinaryCode): the BinaryCode to concatenate. In case\n of int, it will append the cod... | 8,357,015,318,212,129,000 | In-place code concatenation or appendage via *= .
Multiplication with integer will yield appendage, otherwise
concatenation.
Args:
factor (int or BinaryCode): the BinaryCode to concatenate. In case
of int, it will append the code to itself factor times.
Returns (BinaryCode): segmented or concatenated code... | src/openfermion/ops/_binary_code.py | __imul__ | 0tt3r/OpenFermion | python | def __imul__(self, factor):
'In-place code concatenation or appendage via *= .\n Multiplication with integer will yield appendage, otherwise\n concatenation.\n\n Args:\n factor (int or BinaryCode): the BinaryCode to concatenate. In case\n of int, it will append the cod... |
def __mul__(self, factor):
' Concatenation of two codes or appendage the same code factor times\n in case of integer factor.\n\n Args:\n factor (int or BinaryCode): the BinaryCode to concatenate. In case\n of int, it will append the code to itself factor times.\n\n Ret... | 384,758,140,904,999,740 | Concatenation of two codes or appendage the same code factor times
in case of integer factor.
Args:
factor (int or BinaryCode): the BinaryCode to concatenate. In case
of int, it will append the code to itself factor times.
Returns (BinaryCode): segmented or concatenated code | src/openfermion/ops/_binary_code.py | __mul__ | 0tt3r/OpenFermion | python | def __mul__(self, factor):
' Concatenation of two codes or appendage the same code factor times\n in case of integer factor.\n\n Args:\n factor (int or BinaryCode): the BinaryCode to concatenate. In case\n of int, it will append the code to itself factor times.\n\n Ret... |
def __rmul__(self, factor):
' Appending the same code factor times.\n\n Args:\n factor (int): integer defining number of appendages.\n\n Returns (BinaryCode): Segmented code.\n\n Raises:\n TypeError: factor must be an integer\n '
if isinstance(factor, (numpy.int... | 8,989,757,218,044,066,000 | Appending the same code factor times.
Args:
factor (int): integer defining number of appendages.
Returns (BinaryCode): Segmented code.
Raises:
TypeError: factor must be an integer | src/openfermion/ops/_binary_code.py | __rmul__ | 0tt3r/OpenFermion | python | def __rmul__(self, factor):
' Appending the same code factor times.\n\n Args:\n factor (int): integer defining number of appendages.\n\n Returns (BinaryCode): Segmented code.\n\n Raises:\n TypeError: factor must be an integer\n '
if isinstance(factor, (numpy.int... |
def __str__(self):
' Return an easy-to-read string representation.'
string_return = [list(map(list, self.encoder.toarray()))]
dec_str = '['
for term in self.decoder:
dec_str += (term.__str__() + ',')
dec_str = dec_str[:(- 1)]
string_return.append((dec_str + ']'))
return str(string_re... | -746,197,833,112,043,300 | Return an easy-to-read string representation. | src/openfermion/ops/_binary_code.py | __str__ | 0tt3r/OpenFermion | python | def __str__(self):
' '
string_return = [list(map(list, self.encoder.toarray()))]
dec_str = '['
for term in self.decoder:
dec_str += (term.__str__() + ',')
dec_str = dec_str[:(- 1)]
string_return.append((dec_str + ']'))
return str(string_return) |
@app.get('/recommendation')
async def recommend(request):
'\n Gets recommendations for user\n Expects args in query string form -> user=x&count=n\n Returns json object {posts, unvoted, user, meta}\n '
args = request.raw_args
recommender = Recommender(app.predictor, request['accessor'], read_conf... | 3,964,531,211,473,154,000 | Gets recommendations for user
Expects args in query string form -> user=x&count=n
Returns json object {posts, unvoted, user, meta} | kiwi-content/kiwi/app.py | recommend | bubblegumsoldier/kiwi | python | @app.get('/recommendation')
async def recommend(request):
'\n Gets recommendations for user\n Expects args in query string form -> user=x&count=n\n Returns json object {posts, unvoted, user, meta}\n '
args = request.raw_args
recommender = Recommender(app.predictor, request['accessor'], read_conf... |
@app.post('/feedback')
async def feedback(request: Request):
'Stores the feedback for a recommended post. Will return a information object on success and an empty object on failure.\n Think about returning 409-Conflict on failure instead, because the empty object can cause an issue in engine service.'
vote =... | -7,407,837,287,500,175,000 | Stores the feedback for a recommended post. Will return a information object on success and an empty object on failure.
Think about returning 409-Conflict on failure instead, because the empty object can cause an issue in engine service. | kiwi-content/kiwi/app.py | feedback | bubblegumsoldier/kiwi | python | @app.post('/feedback')
async def feedback(request: Request):
'Stores the feedback for a recommended post. Will return a information object on success and an empty object on failure.\n Think about returning 409-Conflict on failure instead, because the empty object can cause an issue in engine service.'
vote =... |
@app.post('/content')
async def content(request: Request):
'\n Inserts posts into the database. The request needs the format\n { "posts": [{"id": string, "tags": string}]}.\n Returns the amout of inserted items and 200-OK.\n '
filtered_posts = [(post['id'], post['tags']) for post in request.json... | 7,956,493,187,145,312,000 | Inserts posts into the database. The request needs the format
{ "posts": [{"id": string, "tags": string}]}.
Returns the amout of inserted items and 200-OK. | kiwi-content/kiwi/app.py | content | bubblegumsoldier/kiwi | python | @app.post('/content')
async def content(request: Request):
'\n Inserts posts into the database. The request needs the format\n { "posts": [{"id": string, "tags": string}]}.\n Returns the amout of inserted items and 200-OK.\n '
filtered_posts = [(post['id'], post['tags']) for post in request.json... |
@app.get('/activation')
async def activation(request: Request):
'\n Returns the activation value for the given set of heuristics\n '
heuristics = request.json['heuristics']
try:
utv = (await app.predictor.get_user_taste_vector(heuristics['user']))
except KeyError:
utv = None
ac... | 2,118,346,643,659,316,500 | Returns the activation value for the given set of heuristics | kiwi-content/kiwi/app.py | activation | bubblegumsoldier/kiwi | python | @app.get('/activation')
async def activation(request: Request):
'\n \n '
heuristics = request.json['heuristics']
try:
utv = (await app.predictor.get_user_taste_vector(heuristics['user']))
except KeyError:
utv = None
ac = ActivationCalculator(heuristics, request['accessor'])
... |
def allocate_buffers(engine):
"Allocates host and device buffer for TRT engine inference.\n\n This function is similair to the one in ../../common.py, but\n converts network outputs (which are np.float32) appropriately\n before writing them to Python buffer. This is needed, since\n TensorRT plugins does... | 4,037,928,288,186,843,600 | Allocates host and device buffer for TRT engine inference.
This function is similair to the one in ../../common.py, but
converts network outputs (which are np.float32) appropriately
before writing them to Python buffer. This is needed, since
TensorRT plugins doesn't support output type description, and
in our particul... | samples/python/uff_ssd/utils/engine.py | allocate_buffers | GreyZzzzzzXh/TensorRT | python | def allocate_buffers(engine):
"Allocates host and device buffer for TRT engine inference.\n\n This function is similair to the one in ../../common.py, but\n converts network outputs (which are np.float32) appropriately\n before writing them to Python buffer. This is needed, since\n TensorRT plugins does... |
def rainbow_to_vector(r, timeformat='h'):
" Convert Rainbow object to np.arrays\n Parameters\n ----------\n r : Rainbow object\n chromatic Rainbow object to convert into array format\n timeformat : str\n (optional, default='hours')\n The time format to use (secon... | 979,080,137,809,821,700 | Convert Rainbow object to np.arrays
Parameters
----------
r : Rainbow object
chromatic Rainbow object to convert into array format
timeformat : str
(optional, default='hours')
The time format to use (seconds, minutes, hours, days etc.)
Returns
----------
rflux : np.array
flux... | src/utils.py | rainbow_to_vector | catrionamurray/chromatic_fitting | python | def rainbow_to_vector(r, timeformat='h'):
" Convert Rainbow object to np.arrays\n Parameters\n ----------\n r : Rainbow object\n chromatic Rainbow object to convert into array format\n timeformat : str\n (optional, default='hours')\n The time format to use (secon... |
def rainbow_to_df(r, timeformat='h'):
" Convert Rainbow object to pandas dataframe\n Parameters\n ----------\n r : Rainbow object\n chromatic Rainbow object to convert into pandas df format\n timeformat : str\n (optional, default='hours')\n The time format to use... | 43,283,962,878,375,304 | Convert Rainbow object to pandas dataframe
Parameters
----------
r : Rainbow object
chromatic Rainbow object to convert into pandas df format
timeformat : str
(optional, default='hours')
The time format to use (seconds, minutes, hours, days etc.)
Returns
----------
pd.DataFrame | src/utils.py | rainbow_to_df | catrionamurray/chromatic_fitting | python | def rainbow_to_df(r, timeformat='h'):
" Convert Rainbow object to pandas dataframe\n Parameters\n ----------\n r : Rainbow object\n chromatic Rainbow object to convert into pandas df format\n timeformat : str\n (optional, default='hours')\n The time format to use... |
def do_import(self, timestamp):
'Call one key import RPC.'
if (self.call == Call.single):
if (self.data == Data.address):
response = self.try_rpc(self.node.importaddress, self.address['address'], self.label, (self.rescan == Rescan.yes))
elif (self.data == Data.pub):
respo... | -4,665,100,780,963,157,000 | Call one key import RPC. | test/functional/import-rescan.py | do_import | BlueScionic/vivarium | python | def do_import(self, timestamp):
if (self.call == Call.single):
if (self.data == Data.address):
response = self.try_rpc(self.node.importaddress, self.address['address'], self.label, (self.rescan == Rescan.yes))
elif (self.data == Data.pub):
response = self.try_rpc(self.no... |
def check(self, txid=None, amount=None, confirmations=None):
'Verify that getbalance/listtransactions return expected values.'
balance = self.node.getbalance(self.label, 0, False, True)
assert_equal(balance, self.expected_balance)
txs = self.node.listtransactions(self.label, 10000, 0, True)
assert_e... | 226,965,051,230,975,360 | Verify that getbalance/listtransactions return expected values. | test/functional/import-rescan.py | check | BlueScionic/vivarium | python | def check(self, txid=None, amount=None, confirmations=None):
balance = self.node.getbalance(self.label, 0, False, True)
assert_equal(balance, self.expected_balance)
txs = self.node.listtransactions(self.label, 10000, 0, True)
assert_equal(len(txs), self.expected_txs)
if (txid is not None):
... |
def filter_func(data, search_notes):
' Return all objects'
search_notes.append(('error', 'Unable to parse search expression', 0, len(query_string)))
return False | 8,760,193,685,689,438,000 | Return all objects | sampledb/frontend/objects.py | filter_func | sciapp/sampledb | python | def filter_func(data, search_notes):
' '
search_notes.append(('error', 'Unable to parse search expression', 0, len(query_string)))
return False |
def elapsed(t0=0.0):
'get elapsed time from the give time\n\n Returns:\n now: the absolute time now\n dt_str: elapsed time in string\n '
now = time()
dt = (now - t0)
dt_sec = Decimal(str(dt)).quantize(Decimal('.0001'), rounding=ROUND_DOWN)
if (dt_sec <= 1):
dt_str = (... | 3,239,857,340,972,211,000 | get elapsed time from the give time
Returns:
now: the absolute time now
dt_str: elapsed time in string | andes/utils/time.py | elapsed | mhdella/andes | python | def elapsed(t0=0.0):
'get elapsed time from the give time\n\n Returns:\n now: the absolute time now\n dt_str: elapsed time in string\n '
now = time()
dt = (now - t0)
dt_sec = Decimal(str(dt)).quantize(Decimal('.0001'), rounding=ROUND_DOWN)
if (dt_sec <= 1):
dt_str = (... |
def __str__(self):
'\n String for representing the Model object (in Admin site etc.)\n '
return self.name | 6,625,425,250,428,873,000 | String for representing the Model object (in Admin site etc.) | src/locallibrary/catalog/models.py | __str__ | zhekazuev/mozilla-django-learning | python | def __str__(self):
'\n \n '
return self.name |
def __str__(self):
'\n String for representing the Model object (in Admin site etc.)\n '
return self.name | 6,625,425,250,428,873,000 | String for representing the Model object (in Admin site etc.) | src/locallibrary/catalog/models.py | __str__ | zhekazuev/mozilla-django-learning | python | def __str__(self):
'\n \n '
return self.name |
def __str__(self):
'\n String for representing the Model object.\n '
return self.title | 8,601,006,417,814,906,000 | String for representing the Model object. | src/locallibrary/catalog/models.py | __str__ | zhekazuev/mozilla-django-learning | python | def __str__(self):
'\n \n '
return self.title |
def get_absolute_url(self):
'\n Returns the url to access a particular book instance.\n '
return reverse('book-detail', args=[str(self.id)]) | -9,027,267,842,509,070,000 | Returns the url to access a particular book instance. | src/locallibrary/catalog/models.py | get_absolute_url | zhekazuev/mozilla-django-learning | python | def get_absolute_url(self):
'\n \n '
return reverse('book-detail', args=[str(self.id)]) |
def __str__(self):
'\n String for representing the Model object\n '
return '{0} ({1})'.format(self.id, self.book.title) | -7,487,876,419,173,492,000 | String for representing the Model object | src/locallibrary/catalog/models.py | __str__ | zhekazuev/mozilla-django-learning | python | def __str__(self):
'\n \n '
return '{0} ({1})'.format(self.id, self.book.title) |
def get_absolute_url(self):
'\n Returns the url to access a particular author instance.\n '
return reverse('author-detail', args=[str(self.id)]) | -894,346,297,409,379,600 | Returns the url to access a particular author instance. | src/locallibrary/catalog/models.py | get_absolute_url | zhekazuev/mozilla-django-learning | python | def get_absolute_url(self):
'\n \n '
return reverse('author-detail', args=[str(self.id)]) |
def __str__(self):
'\n String for representing the Model object.\n '
return '{0} ({1})'.format(self.last_name, self.first_name) | -6,520,315,775,681,146,000 | String for representing the Model object. | src/locallibrary/catalog/models.py | __str__ | zhekazuev/mozilla-django-learning | python | def __str__(self):
'\n \n '
return '{0} ({1})'.format(self.last_name, self.first_name) |
def main(args):
'Main function to parse in Nuclei Dataset from Kaggle and store as HDF5\n\n Parameters\n ----------\n args: ArgumentParser()\n input_dir: str\n directory of the Nuclei data\n output_dir: str\n path to the HDF5 output directory\n '
... | 6,529,766,840,498,225,000 | Main function to parse in Nuclei Dataset from Kaggle and store as HDF5
Parameters
----------
args: ArgumentParser()
input_dir: str
directory of the Nuclei data
output_dir: str
path to the HDF5 output directory | process_data/nuclei_create_hdf5.py | main | marshuang80/CellSegmentation | python | def main(args):
'Main function to parse in Nuclei Dataset from Kaggle and store as HDF5\n\n Parameters\n ----------\n args: ArgumentParser()\n input_dir: str\n directory of the Nuclei data\n output_dir: str\n path to the HDF5 output directory\n '
... |
def train_step(self, *inputs, **kwargs):
'train_step() API for module wrapped by DistributedDataParallel.\n\n This method is basically the same as\n ``DistributedDataParallel.forward()``, while replacing\n ``self.module.forward()`` with ``self.module.train_step()``.\n It is compatible wi... | -3,869,767,233,722,932,700 | train_step() API for module wrapped by DistributedDataParallel.
This method is basically the same as
``DistributedDataParallel.forward()``, while replacing
``self.module.forward()`` with ``self.module.train_step()``.
It is compatible with PyTorch 1.1 - 1.5. | mmcv/parallel/distributed.py | train_step | BIGWangYuDong/mmcv | python | def train_step(self, *inputs, **kwargs):
'train_step() API for module wrapped by DistributedDataParallel.\n\n This method is basically the same as\n ``DistributedDataParallel.forward()``, while replacing\n ``self.module.forward()`` with ``self.module.train_step()``.\n It is compatible wi... |
def val_step(self, *inputs, **kwargs):
'val_step() API for module wrapped by DistributedDataParallel.\n\n This method is basically the same as\n ``DistributedDataParallel.forward()``, while replacing\n ``self.module.forward()`` with ``self.module.val_step()``.\n It is compatible with PyT... | 5,817,637,801,054,475,000 | val_step() API for module wrapped by DistributedDataParallel.
This method is basically the same as
``DistributedDataParallel.forward()``, while replacing
``self.module.forward()`` with ``self.module.val_step()``.
It is compatible with PyTorch 1.1 - 1.5. | mmcv/parallel/distributed.py | val_step | BIGWangYuDong/mmcv | python | def val_step(self, *inputs, **kwargs):
'val_step() API for module wrapped by DistributedDataParallel.\n\n This method is basically the same as\n ``DistributedDataParallel.forward()``, while replacing\n ``self.module.forward()`` with ``self.module.val_step()``.\n It is compatible with PyT... |
def del_none(d):
'\n Delete dict keys with None values, and empty lists, recursively.\n '
for (key, value) in d.items():
if ((value is None) or (isinstance(value, list) and (len(value) == 0))):
del d[key]
elif isinstance(value, dict):
del_none(value)
return d | 8,142,591,104,627,484,000 | Delete dict keys with None values, and empty lists, recursively. | models.py | del_none | cwilso/chromium-dashboard | python | def del_none(d):
'\n \n '
for (key, value) in d.items():
if ((value is None) or (isinstance(value, list) and (len(value) == 0))):
del d[key]
elif isinstance(value, dict):
del_none(value)
return d |
def list_to_chunks(l, n):
'Yield successive n-sized chunk lists from l.'
for i in xrange(0, len(l), n):
(yield l[i:(i + n)]) | -1,047,640,047,794,921,100 | Yield successive n-sized chunk lists from l. | models.py | list_to_chunks | cwilso/chromium-dashboard | python | def list_to_chunks(l, n):
for i in xrange(0, len(l), n):
(yield l[i:(i + n)]) |
@classmethod
def fetch_all_components(self, update_cache=False):
'Returns the list of blink components from live endpoint if unavailable in the cache.'
key = ('%s|blinkcomponents' % settings.MEMCACHE_KEY_PREFIX)
components = memcache.get(key)
if ((components is None) or update_cache):
components... | 2,615,132,007,290,353,000 | Returns the list of blink components from live endpoint if unavailable in the cache. | models.py | fetch_all_components | cwilso/chromium-dashboard | python | @classmethod
def fetch_all_components(self, update_cache=False):
key = ('%s|blinkcomponents' % settings.MEMCACHE_KEY_PREFIX)
components = memcache.get(key)
if ((components is None) or update_cache):
components = []
result = urlfetch.fetch(self.COMPONENTS_ENDPOINT, deadline=60)
i... |
@classmethod
def fetch_wf_content_for_components(self, update_cache=False):
'Returns the /web content that use each blink component.'
key = ('%s|wfcomponents' % settings.MEMCACHE_KEY_PREFIX)
components = memcache.get(key)
if ((components is None) or update_cache):
components = {}
result ... | 3,755,372,784,358,231,600 | Returns the /web content that use each blink component. | models.py | fetch_wf_content_for_components | cwilso/chromium-dashboard | python | @classmethod
def fetch_wf_content_for_components(self, update_cache=False):
key = ('%s|wfcomponents' % settings.MEMCACHE_KEY_PREFIX)
components = memcache.get(key)
if ((components is None) or update_cache):
components = {}
result = urlfetch.fetch(self.WF_CONTENT_ENDPOINT, deadline=60)
... |
@classmethod
def update_db(self):
'Updates the db with new Blink components from the json endpoint'
self.fetch_wf_content_for_components(update_cache=True)
new_components = self.fetch_all_components(update_cache=True)
existing_comps = self.all().fetch(None)
for name in new_components:
if (no... | 1,520,643,486,992,808,700 | Updates the db with new Blink components from the json endpoint | models.py | update_db | cwilso/chromium-dashboard | python | @classmethod
def update_db(self):
self.fetch_wf_content_for_components(update_cache=True)
new_components = self.fetch_all_components(update_cache=True)
existing_comps = self.all().fetch(None)
for name in new_components:
if (not len([x.name for x in existing_comps if (x.name == name)])):
... |
@classmethod
def get_by_name(self, component_name):
'Fetch blink component with given name.'
q = self.all()
q.filter('name =', component_name)
component = q.fetch(1)
if (not component):
logging.error(('%s is an unknown BlinkComponent.' % component_name))
return None
return compon... | 1,578,639,703,039,212,000 | Fetch blink component with given name. | models.py | get_by_name | cwilso/chromium-dashboard | python | @classmethod
def get_by_name(self, component_name):
q = self.all()
q.filter('name =', component_name)
component = q.fetch(1)
if (not component):
logging.error(('%s is an unknown BlinkComponent.' % component_name))
return None
return component[0] |
def __notify_feature_subscribers_of_changes(self, is_update):
'Async notifies subscribers of new features and property changes to features by\n posting to a task queue.'
changed_props = []
for (prop_name, prop) in self.properties().iteritems():
new_val = getattr(self, prop_name, None)
... | 1,186,048,520,462,652,700 | Async notifies subscribers of new features and property changes to features by
posting to a task queue. | models.py | __notify_feature_subscribers_of_changes | cwilso/chromium-dashboard | python | def __notify_feature_subscribers_of_changes(self, is_update):
'Async notifies subscribers of new features and property changes to features by\n posting to a task queue.'
changed_props = []
for (prop_name, prop) in self.properties().iteritems():
new_val = getattr(self, prop_name, None)
... |
def add_to_component_subscribers(self, component_name):
'Adds the user to the list of Blink component subscribers.'
c = BlinkComponent.get_by_name(component_name)
if c:
if (not len(list_with_component(self.blink_components, c))):
self.blink_components.append(c.key())
return s... | 377,905,915,556,286,800 | Adds the user to the list of Blink component subscribers. | models.py | add_to_component_subscribers | cwilso/chromium-dashboard | python | def add_to_component_subscribers(self, component_name):
c = BlinkComponent.get_by_name(component_name)
if c:
if (not len(list_with_component(self.blink_components, c))):
self.blink_components.append(c.key())
return self.put()
return None |
def remove_from_component_subscribers(self, component_name, remove_as_owner=False):
'Removes the user from the list of Blink component subscribers or as the owner\n of the component.'
c = BlinkComponent.get_by_name(component_name)
if c:
if remove_as_owner:
self.primary_blink_compon... | -6,766,741,114,973,741,000 | Removes the user from the list of Blink component subscribers or as the owner
of the component. | models.py | remove_from_component_subscribers | cwilso/chromium-dashboard | python | def remove_from_component_subscribers(self, component_name, remove_as_owner=False):
'Removes the user from the list of Blink component subscribers or as the owner\n of the component.'
c = BlinkComponent.get_by_name(component_name)
if c:
if remove_as_owner:
self.primary_blink_compon... |
def add_as_component_owner(self, component_name):
'Adds the user as the Blink component owner.'
c = BlinkComponent.get_by_name(component_name)
if c:
self.add_to_component_subscribers(component_name)
if (not len(list_with_component(self.primary_blink_components, c))):
self.primary... | 8,702,347,263,936,550,000 | Adds the user as the Blink component owner. | models.py | add_as_component_owner | cwilso/chromium-dashboard | python | def add_as_component_owner(self, component_name):
c = BlinkComponent.get_by_name(component_name)
if c:
self.add_to_component_subscribers(component_name)
if (not len(list_with_component(self.primary_blink_components, c))):
self.primary_blink_components.append(c.key())
ret... |
def get_current_time(format_str: str='%Y-%m-%d %H:%M:%S'):
'\n 获取当前时间，默认为 2020-01-01 00:00:00 格式\n :param format_str: 格式\n :return:\n '
return time.strftime(format_str, time.localtime()) | 2,675,374,514,212,199,400 | 获取当前时间，默认为 2020-01-01 00:00:00 格式
:param format_str: 格式
:return: | src/sogou_wechat/mongoDB.py | get_current_time | matiastang/selenium-learning | python | def get_current_time(format_str: str='%Y-%m-%d %H:%M:%S'):
'\n 获取当前时间，默认为 2020-01-01 00:00:00 格式\n :param format_str: 格式\n :return:\n '
return time.strftime(format_str, time.localtime()) |
def __init__(self):
'初始化\n 初始化 mongo db\n '
mongo_uri = ('mongodb://%s:%s@%s:%s' % (MONGO_CONFIG['user'], MONGO_CONFIG['pwd'], MONGO_CONFIG['host'], MONGO_CONFIG['port']))
self.mongo = MongoClient(mongo_uri)
self.sogou_db = self.mongo['sogou_dev']
self.sogou_search_col = self.sogou_db[... | -2,319,926,698,126,357,000 | 初始化
初始化 mongo db | src/sogou_wechat/mongoDB.py | __init__ | matiastang/selenium-learning | python | def __init__(self):
'初始化\n 初始化 mongo db\n '
mongo_uri = ('mongodb://%s:%s@%s:%s' % (MONGO_CONFIG['user'], MONGO_CONFIG['pwd'], MONGO_CONFIG['host'], MONGO_CONFIG['port']))
self.mongo = MongoClient(mongo_uri)
self.sogou_db = self.mongo['sogou_dev']
self.sogou_search_col = self.sogou_db[... |
def update_sogou_login_cookie(self, username, cookie):
'\n 更新搜狗微信登录 cookie 信息\n :param username:\n :param cookie:\n :return:\n '
col = self.sogou_db['sogou_login_cookies']
ctime = get_current_time()
find_obj = {'nickname': username, 'is_valid': 1}
login_item = col.... | 1,485,805,371,386,330,600 | 更新搜狗微信登录 cookie 信息
:param username:
:param cookie:
:return: | src/sogou_wechat/mongoDB.py | update_sogou_login_cookie | matiastang/selenium-learning | python | def update_sogou_login_cookie(self, username, cookie):
'\n 更新搜狗微信登录 cookie 信息\n :param username:\n :param cookie:\n :return:\n '
col = self.sogou_db['sogou_login_cookies']
ctime = get_current_time()
find_obj = {'nickname': username, 'is_valid': 1}
login_item = col.... |
def insert_sogou_search_result(self, result):
'\n 保存搜狗搜索信息\n :param results: 结果数组\n '
ctime = get_current_time()
find_obj = {'id': result['id'], 'is_valid': 1}
search_item = self.sogou_search_col.find_one(find_obj)
print(search_item)
new_result = result
if (not search_it... | -2,791,544,919,105,490,000 | 保存搜狗搜索信息
:param results: 结果数组 | src/sogou_wechat/mongoDB.py | insert_sogou_search_result | matiastang/selenium-learning | python | def insert_sogou_search_result(self, result):
'\n 保存搜狗搜索信息\n :param results: 结果数组\n '
ctime = get_current_time()
find_obj = {'id': result['id'], 'is_valid': 1}
search_item = self.sogou_search_col.find_one(find_obj)
print(search_item)
new_result = result
if (not search_it... |
@utils.classproperty
def resource_server(cls) -> Optional[str]:
'\n The resource_server name for the API and scopes associated with this client.\n\n This information is pulled from the ``scopes`` attribute of the client class.\n If the client does not have associated scopes, this value will be ... | -8,940,739,177,058,369,000 | The resource_server name for the API and scopes associated with this client.
This information is pulled from the ``scopes`` attribute of the client class.
If the client does not have associated scopes, this value will be ``None``. | src/globus_sdk/client.py | resource_server | rudyardrichter/globus-sdk-python | python | @utils.classproperty
def resource_server(cls) -> Optional[str]:
'\n The resource_server name for the API and scopes associated with this client.\n\n This information is pulled from the ``scopes`` attribute of the client class.\n If the client does not have associated scopes, this value will be ... |
def get(self, path: str, *, query_params: Optional[Dict[(str, Any)]]=None, headers: Optional[Dict[(str, str)]]=None) -> GlobusHTTPResponse:
'\n Make a GET request to the specified path.\n\n See :py:meth:`~.BaseClient.request` for details on the various parameters.\n\n :return: :class:`GlobusHTT... | -8,680,274,190,658,656,000 | Make a GET request to the specified path.
See :py:meth:`~.BaseClient.request` for details on the various parameters.
:return: :class:`GlobusHTTPResponse <globus_sdk.response.GlobusHTTPResponse>` object | src/globus_sdk/client.py | get | rudyardrichter/globus-sdk-python | python | def get(self, path: str, *, query_params: Optional[Dict[(str, Any)]]=None, headers: Optional[Dict[(str, str)]]=None) -> GlobusHTTPResponse:
'\n Make a GET request to the specified path.\n\n See :py:meth:`~.BaseClient.request` for details on the various parameters.\n\n :return: :class:`GlobusHTT... |
def post(self, path: str, *, query_params: Optional[Dict[(str, Any)]]=None, data: Union[(None, Dict[(str, Any)], utils.PayloadWrapper)]=None, headers: Optional[Dict[(str, str)]]=None, encoding: Optional[str]=None) -> GlobusHTTPResponse:
'\n Make a POST request to the specified path.\n\n See :py:meth:`... | -6,405,945,534,864,613,000 | Make a POST request to the specified path.
See :py:meth:`~.BaseClient.request` for details on the various parameters.
:return: :class:`GlobusHTTPResponse <globus_sdk.response.GlobusHTTPResponse>` object | src/globus_sdk/client.py | post | rudyardrichter/globus-sdk-python | python | def post(self, path: str, *, query_params: Optional[Dict[(str, Any)]]=None, data: Union[(None, Dict[(str, Any)], utils.PayloadWrapper)]=None, headers: Optional[Dict[(str, str)]]=None, encoding: Optional[str]=None) -> GlobusHTTPResponse:
'\n Make a POST request to the specified path.\n\n See :py:meth:`... |
def delete(self, path: str, *, query_params: Optional[Dict[(str, Any)]]=None, headers: Optional[Dict[(str, str)]]=None) -> GlobusHTTPResponse:
'\n Make a DELETE request to the specified path.\n\n See :py:meth:`~.BaseClient.request` for details on the various parameters.\n\n :return: :class:`Glo... | 8,448,269,623,345,632,000 | Make a DELETE request to the specified path.
See :py:meth:`~.BaseClient.request` for details on the various parameters.
:return: :class:`GlobusHTTPResponse <globus_sdk.response.GlobusHTTPResponse>` object | src/globus_sdk/client.py | delete | rudyardrichter/globus-sdk-python | python | def delete(self, path: str, *, query_params: Optional[Dict[(str, Any)]]=None, headers: Optional[Dict[(str, str)]]=None) -> GlobusHTTPResponse:
'\n Make a DELETE request to the specified path.\n\n See :py:meth:`~.BaseClient.request` for details on the various parameters.\n\n :return: :class:`Glo... |
def put(self, path: str, *, query_params: Optional[Dict[(str, Any)]]=None, data: Union[(None, Dict[(str, Any)], utils.PayloadWrapper)]=None, headers: Optional[Dict[(str, str)]]=None, encoding: Optional[str]=None) -> GlobusHTTPResponse:
'\n Make a PUT request to the specified path.\n\n See :py:meth:`~.... | -4,209,089,649,074,972,000 | Make a PUT request to the specified path.
See :py:meth:`~.BaseClient.request` for details on the various parameters.
:return: :class:`GlobusHTTPResponse <globus_sdk.response.GlobusHTTPResponse>` object | src/globus_sdk/client.py | put | rudyardrichter/globus-sdk-python | python | def put(self, path: str, *, query_params: Optional[Dict[(str, Any)]]=None, data: Union[(None, Dict[(str, Any)], utils.PayloadWrapper)]=None, headers: Optional[Dict[(str, str)]]=None, encoding: Optional[str]=None) -> GlobusHTTPResponse:
'\n Make a PUT request to the specified path.\n\n See :py:meth:`~.... |
def patch(self, path: str, *, query_params: Optional[Dict[(str, Any)]]=None, data: Union[(None, Dict[(str, Any)], utils.PayloadWrapper)]=None, headers: Optional[Dict[(str, str)]]=None, encoding: Optional[str]=None) -> GlobusHTTPResponse:
'\n Make a PATCH request to the specified path.\n\n See :py:meth... | -6,768,478,994,757,211,000 | Make a PATCH request to the specified path.
See :py:meth:`~.BaseClient.request` for details on the various parameters.
:return: :class:`GlobusHTTPResponse <globus_sdk.response.GlobusHTTPResponse>` object | src/globus_sdk/client.py | patch | rudyardrichter/globus-sdk-python | python | def patch(self, path: str, *, query_params: Optional[Dict[(str, Any)]]=None, data: Union[(None, Dict[(str, Any)], utils.PayloadWrapper)]=None, headers: Optional[Dict[(str, str)]]=None, encoding: Optional[str]=None) -> GlobusHTTPResponse:
'\n Make a PATCH request to the specified path.\n\n See :py:meth... |
def request(self, method: str, path: str, *, query_params: Optional[Dict[(str, Any)]]=None, data: Union[(None, Dict[(str, Any)], utils.PayloadWrapper)]=None, headers: Optional[Dict[(str, str)]]=None, encoding: Optional[str]=None) -> GlobusHTTPResponse:
'\n Send an HTTP request\n\n :param method: HTTP ... | -8,669,220,260,102,745,000 | Send an HTTP request
:param method: HTTP request method, as an all caps string
:type method: str
:param path: Path for the request, with or without leading slash
:type path: str
:param query_params: Parameters to be encoded as a query string
:type query_params: dict, optional
:param headers: HTTP headers to add to the... | src/globus_sdk/client.py | request | rudyardrichter/globus-sdk-python | python | def request(self, method: str, path: str, *, query_params: Optional[Dict[(str, Any)]]=None, data: Union[(None, Dict[(str, Any)], utils.PayloadWrapper)]=None, headers: Optional[Dict[(str, str)]]=None, encoding: Optional[str]=None) -> GlobusHTTPResponse:
'\n Send an HTTP request\n\n :param method: HTTP ... |
@property
def name(self):
'Base name.'
return self._name | 6,807,634,801,631,736,000 | Base name. | robustnessgym/core/identifier.py | name | ANarayan/robustness-gym | python | @property
def name(self):
return self._name |
@property
def index(self):
'Index associated with the identifier.'
return self._index | 1,556,588,699,845,362,400 | Index associated with the identifier. | robustnessgym/core/identifier.py | index | ANarayan/robustness-gym | python | @property
def index(self):
return self._index |
@property
def parameters(self):
'Additional parameters contained in the identifier.'
return self._parameters | 7,311,550,149,156,835,000 | Additional parameters contained in the identifier. | robustnessgym/core/identifier.py | parameters | ANarayan/robustness-gym | python | @property
def parameters(self):
return self._parameters |
@classmethod
def range(cls, n: int, _name: str, **kwargs) -> List[Identifier]:
'Create a list of identifiers, with index varying from 1 to `n`.'
if (n > 1):
return [cls(_name=_name, _index=i, **kwargs) for i in range(1, (n + 1))]
return [cls(_name=_name, **kwargs)] | 319,522,355,280,191,170 | Create a list of identifiers, with index varying from 1 to `n`. | robustnessgym/core/identifier.py | range | ANarayan/robustness-gym | python | @classmethod
def range(cls, n: int, _name: str, **kwargs) -> List[Identifier]:
if (n > 1):
return [cls(_name=_name, _index=i, **kwargs) for i in range(1, (n + 1))]
return [cls(_name=_name, **kwargs)] |
def __call__(self, **kwargs):
'Call the identifier with additional parameters to return a new\n identifier.'
ident = Identifier.loads(self.dumps())
for (parameter, value) in kwargs.items():
ident.add_parameter(parameter, value)
return ident | -3,241,385,535,052,178,400 | Call the identifier with additional parameters to return a new
identifier. | robustnessgym/core/identifier.py | __call__ | ANarayan/robustness-gym | python | def __call__(self, **kwargs):
'Call the identifier with additional parameters to return a new\n identifier.'
ident = Identifier.loads(self.dumps())
for (parameter, value) in kwargs.items():
ident.add_parameter(parameter, value)
return ident |
def dumps(self):
'Dump the identifier to JSON.'
return json.dumps(self.__dict__) | 9,035,629,313,671,611,000 | Dump the identifier to JSON. | robustnessgym/core/identifier.py | dumps | ANarayan/robustness-gym | python | def dumps(self):
return json.dumps(self.__dict__) |
@staticmethod
def _parse_args(s: str):
'https://stackoverflow.com/questions/49723047/parsing-a-string-as-a-\n python-argument-list.'
args = 'f({})'.format(s)
tree = ast.parse(args)
funccall = tree.body[0].value
params = {}
for arg in funccall.keywords:
try:
params[arg.... | -3,864,303,452,000,702,500 | https://stackoverflow.com/questions/49723047/parsing-a-string-as-a-
python-argument-list. | robustnessgym/core/identifier.py | _parse_args | ANarayan/robustness-gym | python | @staticmethod
def _parse_args(s: str):
'https://stackoverflow.com/questions/49723047/parsing-a-string-as-a-\n python-argument-list.'
args = 'f({})'.format(s)
tree = ast.parse(args)
funccall = tree.body[0].value
params = {}
for arg in funccall.keywords:
try:
params[arg.... |
@classmethod
def parse(cls, s: str) -> Identifier:
'Parse in an identifier from string.'
if ('(' in s):
(name_index, params) = s.split('(')
params = params.split(')')[0]
else:
name_index = s
params = None
if ('-' in name_index):
(name, index) = (name_index.split('... | -3,336,107,861,688,347,000 | Parse in an identifier from string. | robustnessgym/core/identifier.py | parse | ANarayan/robustness-gym | python | @classmethod
def parse(cls, s: str) -> Identifier:
if ('(' in s):
(name_index, params) = s.split('(')
params = params.split(')')[0]
else:
name_index = s
params = None
if ('-' in name_index):
(name, index) = (name_index.split('-')[:(- 1)], name_index.split('-')[(-... |
def without(self, *params) -> Identifier:
'Returns an identifier without `params`.'
return Identifier(self.name, self.index, **{k: v for (k, v) in self.parameters.items() if (k not in set(params))}) | -7,226,501,445,548,212,000 | Returns an identifier without `params`. | robustnessgym/core/identifier.py | without | ANarayan/robustness-gym | python | def without(self, *params) -> Identifier:
return Identifier(self.name, self.index, **{k: v for (k, v) in self.parameters.items() if (k not in set(params))}) |
@classmethod
def loads(cls, s: str):
'Load the identifier from JSON.'
identifier = Identifier(_name='')
identifier.__dict__ = json.loads(s)
return identifier | 1,539,369,053,633,276,000 | Load the identifier from JSON. | robustnessgym/core/identifier.py | loads | ANarayan/robustness-gym | python | @classmethod
def loads(cls, s: str):
identifier = Identifier(_name=)
identifier.__dict__ = json.loads(s)
return identifier |
def add_parameter(self, parameter: str, value: Any) -> None:
'Add a parameter to the identifier.'
if isinstance(value, Callable):
self.parameters[parameter] = '.'.join([str(value.__module__), str(value.__name__)])
else:
self.parameters[parameter] = value | 7,490,064,917,028,692,000 | Add a parameter to the identifier. | robustnessgym/core/identifier.py | add_parameter | ANarayan/robustness-gym | python | def add_parameter(self, parameter: str, value: Any) -> None:
if isinstance(value, Callable):
self.parameters[parameter] = '.'.join([str(value.__module__), str(value.__name__)])
else:
self.parameters[parameter] = value |
def get_sinusoid_encoding_table(n_position, d_hid, padding_idx=None):
' Sinusoid position encoding table '
def cal_angle(position, hid_idx):
return (position / np.power(10000, ((2 * (hid_idx // 2)) / d_hid)))
def get_posi_angle_vec(position):
return [cal_angle(position, hid_j) for hid_j in... | 6,906,142,374,917,182,000 | Sinusoid position encoding table | src/onqg/utils/sinusoid.py | get_sinusoid_encoding_table | MrSchnappi/RL-for-Question-Generation | python | def get_sinusoid_encoding_table(n_position, d_hid, padding_idx=None):
' '
def cal_angle(position, hid_idx):
return (position / np.power(10000, ((2 * (hid_idx // 2)) / d_hid)))
def get_posi_angle_vec(position):
return [cal_angle(position, hid_j) for hid_j in range(d_hid)]
sinusoid_tabl... |
def learning_rate_schedule(current_epoch, current_batch, steps_per_epoch, batch_size):
'Handles linear scaling rule, gradual warmup, and LR decay.\n\n Scale learning rate at epoch boundaries provided in LR_SCHEDULE by the\n provided scaling factor.\n\n Args:\n current_epoch: integer, current epoch indexed fro... | 4,805,554,109,677,772,000 | Handles linear scaling rule, gradual warmup, and LR decay.
Scale learning rate at epoch boundaries provided in LR_SCHEDULE by the
provided scaling factor.
Args:
current_epoch: integer, current epoch indexed from 0.
current_batch: integer, current batch in the current epoch, indexed from 0.
steps_per_epoch: inte... | official/vision/image_classification/common.py | learning_rate_schedule | Anku5hk/models | python | def learning_rate_schedule(current_epoch, current_batch, steps_per_epoch, batch_size):
'Handles linear scaling rule, gradual warmup, and LR decay.\n\n Scale learning rate at epoch boundaries provided in LR_SCHEDULE by the\n provided scaling factor.\n\n Args:\n current_epoch: integer, current epoch indexed fro... |
def get_optimizer(learning_rate=0.1):
'Returns optimizer to use.'
return gradient_descent_v2.SGD(learning_rate=learning_rate, momentum=0.9) | 7,685,441,610,714,783,000 | Returns optimizer to use. | official/vision/image_classification/common.py | get_optimizer | Anku5hk/models | python | def get_optimizer(learning_rate=0.1):
return gradient_descent_v2.SGD(learning_rate=learning_rate, momentum=0.9) |
def get_callbacks(steps_per_epoch, learning_rate_schedule_fn=None, pruning_method=None, enable_checkpoint_and_export=False, model_dir=None):
'Returns common callbacks.'
time_callback = keras_utils.TimeHistory(FLAGS.batch_size, FLAGS.log_steps)
callbacks = [time_callback]
if ((not FLAGS.use_tensor_lr) an... | 7,434,335,091,102,080,000 | Returns common callbacks. | official/vision/image_classification/common.py | get_callbacks | Anku5hk/models | python | def get_callbacks(steps_per_epoch, learning_rate_schedule_fn=None, pruning_method=None, enable_checkpoint_and_export=False, model_dir=None):
time_callback = keras_utils.TimeHistory(FLAGS.batch_size, FLAGS.log_steps)
callbacks = [time_callback]
if ((not FLAGS.use_tensor_lr) and learning_rate_schedule_fn... |
def build_stats(history, eval_output, callbacks):
'Normalizes and returns dictionary of stats.\n\n Args:\n history: Results of the training step. Supports both categorical_accuracy\n and sparse_categorical_accuracy.\n eval_output: Output of the eval step. Assumes first value is eval_loss and\n seco... | 1,328,152,771,311,647,200 | Normalizes and returns dictionary of stats.
Args:
history: Results of the training step. Supports both categorical_accuracy
and sparse_categorical_accuracy.
eval_output: Output of the eval step. Assumes first value is eval_loss and
second value is accuracy_top_1.
callbacks: a list of callbacks which migh... | official/vision/image_classification/common.py | build_stats | Anku5hk/models | python | def build_stats(history, eval_output, callbacks):
'Normalizes and returns dictionary of stats.\n\n Args:\n history: Results of the training step. Supports both categorical_accuracy\n and sparse_categorical_accuracy.\n eval_output: Output of the eval step. Assumes first value is eval_loss and\n seco... |
def define_keras_flags(dynamic_loss_scale=True, model=False, optimizer=False, pretrained_filepath=False):
'Define flags for Keras models.'
flags_core.define_base(clean=True, num_gpu=True, run_eagerly=True, train_epochs=True, epochs_between_evals=True, distribution_strategy=True)
flags_core.define_performanc... | 2,800,760,739,962,504,700 | Define flags for Keras models. | official/vision/image_classification/common.py | define_keras_flags | Anku5hk/models | python | def define_keras_flags(dynamic_loss_scale=True, model=False, optimizer=False, pretrained_filepath=False):
flags_core.define_base(clean=True, num_gpu=True, run_eagerly=True, train_epochs=True, epochs_between_evals=True, distribution_strategy=True)
flags_core.define_performance(num_parallel_calls=False, synt... |
def get_synth_data(height, width, num_channels, num_classes, dtype):
'Creates a set of synthetic random data.\n\n Args:\n height: Integer height that will be used to create a fake image tensor.\n width: Integer width that will be used to create a fake image tensor.\n num_channels: Integer depth that will ... | 4,141,754,423,883,289,000 | Creates a set of synthetic random data.
Args:
height: Integer height that will be used to create a fake image tensor.
width: Integer width that will be used to create a fake image tensor.
num_channels: Integer depth that will be used to create a fake image tensor.
num_classes: Number of classes that should be ... | official/vision/image_classification/common.py | get_synth_data | Anku5hk/models | python | def get_synth_data(height, width, num_channels, num_classes, dtype):
'Creates a set of synthetic random data.\n\n Args:\n height: Integer height that will be used to create a fake image tensor.\n width: Integer width that will be used to create a fake image tensor.\n num_channels: Integer depth that will ... |
def define_pruning_flags():
'Define flags for pruning methods.'
flags.DEFINE_string('pruning_method', None, 'Pruning method.None (no pruning) or polynomial_decay.')
flags.DEFINE_float('pruning_initial_sparsity', 0.0, 'Initial sparsity for pruning.')
flags.DEFINE_float('pruning_final_sparsity', 0.5, 'Fin... | -3,593,318,337,141,748,000 | Define flags for pruning methods. | official/vision/image_classification/common.py | define_pruning_flags | Anku5hk/models | python | def define_pruning_flags():
flags.DEFINE_string('pruning_method', None, 'Pruning method.None (no pruning) or polynomial_decay.')
flags.DEFINE_float('pruning_initial_sparsity', 0.0, 'Initial sparsity for pruning.')
flags.DEFINE_float('pruning_final_sparsity', 0.5, 'Final sparsity for pruning.')
flag... |
def get_synth_input_fn(height, width, num_channels, num_classes, dtype=tf.float32, drop_remainder=True):
'Returns an input function that returns a dataset with random data.\n\n This input_fn returns a data set that iterates over a set of random data and\n bypasses all preprocessing, e.g. jpeg decode and copy. The... | 8,003,751,533,849,318,000 | Returns an input function that returns a dataset with random data.
This input_fn returns a data set that iterates over a set of random data and
bypasses all preprocessing, e.g. jpeg decode and copy. The host to device
copy is still included. This used to find the upper throughput bound when
tuning the full input pipel... | official/vision/image_classification/common.py | get_synth_input_fn | Anku5hk/models | python | def get_synth_input_fn(height, width, num_channels, num_classes, dtype=tf.float32, drop_remainder=True):
'Returns an input function that returns a dataset with random data.\n\n This input_fn returns a data set that iterates over a set of random data and\n bypasses all preprocessing, e.g. jpeg decode and copy. The... |
def set_cudnn_batchnorm_mode():
'Set CuDNN batchnorm mode for better performance.\n\n Note: Spatial Persistent mode may lead to accuracy losses for certain\n models.\n '
if FLAGS.batchnorm_spatial_persistent:
os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '1'
else:
os.env... | -4,307,923,414,367,476,000 | Set CuDNN batchnorm mode for better performance.
Note: Spatial Persistent mode may lead to accuracy losses for certain
models. | official/vision/image_classification/common.py | set_cudnn_batchnorm_mode | Anku5hk/models | python | def set_cudnn_batchnorm_mode():
'Set CuDNN batchnorm mode for better performance.\n\n Note: Spatial Persistent mode may lead to accuracy losses for certain\n models.\n '
if FLAGS.batchnorm_spatial_persistent:
os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '1'
else:
os.env... |
def on_batch_begin(self, batch, logs=None):
'Executes before step begins.'
lr = self.schedule(self.epochs, batch, self.steps_per_epoch, self.batch_size)
if (not isinstance(lr, (float, np.float32, np.float64))):
raise ValueError('The output of the "schedule" function should be float.')
if (lr != ... | 7,123,145,724,043,767,000 | Executes before step begins. | official/vision/image_classification/common.py | on_batch_begin | Anku5hk/models | python | def on_batch_begin(self, batch, logs=None):
lr = self.schedule(self.epochs, batch, self.steps_per_epoch, self.batch_size)
if (not isinstance(lr, (float, np.float32, np.float64))):
raise ValueError('The output of the "schedule" function should be float.')
if (lr != self.prev_lr):
self.mo... |
def _get_learning_rate(self, step):
'Compute learning rate at given step.'
with tf.compat.v1.name_scope(self.name, 'PiecewiseConstantDecayWithWarmup', [self.rescaled_lr, self.step_boundaries, self.lr_values, self.warmup_steps, self.compute_lr_on_cpu]):
def warmup_lr(step):
return (self.resc... | -9,149,486,914,277,548,000 | Compute learning rate at given step. | official/vision/image_classification/common.py | _get_learning_rate | Anku5hk/models | python | def _get_learning_rate(self, step):
with tf.compat.v1.name_scope(self.name, 'PiecewiseConstantDecayWithWarmup', [self.rescaled_lr, self.step_boundaries, self.lr_values, self.warmup_steps, self.compute_lr_on_cpu]):
def warmup_lr(step):
return (self.rescaled_lr * (tf.cast(step, tf.float32) /... |
def input_fn(is_training, data_dir, batch_size, *args, **kwargs):
'Returns dataset filled with random data.'
(inputs, labels) = get_synth_data(height=height, width=width, num_channels=num_channels, num_classes=num_classes, dtype=dtype)
labels = tf.cast(labels, dtype=tf.float32)
data = tf.data.Dataset.fr... | 533,578,729,328,800,500 | Returns dataset filled with random data. | official/vision/image_classification/common.py | input_fn | Anku5hk/models | python | def input_fn(is_training, data_dir, batch_size, *args, **kwargs):
(inputs, labels) = get_synth_data(height=height, width=width, num_channels=num_channels, num_classes=num_classes, dtype=dtype)
labels = tf.cast(labels, dtype=tf.float32)
data = tf.data.Dataset.from_tensors((inputs, labels)).repeat()
... |
def default_keygen(self, *args, **kwargs) -> Tuple[(Hashable, ...)]:
'Returns all params (args, kwargs, and missing default kwargs) for function as kwargs.'
return tuple(self.get_args_as_kwargs(*args, **kwargs).values()) | -423,157,303,730,307,500 | Returns all params (args, kwargs, and missing default kwargs) for function as kwargs. | atools/_memoize_decorator.py | default_keygen | cevans87/atools | python | def default_keygen(self, *args, **kwargs) -> Tuple[(Hashable, ...)]:
return tuple(self.get_args_as_kwargs(*args, **kwargs).values()) |
def __init__(self, keylist, header=None):
'\n Initializes the ConfigList object by tranfsforming\n a list of keywords into a structured list including\n beams descriptions\n\n keylist: list\n List of configuration keys\n header: str\n the header string\n ... | 6,519,035,064,426,874,000 | Initializes the ConfigList object by tranfsforming
a list of keywords into a structured list including
beams descriptions
keylist: list
List of configuration keys
header: str
the header string | pyaxe/axesrc/configfile.py | __init__ | sosey/pyaxe | python | def __init__(self, keylist, header=None):
'\n Initializes the ConfigList object by tranfsforming\n a list of keywords into a structured list including\n beams descriptions\n\n keylist: list\n List of configuration keys\n header: str\n the header string\n ... |
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