code stringlengths 20 4.93k | docstring stringlengths 33 1.27k | source stringclasses 3
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def __init__(self, chain):
self.queue = []
self._fsm = JTAGStateMachine()
self._chain = chain | Create a new CommandQueue to manage, compile, and run Primitives.
Args:
chain: A JTAGScanChain instance that this queue will be associated with. | juraj-google-style |
def _last_path_token(builder: expressions.Builder) -> str:
if isinstance(builder.node, _evaluation.RootMessageNode):
return ''
return builder.node.to_path_token() | Returns `builder`'s last path token less the resource type.
For example:
* "Foo" returns "" (empty string)
* "Foo.bar" returns "bar"
* "Foo.bar.bats" returns "bats"
Args:
builder: The `builder` whose relative path to return. | github-repos |
def dna_transformation(prev_image, dna_input, dna_kernel_size, relu_shift):
prev_image_pad = tf.pad(prev_image, [[0, 0], [2, 2], [2, 2], [0, 0]])
image_height = int(prev_image.get_shape()[1])
image_width = int(prev_image.get_shape()[2])
inputs = []
for xkern in range(dna_kernel_size):
for ykern in ... | Apply dynamic neural advection to previous image.
Args:
prev_image: previous image to be transformed.
dna_input: hidden lyaer to be used for computing DNA transformation.
dna_kernel_size: dna kernel size.
relu_shift: shift for ReLU function.
Returns:
List of images transformed by the predicted CDNA kernels. | juraj-google-style |
def serialize_quantity(o):
return dict(
_type='astropy.units.Quantity',
value=o.value,
unit=o.unit.to_string()) | Serializes an :obj:`astropy.units.Quantity`, for JSONification.
Args:
o (:obj:`astropy.units.Quantity`): :obj:`Quantity` to be serialized.
Returns:
A dictionary that can be passed to :obj:`json.dumps`. | juraj-google-style |
def normalize_json(template):
obj = parse_cloudformation_template(template)
json_str = json.dumps(obj, sort_keys=True, indent=4, default=str, separators=(',', ': '))
result = []
lines = json_str.split('\n')
for line in lines:
result.append((line + '\n'))
return result | Normalize our template for diffing.
Args:
template(str): string representing the template
Returns:
list: json representation of the parameters | codesearchnet |
def _from_yaml_v0(cls, job):
job_metadata = {}
for key in ['job-id', 'job-name', 'create-time']:
job_metadata[key] = job.get(key)
job_metadata['create-time'] = dsub_util.replace_timezone(datetime.datetime.strptime(job['create-time'], '%Y-%m-%d %H:%M:%S.%f'), tzlocal())
job_resources = Resources(... | Populate a JobDescriptor from the local provider's original meta.yaml.
The local job provider had the first incarnation of a YAML file for each
task. That idea was extended here in the JobDescriptor and the local
provider adopted the JobDescriptor.to_yaml() call to write its meta.yaml.
The JobDescriptor.from_yaml() d... | codesearchnet |
def _iterate_through_class(self, class_dict):
output_dict = {}
for key in class_dict:
val = class_dict[key]
try:
val = val.__dict__
except AttributeError:
pass
if (type(val) is dict):
val = self._iterate_through_class(val)
if (type(val)... | Recursive function for output dictionary creation.
Function will check each value in a dictionary to see if it is a
class, list, or dictionary object. The idea is to turn all class objects into
dictionaries. If it is a class object it will pass its ``class.__dict__``
recursively through this function again. If it is a... | codesearchnet |
def cdna_transformation(prev_image, cdna_input, num_masks, color_channels, dna_kernel_size, relu_shift):
batch_size = tf.shape(cdna_input)[0]
height = int(prev_image.get_shape()[1])
width = int(prev_image.get_shape()[2])
cdna_kerns = tfl.dense(cdna_input, ((dna_kernel_size * dna_kernel_size) * num_masks... | Apply convolutional dynamic neural advection to previous image.
Args:
prev_image: previous image to be transformed.
cdna_input: hidden lyaer to be used for computing CDNA kernels.
num_masks: number of masks and hence the number of CDNA transformations.
color_channels: the number of color channels in the images.
dna_ke... | codesearchnet |
class _ImageEmbeddingHandler(_EmbeddingHandler):
def _validate_column_data(self, batch):
if isinstance(batch[0], (int, str, float, bool)):
raise TypeError(f'Embeddings can only be generated on dict[str, Image].Got dict[str, {type(batch[0])}] instead.')
def get_metrics_namespace(self) -> st... | A ModelHandler intended to be work on list[dict[str, Image]] inputs.
The inputs to the model handler are expected to be a list of dicts.
For example, if the original mode is used with RunInference to take a
PCollection[E] to a PCollection[P], this ModelHandler would take a
PCollection[dict[str, E]] to a PCollection[d... | github-repos |
def plugin_test_validation(self, handler):
methods = {name:func for name, func in inspect.getmembers(handler, callable)}
if 'test' not in methods.keys():
print 'Failure for plugin: %s' % (handler.__name__)
print 'Validation Error: The file must have a top level test() me... | Plugin validation.
Every workbench plugin must have top level test method.
Args:
handler: The loaded plugin.
Returns:
None if the test fails or the test function. | juraj-google-style |
def end_entry(self):
if (self.in_progress is None):
return Error.NO_ERROR
if (self.in_progress.data_space() == 2):
return Error.INPUT_BUFFER_WRONG_SIZE
for entry in self.entries:
if ((entry.target == self.in_progress.target) and (entry.var_id == self.in_progress.var_id)):
... | Finish a previously started config database entry.
This commits the currently in progress entry. The expected flow is
that start_entry() is called followed by 1 or more calls to add_data()
followed by a single call to end_entry().
Returns:
int: An error code | codesearchnet |
def add(self, private_key):
if not isinstance(private_key, PaillierPrivateKey):
raise TypeError("private_key should be of type PaillierPrivateKey, "
"not %s" % type(private_key))
self.__keyring[private_key.public_key] = private_key | Add a key to the keyring.
Args:
private_key (PaillierPrivateKey): a key to add to this keyring. | juraj-google-style |
def load_fasta_file_as_dict_of_seqrecords(filename):
results = {}
records = load_fasta_file(filename)
for r in records:
results[r.id] = r
return results | Load a FASTA file and return the sequences as a dict of {ID: SeqRecord}
Args:
filename (str): Path to the FASTA file to load
Returns:
dict: Dictionary of IDs to their SeqRecords | juraj-google-style |
def __method_descriptor(self, service, method_info, operation_id,
protorpc_method_info, security_definitions):
descriptor = {}
request_message_type = (resource_container.ResourceContainer.
get_request_message(protorpc_method_info.remote))
request_k... | Describes a method.
Args:
service: endpoints.Service, Implementation of the API as a service.
method_info: _MethodInfo, Configuration for the method.
operation_id: string, Operation ID of the method
protorpc_method_info: protorpc.remote._RemoteMethodInfo, ProtoRPC
description of the method.
security_definitions: list ... | juraj-google-style |
def fetch_time_output(marker, format_s, ins):
from parse import parse
timings = [x for x in ins if marker in x]
res = [parse(format_s, t) for t in timings]
return [_f for _f in res if _f] | Fetch the output /usr/bin/time from a.
Args:
marker: The marker that limits the time output
format_s: The format string used to parse the timings
ins: A list of lines we look for the output.
Returns:
A list of timing tuples | juraj-google-style |
def post(self, json=None):
return self._call('post', url=self.endpoint, json=json) | Send a POST request and return the JSON decoded result.
Args:
json (dict, optional): Object to encode and send in request.
Returns:
mixed: JSON decoded response data. | juraj-google-style |
def get_variants(self, chromosome=None, start=None, end=None):
query = {}
if chromosome:
query['chrom'] = chromosome
if start:
query['start'] = {'$lte': end}
query['end'] = {'$gte': start}
LOG.info('Find all variants {}'.format(query))
return self.db.variant.find(query).sort(... | Return all variants in the database
If no region is specified all variants will be returned.
Args:
chromosome(str)
start(int)
end(int)
Returns:
variants(Iterable(Variant)) | codesearchnet |
def generate_hpo_gene_list(self, *hpo_terms):
genes = {}
for term in hpo_terms:
hpo_obj = self.hpo_term(term)
if hpo_obj:
for hgnc_id in hpo_obj['genes']:
if hgnc_id in genes:
genes[hgnc_id] += 1
... | Generate a sorted list with namedtuples of hpogenes
Each namedtuple of the list looks like (hgnc_id, count)
Args:
hpo_terms(iterable(str))
Returns:
hpo_genes(list(HpoGene)) | juraj-google-style |
def get_username(self, userid):
username = self.user_map.get(userid)
if not username:
users = self.get_users()
if users:
members = {
m['id']: m['name']
for m in users.get('members', [{}])
if m.g... | Perform a lookup of users to resolve a userid to a username
Args:
userid (string): Slack userid to lookup.
Returns:
string: Human-friendly name of the user | juraj-google-style |
def classifier_factory(clf):
required_methods = ['fit', 'score', 'predict']
for method in required_methods:
if not hasattr(clf, method):
raise TypeError('"{}" is not in clf. Did you pass a '
'classifier instance?'.format(method))
optional_methods = ['pr... | Embeds scikit-plot instance methods in an sklearn classifier.
Args:
clf: Scikit-learn classifier instance
Returns:
The same scikit-learn classifier instance passed in **clf**
with embedded scikit-plot instance methods.
Raises:
ValueError: If **clf** does not contain the instance methods
necessary for scikit-plot ins... | juraj-google-style |
def exe_cmd(*cmds):
cmd = ' '.join(cmds)
proc = Popen(cmd, stdout=PIPE, stderr=PIPE, shell=True)
(out, err) = proc.communicate()
if not err:
return out
return err | Executes commands in a new shell. Directing stderr to PIPE.
This is fastboot's own exe_cmd because of its peculiar way of writing
non-error info to stderr.
Args:
cmds: A sequence of commands and arguments.
Returns:
The output of the command run.
Raises:
Exception: An error occurred during the command execution. | juraj-google-style |
def clear(self, size=(- 1), *, offset=0, chunk=None) -> None:
self.mglo.clear(size, offset, chunk) | Clear the content.
Args:
size (int): The size. Value ``-1`` means all.
Keyword Args:
offset (int): The offset.
chunk (bytes): The chunk to use repeatedly. | codesearchnet |
def make_encoder(base_depth, activation, latent_size, code_size):
conv = functools.partial(
tf.keras.layers.Conv2D, padding="SAME", activation=activation)
encoder_net = tf.keras.Sequential([
conv(base_depth, 5, 1),
conv(base_depth, 5, 2),
conv(2 * base_depth, 5, 1),
conv(2 * base_d... | Creates the encoder function.
Args:
base_depth: Layer base depth in encoder net.
activation: Activation function in hidden layers.
latent_size: The number of latent variables in the code.
code_size: The dimensionality of each latent variable.
Returns:
encoder: A `callable` mapping a `Tensor` of images to a `Tensor` o... | juraj-google-style |
def num_embedding_devices_per_chip(self):
return self.tpu_hardware_feature_proto.num_embedding_devices_per_chip | Number of embedding accelerator devices per chip.
Returns:
Number of embedding devices per chip. | github-repos |
def __init__(self, projection=None, orientation0=(0, 0, -1), **kwargs):
kwargs['orientation0'] = orientation0
super(Camera, self).__init__(**kwargs)
self.projection = PerspectiveProjection() if not projection else projection
self.reset_uniforms() | Returns a camera object
Args:
projection (obj): the projection type for the camera. It can either be an instance of OrthoProjection or PerspeectiveProjection
orientation0 (tuple):
Returns:
Camera instance | juraj-google-style |
def set_sflow(self, name, value=None, default=False, disable=False):
if value not in [True, False, None]:
raise ValueError
commands = ['interface %s' % name]
commands.append(self.command_builder('sflow enable', value=value,
defau... | Configures the sFlow state on the interface
Args:
name (string): The interface identifier. It must be a full
interface name (ie Ethernet, not Et)
value (boolean): True if sFlow should be enabled otherwise False
default (boolean): Specifies the default value for sFlow
disable (boolean): Specifies to disable sFlow
... | juraj-google-style |
def _ParseOriginalFilename(self, file_object, format_version):
file_offset = file_object.tell()
if (format_version == 1):
data_type_map = self._GetDataTypeMap('recycle_bin_metadata_utf16le_string')
else:
data_type_map = self._GetDataTypeMap('recycle_bin_metadata_utf16le_string_with_size')
... | Parses the original filename.
Args:
file_object (FileIO): file-like object.
format_version (int): format version.
Returns:
str: filename or None on error.
Raises:
ParseError: if the original filename cannot be read. | codesearchnet |
def _assert_weights_created(self):
if self.dynamic:
return
if 'build' in self.__class__.__dict__ and self.__class__ != Model and (not self.built):
raise ValueError('Weights for model %s have not yet been created. Weights are created when the Model is first called on inputs or `build()` is called... | Asserts that all the weights for the model have been created.
For a non-dynamic model, the weights must already be created after the
layer has been called. For a dynamic model, the exact list of weights can
never be known for certain since it may change at any time during execution.
We run this check right before acc... | github-repos |
def output(self, _filename):
txt = ''
for contract in self.slither.contracts_derived:
txt += '\n{}:\n'.format(contract.name)
table = PrettyTable(['Name', 'Type'])
for variable in contract.state_variables:
if not variable.is_constant:
... | _filename is not used
Args:
_filename(string) | juraj-google-style |
def find_nearest_color_hexstr(hexdigits, color_table=None, method='euclid'):
triplet = []
try:
if (len(hexdigits) == 3):
for digit in hexdigits:
digit = int(digit, 16)
triplet.append(((digit * 16) + digit))
elif (len(hexdigits) == 6):
tripl... | Given a three or six-character hex digit string, return the nearest
color index.
Arguments:
hexdigits: a three/6 digit hex string, e.g. 'b0b', '123456'
Returns:
int, None: index, or None on error. | codesearchnet |
def is_github_task(task):
return any((
task.get('schedulerId') == 'taskcluster-github',
task.get('extra', {}).get('tasks_for', '').startswith('github-'),
is_github_url(task.get('metadata', {}).get('source', '')),
)) | Determine if a task is related to GitHub.
This function currently looks into the ``schedulerId``, ``extra.tasks_for``, and
``metadata.source``.
Args:
task (dict): the task definition to check.
Returns:
bool: True if a piece of data refers to GitHub | juraj-google-style |
def inputs(self) -> Mapping[str, Mapping[int, str]]:
raise NotImplementedError() | Mapping containing the axis definition of the input tensors to provide to the model
Returns:
For each input: its name associated to the axes symbolic name and the axis position within the tensor | github-repos |
def set_pattern_additional_cycles(self, patternnumber, value):
_checkPatternNumber(patternnumber)
minimalmodbus._checkInt(value, minvalue=0, maxvalue=99, description='number of additional cycles')
address = _calculateRegisterAddress('cycles', patternnumber)
self.wr... | Set the number of additional cycles for a given pattern.
Args:
* patternnumber (integer): 0-7
* value (integer): 0-99 | juraj-google-style |
def generate_files(generator, output_filenames, max_cases=None, cycle_every_n=1):
if outputs_exist(output_filenames):
tf.logging.info('Skipping generator because outputs files exists at {}'.format(output_filenames))
return
tmp_filenames = [(fname + '.incomplete') for fname in output_filenames]
... | Generate cases from a generator and save as TFRecord files.
Generated cases are transformed to tf.Example protos and saved as TFRecords
in sharded files named output_dir/output_name-00..N-of-00..M=num_shards.
Args:
generator: a generator yielding (string -> int/float/str list) dictionaries.
output_filenames: List of ... | codesearchnet |
def calc_checksum(sentence):
if sentence.startswith('$'):
sentence = sentence[1:]
sentence = sentence.split('*')[0]
return reduce(xor, map(ord, sentence)) | Calculate a NMEA 0183 checksum for the given sentence.
NMEA checksums are a simple XOR of all the characters in the sentence
between the leading "$" symbol, and the "*" checksum separator.
Args:
sentence (str): NMEA 0183 formatted sentence | codesearchnet |
def create_gpu_capa_map(match_list, generate_csv=False, filename='compute_capability'):
gpu_capa = collections.OrderedDict()
include = False
gpu = ''
cnt = 0
mismatch_cnt = 0
for match in match_list:
if 'Products' in match:
if not include:
include = True
... | Generates a map between GPU types and corresponding compute capability.
This method is used for retrieving CUDA compute capability from the web only.
Args:
match_list: List of all CUDA compute capability detected from the webpage.
generate_csv: Boolean for creating csv file to store results.
filename: String that is ... | github-repos |
def pop(self):
return self._queue.popleft() | Removes and returns the oldest value from the data window (FIFO).
Returns:
The oldest value from the window. | github-repos |
def get_qa_logit_layer(self) -> nn.Module:
if hasattr(self, 'answer_head'):
return self.answer_head.logit_fc[-1] | Returns the linear layer that produces question answering logits.
Returns:
`nn.Module`: A torch module mapping the question answering prediction hidden states or `None` if LXMERT
does not have a visual answering head. | github-repos |
def bulk_load_docs(es, docs):
chunk_size = 200
try:
results = elasticsearch.helpers.bulk(es, docs, chunk_size=chunk_size)
log.debug(f"Elasticsearch documents loaded: {results[0]}")
if len(results[1]) > 0:
log.error("Bulk load errors {}".format(results))
e... | Bulk load docs
Args:
es: elasticsearch handle
docs: Iterator of doc objects - includes index_name | juraj-google-style |
def get_parameter_bounds(self, include_frozen=False):
if include_frozen:
return self.parameter_bounds
return list((p for (p, f) in zip(self.parameter_bounds, self.unfrozen_mask) if f)) | Get a list of the parameter bounds
Args:
include_frozen (Optional[bool]): Should the frozen parameters be
included in the returned value? (default: ``False``) | codesearchnet |
def wait_for_plug_update(self, plug_name, remote_state, timeout_s):
plug = self._plugs_by_name.get(plug_name)
if (plug is None):
raise InvalidPlugError(('Cannot wait on unknown plug "%s".' % plug_name))
if (not isinstance(plug, FrontendAwareBasePlug)):
raise InvalidPlugError(('Cannot wait on... | Wait for a change in the state of a frontend-aware plug.
Args:
plug_name: Plug name, e.g. 'openhtf.plugs.user_input.UserInput'.
remote_state: The last observed state.
timeout_s: Number of seconds to wait for an update.
Returns:
An updated state, or None if the timeout runs out.
Raises:
InvalidPlugError: The plug can... | codesearchnet |
def generate_index(fn, cols=None, names=None, sep=" "):
assert cols is not None, "'cols' was not set"
assert names is not None, "'names' was not set"
assert len(cols) == len(names)
bgzip, open_func = get_open_func(fn, return_fmt=True)
data = pd.read_csv(fn, sep=sep, engine="c",... | Build a index for the given file.
Args:
fn (str): the name of the file.
cols (list): a list containing column to keep (as int).
names (list): the name corresponding to the column to keep (as str).
sep (str): the field separator.
Returns:
pandas.DataFrame: the index. | juraj-google-style |
def get_axis_value_discrete(self, axis):
if (self.type != EventType.POINTER_AXIS):
raise AttributeError(_wrong_meth.format(self.type))
return self._libinput.libinput_event_pointer_get_axis_value_discrete(self._handle, axis) | Return the axis value in discrete steps for a given axis event.
How a value translates into a discrete step depends on the source.
If the source is :attr:`~libinput.constant.PointerAxisSource.WHEEL`,
the discrete value correspond to the number of physical mouse wheel
clicks.
If the source is :attr:`~libinput.constant... | codesearchnet |
def _DeserializeResponse(self, payload):
status_line, payload = payload.split('\n', 1)
_, status, _ = status_line.split(' ', 2)
parser = email_parser.Parser()
msg = parser.parsestr(payload)
info = dict(msg)
info['status'] = status
... | Convert string into Response and content.
Args:
payload: Header and body string to be deserialized.
Returns:
A Response object | juraj-google-style |
def ReadLine(self, file_object):
(line, _, self.lines) = self.lines.partition('\n')
if (not line):
self.ReadLines(file_object)
(line, _, self.lines) = self.lines.partition('\n')
return line | Reads a line.
Args:
file_object (dfvfs.FileIO): file-like object.
Returns:
str: line read from the lines buffer. | codesearchnet |
def is_scheduled(configuration, task={}):
days = configuration.recipe.get('setup', {}).get('day', [])
hours = [int(h) for h in task.get('hour', configuration.recipe.get('setup', {}).get('hour', []))]
if days == [] or configuration.date.strftime('%a') in days:
if hours == [] or configuration.hour in ... | Wrapper for day_hour_scheduled that returns True if current time zone safe hour is in recipe schedule.
Used as a helper for any cron job running projects. Keeping this logic in
project
helps avoid time zone detection issues and scheduling discrepencies between
machines.
Args:
* recipe: (Recipe JSON) The JSON of a re... | github-repos |
def make_test_function(self):
if self.test_function is not None:
return self.test_function
def step_function(model, iterator):
def run_step(data):
outputs = model.test_step(data)
with ops.control_dependencies(_minimum_control_deps(outputs)):
mod... | Creates a function that executes one step of evaluation.
This method can be overridden to support custom evaluation logic.
This method is called by `Model.evaluate` and `Model.test_on_batch`.
Typically, this method directly controls `tf.function` and
`tf.distribute.Strategy` settings, and delegates the actual evaluat... | github-repos |
def __init__(self, key, items):
self._key = key
sequence = list(items)
super(Grouping, self).__init__(sequence) | Create a Grouping with a given key and a collection of members.
Args:
key: The key corresponding to this Grouping
items: An iterable collection of the members of the group. | juraj-google-style |
def to_dms(angle, style='dms'):
sign = (1 if (angle >= 0) else (- 1))
angle = (abs(angle) * 3600)
(minutes, seconds) = divmod(angle, 60)
(degrees, minutes) = divmod(minutes, 60)
if (style == 'dms'):
return tuple(((sign * abs(i)) for i in (int(degrees), int(minutes), seconds)))
elif (styl... | Convert decimal angle to degrees, minutes and possibly seconds.
Args:
angle (float): Angle to convert
style (str): Return fractional or whole minutes values
Returns:
tuple of int: Angle converted to degrees, minutes and possibly seconds
Raises:
ValueError: Unknown value for ``style`` | codesearchnet |
def showbox(self, force_rerun=False):
log.debug('{}: running box maker...'.format(self.id))
if not self.sphsel_path:
return ValueError('Please run sphere_selector_using_residues')
boxfile = op.join(self.dock_dir, "{}_box.pdb".format(self.id))
boxscript = op.join(se... | Create the dummy PDB box around the selected spheres.
Args:
force_rerun (bool): If method should be rerun even if output file exists | juraj-google-style |
def process_result_value(self, value, dialect):
masks = list()
if value:
for e in enums.CryptographicUsageMask:
if (e.value & value):
masks.append(e)
return masks | Returns a new list of enums.CryptographicUsageMask Enums. This converts
the integer value into the list of enums.
Args:
value(int): The integer value stored in the database that is used
to create the list of enums.CryptographicUsageMask Enums.
dialect(string): SQL dialect | codesearchnet |
def _set_mode(self, discover_mode, connect_mode):
payload = struct.pack('<BB', discover_mode, connect_mode)
response = self._send_command(6, 1, payload)
(result,) = unpack('<H', response.payload)
if (result != 0):
return (False, {'reason': 'Error code from BLED112 setting mode', 'code': result})... | Set the mode of the BLED112, used to enable and disable advertising
To enable advertising, use 4, 2.
To disable advertising use 0, 0.
Args:
discover_mode (int): The discoverability mode, 0 for off, 4 for on (user data)
connect_mode (int): The connectability mode, 0 for of, 2 for undirected connectable | codesearchnet |
def EnqueueBreakpointUpdate(self, breakpoint):
with self._transmission_thread_startup_lock:
if self._transmission_thread is None:
self._transmission_thread = threading.Thread(
target=self._TransmissionThreadProc)
self._transmission_thread.name = 'Cloud Debugger transmission th... | Asynchronously updates the specified breakpoint on the backend.
This function returns immediately. The worker thread is actually doing
all the work. The worker thread is responsible to retry the transmission
in case of transient errors.
Args:
breakpoint: breakpoint in either final or non-final state. | juraj-google-style |
def create_primes(threshold):
if (threshold == 2):
return [2]
elif (threshold < 2):
return []
numbers = list(range(3, (threshold + 1), 2))
root_of_threshold = (threshold ** 0.5)
half = int((((threshold + 1) / 2) - 1))
idx = 0
counter = 3
while (counter <= root_of_threshol... | Generate prime values using sieve of Eratosthenes method.
Args:
threshold (int):
The upper bound for the size of the prime values.
Returns (List[int]):
All primes from 2 and up to ``threshold``. | codesearchnet |
def convert_item_to_command_line_arg(self, action, key, value):
args = []
if (action is None):
command_line_key = self.get_command_line_key_for_unknown_config_file_setting(key)
else:
command_line_key = action.option_strings[(- 1)]
if ((action is not None) and isinstance(action, ACTION_TY... | Converts a config file or env var key + value to a list of
commandline args to append to the commandline.
Args:
action: The argparse Action object for this setting, or None if this
config file setting doesn't correspond to any defined
configargparse arg.
key: string (config file key or env var name)
value: parsed valu... | codesearchnet |
def build_ring_all_reduce(input_tensors, num_workers, num_subchunks, gpu_perm, red_op, un_op=None):
if len(input_tensors) < 2:
raise ValueError('input_tensors must be length 2 or longer')
input_tensors, shape = _flatten_tensors(input_tensors)
devices = [t.device for t in input_tensors]
pred_by_s... | Construct a subgraph performing a ring-style all-reduce of input_tensors.
Args:
input_tensors: a list of `tf.Tensor` objects, which must all
have the same shape and type.
num_workers: number of worker tasks spanned by input_tensors.
num_subchunks: number of subchunks each device should process in one tick.
gpu_perm: a... | github-repos |
def close(self, file_des):
file_handle = self.filesystem.get_open_file(file_des)
file_handle.close() | Close a file descriptor.
Args:
file_des: An integer file descriptor for the file object requested.
Raises:
OSError: bad file descriptor.
TypeError: if file descriptor is not an integer. | codesearchnet |
def delete_case(self, case_id=None, institute_id=None, display_name=None):
query = {}
if case_id:
query['_id'] = case_id
LOG.info('Deleting case %s', case_id)
else:
if (not (institute_id and display_name)):
raise ValueError('Have to provide both institute_id and display_n... | Delete a single case from database
Args:
institute_id(str)
case_id(str)
Returns:
case_obj(dict): The case that was deleted | codesearchnet |
def WriteEventMACBGroup(self, event_macb_group):
output_values = self._GetOutputValues(event_macb_group[0])
timestamp_descriptions = [
event.timestamp_desc for event in event_macb_group]
output_values[3] = (
self._output_mediator.GetMACBRepresentationFromDescriptions(
times... | Writes an event MACB group to the output.
Args:
event_macb_group (list[EventObject]): event MACB group. | juraj-google-style |
def set_napp(self, user, napp, version=None):
self.user = user
self.napp = napp
self.version = (version or 'latest') | Set info about NApp.
Args:
user (str): NApps Server username.
napp (str): NApp name.
version (str): NApp version. | codesearchnet |
def __init__(self, filenames, record_bytes, header_bytes=None, footer_bytes=None, buffer_size=None, compression_type=None, name=None):
self._filenames = filenames
self._record_bytes = ops.convert_to_tensor(record_bytes, dtype=dtypes.int64, name='record_bytes')
self._header_bytes = convert.optional_param_to_... | Creates a `FixedLengthRecordDataset`.
Args:
filenames: A `tf.string` tensor containing one or more filenames.
record_bytes: A `tf.int64` scalar representing the number of bytes in each
record.
header_bytes: (Optional.) A `tf.int64` scalar representing the number of
bytes to skip at the start of a file.
footer_bytes: (... | github-repos |
def _assert_at_most_n_true(predicates, n, msg):
preds_c = array_ops_stack.stack(predicates, name='preds_c')
num_true_conditions = math_ops.reduce_sum(math_ops.cast(preds_c, dtypes.int32), name='num_true_conds')
condition = math_ops.less_equal(num_true_conditions, constant_op.constant(n, name='n_true_conds')... | Returns an Assert op that checks that at most n predicates are True.
Args:
predicates: list of bool scalar tensors.
n: maximum number of true predicates allowed.
msg: Error message. | github-repos |
def GetEntries(self, parser_mediator, match=None, **unused_kwargs):
version = match.get('LastAttemptSystemVersion', 'N/A')
pending = match.get('LastUpdatesAvailable', None)
event_data = plist_event.PlistTimeEventData()
event_data.desc = 'Last MacOS {0:s} full update.'.format(version)
event_dat... | Extracts relevant MacOS update entries.
Args:
parser_mediator (ParserMediator): mediates interactions between parsers
and other components, such as storage and dfvfs.
match (Optional[dict[str: object]]): keys extracted from PLIST_KEYS. | juraj-google-style |
def __init__(self, data_file, vocab_data_file):
def reading_function(file_name):
for root in self.ROOTS:
file_path = os.path.join(root, file_name)
if os.path.exists(file_path):
break
file_path = None
assert file_path is not None, ("Couldn't locate %s in %r" %
... | Creates a TokenDataSource instance.
Args:
data_file: file object containing text data to be tokenized.
vocab_data_file: file object containing text data used to initialize
the vocabulary. | juraj-google-style |
def add_edge_bias(x, filter_size):
x_shape = common_layers.shape_list(x)
if ((filter_size[0] == 1) and (filter_size[1] == 1)):
return x
a = ((filter_size[0] - 1)
b = ((filter_size[1] - 1)
padding = [[0, 0], [a, a], [b, b], [0, 0]]
x_bias = tf.zeros((x_shape[:(- 1)] + [1]))
x = tf.p... | Pad x and concatenates an edge bias across the depth of x.
The edge bias can be thought of as a binary feature which is unity when
the filter is being convolved over an edge and zero otherwise.
Args:
x: Input tensor, shape (NHWC)
filter_size: filter_size to determine padding.
Returns:
x_pad: Input tensor, shape (NHW(... | codesearchnet |
def Add(self, other):
if len(self.data) != len(other.data):
raise RuntimeError("Can only add series of identical lengths.")
for i in range(len(self.data)):
if self.data[i][1] != other.data[i][1]:
raise RuntimeError("Timestamp mismatch.")
if self.data[i][0] is None and other.data[i... | Add other to self pointwise.
Requires that both self and other are of the same length, and contain
identical timestamps. Typically this means that Normalize has been called
on both with identical time parameters.
Args:
other: The sequence to add to self.
Raises:
RuntimeError: other does not contain the same timestam... | juraj-google-style |
def MakeCdfFromPmf(pmf, name=None):
if (name == None):
name = pmf.name
return MakeCdfFromItems(pmf.Items(), name) | Makes a CDF from a Pmf object.
Args:
pmf: Pmf.Pmf object
name: string name for the data.
Returns:
Cdf object | codesearchnet |
def save(self, items):
rows = []
indx = self.indx
size = 0
tick = s_common.now()
for item in items:
byts = s_msgpack.en(item)
size += len(byts)
lkey = s_common.int64en(indx)
indx += 1
rows.append((lkey, byts))
self.slab.putmulti(rows, append=True, db=self.... | Save a series of items to a sequence.
Args:
items (tuple): The series of items to save into the sequence.
Returns:
The index of the first item | codesearchnet |
def emit(self, record):
if (record.levelno < logging.getLevelName(self.min_level)):
return
evt = LogEvent()
evt.level = record.levelname
evt.levelno = record.levelno
evt.timestamp = datetime.fromtimestamp(record.created)
evt.message = record.message
evt.filename = record.filename
... | Persist a record into the database
Args:
record (`logging.Record`): The logging.Record object to store
Returns:
`None` | codesearchnet |
def get_structure_from_prev_run(vasprun, outcar=None, sym_prec=0.1, international_monoclinic=True):
structure = vasprun.final_structure
site_properties = {}
if vasprun.is_spin:
if (outcar and outcar.magnetization):
site_properties.update({'magmom': [i['tot'] for i in outcar.magnetization... | Process structure from previous run.
Args:
vasprun (Vasprun): Vasprun that contains the final structure
from previous run.
outcar (Outcar): Outcar that contains the magnetization info from
previous run.
sym_prec (float): Tolerance for symmetry finding for standardization. If
no standardization is desired, set to 0 or ... | codesearchnet |
def is_in_path(program):
if (sys.version_info.major == 2):
path = os.getenv('PATH')
if (os.name == 'nt'):
path = path.split(';')
else:
path = path.split(':')
else:
path = os.get_exec_path()
for i in path:
if os.path.isdir(i):
if (pr... | Check if a program is in the system ``PATH``.
Checks if a given program is in the user's ``PATH`` or not.
Args:
program (str): The program to try to find in ``PATH``.
Returns:
bool: Is the program in ``PATH``? | codesearchnet |
def _compute_edge_nodes(nodes, degree):
(dimension, _) = np.shape(nodes)
nodes1 = np.empty((dimension, (degree + 1)), order='F')
nodes2 = np.empty((dimension, (degree + 1)), order='F')
nodes3 = np.empty((dimension, (degree + 1)), order='F')
curr2 = degree
curr3 = (- 1)
for i in six.moves.xra... | Compute the nodes of each edges of a surface.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): Control point nodes that define the surface.
degree (int): The degree of the surface define by ``nodes``.
Returns:
Tuple[numpy.ndarray,... | codesearchnet |
def item_status(self, **kwargs):
path = self._get_id_path('item_status')
response = self._GET(path, kwargs)
self._set_attrs_to_values(response)
return response | Check to see if a movie id is already added to a list.
Args:
movie_id: The id of the movie.
Returns:
A dict respresentation of the JSON returned from the API. | juraj-google-style |
def __init__(self, channel):
self.AuthEnable = channel.unary_unary(
'/etcdserverpb.Auth/AuthEnable',
request_serializer=rpc__pb2.AuthEnableRequest.SerializeToString,
response_deserializer=rpc__pb2.AuthEnableResponse.FromString,
)
self.AuthDisable = channel.unary_unary(
... | Constructor.
Args:
channel: A grpc.Channel. | juraj-google-style |
def make_train_function(self):
if self.train_function is not None:
return self.train_function
def step_function(model, iterator):
def run_step(data):
outputs = model.train_step(data)
with ops.control_dependencies(_minimum_control_deps(outputs)):
... | Creates a function that executes one step of training.
This method can be overridden to support custom training logic.
This method is called by `Model.fit` and `Model.train_on_batch`.
Typically, this method directly controls `tf.function` and
`tf.distribute.Strategy` settings, and delegates the actual training
logic ... | github-repos |
def sendResponse(self, message, UUID, routing_key):
self.sendMessage(
exchange=self.output_exchange,
routing_key=routing_key,
message=message,
UUID=UUID
) | Send `message` to ``self.output_exchange`` with routing key
``self.output_key``, ``self.content_type`` in ``delivery_mode=2``.
Args:
message (str): message which will be sent
UUID: unique identification of message
routing_key (str): which routing key to use to send message back | juraj-google-style |
def start_trial(self, trial, checkpoint=None):
self._commit_resources(trial.resources)
try:
self._start_trial(trial, checkpoint)
except Exception as e:
logger.exception('Error starting runner for Trial %s', str(trial))
error_msg = traceback.format_exc()
time.sleep(2)
... | Starts the trial.
Will not return resources if trial repeatedly fails on start.
Args:
trial (Trial): Trial to be started.
checkpoint (Checkpoint): A Python object or path storing the state
of trial. | codesearchnet |
def _select_forward_and_backward_functions(self, args, possible_gradient_type, executing_eagerly):
if executing_eagerly:
input_tangents = forwardprop_util.pack_tangents(args)
else:
input_tangents = forwardprop_util.TangentInfo()
need_gradients_for_jvps = record.should_record_backprop(input_t... | Selects forward and backward functions based on the calling context.
The forward function computes the "real" function outputs, `self._outputs`,
and any extra values needed by the corresponding backward function.
Args:
args: A flat list of Tensors with all of the inputs to the forward
function (including user-specifi... | github-repos |
def MessageToRepr(msg, multiline=False, **kwargs):
indent = kwargs.get('indent', 0)
def IndentKwargs(kwargs):
kwargs = dict(kwargs)
kwargs['indent'] = (kwargs.get('indent', 0) + 4)
return kwargs
if isinstance(msg, list):
s = '['
for item in msg:
if multil... | Return a repr-style string for a protorpc message.
protorpc.Message.__repr__ does not return anything that could be considered
python code. Adding this function lets us print a protorpc message in such
a way that it could be pasted into code later, and used to compare against
other things.
Args:
msg: protorpc.Message... | codesearchnet |
def _make_spec_file(self):
if issubclass(BdistRPMCommand, object):
spec_file = super(BdistRPMCommand, self)._make_spec_file()
else:
spec_file = bdist_rpm._make_spec_file(self)
if (sys.version_info[0] < 3):
python_package = 'python'
else:
python_package = 'python3'
des... | Generates the text of an RPM spec file.
Returns:
A list of strings containing the lines of text. | codesearchnet |
def observation_spec(self):
obs_spec = named_array.NamedDict({'action_result': (0,), 'alerts': (0,), 'available_actions': (0,), 'build_queue': (0, len(UnitLayer)), 'cargo': (0, len(UnitLayer)), 'cargo_slots_available': (1,), 'control_groups': (10, 2), 'game_loop': (1,), 'last_actions': (0,), 'multi_select': (0, len... | The observation spec for the SC2 environment.
It's worth noting that the image-like observations are in y,x/row,column
order which is different than the actions which are in x,y order. This is
due to conflicting conventions, and to facilitate printing of the images.
Returns:
The dict of observation names to their ten... | codesearchnet |
def plot_probabilities_histogram(Y_p, title=None):
if Y_p.ndim > 1:
msg = (
f"Arg Y_p should be a 1-dimensional np.ndarray, not of shape "
f"{Y_p.shape}."
)
raise ValueError(msg)
plt.hist(Y_p, bins=20)
plt.xlim((0, 1.025))
plt.xlabel("Probability")
... | Plot a histogram from a numpy array of probabilities
Args:
Y_p: An [n] or [n, 1] np.ndarray of probabilities (floats in [0,1]) | juraj-google-style |
def get_accounts_for_service(cls, service_type):
return [
a for a in cls.get_accounts().values()
if a.service_type == service_type
] | Get a list of accounts for a given music service.
Args:
service_type (str): The service_type to use.
Returns:
list: A list of `Account` instances. | juraj-google-style |
def discovery(self, logfile=None, tracefile=None):
self._enable_logging(logfile=logfile, tracefile=tracefile)
self.log("'discovery' method is deprecated. Please 'connect' with force_discovery=True.")
self.log("Device discovery process started")
self.connect(logfile=logfile, for... | Discover the device details.
This method discover several device attributes.
Args:
logfile (file): Optional file descriptor for session logging. The file must be open for write.
The session is logged only if ``log_session=True`` was passed to the constructor.
It the parameter is not passed then the default *session.l... | juraj-google-style |
def stage_tc_create_tag(self, tag, resource):
tag_resource = resource.tags(self.tcex.safetag(tag))
tag_resource.http_method = 'POST'
t_response = tag_resource.request()
if (t_response.get('status') != 'Success'):
self.log.warning('[tcex] Failed adding tag "{}" ({}).'.format(tag, t_response.get('... | Add a tag to a resource.
Args:
tag (str): The tag to be added to the resource.
resource (obj): An instance of tcex resource class. | codesearchnet |
def get_legacy_output_classes(dataset_or_iterator):
return nest.map_structure(lambda component_spec: component_spec._to_legacy_output_classes(), get_structure(dataset_or_iterator)) | Returns the output classes for elements of the input dataset / iterator.
Args:
dataset_or_iterator: A `tf.data.Dataset` or `tf.data.Iterator`.
Returns:
A (nested) structure of Python `type` objects matching the structure of the
dataset / iterator elements and specifying the class of the individual
components.
@compa... | github-repos |
def register_binary_elementwise_assert_api(func):
_BINARY_ELEMENTWISE_ASSERT_APIS.append(func)
for args, handler in _ELEMENTWISE_API_HANDLERS.items():
if len(args) == 3 and args[2] is _ASSERT_API_TAG:
_add_dispatch_for_binary_elementwise_api(func, args[0], args[1], handler)
return func | Decorator that registers a TensorFlow op as a binary elementwise assert API.
Different from `dispatch_for_binary_elementwise_apis`, this decorator is used
for assert apis, such as assert_equal, assert_none_equal, etc, which return
None in eager mode and an op in graph mode.
Args:
func: The function that implements th... | github-repos |
def global_env_valid(env):
if env not in EFConfig.ACCOUNT_SCOPED_ENVS:
raise ValueError("Invalid global env: {}; global envs are: {}".format(env, EFConfig.ACCOUNT_SCOPED_ENVS))
return True | Given an env, determine if it's a valid "global" or "mgmt" env as listed in EFConfig
Args:
env: the env to check
Returns:
True if the env is a valid global env in EFConfig
Raises:
ValueError with message if the env is not valid | juraj-google-style |
def _bit_list_to_bytes(bit_list):
num_bits = len(bit_list)
byte_vals = bytearray()
for start in six.moves.xrange(0, num_bits, 8):
curr_bits = bit_list[start:start + 8]
char_val = sum(
val * digit for val, digit in six.moves.zip(_POW2, curr_bits))
byte_vals.append(cha... | Converts an iterable of 1s and 0s to bytes.
Combines the list 8 at a time, treating each group of 8 bits
as a single byte.
Args:
bit_list (Sequence): Sequence of 1s and 0s.
Returns:
bytes: The decoded bytes. | juraj-google-style |
def get_energy_buckingham(structure, gulp_cmd='gulp', keywords=('optimise', 'conp', 'qok'), valence_dict=None):
gio = GulpIO()
gc = GulpCaller(gulp_cmd)
gin = gio.buckingham_input(structure, keywords, valence_dict=valence_dict)
gout = gc.run(gin)
return gio.get_energy(gout) | Compute the energy of a structure using Buckingham potential.
Args:
structure: pymatgen.core.structure.Structure
gulp_cmd: GULP command if not in standard place
keywords: GULP first line keywords
valence_dict: {El: valence}. Needed if the structure is not charge
neutral. | codesearchnet |
def get_dataset(self, dsid, dsinfo):
data = self[dsinfo.get('file_key', dsid.name)]
data.attrs.update(dsinfo)
data.attrs["platform_name"] = self['/attr/satellite_name']
data.attrs["sensor"] = self['/attr/instrument_name']
return data | Get dataset function
Args:
dsid: Dataset ID
param2: Dataset Information
Returns:
Dask DataArray: Data | juraj-google-style |
def is_evenly_distributed_thresholds(thresholds):
num_thresholds = len(thresholds)
if num_thresholds < 3:
return False
even_thresholds = np.arange(num_thresholds, dtype=np.float32) / (num_thresholds - 1)
return np.allclose(thresholds, even_thresholds, atol=backend.epsilon()) | Check if the thresholds list is evenly distributed.
We could leverage evenly distributed thresholds to use less memory when
calculate metrcis like AUC where each individual threshold need to be
evaluated.
Args:
thresholds: A python list or tuple, or 1D numpy array whose value is ranged
in [0, 1].
Returns:
boolean, w... | github-repos |
def __init__(self, config, start=True):
if config.dispatcher_address is None:
raise ValueError('Must specify a `dispatcher_address` in the `config` passed to `WorkerServer`.')
if isinstance(config, service_config_pb2.WorkerConfig):
config_proto = config
else:
config_proto = service_c... | Creates a new worker server.
Args:
config: A `tf.data.experimental.service.WorkerConfig` configuration.
start: (Optional.) Boolean, indicating whether to start the server after
creating it. Defaults to True. | github-repos |
def from_symmop(cls, symmop, time_reversal):
magsymmop = cls(symmop.affine_matrix, time_reversal, symmop.tol)
return magsymmop | Initialize a MagSymmOp from a SymmOp and time reversal operator.
Args:
symmop (SymmOp): SymmOp
time_reversal (int): Time reversal operator, +1 or -1.
Returns:
MagSymmOp object | juraj-google-style |
def _ring_2d(m, n):
if m == 1:
return [(0, i) for i in range(n)]
if n == 1:
return [(i, 0) for i in range(m)]
if m % 2 != 0:
tf.logging.warning("Odd dimension")
return [(i % m, i
ret = [(0, 0)]
for i in range(m
for j in range(1, n):
ret.append((2 * i, j))
for j in range(n-1,... | Ring-order of a mxn mesh.
Args:
m: an integer
n: an integer
Returns:
a list of mxn pairs | juraj-google-style |
def _check_for_fail_message(self, transport, exc_info, timeout):
try:
transport.read_message(timeout)
except usb_exceptions.CommonUsbError:
if (sys.exc_info()[0] is usb_exceptions.AdbRemoteError):
raise
raise_with_traceback(exc_info[0](exc_info[1]), traceback=exc_info[2]) | Check for a 'FAIL' message from transport.
This method always raises, if 'FAIL' was read, it will raise an
AdbRemoteError with the message, otherwise it will raise based on
exc_info, which should be a tuple as per sys.exc_info().
Args:
transport: Transport from which to read for a 'FAIL' message.
exc_info: Exception ... | codesearchnet |
def create_dummy_class(klass, dependency):
assert not building_rtfd()
class _DummyMetaClass(type):
def __getattr__(_, __):
raise AttributeError("Cannot import '{}', therefore '{}' is not available".format(dependency, klass))
@six.add_metaclass(_DummyMetaClass)
class _... | When a dependency of a class is not available, create a dummy class which throws ImportError when used.
Args:
klass (str): name of the class.
dependency (str): name of the dependency.
Returns:
class: a class object | juraj-google-style |
def _filter_out_metaclasses(bases, ctx):
non_meta = []
meta = None
for base in bases:
with_metaclass = False
for b in base.data:
if isinstance(b, metaclass.WithMetaclassInstance):
with_metaclass = True
if not meta:
meta = b.cls.... | Process the temporary classes created by six.with_metaclass.
six.with_metaclass constructs an anonymous class holding a metaclass and a
list of base classes; if we find instances in `bases`, store the first
metaclass we find and remove all metaclasses from `bases`.
Args:
bases: The list of base classes for the class ... | github-repos |
def get_min_muO2(self, min_voltage=None, max_voltage=None):
data = []
for pair in self._select_in_voltage_range(min_voltage, max_voltage):
if pair.muO2_discharge is not None:
data.extend([d['chempot'] for d in pair.muO2_discharge])
if pair.muO2_charge is ... | Minimum critical oxygen chemical potential along path.
Args:
min_voltage: The minimum allowable voltage for a given step
max_voltage: The maximum allowable voltage allowable for a given
step
Returns:
Minimum critical oxygen chemical of all compounds along the
insertion path (a subset of the path can be chosen by the ... | juraj-google-style |
def grad_dot(dy, x1, x2):
if (len(numpy.shape(x1)) == 1):
dy = numpy.atleast_2d(dy)
elif (len(numpy.shape(x2)) == 1):
dy = numpy.transpose(numpy.atleast_2d(dy))
x2 = numpy.transpose(numpy.atleast_2d(x2))
x2_t = numpy.transpose(numpy.atleast_2d(numpy.sum(x2, axis=tuple(numpy.arange((n... | Gradient of NumPy dot product w.r.t. to the left hand side.
Args:
dy: The gradient with respect to the output.
x1: The left hand side of the `numpy.dot` function.
x2: The right hand side
Returns:
The gradient with respect to `x1` i.e. `x2.dot(dy.T)` with all the
broadcasting involved. | codesearchnet |
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