id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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32,100 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.ndims | def ndims(self):
"""Returns the rank of this shape, or None if it is unspecified."""
if self._dims is None:
return None
else:
if self._ndims is None:
self._ndims = len(self._dims)
return self._ndims | python | def ndims(self):
"""Returns the rank of this shape, or None if it is unspecified."""
if self._dims is None:
return None
else:
if self._ndims is None:
self._ndims = len(self._dims)
return self._ndims | [
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32,101 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.num_elements | def num_elements(self):
"""Returns the total number of elements, or none for incomplete shapes."""
if self.is_fully_defined():
size = 1
for dim in self._dims:
size *= dim.value
return size
else:
return None | python | def num_elements(self):
"""Returns the total number of elements, or none for incomplete shapes."""
if self.is_fully_defined():
size = 1
for dim in self._dims:
size *= dim.value
return size
else:
return None | [
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32,102 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.merge_with | def merge_with(self, other):
"""Returns a `TensorShape` combining the information in `self` and `other`.
The dimensions in `self` and `other` are merged elementwise,
according to the rules defined for `Dimension.merge_with()`.
Args:
other: Another `TensorShape`.
Retu... | python | def merge_with(self, other):
"""Returns a `TensorShape` combining the information in `self` and `other`.
The dimensions in `self` and `other` are merged elementwise,
according to the rules defined for `Dimension.merge_with()`.
Args:
other: Another `TensorShape`.
Retu... | [
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32,103 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.concatenate | def concatenate(self, other):
"""Returns the concatenation of the dimension in `self` and `other`.
*N.B.* If either `self` or `other` is completely unknown,
concatenation will discard information about the other shape. In
future, we might support concatenation that preserves this
... | python | def concatenate(self, other):
"""Returns the concatenation of the dimension in `self` and `other`.
*N.B.* If either `self` or `other` is completely unknown,
concatenation will discard information about the other shape. In
future, we might support concatenation that preserves this
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32,104 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.assert_same_rank | def assert_same_rank(self, other):
"""Raises an exception if `self` and `other` do not have convertible ranks.
Args:
other: Another `TensorShape`.
Raises:
ValueError: If `self` and `other` do not represent shapes with the
same rank.
"""
other = a... | python | def assert_same_rank(self, other):
"""Raises an exception if `self` and `other` do not have convertible ranks.
Args:
other: Another `TensorShape`.
Raises:
ValueError: If `self` and `other` do not represent shapes with the
same rank.
"""
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32,105 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.with_rank | def with_rank(self, rank):
"""Returns a shape based on `self` with the given rank.
This method promotes a completely unknown shape to one with a
known rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with the given rank.... | python | def with_rank(self, rank):
"""Returns a shape based on `self` with the given rank.
This method promotes a completely unknown shape to one with a
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Args:
rank: An integer.
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32,106 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.with_rank_at_least | def with_rank_at_least(self, rank):
"""Returns a shape based on `self` with at least the given rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with at least the given
rank.
Raises:
ValueError: If `self` does... | python | def with_rank_at_least(self, rank):
"""Returns a shape based on `self` with at least the given rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with at least the given
rank.
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32,107 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.with_rank_at_most | def with_rank_at_most(self, rank):
"""Returns a shape based on `self` with at most the given rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with at most the given
rank.
Raises:
ValueError: If `self` does no... | python | def with_rank_at_most(self, rank):
"""Returns a shape based on `self` with at most the given rank.
Args:
rank: An integer.
Returns:
A shape that is at least as specific as `self` with at most the given
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32,108 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.is_convertible_with | def is_convertible_with(self, other):
"""Returns True iff `self` is convertible with `other`.
Two possibly-partially-defined shapes are convertible if there
exists a fully-defined shape that both shapes can represent. Thus,
convertibility allows the shape inference code to reason about
... | python | def is_convertible_with(self, other):
"""Returns True iff `self` is convertible with `other`.
Two possibly-partially-defined shapes are convertible if there
exists a fully-defined shape that both shapes can represent. Thus,
convertibility allows the shape inference code to reason about
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32,109 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.most_specific_convertible_shape | def most_specific_convertible_shape(self, other):
"""Returns the most specific TensorShape convertible with `self` and `other`.
* TensorShape([None, 1]) is the most specific TensorShape convertible with
both TensorShape([2, 1]) and TensorShape([5, 1]). Note that
TensorShape(None) is... | python | def most_specific_convertible_shape(self, other):
"""Returns the most specific TensorShape convertible with `self` and `other`.
* TensorShape([None, 1]) is the most specific TensorShape convertible with
both TensorShape([2, 1]) and TensorShape([5, 1]). Note that
TensorShape(None) is... | [
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32,110 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.is_fully_defined | def is_fully_defined(self):
"""Returns True iff `self` is fully defined in every dimension."""
return self._dims is not None and all(
dim.value is not None for dim in self._dims
) | python | def is_fully_defined(self):
"""Returns True iff `self` is fully defined in every dimension."""
return self._dims is not None and all(
dim.value is not None for dim in self._dims
) | [
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32,111 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.as_list | def as_list(self):
"""Returns a list of integers or `None` for each dimension.
Returns:
A list of integers or `None` for each dimension.
Raises:
ValueError: If `self` is an unknown shape with an unknown rank.
"""
if self._dims is None:
raise Valu... | python | def as_list(self):
"""Returns a list of integers or `None` for each dimension.
Returns:
A list of integers or `None` for each dimension.
Raises:
ValueError: If `self` is an unknown shape with an unknown rank.
"""
if self._dims is None:
raise Valu... | [
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32,112 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/tensor_shape.py | TensorShape.as_proto | def as_proto(self):
"""Returns this shape as a `TensorShapeProto`."""
if self._dims is None:
return tensor_shape_pb2.TensorShapeProto(unknown_rank=True)
else:
return tensor_shape_pb2.TensorShapeProto(
dim=[
tensor_shape_pb2.TensorShapeP... | python | def as_proto(self):
"""Returns this shape as a `TensorShapeProto`."""
if self._dims is None:
return tensor_shape_pb2.TensorShapeProto(unknown_rank=True)
else:
return tensor_shape_pb2.TensorShapeProto(
dim=[
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32,113 | tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/common_utils.py | convert_predict_response | def convert_predict_response(pred, serving_bundle):
"""Converts a PredictResponse to ClassificationResponse or RegressionResponse.
Args:
pred: PredictResponse to convert.
serving_bundle: A `ServingBundle` object that contains the information about
the serving request that the response was generated b... | python | def convert_predict_response(pred, serving_bundle):
"""Converts a PredictResponse to ClassificationResponse or RegressionResponse.
Args:
pred: PredictResponse to convert.
serving_bundle: A `ServingBundle` object that contains the information about
the serving request that the response was generated b... | [
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32,114 | tensorflow/tensorboard | tensorboard/plugins/interactive_inference/utils/common_utils.py | convert_prediction_values | def convert_prediction_values(values, serving_bundle, model_spec=None):
"""Converts tensor values into ClassificationResponse or RegressionResponse.
Args:
values: For classification, a 2D list of numbers. The first dimension is for
each example being predicted. The second dimension are the probabilities
... | python | def convert_prediction_values(values, serving_bundle, model_spec=None):
"""Converts tensor values into ClassificationResponse or RegressionResponse.
Args:
values: For classification, a 2D list of numbers. The first dimension is for
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32,115 | tensorflow/tensorboard | tensorboard/plugins/graph/keras_util.py | _update_dicts | def _update_dicts(name_scope,
model_layer,
input_to_in_layer,
model_name_to_output,
prev_node_name):
"""Updates input_to_in_layer, model_name_to_output, and prev_node_name
based on the model_layer.
Args:
name_scope: a string representing... | python | def _update_dicts(name_scope,
model_layer,
input_to_in_layer,
model_name_to_output,
prev_node_name):
"""Updates input_to_in_layer, model_name_to_output, and prev_node_name
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32,116 | tensorflow/tensorboard | tensorboard/plugins/graph/keras_util.py | keras_model_to_graph_def | def keras_model_to_graph_def(keras_layer):
"""Returns a GraphDef representation of the Keras model in a dict form.
Note that it only supports models that implemented to_json().
Args:
keras_layer: A dict from Keras model.to_json().
Returns:
A GraphDef representation of the layers in the model.
"""
... | python | def keras_model_to_graph_def(keras_layer):
"""Returns a GraphDef representation of the Keras model in a dict form.
Note that it only supports models that implemented to_json().
Args:
keras_layer: A dict from Keras model.to_json().
Returns:
A GraphDef representation of the layers in the model.
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32,117 | tensorflow/tensorboard | tensorboard/plugins/hparams/hparams_plugin.py | HParamsPlugin.is_active | def is_active(self):
"""Returns True if the hparams plugin is active.
The hparams plugin is active iff there is a tag with
the hparams plugin name as its plugin name and the scalars plugin is
registered and active.
"""
if not self._context.multiplexer:
return False
scalars_plugin = se... | python | def is_active(self):
"""Returns True if the hparams plugin is active.
The hparams plugin is active iff there is a tag with
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registered and active.
"""
if not self._context.multiplexer:
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32,118 | tensorflow/tensorboard | tensorboard/plugin_util.py | markdown_to_safe_html | def markdown_to_safe_html(markdown_string):
"""Convert Markdown to HTML that's safe to splice into the DOM.
Arguments:
markdown_string: A Unicode string or UTF-8--encoded bytestring
containing Markdown source. Markdown tables are supported.
Returns:
A string containing safe HTML.
"""
warning =... | python | def markdown_to_safe_html(markdown_string):
"""Convert Markdown to HTML that's safe to splice into the DOM.
Arguments:
markdown_string: A Unicode string or UTF-8--encoded bytestring
containing Markdown source. Markdown tables are supported.
Returns:
A string containing safe HTML.
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32,119 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/dtypes.py | as_dtype | def as_dtype(type_value):
"""Converts the given `type_value` to a `DType`.
Args:
type_value: A value that can be converted to a `tf.DType` object. This may
currently be a `tf.DType` object, a [`DataType`
enum](https://www.tensorflow.org/code/tensorflow/core/framework/types.proto),
... | python | def as_dtype(type_value):
"""Converts the given `type_value` to a `DType`.
Args:
type_value: A value that can be converted to a `tf.DType` object. This may
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32,120 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/dtypes.py | DType.real_dtype | def real_dtype(self):
"""Returns the dtype correspond to this dtype's real part."""
base = self.base_dtype
if base == complex64:
return float32
elif base == complex128:
return float64
else:
return self | python | def real_dtype(self):
"""Returns the dtype correspond to this dtype's real part."""
base = self.base_dtype
if base == complex64:
return float32
elif base == complex128:
return float64
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32,121 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/dtypes.py | DType.min | def min(self):
"""Returns the minimum representable value in this data type.
Raises:
TypeError: if this is a non-numeric, unordered, or quantized type.
"""
if self.is_quantized or self.base_dtype in (
bool,
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co... | python | def min(self):
"""Returns the minimum representable value in this data type.
Raises:
TypeError: if this is a non-numeric, unordered, or quantized type.
"""
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32,122 | tensorflow/tensorboard | tensorboard/compat/tensorflow_stub/dtypes.py | DType.is_compatible_with | def is_compatible_with(self, other):
"""Returns True if the `other` DType will be converted to this DType.
The conversion rules are as follows:
```python
DType(T) .is_compatible_with(DType(T)) == True
DType(T) .is_compatible_with(DType(T).as_ref) == True
... | python | def is_compatible_with(self, other):
"""Returns True if the `other` DType will be converted to this DType.
The conversion rules are as follows:
```python
DType(T) .is_compatible_with(DType(T)) == True
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32,123 | tensorflow/tensorboard | tensorboard/plugins/debugger/interactive_debugger_plugin.py | InteractiveDebuggerPlugin.get_plugin_apps | def get_plugin_apps(self):
"""Obtains a mapping between routes and handlers.
This function also starts a debugger data server on separate thread if the
plugin has not started one yet.
Returns:
A mapping between routes and handlers (functions that respond to
requests).
"""
return {
... | python | def get_plugin_apps(self):
"""Obtains a mapping between routes and handlers.
This function also starts a debugger data server on separate thread if the
plugin has not started one yet.
Returns:
A mapping between routes and handlers (functions that respond to
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32,124 | tensorflow/tensorboard | tensorboard/plugins/audio/audio_plugin.py | AudioPlugin.is_active | def is_active(self):
"""The audio plugin is active iff any run has at least one relevant tag."""
if not self._multiplexer:
return False
return bool(self._multiplexer.PluginRunToTagToContent(metadata.PLUGIN_NAME)) | python | def is_active(self):
"""The audio plugin is active iff any run has at least one relevant tag."""
if not self._multiplexer:
return False
return bool(self._multiplexer.PluginRunToTagToContent(metadata.PLUGIN_NAME)) | [
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32,125 | tensorflow/tensorboard | tensorboard/plugins/audio/audio_plugin.py | AudioPlugin._index_impl | def _index_impl(self):
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{
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"tagName1": {
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"""Return information about the tags in each run.
Result is a dictionary of the form
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32,126 | tensorflow/tensorboard | tensorboard/plugins/audio/audio_plugin.py | AudioPlugin._serve_audio_metadata | def _serve_audio_metadata(self, request):
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Note that the actual audio data are not sent; instead, we respond
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... | python | def _serve_audio_metadata(self, request):
"""Given a tag and list of runs, serve a list of metadata for audio.
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32,127 | tensorflow/tensorboard | tensorboard/plugins/audio/audio_plugin.py | AudioPlugin._audio_response_for_run | def _audio_response_for_run(self, tensor_events, run, tag, sample):
"""Builds a JSON-serializable object with information about audio.
Args:
tensor_events: A list of image event_accumulator.TensorEvent objects.
run: The name of the run.
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32,128 | tensorflow/tensorboard | tensorboard/plugins/audio/audio_plugin.py | AudioPlugin._query_for_individual_audio | def _query_for_individual_audio(self, run, tag, sample, index):
"""Builds a URL for accessing the specified audio.
This should be kept in sync with _serve_audio_metadata. Note that the URL is
*not* guaranteed to always return the same audio, since audio may be
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32,129 | tensorflow/tensorboard | tensorboard/plugins/audio/audio_plugin.py | AudioPlugin._serve_individual_audio | def _serve_individual_audio(self, request):
"""Serve encoded audio data."""
tag = request.args.get('tag')
run = request.args.get('run')
index = int(request.args.get('index'))
sample = int(request.args.get('sample', 0))
events = self._filter_by_sample(self._multiplexer.Tensors(run, tag), sample)
... | python | def _serve_individual_audio(self, request):
"""Serve encoded audio data."""
tag = request.args.get('tag')
run = request.args.get('run')
index = int(request.args.get('index'))
sample = int(request.args.get('sample', 0))
events = self._filter_by_sample(self._multiplexer.Tensors(run, tag), sample)
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32,130 | tensorflow/tensorboard | tensorboard/plugins/image/summary.py | op | def op(name,
images,
max_outputs=3,
display_name=None,
description=None,
collections=None):
"""Create a legacy image summary op for use in a TensorFlow graph.
Arguments:
name: A unique name for the generated summary node.
images: A `Tensor` representing pixel data with sh... | python | def op(name,
images,
max_outputs=3,
display_name=None,
description=None,
collections=None):
"""Create a legacy image summary op for use in a TensorFlow graph.
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name: A unique name for the generated summary node.
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32,131 | tensorflow/tensorboard | tensorboard/plugins/image/summary.py | pb | def pb(name, images, max_outputs=3, display_name=None, description=None):
"""Create a legacy image summary protobuf.
This behaves as if you were to create an `op` with the same arguments
(wrapped with constant tensors where appropriate) and then execute
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Arguments:
... | python | def pb(name, images, max_outputs=3, display_name=None, description=None):
"""Create a legacy image summary protobuf.
This behaves as if you were to create an `op` with the same arguments
(wrapped with constant tensors where appropriate) and then execute
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Arguments:
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32,132 | tensorflow/tensorboard | tensorboard/backend/application.py | tensor_size_guidance_from_flags | def tensor_size_guidance_from_flags(flags):
"""Apply user per-summary size guidance overrides."""
tensor_size_guidance = dict(DEFAULT_TENSOR_SIZE_GUIDANCE)
if not flags or not flags.samples_per_plugin:
return tensor_size_guidance
for token in flags.samples_per_plugin.split(','):
k, v = token.strip().s... | python | def tensor_size_guidance_from_flags(flags):
"""Apply user per-summary size guidance overrides."""
tensor_size_guidance = dict(DEFAULT_TENSOR_SIZE_GUIDANCE)
if not flags or not flags.samples_per_plugin:
return tensor_size_guidance
for token in flags.samples_per_plugin.split(','):
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32,133 | tensorflow/tensorboard | tensorboard/backend/application.py | standard_tensorboard_wsgi | def standard_tensorboard_wsgi(flags, plugin_loaders, assets_zip_provider):
"""Construct a TensorBoardWSGIApp with standard plugins and multiplexer.
Args:
flags: An argparse.Namespace containing TensorBoard CLI flags.
plugin_loaders: A list of TBLoader instances.
assets_zip_provider: See TBContext docum... | python | def standard_tensorboard_wsgi(flags, plugin_loaders, assets_zip_provider):
"""Construct a TensorBoardWSGIApp with standard plugins and multiplexer.
Args:
flags: An argparse.Namespace containing TensorBoard CLI flags.
plugin_loaders: A list of TBLoader instances.
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32,134 | tensorflow/tensorboard | tensorboard/backend/application.py | TensorBoardWSGIApp | def TensorBoardWSGIApp(logdir, plugins, multiplexer, reload_interval,
path_prefix='', reload_task='auto'):
"""Constructs the TensorBoard application.
Args:
logdir: the logdir spec that describes where data will be loaded.
may be a directory, or comma,separated list of directories, ... | python | def TensorBoardWSGIApp(logdir, plugins, multiplexer, reload_interval,
path_prefix='', reload_task='auto'):
"""Constructs the TensorBoard application.
Args:
logdir: the logdir spec that describes where data will be loaded.
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32,135 | tensorflow/tensorboard | tensorboard/backend/application.py | parse_event_files_spec | def parse_event_files_spec(logdir):
"""Parses `logdir` into a map from paths to run group names.
The events files flag format is a comma-separated list of path specifications.
A path specification either looks like 'group_name:/path/to/directory' or
'/path/to/directory'; in the latter case, the group is unname... | python | def parse_event_files_spec(logdir):
"""Parses `logdir` into a map from paths to run group names.
The events files flag format is a comma-separated list of path specifications.
A path specification either looks like 'group_name:/path/to/directory' or
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32,136 | tensorflow/tensorboard | tensorboard/backend/application.py | start_reloading_multiplexer | def start_reloading_multiplexer(multiplexer, path_to_run, load_interval,
reload_task):
"""Starts automatically reloading the given multiplexer.
If `load_interval` is positive, the thread will reload the multiplexer
by calling `ReloadMultiplexer` every `load_interval` seconds, star... | python | def start_reloading_multiplexer(multiplexer, path_to_run, load_interval,
reload_task):
"""Starts automatically reloading the given multiplexer.
If `load_interval` is positive, the thread will reload the multiplexer
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32,137 | tensorflow/tensorboard | tensorboard/backend/application.py | get_database_info | def get_database_info(db_uri):
"""Returns TBContext fields relating to SQL database.
Args:
db_uri: A string URI expressing the DB file, e.g. "sqlite:~/tb.db".
Returns:
A tuple with the db_module and db_connection_provider TBContext fields. If
db_uri was empty, then (None, None) is returned.
Raise... | python | def get_database_info(db_uri):
"""Returns TBContext fields relating to SQL database.
Args:
db_uri: A string URI expressing the DB file, e.g. "sqlite:~/tb.db".
Returns:
A tuple with the db_module and db_connection_provider TBContext fields. If
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32,138 | tensorflow/tensorboard | tensorboard/backend/application.py | create_sqlite_connection_provider | def create_sqlite_connection_provider(db_uri):
"""Returns function that returns SQLite Connection objects.
Args:
db_uri: A string URI expressing the DB file, e.g. "sqlite:~/tb.db".
Returns:
A function that returns a new PEP-249 DB Connection, which must be closed,
each time it is called.
Raises:
... | python | def create_sqlite_connection_provider(db_uri):
"""Returns function that returns SQLite Connection objects.
Args:
db_uri: A string URI expressing the DB file, e.g. "sqlite:~/tb.db".
Returns:
A function that returns a new PEP-249 DB Connection, which must be closed,
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32,139 | tensorflow/tensorboard | tensorboard/backend/application.py | TensorBoardWSGI._serve_plugins_listing | def _serve_plugins_listing(self, request):
"""Serves an object mapping plugin name to whether it is enabled.
Args:
request: The werkzeug.Request object.
Returns:
A werkzeug.Response object.
"""
response = {}
for plugin in self._plugins:
start = time.time()
response[plug... | python | def _serve_plugins_listing(self, request):
"""Serves an object mapping plugin name to whether it is enabled.
Args:
request: The werkzeug.Request object.
Returns:
A werkzeug.Response object.
"""
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32,140 | tensorflow/tensorboard | tensorboard/plugins/debugger/tensor_helper.py | parse_time_indices | def parse_time_indices(s):
"""Parse a string as time indices.
Args:
s: A valid slicing string for time indices. E.g., '-1', '[:]', ':', '2:10'
Returns:
A slice object.
Raises:
ValueError: If `s` does not represent valid time indices.
"""
if not s.startswith('['):
s = '[' + s + ']'
parse... | python | def parse_time_indices(s):
"""Parse a string as time indices.
Args:
s: A valid slicing string for time indices. E.g., '-1', '[:]', ':', '2:10'
Returns:
A slice object.
Raises:
ValueError: If `s` does not represent valid time indices.
"""
if not s.startswith('['):
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32,141 | tensorflow/tensorboard | tensorboard/plugins/debugger/tensor_helper.py | process_buffers_for_display | def process_buffers_for_display(s, limit=40):
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32,142 | tensorflow/tensorboard | tensorboard/plugins/debugger/tensor_helper.py | array_view | def array_view(array, slicing=None, mapping=None):
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Args:
array: The input array, as an numpy.ndarray.
slicing: Optional slicing string, e.g., "[:, 1:3, :]".
mapping: Optional mapping string. Supported mappings:
`None` or case-insensitive `'None'`: Un... | python | def array_view(array, slicing=None, mapping=None):
"""View a slice or the entirety of an ndarray.
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array: The input array, as an numpy.ndarray.
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32,143 | tensorflow/tensorboard | tensorboard/plugins/debugger/tensor_helper.py | array_to_base64_png | def array_to_base64_png(array):
"""Convert an array into base64-enoded PNG image.
Args:
array: A 2D np.ndarray or nested list of items.
Returns:
A base64-encoded string the image. The image is grayscale if the array is
2D. The image is RGB color if the image is 3D with lsat dimension equal to
3.... | python | def array_to_base64_png(array):
"""Convert an array into base64-enoded PNG image.
Args:
array: A 2D np.ndarray or nested list of items.
Returns:
A base64-encoded string the image. The image is grayscale if the array is
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32,144 | tensorflow/tensorboard | tensorboard/plugins/graph/graph_util.py | _safe_copy_proto_list_values | def _safe_copy_proto_list_values(dst_proto_list, src_proto_list, get_key):
"""Safely merge values from `src_proto_list` into `dst_proto_list`.
Each element in `dst_proto_list` must be mapped by `get_key` to a key
value that is unique within that list; likewise for `src_proto_list`.
If an element of `src_proto_... | python | def _safe_copy_proto_list_values(dst_proto_list, src_proto_list, get_key):
"""Safely merge values from `src_proto_list` into `dst_proto_list`.
Each element in `dst_proto_list` must be mapped by `get_key` to a key
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32,145 | tensorflow/tensorboard | tensorboard/plugins/graph/graph_util.py | combine_graph_defs | def combine_graph_defs(to_proto, from_proto):
"""Combines two GraphDefs by adding nodes from from_proto into to_proto.
All GraphDefs are expected to be of TensorBoard's.
It assumes node names are unique across GraphDefs if contents differ. The
names can be the same if the NodeDef content are exactly the same.
... | python | def combine_graph_defs(to_proto, from_proto):
"""Combines two GraphDefs by adding nodes from from_proto into to_proto.
All GraphDefs are expected to be of TensorBoard's.
It assumes node names are unique across GraphDefs if contents differ. The
names can be the same if the NodeDef content are exactly the same.
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32,146 | tensorflow/tensorboard | tensorboard/plugins/scalar/summary_v2.py | scalar | def scalar(name, data, step=None, description=None):
"""Write a scalar summary.
Arguments:
name: A name for this summary. The summary tag used for TensorBoard will
be this name prefixed by any active name scopes.
data: A real numeric scalar value, convertible to a `float32` Tensor.
step: Explicit... | python | def scalar(name, data, step=None, description=None):
"""Write a scalar summary.
Arguments:
name: A name for this summary. The summary tag used for TensorBoard will
be this name prefixed by any active name scopes.
data: A real numeric scalar value, convertible to a `float32` Tensor.
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32,147 | tensorflow/tensorboard | tensorboard/plugins/scalar/summary_v2.py | scalar_pb | def scalar_pb(tag, data, description=None):
"""Create a scalar summary_pb2.Summary protobuf.
Arguments:
tag: String tag for the summary.
data: A 0-dimensional `np.array` or a compatible python number type.
description: Optional long-form description for this summary, as a
`str`. Markdown is suppo... | python | def scalar_pb(tag, data, description=None):
"""Create a scalar summary_pb2.Summary protobuf.
Arguments:
tag: String tag for the summary.
data: A 0-dimensional `np.array` or a compatible python number type.
description: Optional long-form description for this summary, as a
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32,148 | tensorflow/tensorboard | tensorboard/plugins/profile/profile_demo.py | dump_data | def dump_data(logdir):
"""Dumps plugin data to the log directory."""
# Create a tfevents file in the logdir so it is detected as a run.
write_empty_event_file(logdir)
plugin_logdir = plugin_asset_util.PluginDirectory(
logdir, profile_plugin.ProfilePlugin.plugin_name)
_maybe_create_directory(plugin_logd... | python | def dump_data(logdir):
"""Dumps plugin data to the log directory."""
# Create a tfevents file in the logdir so it is detected as a run.
write_empty_event_file(logdir)
plugin_logdir = plugin_asset_util.PluginDirectory(
logdir, profile_plugin.ProfilePlugin.plugin_name)
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32,149 | tensorflow/tensorboard | tensorboard/plugins/debugger/health_pill_calc.py | calc_health_pill | def calc_health_pill(tensor):
"""Calculate health pill of a tensor.
Args:
tensor: An instance of `np.array` (for initialized tensors) or
`tensorflow.python.debug.lib.debug_data.InconvertibleTensorProto`
(for unininitialized tensors).
Returns:
If `tensor` is an initialized tensor of numeric o... | python | def calc_health_pill(tensor):
"""Calculate health pill of a tensor.
Args:
tensor: An instance of `np.array` (for initialized tensors) or
`tensorflow.python.debug.lib.debug_data.InconvertibleTensorProto`
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Returns:
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32,150 | tensorflow/tensorboard | tensorboard/plugins/beholder/beholder.py | Beholder._get_config | def _get_config(self):
'''Reads the config file from disk or creates a new one.'''
filename = '{}/{}'.format(self.PLUGIN_LOGDIR, CONFIG_FILENAME)
modified_time = os.path.getmtime(filename)
if modified_time != self.config_last_modified_time:
config = read_pickle(filename, default=self.previous_con... | python | def _get_config(self):
'''Reads the config file from disk or creates a new one.'''
filename = '{}/{}'.format(self.PLUGIN_LOGDIR, CONFIG_FILENAME)
modified_time = os.path.getmtime(filename)
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32,151 | tensorflow/tensorboard | tensorboard/plugins/beholder/beholder.py | Beholder._write_summary | def _write_summary(self, session, frame):
'''Writes the frame to disk as a tensor summary.'''
summary = session.run(self.summary_op, feed_dict={
self.frame_placeholder: frame
})
path = '{}/{}'.format(self.PLUGIN_LOGDIR, SUMMARY_FILENAME)
write_file(summary, path) | python | def _write_summary(self, session, frame):
'''Writes the frame to disk as a tensor summary.'''
summary = session.run(self.summary_op, feed_dict={
self.frame_placeholder: frame
})
path = '{}/{}'.format(self.PLUGIN_LOGDIR, SUMMARY_FILENAME)
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32,152 | tensorflow/tensorboard | tensorboard/plugins/beholder/beholder.py | Beholder._enough_time_has_passed | def _enough_time_has_passed(self, FPS):
'''For limiting how often frames are computed.'''
if FPS == 0:
return False
else:
earliest_time = self.last_update_time + (1.0 / FPS)
return time.time() >= earliest_time | python | def _enough_time_has_passed(self, FPS):
'''For limiting how often frames are computed.'''
if FPS == 0:
return False
else:
earliest_time = self.last_update_time + (1.0 / FPS)
return time.time() >= earliest_time | [
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32,153 | tensorflow/tensorboard | tensorboard/plugins/beholder/beholder.py | Beholder._update_recording | def _update_recording(self, frame, config):
'''Adds a frame to the current video output.'''
# pylint: disable=redefined-variable-type
should_record = config['is_recording']
if should_record:
if not self.is_recording:
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'''Adds a frame to the current video output.'''
# pylint: disable=redefined-variable-type
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if should_record:
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32,154 | tensorflow/tensorboard | tensorboard/plugins/beholder/beholder.py | Beholder.update | def update(self, session, arrays=None, frame=None):
'''Creates a frame and writes it to disk.
Args:
arrays: a list of np arrays. Use the "custom" option in the client.
frame: a 2D np array. This way the plugin can be used for video of any
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32,155 | tensorflow/tensorboard | tensorboard/plugins/beholder/beholder.py | Beholder.gradient_helper | def gradient_helper(optimizer, loss, var_list=None):
'''A helper to get the gradients out at each step.
Args:
optimizer: the optimizer op.
loss: the op that computes your loss value.
Returns: the gradient tensors and the train_step op.
'''
if var_list is None:
var_list = tf.compa... | python | def gradient_helper(optimizer, loss, var_list=None):
'''A helper to get the gradients out at each step.
Args:
optimizer: the optimizer op.
loss: the op that computes your loss value.
Returns: the gradient tensors and the train_step op.
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32,156 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_extractors | def _create_extractors(col_params):
"""Creates extractors to extract properties corresponding to 'col_params'.
Args:
col_params: List of ListSessionGroupsRequest.ColParam protobufs.
Returns:
A list of extractor functions. The ith element in the
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"""Creates extractors to extract properties corresponding to 'col_params'.
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col_params: List of ListSessionGroupsRequest.ColParam protobufs.
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A list of extractor functions. The ith element in the
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32,157 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_metric_extractor | def _create_metric_extractor(metric_name):
"""Returns function that extracts a metric from a session group or a session.
Args:
metric_name: tensorboard.hparams.MetricName protobuffer. Identifies the
metric to extract from the session group.
Returns:
A function that takes a tensorboard.hparams.Session... | python | def _create_metric_extractor(metric_name):
"""Returns function that extracts a metric from a session group or a session.
Args:
metric_name: tensorboard.hparams.MetricName protobuffer. Identifies the
metric to extract from the session group.
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32,158 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _find_metric_value | def _find_metric_value(session_or_group, metric_name):
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Args:
session_or_group: A Session protobuffer or SessionGroup protobuffer.
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32,159 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_hparam_extractor | def _create_hparam_extractor(hparam_name):
"""Returns an extractor function that extracts an hparam from a session group.
Args:
hparam_name: str. Identies the hparam to extract from the session group.
Returns:
A function that takes a tensorboard.hparams.SessionGroup protobuffer and
returns the value,... | python | def _create_hparam_extractor(hparam_name):
"""Returns an extractor function that extracts an hparam from a session group.
Args:
hparam_name: str. Identies the hparam to extract from the session group.
Returns:
A function that takes a tensorboard.hparams.SessionGroup protobuffer and
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32,160 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_filters | def _create_filters(col_params, extractors):
"""Creates filters for the given col_params.
Args:
col_params: List of ListSessionGroupsRequest.ColParam protobufs.
extractors: list of extractor functions of the same length as col_params.
Each element should extract the column described by the correspond... | python | def _create_filters(col_params, extractors):
"""Creates filters for the given col_params.
Args:
col_params: List of ListSessionGroupsRequest.ColParam protobufs.
extractors: list of extractor functions of the same length as col_params.
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32,161 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_filter | def _create_filter(col_param, extractor):
"""Creates a filter for the given col_param and extractor.
Args:
col_param: A tensorboard.hparams.ColParams object identifying the column
and describing the filter to apply.
extractor: A function that extract the column value identified by
'col_param' f... | python | def _create_filter(col_param, extractor):
"""Creates a filter for the given col_param and extractor.
Args:
col_param: A tensorboard.hparams.ColParams object identifying the column
and describing the filter to apply.
extractor: A function that extract the column value identified by
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32,162 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_regexp_filter | def _create_regexp_filter(regex):
"""Returns a boolean function that filters strings based on a regular exp.
Args:
regex: A string describing the regexp to use.
Returns:
A function taking a string and returns True if any of its substrings
matches regex.
"""
# Warning: Note that python's regex lib... | python | def _create_regexp_filter(regex):
"""Returns a boolean function that filters strings based on a regular exp.
Args:
regex: A string describing the regexp to use.
Returns:
A function taking a string and returns True if any of its substrings
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32,163 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _create_interval_filter | def _create_interval_filter(interval):
"""Returns a function that checkes whether a number belongs to an interval.
Args:
interval: A tensorboard.hparams.Interval protobuf describing the interval.
Returns:
A function taking a number (a float or an object of a type in
six.integer_types) that returns Tr... | python | def _create_interval_filter(interval):
"""Returns a function that checkes whether a number belongs to an interval.
Args:
interval: A tensorboard.hparams.Interval protobuf describing the interval.
Returns:
A function taking a number (a float or an object of a type in
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32,164 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _value_to_python | def _value_to_python(value):
"""Converts a google.protobuf.Value to a native Python object."""
assert isinstance(value, struct_pb2.Value)
field = value.WhichOneof('kind')
if field == 'number_value':
return value.number_value
elif field == 'string_value':
return value.string_value
elif field == 'boo... | python | def _value_to_python(value):
"""Converts a google.protobuf.Value to a native Python object."""
assert isinstance(value, struct_pb2.Value)
field = value.WhichOneof('kind')
if field == 'number_value':
return value.number_value
elif field == 'string_value':
return value.string_value
elif field == 'boo... | [
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32,165 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _set_avg_session_metrics | def _set_avg_session_metrics(session_group):
"""Sets the metrics for the group to be the average of its sessions.
The resulting session group metrics consist of the union of metrics across
the group's sessions. The value of each session group metric is the average
of that metric values across the sessions in t... | python | def _set_avg_session_metrics(session_group):
"""Sets the metrics for the group to be the average of its sessions.
The resulting session group metrics consist of the union of metrics across
the group's sessions. The value of each session group metric is the average
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32,166 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _set_median_session_metrics | def _set_median_session_metrics(session_group, aggregation_metric):
"""Sets the metrics for session_group to those of its "median session".
The median session is the session in session_group with the median value
of the metric given by 'aggregation_metric'. The median is taken over the
subset of sessions in th... | python | def _set_median_session_metrics(session_group, aggregation_metric):
"""Sets the metrics for session_group to those of its "median session".
The median session is the session in session_group with the median value
of the metric given by 'aggregation_metric'. The median is taken over the
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32,167 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _set_extremum_session_metrics | def _set_extremum_session_metrics(session_group, aggregation_metric,
extremum_fn):
"""Sets the metrics for session_group to those of its "extremum session".
The extremum session is the session in session_group with the extremum value
of the metric given by 'aggregation_metric'. ... | python | def _set_extremum_session_metrics(session_group, aggregation_metric,
extremum_fn):
"""Sets the metrics for session_group to those of its "extremum session".
The extremum session is the session in session_group with the extremum value
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32,168 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | _measurements | def _measurements(session_group, metric_name):
"""A generator for the values of the metric across the sessions in the group.
Args:
session_group: A SessionGroup protobuffer.
metric_name: A MetricName protobuffer.
Yields:
The next metric value wrapped in a _Measurement instance.
"""
for session_in... | python | def _measurements(session_group, metric_name):
"""A generator for the values of the metric across the sessions in the group.
Args:
session_group: A SessionGroup protobuffer.
metric_name: A MetricName protobuffer.
Yields:
The next metric value wrapped in a _Measurement instance.
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32,169 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | Handler._build_session_groups | def _build_session_groups(self):
"""Returns a list of SessionGroups protobuffers from the summary data."""
# Algorithm: We keep a dict 'groups_by_name' mapping a SessionGroup name
# (str) to a SessionGroup protobuffer. We traverse the runs associated with
# the plugin--each representing a single sessio... | python | def _build_session_groups(self):
"""Returns a list of SessionGroups protobuffers from the summary data."""
# Algorithm: We keep a dict 'groups_by_name' mapping a SessionGroup name
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32,170 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | Handler._add_session | def _add_session(self, session, start_info, groups_by_name):
"""Adds a new Session protobuffer to the 'groups_by_name' dictionary.
Called by _build_session_groups when we encounter a new session. Creates
the Session protobuffer and adds it to the relevant group in the
'groups_by_name' dict. Creates the... | python | def _add_session(self, session, start_info, groups_by_name):
"""Adds a new Session protobuffer to the 'groups_by_name' dictionary.
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32,171 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | Handler._build_session | def _build_session(self, name, start_info, end_info):
"""Builds a session object."""
assert start_info is not None
result = api_pb2.Session(
name=name,
start_time_secs=start_info.start_time_secs,
model_uri=start_info.model_uri,
metric_values=self._build_session_metric_values... | python | def _build_session(self, name, start_info, end_info):
"""Builds a session object."""
assert start_info is not None
result = api_pb2.Session(
name=name,
start_time_secs=start_info.start_time_secs,
model_uri=start_info.model_uri,
metric_values=self._build_session_metric_values... | [
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32,172 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | Handler._build_session_metric_values | def _build_session_metric_values(self, session_name):
"""Builds the session metric values."""
# result is a list of api_pb2.MetricValue instances.
result = []
metric_infos = self._experiment.metric_infos
for metric_info in metric_infos:
metric_name = metric_info.name
try:
metric... | python | def _build_session_metric_values(self, session_name):
"""Builds the session metric values."""
# result is a list of api_pb2.MetricValue instances.
result = []
metric_infos = self._experiment.metric_infos
for metric_info in metric_infos:
metric_name = metric_info.name
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metric... | [
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32,173 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | Handler._aggregate_metrics | def _aggregate_metrics(self, session_group):
"""Sets the metrics of the group based on aggregation_type."""
if (self._request.aggregation_type == api_pb2.AGGREGATION_AVG or
self._request.aggregation_type == api_pb2.AGGREGATION_UNSET):
_set_avg_session_metrics(session_group)
elif self._request... | python | def _aggregate_metrics(self, session_group):
"""Sets the metrics of the group based on aggregation_type."""
if (self._request.aggregation_type == api_pb2.AGGREGATION_AVG or
self._request.aggregation_type == api_pb2.AGGREGATION_UNSET):
_set_avg_session_metrics(session_group)
elif self._request... | [
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32,174 | tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | Handler._sort | def _sort(self, session_groups):
"""Sorts 'session_groups' in place according to _request.col_params."""
# Sort by session_group name so we have a deterministic order.
session_groups.sort(key=operator.attrgetter('name'))
# Sort by lexicographical order of the _request.col_params whose order
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"""Sorts 'session_groups' in place according to _request.col_params."""
# Sort by session_group name so we have a deterministic order.
session_groups.sort(key=operator.attrgetter('name'))
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32,175 | tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioBasicIO.py | readAudioFile | def readAudioFile(path):
'''
This function returns a numpy array that stores the audio samples of a specified WAV of AIFF file
'''
extension = os.path.splitext(path)[1]
try:
#if extension.lower() == '.wav':
#[Fs, x] = wavfile.read(path)
if extension.lower() == '.aif' or ... | python | def readAudioFile(path):
'''
This function returns a numpy array that stores the audio samples of a specified WAV of AIFF file
'''
extension = os.path.splitext(path)[1]
try:
#if extension.lower() == '.wav':
#[Fs, x] = wavfile.read(path)
if extension.lower() == '.aif' or ... | [
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32,176 | tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | computePreRec | def computePreRec(cm, class_names):
'''
This function computes the precision, recall and f1 measures,
given a confusion matrix
'''
n_classes = cm.shape[0]
if len(class_names) != n_classes:
print("Error in computePreRec! Confusion matrix and class_names "
"list must be of th... | python | def computePreRec(cm, class_names):
'''
This function computes the precision, recall and f1 measures,
given a confusion matrix
'''
n_classes = cm.shape[0]
if len(class_names) != n_classes:
print("Error in computePreRec! Confusion matrix and class_names "
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32,177 | tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | trainHMM_computeStatistics | def trainHMM_computeStatistics(features, labels):
'''
This function computes the statistics used to train an HMM joint segmentation-classification model
using a sequence of sequential features and respective labels
ARGUMENTS:
- features: a numpy matrix of feature vectors (numOfDimensions x n_wi... | python | def trainHMM_computeStatistics(features, labels):
'''
This function computes the statistics used to train an HMM joint segmentation-classification model
using a sequence of sequential features and respective labels
ARGUMENTS:
- features: a numpy matrix of feature vectors (numOfDimensions x n_wi... | [
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32,178 | tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioVisualization.py | levenshtein | def levenshtein(str1, s2):
'''
Distance between two strings
'''
N1 = len(str1)
N2 = len(s2)
stringRange = [range(N1 + 1)] * (N2 + 1)
for i in range(N2 + 1):
stringRange[i] = range(i,i + N1 + 1)
for i in range(0,N2):
for j in range(0,N1):
if str1[j] == s2[i]:
... | python | def levenshtein(str1, s2):
'''
Distance between two strings
'''
N1 = len(str1)
N2 = len(s2)
stringRange = [range(N1 + 1)] * (N2 + 1)
for i in range(N2 + 1):
stringRange[i] = range(i,i + N1 + 1)
for i in range(0,N2):
for j in range(0,N1):
if str1[j] == s2[i]:
... | [
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32,179 | tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioVisualization.py | chordialDiagram | def chordialDiagram(fileStr, SM, Threshold, names, namesCategories):
'''
Generates a d3js chordial diagram that illustrates similarites
'''
colors = text_list_to_colors_simple(namesCategories)
SM2 = SM.copy()
SM2 = (SM2 + SM2.T) / 2.0
for i in range(SM2.shape[0]):
M = Threshold
# ... | python | def chordialDiagram(fileStr, SM, Threshold, names, namesCategories):
'''
Generates a d3js chordial diagram that illustrates similarites
'''
colors = text_list_to_colors_simple(namesCategories)
SM2 = SM.copy()
SM2 = (SM2 + SM2.T) / 2.0
for i in range(SM2.shape[0]):
M = Threshold
# ... | [
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32,180 | tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stZCR | def stZCR(frame):
"""Computes zero crossing rate of frame"""
count = len(frame)
countZ = numpy.sum(numpy.abs(numpy.diff(numpy.sign(frame)))) / 2
return (numpy.float64(countZ) / numpy.float64(count-1.0)) | python | def stZCR(frame):
"""Computes zero crossing rate of frame"""
count = len(frame)
countZ = numpy.sum(numpy.abs(numpy.diff(numpy.sign(frame)))) / 2
return (numpy.float64(countZ) / numpy.float64(count-1.0)) | [
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32,181 | tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stHarmonic | def stHarmonic(frame, fs):
"""
Computes harmonic ratio and pitch
"""
M = numpy.round(0.016 * fs) - 1
R = numpy.correlate(frame, frame, mode='full')
g = R[len(frame)-1]
R = R[len(frame):-1]
# estimate m0 (as the first zero crossing of R)
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"""
Computes harmonic ratio and pitch
"""
M = numpy.round(0.016 * fs) - 1
R = numpy.correlate(frame, frame, mode='full')
g = R[len(frame)-1]
R = R[len(frame):-1]
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32,182 | tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stMFCC | def stMFCC(X, fbank, n_mfcc_feats):
"""
Computes the MFCCs of a frame, given the fft mag
ARGUMENTS:
X: fft magnitude abs(FFT)
fbank: filter bank (see mfccInitFilterBanks)
RETURN
ceps: MFCCs (13 element vector)
Note: MFCC calculation is, in general, taken fr... | python | def stMFCC(X, fbank, n_mfcc_feats):
"""
Computes the MFCCs of a frame, given the fft mag
ARGUMENTS:
X: fft magnitude abs(FFT)
fbank: filter bank (see mfccInitFilterBanks)
RETURN
ceps: MFCCs (13 element vector)
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32,183 | tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stChromaFeaturesInit | def stChromaFeaturesInit(nfft, fs):
"""
This function initializes the chroma matrices used in the calculation of the chroma features
"""
freqs = numpy.array([((f + 1) * fs) / (2 * nfft) for f in range(nfft)])
Cp = 27.50
nChroma = numpy.round(12.0 * numpy.log2(freqs / Cp)).astype(int)
... | python | def stChromaFeaturesInit(nfft, fs):
"""
This function initializes the chroma matrices used in the calculation of the chroma features
"""
freqs = numpy.array([((f + 1) * fs) / (2 * nfft) for f in range(nfft)])
Cp = 27.50
nChroma = numpy.round(12.0 * numpy.log2(freqs / Cp)).astype(int)
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32,184 | tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stFeatureExtraction | def stFeatureExtraction(signal, fs, win, step):
"""
This function implements the shor-term windowing process. For each short-term window a set of features is extracted.
This results to a sequence of feature vectors, stored in a numpy matrix.
ARGUMENTS
signal: the input signal samples
... | python | def stFeatureExtraction(signal, fs, win, step):
"""
This function implements the shor-term windowing process. For each short-term window a set of features is extracted.
This results to a sequence of feature vectors, stored in a numpy matrix.
ARGUMENTS
signal: the input signal samples
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32,185 | tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | mtFeatureExtraction | def mtFeatureExtraction(signal, fs, mt_win, mt_step, st_win, st_step):
"""
Mid-term feature extraction
"""
mt_win_ratio = int(round(mt_win / st_step))
mt_step_ratio = int(round(mt_step / st_step))
mt_features = []
st_features, f_names = stFeatureExtraction(signal, fs, st_win, st_step)
... | python | def mtFeatureExtraction(signal, fs, mt_win, mt_step, st_win, st_step):
"""
Mid-term feature extraction
"""
mt_win_ratio = int(round(mt_win / st_step))
mt_step_ratio = int(round(mt_step / st_step))
mt_features = []
st_features, f_names = stFeatureExtraction(signal, fs, st_win, st_step)
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32,186 | tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | dirWavFeatureExtraction | def dirWavFeatureExtraction(dirName, mt_win, mt_step, st_win, st_step,
compute_beat=False):
"""
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32,187 | tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | dirWavFeatureExtractionNoAveraging | def dirWavFeatureExtractionNoAveraging(dirName, mt_win, mt_step, st_win, st_step):
"""
This function extracts the mid-term features of the WAVE
files of a particular folder without averaging each file.
ARGUMENTS:
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"""
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32,188 | ricequant/rqalpha | rqalpha/__main__.py | update_bundle | def update_bundle(data_bundle_path, locale):
"""
Sync Data Bundle
"""
import rqalpha.utils.bundle_helper
rqalpha.utils.bundle_helper.update_bundle(data_bundle_path, locale) | python | def update_bundle(data_bundle_path, locale):
"""
Sync Data Bundle
"""
import rqalpha.utils.bundle_helper
rqalpha.utils.bundle_helper.update_bundle(data_bundle_path, locale) | [
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32,189 | ricequant/rqalpha | rqalpha/__main__.py | run | def run(**kwargs):
"""
Start to run a strategy
"""
config_path = kwargs.get('config_path', None)
if config_path is not None:
config_path = os.path.abspath(config_path)
kwargs.pop('config_path')
if not kwargs.get('base__securities', None):
kwargs.pop('base__securities', No... | python | def run(**kwargs):
"""
Start to run a strategy
"""
config_path = kwargs.get('config_path', None)
if config_path is not None:
config_path = os.path.abspath(config_path)
kwargs.pop('config_path')
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32,190 | ricequant/rqalpha | rqalpha/__main__.py | examples | def examples(directory):
"""
Generate example strategies to target folder
"""
source_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples")
try:
shutil.copytree(source_dir, os.path.join(directory, "examples"))
except OSError as e:
if e.errno == errno.EEXIST:... | python | def examples(directory):
"""
Generate example strategies to target folder
"""
source_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples")
try:
shutil.copytree(source_dir, os.path.join(directory, "examples"))
except OSError as e:
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32,191 | ricequant/rqalpha | rqalpha/__main__.py | generate_config | def generate_config(directory):
"""
Generate default config file
"""
default_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "config.yml")
target_config_path = os.path.abspath(os.path.join(directory, 'config.yml'))
shutil.copy(default_config, target_config_path)
six.print_... | python | def generate_config(directory):
"""
Generate default config file
"""
default_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "config.yml")
target_config_path = os.path.abspath(os.path.join(directory, 'config.yml'))
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32,192 | elastic/elasticsearch-py | elasticsearch/transport.py | Transport.perform_request | def perform_request(self, method, url, headers=None, params=None, body=None):
"""
Perform the actual request. Retrieve a connection from the connection
pool, pass all the information to it's perform_request method and
return the data.
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32,193 | elastic/elasticsearch-py | example/load.py | parse_commits | def parse_commits(head, name):
"""
Go through the git repository log and generate a document per commit
containing all the metadata.
"""
for commit in head.traverse():
yield {
'_id': commit.hexsha,
'repository': name,
'committed_date': datetime.fromtimesta... | python | def parse_commits(head, name):
"""
Go through the git repository log and generate a document per commit
containing all the metadata.
"""
for commit in head.traverse():
yield {
'_id': commit.hexsha,
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32,194 | elastic/elasticsearch-py | example/load.py | load_repo | def load_repo(client, path=None, index='git'):
"""
Parse a git repository with all it's commits and load it into elasticsearch
using `client`. If the index doesn't exist it will be created.
"""
path = dirname(dirname(abspath(__file__))) if path is None else path
repo_name = basename(path)
re... | python | def load_repo(client, path=None, index='git'):
"""
Parse a git repository with all it's commits and load it into elasticsearch
using `client`. If the index doesn't exist it will be created.
"""
path = dirname(dirname(abspath(__file__))) if path is None else path
repo_name = basename(path)
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32,195 | elastic/elasticsearch-py | elasticsearch/helpers/actions.py | parallel_bulk | def parallel_bulk(
client,
actions,
thread_count=4,
chunk_size=500,
max_chunk_bytes=100 * 1024 * 1024,
queue_size=4,
expand_action_callback=expand_action,
*args,
**kwargs
):
"""
Parallel version of the bulk helper run in multiple threads at once.
:arg client: instance of... | python | def parallel_bulk(
client,
actions,
thread_count=4,
chunk_size=500,
max_chunk_bytes=100 * 1024 * 1024,
queue_size=4,
expand_action_callback=expand_action,
*args,
**kwargs
):
"""
Parallel version of the bulk helper run in multiple threads at once.
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32,196 | elastic/elasticsearch-py | elasticsearch/client/indices.py | IndicesClient.forcemerge | def forcemerge(self, index=None, params=None):
"""
The force merge API allows to force merging of one or more indices
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index holds within each shard. The force merge operation allows to
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"""
The force merge API allows to force merging of one or more indices
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32,197 | elastic/elasticsearch-py | elasticsearch/client/indices.py | IndicesClient.rollover | def rollover(self, alias, new_index=None, body=None, params=None):
"""
The rollover index API rolls an alias over to a new index when the
existing index is considered to be too large or too old.
The API accepts a single alias name and a list of conditions. The alias
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32,198 | elastic/elasticsearch-py | elasticsearch/client/utils.py | _escape | def _escape(value):
"""
Escape a single value of a URL string or a query parameter. If it is a list
or tuple, turn it into a comma-separated string first.
"""
# make sequences into comma-separated stings
if isinstance(value, (list, tuple)):
value = ",".join(value)
# dates and datet... | python | def _escape(value):
"""
Escape a single value of a URL string or a query parameter. If it is a list
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"""
# make sequences into comma-separated stings
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32,199 | elastic/elasticsearch-py | elasticsearch/client/utils.py | _make_path | def _make_path(*parts):
"""
Create a URL string from parts, omit all `None` values and empty strings.
Convert lists and tuples to comma separated values.
"""
# TODO: maybe only allow some parts to be lists/tuples ?
return "/" + "/".join(
# preserve ',' and '*' in url for nicer URLs in lo... | python | def _make_path(*parts):
"""
Create a URL string from parts, omit all `None` values and empty strings.
Convert lists and tuples to comma separated values.
"""
# TODO: maybe only allow some parts to be lists/tuples ?
return "/" + "/".join(
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Convert lists and tuples to comma separated values. | [
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] | 2aab285c8f506f3863cbdaba3c90a685c510ba00 | https://github.com/elastic/elasticsearch-py/blob/2aab285c8f506f3863cbdaba3c90a685c510ba00/elasticsearch/client/utils.py#L44-L55 |
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