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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
30,200 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | SavedModelHandler.add_graph_copy | def add_graph_copy(self, graph, tags=None):
"""Adds a copy of Graph with the specified set of tags."""
with graph.as_default():
# Remove default attrs so that Modules created by a tensorflow version
# with ops that have new attrs that are left to their default values can
# still be loaded by older versions unware of those attributes.
meta_graph = tf_v1.train.export_meta_graph(strip_default_attrs=True)
_export_tags(meta_graph, tags)
_export_signatures(meta_graph)
_export_module_attachments(meta_graph)
self._proto.meta_graphs.extend([meta_graph]) | python | def add_graph_copy(self, graph, tags=None):
"""Adds a copy of Graph with the specified set of tags."""
with graph.as_default():
# Remove default attrs so that Modules created by a tensorflow version
# with ops that have new attrs that are left to their default values can
# still be loaded by older versions unware of those attributes.
meta_graph = tf_v1.train.export_meta_graph(strip_default_attrs=True)
_export_tags(meta_graph, tags)
_export_signatures(meta_graph)
_export_module_attachments(meta_graph)
self._proto.meta_graphs.extend([meta_graph]) | [
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30,201 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | SavedModelHandler.get_meta_graph_copy | def get_meta_graph_copy(self, tags=None):
"""Returns a copy of a MetaGraph with the identical set of tags."""
meta_graph = self.get_meta_graph(tags)
copy = tf_v1.MetaGraphDef()
copy.CopyFrom(meta_graph)
return copy | python | def get_meta_graph_copy(self, tags=None):
"""Returns a copy of a MetaGraph with the identical set of tags."""
meta_graph = self.get_meta_graph(tags)
copy = tf_v1.MetaGraphDef()
copy.CopyFrom(meta_graph)
return copy | [
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30,202 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | SavedModelHandler.get_tags | def get_tags(self):
"""Returns a list of set of tags."""
return sorted([frozenset(meta_graph.meta_info_def.tags)
for meta_graph in self.meta_graphs]) | python | def get_tags(self):
"""Returns a list of set of tags."""
return sorted([frozenset(meta_graph.meta_info_def.tags)
for meta_graph in self.meta_graphs]) | [
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30,203 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | SavedModelHandler.export | def export(self, path, variables_saver=None):
"""Exports to SavedModel directory.
Args:
path: path where to export the SavedModel to.
variables_saver: lambda that receives a directory path where to
export checkpoints of variables.
"""
# Operate on a copy of self._proto since it needs to be modified.
proto = saved_model_pb2.SavedModel()
proto.CopyFrom(self._proto)
assets_map = _make_assets_key_collection(proto, path)
self._save_all_assets(path, assets_map)
self._save_variables(path, variables_saver)
self._save_proto(path, proto) | python | def export(self, path, variables_saver=None):
"""Exports to SavedModel directory.
Args:
path: path where to export the SavedModel to.
variables_saver: lambda that receives a directory path where to
export checkpoints of variables.
"""
# Operate on a copy of self._proto since it needs to be modified.
proto = saved_model_pb2.SavedModel()
proto.CopyFrom(self._proto)
assets_map = _make_assets_key_collection(proto, path)
self._save_all_assets(path, assets_map)
self._save_variables(path, variables_saver)
self._save_proto(path, proto) | [
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30,204 | tensorflow/hub | tensorflow_hub/saved_model_lib.py | SavedModelHandler.get_meta_graph | def get_meta_graph(self, tags=None):
"""Returns the matching MetaGraphDef or raises KeyError."""
matches = [meta_graph
for meta_graph in self.meta_graphs
if set(meta_graph.meta_info_def.tags) == set(tags or [])]
if not matches:
raise KeyError("SavedModelHandler has no graph with tags: %r" % tags)
if len(matches) != 1:
raise KeyError(
"SavedModelHandler has multiple graphs with tags %r" % tags)
return matches[0] | python | def get_meta_graph(self, tags=None):
"""Returns the matching MetaGraphDef or raises KeyError."""
matches = [meta_graph
for meta_graph in self.meta_graphs
if set(meta_graph.meta_info_def.tags) == set(tags or [])]
if not matches:
raise KeyError("SavedModelHandler has no graph with tags: %r" % tags)
if len(matches) != 1:
raise KeyError(
"SavedModelHandler has multiple graphs with tags %r" % tags)
return matches[0] | [
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30,205 | tensorflow/hub | tensorflow_hub/module.py | _convert_dict_inputs | def _convert_dict_inputs(inputs, tensor_info_map):
"""Converts from inputs into dict of input tensors.
This handles:
- putting inputs into a dict, per _prepare_dict_inputs(),
- converting all input values into tensors compatible with the
expected input tensor (dtype, shape).
- check sparse/non-sparse tensor types.
Args:
inputs: inputs fed to Module.__call__().
tensor_info_map: A map from string to `tensor_info.ParsedTensorInfo`
describing the signature inputs.
Returns:
A dict of tensors to feed to the signature instantiation.
Raises:
TypeError: If it fails to convert the input values into a dict of tensors
to feed to the signature instantiation.
"""
dict_inputs = _prepare_dict_inputs(inputs, tensor_info_map)
return tensor_info.convert_dict_to_compatible_tensor(dict_inputs,
tensor_info_map) | python | def _convert_dict_inputs(inputs, tensor_info_map):
"""Converts from inputs into dict of input tensors.
This handles:
- putting inputs into a dict, per _prepare_dict_inputs(),
- converting all input values into tensors compatible with the
expected input tensor (dtype, shape).
- check sparse/non-sparse tensor types.
Args:
inputs: inputs fed to Module.__call__().
tensor_info_map: A map from string to `tensor_info.ParsedTensorInfo`
describing the signature inputs.
Returns:
A dict of tensors to feed to the signature instantiation.
Raises:
TypeError: If it fails to convert the input values into a dict of tensors
to feed to the signature instantiation.
"""
dict_inputs = _prepare_dict_inputs(inputs, tensor_info_map)
return tensor_info.convert_dict_to_compatible_tensor(dict_inputs,
tensor_info_map) | [
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30,206 | tensorflow/hub | tensorflow_hub/module.py | eval_function_for_module | def eval_function_for_module(spec, tags=None):
"""Context manager that yields a function to directly evaluate a Module.
This creates a separate graph, in which all of the signatures of the module
are instantiated. Then, it creates a session and initializes the module
variables. Finally, it returns a function which can be used to evaluate the
module signatures.
The function returned by eval_function_for_module has the same syntax as
Module.__call__ , except that inputs and outputs are not tensors but actual
values as used with Session.run().
```python
with hub.eval_function_for_module("/tmp/text-embedding") as f:
# The module can be directly evaluated using f without constructing a graph.
embeddings = f(["Hello world!",], signature="mysignature")
```
Args:
spec: A ModuleSpec defining the Module to instantiate or a path where to
load a ModuleSpec from via `load_module_spec`.
tags: A set of strings specifying the graph variant to use.
Yields:
A function whose keyword arguments are fed into the tfhub module and which
returns a dictionary with the value of the output tensors.
Raises:
RuntimeError: explaning the reason why it failed to instantiate the
Module.
ValueError: if the requested graph variant does not exists.
"""
# We create a separate graph and add all the signatures of the module to it.
original_graph = tf_v1.get_default_graph()
with tf.Graph().as_default():
module = Module(spec, tags=tags)
input_tensors_per_signature = {}
output_tensors_per_signature = {}
for signature in module.get_signature_names():
# We scope with the signature name as different signatures will likely
# contain tensors with the same name (e.g. the input and output tensors).
with tf_v1.variable_scope(signature):
input_tensors = {}
for name, tensorinfo in module.get_input_info_dict(signature).items():
# We need to be care with the shape as it may be fully-known,
# partially-known or even unknown.
shape = tensorinfo.get_shape()
effective_shape = None if shape.dims is None else shape.as_list()
if tensorinfo.is_sparse:
input_tensors[name] = tf_v1.sparse_placeholder(
tensorinfo.dtype, shape=effective_shape, name=name)
else:
input_tensors[name] = tf_v1.placeholder(
tensorinfo.dtype, shape=effective_shape, name=name)
input_tensors_per_signature[signature] = input_tensors
output_tensors_per_signature[signature] = module(
input_tensors_per_signature[signature],
signature=signature,
as_dict=True)
# Evaluating the tfhub module requires an active tensorflow session.
with tf_v1.train.SingularMonitoredSession() as sess:
def func(
inputs=None,
_sentinel=None, # pylint: disable=invalid-name
signature=None,
as_dict=None):
"""Function that directly evaluates a signature in the module."""
signature = signature or "default"
input_tensors = input_tensors_per_signature[signature]
dict_inputs = _prepare_dict_inputs(inputs, input_tensors)
# The input arguments are directly fed into the session.
feed_dict = {
input_tensors[key]: value for key, value in dict_inputs.items()
}
output = output_tensors_per_signature[signature]
output = _prepare_outputs(output, as_dict)
return sess.run(output, feed_dict=feed_dict)
with original_graph.as_default():
# Yield the function since that will keep the session alive until the
# user exits the context.
yield func | python | def eval_function_for_module(spec, tags=None):
"""Context manager that yields a function to directly evaluate a Module.
This creates a separate graph, in which all of the signatures of the module
are instantiated. Then, it creates a session and initializes the module
variables. Finally, it returns a function which can be used to evaluate the
module signatures.
The function returned by eval_function_for_module has the same syntax as
Module.__call__ , except that inputs and outputs are not tensors but actual
values as used with Session.run().
```python
with hub.eval_function_for_module("/tmp/text-embedding") as f:
# The module can be directly evaluated using f without constructing a graph.
embeddings = f(["Hello world!",], signature="mysignature")
```
Args:
spec: A ModuleSpec defining the Module to instantiate or a path where to
load a ModuleSpec from via `load_module_spec`.
tags: A set of strings specifying the graph variant to use.
Yields:
A function whose keyword arguments are fed into the tfhub module and which
returns a dictionary with the value of the output tensors.
Raises:
RuntimeError: explaning the reason why it failed to instantiate the
Module.
ValueError: if the requested graph variant does not exists.
"""
# We create a separate graph and add all the signatures of the module to it.
original_graph = tf_v1.get_default_graph()
with tf.Graph().as_default():
module = Module(spec, tags=tags)
input_tensors_per_signature = {}
output_tensors_per_signature = {}
for signature in module.get_signature_names():
# We scope with the signature name as different signatures will likely
# contain tensors with the same name (e.g. the input and output tensors).
with tf_v1.variable_scope(signature):
input_tensors = {}
for name, tensorinfo in module.get_input_info_dict(signature).items():
# We need to be care with the shape as it may be fully-known,
# partially-known or even unknown.
shape = tensorinfo.get_shape()
effective_shape = None if shape.dims is None else shape.as_list()
if tensorinfo.is_sparse:
input_tensors[name] = tf_v1.sparse_placeholder(
tensorinfo.dtype, shape=effective_shape, name=name)
else:
input_tensors[name] = tf_v1.placeholder(
tensorinfo.dtype, shape=effective_shape, name=name)
input_tensors_per_signature[signature] = input_tensors
output_tensors_per_signature[signature] = module(
input_tensors_per_signature[signature],
signature=signature,
as_dict=True)
# Evaluating the tfhub module requires an active tensorflow session.
with tf_v1.train.SingularMonitoredSession() as sess:
def func(
inputs=None,
_sentinel=None, # pylint: disable=invalid-name
signature=None,
as_dict=None):
"""Function that directly evaluates a signature in the module."""
signature = signature or "default"
input_tensors = input_tensors_per_signature[signature]
dict_inputs = _prepare_dict_inputs(inputs, input_tensors)
# The input arguments are directly fed into the session.
feed_dict = {
input_tensors[key]: value for key, value in dict_inputs.items()
}
output = output_tensors_per_signature[signature]
output = _prepare_outputs(output, as_dict)
return sess.run(output, feed_dict=feed_dict)
with original_graph.as_default():
# Yield the function since that will keep the session alive until the
# user exits the context.
yield func | [
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module signatures.
The function returned by eval_function_for_module has the same syntax as
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```python
with hub.eval_function_for_module("/tmp/text-embedding") as f:
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```
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spec: A ModuleSpec defining the Module to instantiate or a path where to
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30,207 | tensorflow/hub | tensorflow_hub/module.py | Module.get_input_info_dict | def get_input_info_dict(self, signature=None):
"""Describes the inputs required by a signature.
Args:
signature: A string with the signature to get inputs information for.
If None, the default signature is used if defined.
Returns:
The result of ModuleSpec.get_input_info_dict() for the given signature,
and the graph variant selected by `tags` when this Module was initialized.
Raises:
KeyError: if there is no such signature.
"""
return self._spec.get_input_info_dict(signature=signature, tags=self._tags) | python | def get_input_info_dict(self, signature=None):
"""Describes the inputs required by a signature.
Args:
signature: A string with the signature to get inputs information for.
If None, the default signature is used if defined.
Returns:
The result of ModuleSpec.get_input_info_dict() for the given signature,
and the graph variant selected by `tags` when this Module was initialized.
Raises:
KeyError: if there is no such signature.
"""
return self._spec.get_input_info_dict(signature=signature, tags=self._tags) | [
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30,208 | tensorflow/hub | tensorflow_hub/module.py | Module.get_output_info_dict | def get_output_info_dict(self, signature=None):
"""Describes the outputs provided by a signature.
Args:
signature: A string with the signature to get ouputs information for.
If None, the default signature is used if defined.
Returns:
The result of ModuleSpec.get_output_info_dict() for the given signature,
and the graph variant selected by `tags` when this Module was initialized.
Raises:
KeyError: if there is no such signature.
"""
return self._spec.get_output_info_dict(signature=signature, tags=self._tags) | python | def get_output_info_dict(self, signature=None):
"""Describes the outputs provided by a signature.
Args:
signature: A string with the signature to get ouputs information for.
If None, the default signature is used if defined.
Returns:
The result of ModuleSpec.get_output_info_dict() for the given signature,
and the graph variant selected by `tags` when this Module was initialized.
Raises:
KeyError: if there is no such signature.
"""
return self._spec.get_output_info_dict(signature=signature, tags=self._tags) | [
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30,209 | tensorflow/hub | tensorflow_hub/module.py | Module.export | def export(self, path, session):
"""Exports the module with the variables from the session in `path`.
Note that it is the module definition in the ModuleSpec used to create this
module that gets exported. The session is only used to provide the value
of variables.
Args:
path: path where to export the module to.
session: session where to export the variables from.
Raises:
RuntimeError: if there is an issue during the export.
"""
if self._graph is not tf_v1.get_default_graph():
raise RuntimeError("default graph differs from the graph where the "
"module was instantiated.")
if self._graph is not session.graph:
raise RuntimeError("session graph differs from the graph where the "
"module was instantiated.")
self._impl.export(path, session) | python | def export(self, path, session):
"""Exports the module with the variables from the session in `path`.
Note that it is the module definition in the ModuleSpec used to create this
module that gets exported. The session is only used to provide the value
of variables.
Args:
path: path where to export the module to.
session: session where to export the variables from.
Raises:
RuntimeError: if there is an issue during the export.
"""
if self._graph is not tf_v1.get_default_graph():
raise RuntimeError("default graph differs from the graph where the "
"module was instantiated.")
if self._graph is not session.graph:
raise RuntimeError("session graph differs from the graph where the "
"module was instantiated.")
self._impl.export(path, session) | [
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30,210 | tensorflow/hub | tensorflow_hub/module.py | Module.variables | def variables(self):
"""Returns the list of all tf.Variables created by module instantiation."""
result = []
for _, value in sorted(self.variable_map.items()):
if isinstance(value, list):
result.extend(value)
else:
result.append(value)
return result | python | def variables(self):
"""Returns the list of all tf.Variables created by module instantiation."""
result = []
for _, value in sorted(self.variable_map.items()):
if isinstance(value, list):
result.extend(value)
else:
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30,211 | tensorflow/hub | tensorflow_hub/feature_column.py | text_embedding_column | def text_embedding_column(key, module_spec, trainable=False):
"""Uses a Module to construct a dense representation from a text feature.
This feature column can be used on an input feature whose values are strings
of arbitrary size.
The result of this feature column is the result of passing its `input`
through the module `m` instantiated from `module_spec`, as per
`result = m(input)`. The `result` must have dtype float32 and shape
`[batch_size, num_features]` with a known value of num_features.
Example:
```python
comment = text_embedding_column("comment", "/tmp/text-module")
feature_columns = [comment, ...]
...
features = {
"comment": np.array(["wow, much amazing", "so easy", ...]),
...
}
labels = np.array([[1], [0], ...])
# If running TF 2.x, use `tf.compat.v1.estimator.inputs.numpy_input_fn`
input_fn = tf.estimator.inputs.numpy_input_fn(features, labels,
shuffle=True)
estimator = tf.estimator.DNNClassifier(hidden_units, feature_columns)
estimator.train(input_fn, max_steps=100)
```
Args:
key: A string or `_FeatureColumn` identifying the text feature.
module_spec: A ModuleSpec defining the Module to instantiate or a path where
to load a ModuleSpec via `load_module_spec`
trainable: Whether or not the Module is trainable. False by default,
meaning the pre-trained weights are frozen. This is different from the
ordinary tf.feature_column.embedding_column(), but that one is intended
for training from scratch.
Returns:
`_DenseColumn` that converts from text input.
Raises:
ValueError: if module_spec is not suitable for use in this feature column.
"""
module_spec = module.as_module_spec(module_spec)
_check_module_is_text_embedding(module_spec)
return _TextEmbeddingColumn(key=key, module_spec=module_spec,
trainable=trainable) | python | def text_embedding_column(key, module_spec, trainable=False):
"""Uses a Module to construct a dense representation from a text feature.
This feature column can be used on an input feature whose values are strings
of arbitrary size.
The result of this feature column is the result of passing its `input`
through the module `m` instantiated from `module_spec`, as per
`result = m(input)`. The `result` must have dtype float32 and shape
`[batch_size, num_features]` with a known value of num_features.
Example:
```python
comment = text_embedding_column("comment", "/tmp/text-module")
feature_columns = [comment, ...]
...
features = {
"comment": np.array(["wow, much amazing", "so easy", ...]),
...
}
labels = np.array([[1], [0], ...])
# If running TF 2.x, use `tf.compat.v1.estimator.inputs.numpy_input_fn`
input_fn = tf.estimator.inputs.numpy_input_fn(features, labels,
shuffle=True)
estimator = tf.estimator.DNNClassifier(hidden_units, feature_columns)
estimator.train(input_fn, max_steps=100)
```
Args:
key: A string or `_FeatureColumn` identifying the text feature.
module_spec: A ModuleSpec defining the Module to instantiate or a path where
to load a ModuleSpec via `load_module_spec`
trainable: Whether or not the Module is trainable. False by default,
meaning the pre-trained weights are frozen. This is different from the
ordinary tf.feature_column.embedding_column(), but that one is intended
for training from scratch.
Returns:
`_DenseColumn` that converts from text input.
Raises:
ValueError: if module_spec is not suitable for use in this feature column.
"""
module_spec = module.as_module_spec(module_spec)
_check_module_is_text_embedding(module_spec)
return _TextEmbeddingColumn(key=key, module_spec=module_spec,
trainable=trainable) | [
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Example:
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comment = text_embedding_column("comment", "/tmp/text-module")
feature_columns = [comment, ...]
...
features = {
"comment": np.array(["wow, much amazing", "so easy", ...]),
...
}
labels = np.array([[1], [0], ...])
# If running TF 2.x, use `tf.compat.v1.estimator.inputs.numpy_input_fn`
input_fn = tf.estimator.inputs.numpy_input_fn(features, labels,
shuffle=True)
estimator = tf.estimator.DNNClassifier(hidden_units, feature_columns)
estimator.train(input_fn, max_steps=100)
```
Args:
key: A string or `_FeatureColumn` identifying the text feature.
module_spec: A ModuleSpec defining the Module to instantiate or a path where
to load a ModuleSpec via `load_module_spec`
trainable: Whether or not the Module is trainable. False by default,
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Returns:
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30,212 | tensorflow/hub | tensorflow_hub/feature_column.py | _check_module_is_text_embedding | def _check_module_is_text_embedding(module_spec):
"""Raises ValueError if `module_spec` is not a text-embedding module.
Args:
module_spec: A `ModuleSpec` to test.
Raises:
ValueError: if `module_spec` default signature is not compatible with
Tensor(string, shape=(?,)) -> Tensor(float32, shape=(?,K)).
"""
issues = []
# Find issues with signature inputs.
input_info_dict = module_spec.get_input_info_dict()
if len(input_info_dict) != 1:
issues.append("Module default signature must require only one input")
else:
input_info, = input_info_dict.values()
input_shape = input_info.get_shape()
if not (input_info.dtype == tf.string and input_shape.ndims == 1 and
input_shape.as_list() == [None]):
issues.append(
"Module default signature must have only one input "
"tf.Tensor(shape=(?,), dtype=string)"
)
# Find issues with signature outputs.
output_info_dict = module_spec.get_output_info_dict()
if "default" not in output_info_dict:
issues.append("Module default signature must have a 'default' output.")
else:
output_info = output_info_dict["default"]
output_shape = output_info.get_shape()
if not (output_info.dtype == tf.float32 and output_shape.ndims == 2 and
not output_shape.as_list()[0] and output_shape.as_list()[1]):
issues.append(
"Module default signature must have a 'default' output of "
"tf.Tensor(shape=(?,K), dtype=float32)."
)
if issues:
raise ValueError("Module is not a text-embedding: %r" % issues) | python | def _check_module_is_text_embedding(module_spec):
"""Raises ValueError if `module_spec` is not a text-embedding module.
Args:
module_spec: A `ModuleSpec` to test.
Raises:
ValueError: if `module_spec` default signature is not compatible with
Tensor(string, shape=(?,)) -> Tensor(float32, shape=(?,K)).
"""
issues = []
# Find issues with signature inputs.
input_info_dict = module_spec.get_input_info_dict()
if len(input_info_dict) != 1:
issues.append("Module default signature must require only one input")
else:
input_info, = input_info_dict.values()
input_shape = input_info.get_shape()
if not (input_info.dtype == tf.string and input_shape.ndims == 1 and
input_shape.as_list() == [None]):
issues.append(
"Module default signature must have only one input "
"tf.Tensor(shape=(?,), dtype=string)"
)
# Find issues with signature outputs.
output_info_dict = module_spec.get_output_info_dict()
if "default" not in output_info_dict:
issues.append("Module default signature must have a 'default' output.")
else:
output_info = output_info_dict["default"]
output_shape = output_info.get_shape()
if not (output_info.dtype == tf.float32 and output_shape.ndims == 2 and
not output_shape.as_list()[0] and output_shape.as_list()[1]):
issues.append(
"Module default signature must have a 'default' output of "
"tf.Tensor(shape=(?,K), dtype=float32)."
)
if issues:
raise ValueError("Module is not a text-embedding: %r" % issues) | [
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30,213 | tensorflow/hub | tensorflow_hub/feature_column.py | image_embedding_column | def image_embedding_column(key, module_spec):
"""Uses a Module to get a dense 1-D representation from the pixels of images.
This feature column can be used on images, represented as float32 tensors of
RGB pixel data in the range [0,1]. This can be read from a numeric_column()
if the tf.Example input data happens to have decoded images, all with the
same shape [height, width, 3]. More commonly, the input_fn will have code to
explicitly decode images, resize them (possibly after performing data
augmentation such as random crops etc.), and provide a batch of shape
[batch_size, height, width, 3].
The result of this feature column is the result of passing its `input`
through the module `m` instantiated from `module_spec`, as per
`result = m({"images": input})`. The `result` must have dtype float32 and
shape `[batch_size, num_features]` with a known value of num_features.
Example:
```python
image_column = hub.image_embedding_column("embeddings", "/tmp/image-module")
feature_columns = [image_column, ...]
estimator = tf.estimator.LinearClassifier(feature_columns, ...)
height, width = hub.get_expected_image_size(image_column.module_spec)
input_fn = ... # Provides "embeddings" with shape [None, height, width, 3].
estimator.train(input_fn, ...)
```
Args:
key: A string or `_FeatureColumn` identifying the input image data.
module_spec: A string handle or a `ModuleSpec` identifying the module.
Returns:
`_DenseColumn` that converts from pixel data.
Raises:
ValueError: if module_spec is not suitable for use in this feature column.
"""
module_spec = module.as_module_spec(module_spec)
_check_module_is_image_embedding(module_spec)
return _ImageEmbeddingColumn(key=key, module_spec=module_spec) | python | def image_embedding_column(key, module_spec):
"""Uses a Module to get a dense 1-D representation from the pixels of images.
This feature column can be used on images, represented as float32 tensors of
RGB pixel data in the range [0,1]. This can be read from a numeric_column()
if the tf.Example input data happens to have decoded images, all with the
same shape [height, width, 3]. More commonly, the input_fn will have code to
explicitly decode images, resize them (possibly after performing data
augmentation such as random crops etc.), and provide a batch of shape
[batch_size, height, width, 3].
The result of this feature column is the result of passing its `input`
through the module `m` instantiated from `module_spec`, as per
`result = m({"images": input})`. The `result` must have dtype float32 and
shape `[batch_size, num_features]` with a known value of num_features.
Example:
```python
image_column = hub.image_embedding_column("embeddings", "/tmp/image-module")
feature_columns = [image_column, ...]
estimator = tf.estimator.LinearClassifier(feature_columns, ...)
height, width = hub.get_expected_image_size(image_column.module_spec)
input_fn = ... # Provides "embeddings" with shape [None, height, width, 3].
estimator.train(input_fn, ...)
```
Args:
key: A string or `_FeatureColumn` identifying the input image data.
module_spec: A string handle or a `ModuleSpec` identifying the module.
Returns:
`_DenseColumn` that converts from pixel data.
Raises:
ValueError: if module_spec is not suitable for use in this feature column.
"""
module_spec = module.as_module_spec(module_spec)
_check_module_is_image_embedding(module_spec)
return _ImageEmbeddingColumn(key=key, module_spec=module_spec) | [
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if the tf.Example input data happens to have decoded images, all with the
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shape `[batch_size, num_features]` with a known value of num_features.
Example:
```python
image_column = hub.image_embedding_column("embeddings", "/tmp/image-module")
feature_columns = [image_column, ...]
estimator = tf.estimator.LinearClassifier(feature_columns, ...)
height, width = hub.get_expected_image_size(image_column.module_spec)
input_fn = ... # Provides "embeddings" with shape [None, height, width, 3].
estimator.train(input_fn, ...)
```
Args:
key: A string or `_FeatureColumn` identifying the input image data.
module_spec: A string handle or a `ModuleSpec` identifying the module.
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`_DenseColumn` that converts from pixel data.
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30,214 | tensorflow/hub | tensorflow_hub/feature_column.py | _check_module_is_image_embedding | def _check_module_is_image_embedding(module_spec):
"""Raises ValueError if `module_spec` is not usable as image embedding.
Args:
module_spec: A `_ModuleSpec` to test.
Raises:
ValueError: if `module_spec` default signature is not compatible with
mappingan "images" input to a Tensor(float32, shape=(_,K)).
"""
issues = []
# Find issues with "default" signature inputs. The common signatures for
# image models prescribe a specific name; we trust it if we find it
# and if we can do the necessary inference of input shapes from it.
input_info_dict = module_spec.get_input_info_dict()
if (list(input_info_dict.keys()) != ["images"] or
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issues.append("Module 'default' signature must require a single input, "
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else:
try:
image_util.get_expected_image_size(module_spec)
except ValueError as e:
issues.append("Module does not support hub.get_expected_image_size(); "
"original error was:\n" + str(e)) # Raised again below.
# Find issues with "default" signature outputs. We test that the dtype and
# shape is appropriate for use in input_layer().
output_info_dict = module_spec.get_output_info_dict()
if "default" not in output_info_dict:
issues.append("Module 'default' signature must have a 'default' output.")
else:
output_type = output_info_dict["default"].dtype
output_shape = output_info_dict["default"].get_shape()
if not (output_type == tf.float32 and output_shape.ndims == 2 and
output_shape.dims[1].value):
issues.append("Module 'default' signature must have a 'default' output "
"of tf.Tensor(shape=(_,K), dtype=float32).")
if issues:
raise ValueError("Module is not usable as image embedding: %r" % issues) | python | def _check_module_is_image_embedding(module_spec):
"""Raises ValueError if `module_spec` is not usable as image embedding.
Args:
module_spec: A `_ModuleSpec` to test.
Raises:
ValueError: if `module_spec` default signature is not compatible with
mappingan "images" input to a Tensor(float32, shape=(_,K)).
"""
issues = []
# Find issues with "default" signature inputs. The common signatures for
# image models prescribe a specific name; we trust it if we find it
# and if we can do the necessary inference of input shapes from it.
input_info_dict = module_spec.get_input_info_dict()
if (list(input_info_dict.keys()) != ["images"] or
input_info_dict["images"].dtype != tf.float32):
issues.append("Module 'default' signature must require a single input, "
"which must have type float32 and name 'images'.")
else:
try:
image_util.get_expected_image_size(module_spec)
except ValueError as e:
issues.append("Module does not support hub.get_expected_image_size(); "
"original error was:\n" + str(e)) # Raised again below.
# Find issues with "default" signature outputs. We test that the dtype and
# shape is appropriate for use in input_layer().
output_info_dict = module_spec.get_output_info_dict()
if "default" not in output_info_dict:
issues.append("Module 'default' signature must have a 'default' output.")
else:
output_type = output_info_dict["default"].dtype
output_shape = output_info_dict["default"].get_shape()
if not (output_type == tf.float32 and output_shape.ndims == 2 and
output_shape.dims[1].value):
issues.append("Module 'default' signature must have a 'default' output "
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if issues:
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30,215 | tensorflow/hub | tensorflow_hub/feature_column.py | _TextEmbeddingColumn.name | def name(self):
"""Returns string. Used for variable_scope and naming."""
if not hasattr(self, "_name"):
self._name = "{}_hub_module_embedding".format(self.key)
return self._name | python | def name(self):
"""Returns string. Used for variable_scope and naming."""
if not hasattr(self, "_name"):
self._name = "{}_hub_module_embedding".format(self.key)
return self._name | [
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30,216 | tensorflow/hub | tensorflow_hub/feature_column.py | _TextEmbeddingColumn._get_dense_tensor | def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None):
"""Returns a `Tensor`."""
del weight_collections
text_batch = tf.reshape(inputs.get(self), shape=[-1])
m = module.Module(self.module_spec, trainable=self.trainable and trainable)
return m(text_batch) | python | def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None):
"""Returns a `Tensor`."""
del weight_collections
text_batch = tf.reshape(inputs.get(self), shape=[-1])
m = module.Module(self.module_spec, trainable=self.trainable and trainable)
return m(text_batch) | [
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30,217 | tensorflow/hub | tensorflow_hub/feature_column.py | _ImageEmbeddingColumn._parse_example_spec | def _parse_example_spec(self):
"""Returns a `tf.Example` parsing spec as dict."""
height, width = image_util.get_expected_image_size(self.module_spec)
input_shape = [height, width, 3]
return {self.key: tf_v1.FixedLenFeature(input_shape, tf.float32)} | python | def _parse_example_spec(self):
"""Returns a `tf.Example` parsing spec as dict."""
height, width = image_util.get_expected_image_size(self.module_spec)
input_shape = [height, width, 3]
return {self.key: tf_v1.FixedLenFeature(input_shape, tf.float32)} | [
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30,218 | tensorflow/hub | tensorflow_hub/resolver.py | tfhub_cache_dir | def tfhub_cache_dir(default_cache_dir=None, use_temp=False):
"""Returns cache directory.
Returns cache directory from either TFHUB_CACHE_DIR environment variable
or --tfhub_cache_dir or default, if set.
Args:
default_cache_dir: Default cache location to use if neither TFHUB_CACHE_DIR
environment variable nor --tfhub_cache_dir are
not specified.
use_temp: bool, Optional to enable using system's temp directory as a
module cache directory if neither default_cache_dir nor
--tfhub_cache_dir nor TFHUB_CACHE_DIR environment variable are
specified .
"""
# Note: We are using FLAGS["tfhub_cache_dir"] (and not FLAGS.tfhub_cache_dir)
# to access the flag value in order to avoid parsing argv list. The flags
# should have been parsed by now in main() by tf.app.run(). If that was not
# the case (say in Colab env) we skip flag parsing because argv may contain
# unknown flags.
cache_dir = (
os.getenv(_TFHUB_CACHE_DIR, "") or FLAGS["tfhub_cache_dir"].value or
default_cache_dir)
if not cache_dir and use_temp:
# Place all TF-Hub modules under <system's temp>/tfhub_modules.
cache_dir = os.path.join(tempfile.gettempdir(), "tfhub_modules")
if cache_dir:
logging.log_first_n(logging.INFO, "Using %s to cache modules.", 1,
cache_dir)
return cache_dir | python | def tfhub_cache_dir(default_cache_dir=None, use_temp=False):
"""Returns cache directory.
Returns cache directory from either TFHUB_CACHE_DIR environment variable
or --tfhub_cache_dir or default, if set.
Args:
default_cache_dir: Default cache location to use if neither TFHUB_CACHE_DIR
environment variable nor --tfhub_cache_dir are
not specified.
use_temp: bool, Optional to enable using system's temp directory as a
module cache directory if neither default_cache_dir nor
--tfhub_cache_dir nor TFHUB_CACHE_DIR environment variable are
specified .
"""
# Note: We are using FLAGS["tfhub_cache_dir"] (and not FLAGS.tfhub_cache_dir)
# to access the flag value in order to avoid parsing argv list. The flags
# should have been parsed by now in main() by tf.app.run(). If that was not
# the case (say in Colab env) we skip flag parsing because argv may contain
# unknown flags.
cache_dir = (
os.getenv(_TFHUB_CACHE_DIR, "") or FLAGS["tfhub_cache_dir"].value or
default_cache_dir)
if not cache_dir and use_temp:
# Place all TF-Hub modules under <system's temp>/tfhub_modules.
cache_dir = os.path.join(tempfile.gettempdir(), "tfhub_modules")
if cache_dir:
logging.log_first_n(logging.INFO, "Using %s to cache modules.", 1,
cache_dir)
return cache_dir | [
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30,219 | tensorflow/hub | tensorflow_hub/resolver.py | create_local_module_dir | def create_local_module_dir(cache_dir, module_name):
"""Creates and returns the name of directory where to cache a module."""
tf_v1.gfile.MakeDirs(cache_dir)
return os.path.join(cache_dir, module_name) | python | def create_local_module_dir(cache_dir, module_name):
"""Creates and returns the name of directory where to cache a module."""
tf_v1.gfile.MakeDirs(cache_dir)
return os.path.join(cache_dir, module_name) | [
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30,220 | tensorflow/hub | tensorflow_hub/resolver.py | _write_module_descriptor_file | def _write_module_descriptor_file(handle, module_dir):
"""Writes a descriptor file about the directory containing a module.
Args:
handle: Module name/handle.
module_dir: Directory where a module was downloaded.
"""
readme = _module_descriptor_file(module_dir)
readme_content = (
"Module: %s\nDownload Time: %s\nDownloader Hostname: %s (PID:%d)" %
(handle, str(datetime.datetime.today()), socket.gethostname(),
os.getpid()))
# The descriptor file has no semantic meaning so we allow 'overwrite' since
# there is a chance that another process might have written the file (and
# crashed), we just overwrite it.
tf_utils.atomic_write_string_to_file(readme, readme_content, overwrite=True) | python | def _write_module_descriptor_file(handle, module_dir):
"""Writes a descriptor file about the directory containing a module.
Args:
handle: Module name/handle.
module_dir: Directory where a module was downloaded.
"""
readme = _module_descriptor_file(module_dir)
readme_content = (
"Module: %s\nDownload Time: %s\nDownloader Hostname: %s (PID:%d)" %
(handle, str(datetime.datetime.today()), socket.gethostname(),
os.getpid()))
# The descriptor file has no semantic meaning so we allow 'overwrite' since
# there is a chance that another process might have written the file (and
# crashed), we just overwrite it.
tf_utils.atomic_write_string_to_file(readme, readme_content, overwrite=True) | [
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30,221 | tensorflow/hub | tensorflow_hub/resolver.py | _locked_tmp_dir_size | def _locked_tmp_dir_size(lock_filename):
"""Returns the size of the temp dir pointed to by the given lock file."""
task_uid = _task_uid_from_lock_file(lock_filename)
try:
return _dir_size(
_temp_download_dir(_module_dir(lock_filename), task_uid))
except tf.errors.NotFoundError:
return 0 | python | def _locked_tmp_dir_size(lock_filename):
"""Returns the size of the temp dir pointed to by the given lock file."""
task_uid = _task_uid_from_lock_file(lock_filename)
try:
return _dir_size(
_temp_download_dir(_module_dir(lock_filename), task_uid))
except tf.errors.NotFoundError:
return 0 | [
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30,222 | tensorflow/hub | tensorflow_hub/resolver.py | _wait_for_lock_to_disappear | def _wait_for_lock_to_disappear(handle, lock_file, lock_file_timeout_sec):
"""Waits for the lock file to disappear.
The lock file was created by another process that is performing a download
into its own temporary directory. The name of this temp directory is
sha1(<module>).<uuid>.tmp where <uuid> comes from the lock file.
Args:
handle: The location from where a module is being download.
lock_file: Lock file created by another process downloading this module.
lock_file_timeout_sec: The amount of time to wait (in seconds) before we
can declare that the other downloaded has been
abandoned. The download is declared abandoned if
there is no file size change in the temporary
directory within the last 'lock_file_timeout_sec'.
"""
locked_tmp_dir_size = 0
locked_tmp_dir_size_check_time = time.time()
lock_file_content = None
while tf_v1.gfile.Exists(lock_file):
try:
logging.log_every_n(
logging.INFO,
"Module '%s' already being downloaded by '%s'. Waiting.", 10,
handle, tf_utils.read_file_to_string(lock_file))
if (time.time() - locked_tmp_dir_size_check_time >
lock_file_timeout_sec):
# Check whether the holder of the current lock downloaded anything
# in its temporary directory in the last 'lock_file_timeout_sec'.
cur_locked_tmp_dir_size = _locked_tmp_dir_size(lock_file)
cur_lock_file_content = tf_utils.read_file_to_string(lock_file)
if (cur_locked_tmp_dir_size == locked_tmp_dir_size and
cur_lock_file_content == lock_file_content):
# There is was no data downloaded in the past
# 'lock_file_timeout_sec'. Steal the lock and proceed with the
# local download.
logging.warning("Deleting lock file %s due to inactivity.",
lock_file)
tf_v1.gfile.Remove(lock_file)
break
locked_tmp_dir_size = cur_locked_tmp_dir_size
locked_tmp_dir_size_check_time = time.time()
lock_file_content = cur_lock_file_content
except tf.errors.NotFoundError:
# Lock file or temp directory were deleted during check. Continue
# to check whether download succeeded or we need to start our own
# download.
pass
finally:
time.sleep(5) | python | def _wait_for_lock_to_disappear(handle, lock_file, lock_file_timeout_sec):
"""Waits for the lock file to disappear.
The lock file was created by another process that is performing a download
into its own temporary directory. The name of this temp directory is
sha1(<module>).<uuid>.tmp where <uuid> comes from the lock file.
Args:
handle: The location from where a module is being download.
lock_file: Lock file created by another process downloading this module.
lock_file_timeout_sec: The amount of time to wait (in seconds) before we
can declare that the other downloaded has been
abandoned. The download is declared abandoned if
there is no file size change in the temporary
directory within the last 'lock_file_timeout_sec'.
"""
locked_tmp_dir_size = 0
locked_tmp_dir_size_check_time = time.time()
lock_file_content = None
while tf_v1.gfile.Exists(lock_file):
try:
logging.log_every_n(
logging.INFO,
"Module '%s' already being downloaded by '%s'. Waiting.", 10,
handle, tf_utils.read_file_to_string(lock_file))
if (time.time() - locked_tmp_dir_size_check_time >
lock_file_timeout_sec):
# Check whether the holder of the current lock downloaded anything
# in its temporary directory in the last 'lock_file_timeout_sec'.
cur_locked_tmp_dir_size = _locked_tmp_dir_size(lock_file)
cur_lock_file_content = tf_utils.read_file_to_string(lock_file)
if (cur_locked_tmp_dir_size == locked_tmp_dir_size and
cur_lock_file_content == lock_file_content):
# There is was no data downloaded in the past
# 'lock_file_timeout_sec'. Steal the lock and proceed with the
# local download.
logging.warning("Deleting lock file %s due to inactivity.",
lock_file)
tf_v1.gfile.Remove(lock_file)
break
locked_tmp_dir_size = cur_locked_tmp_dir_size
locked_tmp_dir_size_check_time = time.time()
lock_file_content = cur_lock_file_content
except tf.errors.NotFoundError:
# Lock file or temp directory were deleted during check. Continue
# to check whether download succeeded or we need to start our own
# download.
pass
finally:
time.sleep(5) | [
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30,223 | tensorflow/hub | tensorflow_hub/resolver.py | atomic_download | def atomic_download(handle,
download_fn,
module_dir,
lock_file_timeout_sec=10 * 60):
"""Returns the path to a Module directory for a given TF-Hub Module handle.
Args:
handle: (string) Location of a TF-Hub Module.
download_fn: Callback function that actually performs download. The callback
receives two arguments, handle and the location of a temporary
directory to download the content into.
module_dir: Directory where to download the module files to.
lock_file_timeout_sec: The amount of time we give the current holder of
the lock to make progress in downloading a module.
If no progress is made, the lock is revoked.
Returns:
A string containing the path to a TF-Hub Module directory.
Raises:
ValueError: if the Module is not found.
"""
lock_file = _lock_filename(module_dir)
task_uid = uuid.uuid4().hex
lock_contents = _lock_file_contents(task_uid)
tmp_dir = _temp_download_dir(module_dir, task_uid)
# Attempt to protect against cases of processes being cancelled with
# KeyboardInterrupt by using a try/finally clause to remove the lock
# and tmp_dir.
try:
while True:
try:
tf_utils.atomic_write_string_to_file(lock_file, lock_contents,
overwrite=False)
# Must test condition again, since another process could have created
# the module and deleted the old lock file since last test.
if tf_v1.gfile.Exists(module_dir):
# Lock file will be deleted in the finally-clause.
return module_dir
break # Proceed to downloading the module.
except tf.errors.OpError:
pass
# Wait for lock file to disappear.
_wait_for_lock_to_disappear(handle, lock_file, lock_file_timeout_sec)
# At this point we either deleted a lock or a lock got removed by the
# owner or another process. Perform one more iteration of the while-loop,
# we would either terminate due tf_v1.gfile.Exists(module_dir) or because
# we would obtain a lock ourselves, or wait again for the lock to
# disappear.
# Lock file acquired.
logging.info("Downloading TF-Hub Module '%s'.", handle)
tf_v1.gfile.MakeDirs(tmp_dir)
download_fn(handle, tmp_dir)
# Write module descriptor to capture information about which module was
# downloaded by whom and when. The file stored at the same level as a
# directory in order to keep the content of the 'model_dir' exactly as it
# was define by the module publisher.
#
# Note: The descriptor is written purely to help the end-user to identify
# which directory belongs to which module. The descriptor is not part of the
# module caching protocol and no code in the TF-Hub library reads its
# content.
_write_module_descriptor_file(handle, module_dir)
try:
tf_v1.gfile.Rename(tmp_dir, module_dir)
logging.info("Downloaded TF-Hub Module '%s'.", handle)
except tf.errors.AlreadyExistsError:
logging.warning("Module already exists in %s", module_dir)
finally:
try:
# Temp directory is owned by the current process, remove it.
tf_v1.gfile.DeleteRecursively(tmp_dir)
except tf.errors.NotFoundError:
pass
try:
contents = tf_utils.read_file_to_string(lock_file)
except tf.errors.NotFoundError:
contents = ""
if contents == lock_contents:
# Lock file exists and is owned by this process.
try:
tf_v1.gfile.Remove(lock_file)
except tf.errors.NotFoundError:
pass
return module_dir | python | def atomic_download(handle,
download_fn,
module_dir,
lock_file_timeout_sec=10 * 60):
"""Returns the path to a Module directory for a given TF-Hub Module handle.
Args:
handle: (string) Location of a TF-Hub Module.
download_fn: Callback function that actually performs download. The callback
receives two arguments, handle and the location of a temporary
directory to download the content into.
module_dir: Directory where to download the module files to.
lock_file_timeout_sec: The amount of time we give the current holder of
the lock to make progress in downloading a module.
If no progress is made, the lock is revoked.
Returns:
A string containing the path to a TF-Hub Module directory.
Raises:
ValueError: if the Module is not found.
"""
lock_file = _lock_filename(module_dir)
task_uid = uuid.uuid4().hex
lock_contents = _lock_file_contents(task_uid)
tmp_dir = _temp_download_dir(module_dir, task_uid)
# Attempt to protect against cases of processes being cancelled with
# KeyboardInterrupt by using a try/finally clause to remove the lock
# and tmp_dir.
try:
while True:
try:
tf_utils.atomic_write_string_to_file(lock_file, lock_contents,
overwrite=False)
# Must test condition again, since another process could have created
# the module and deleted the old lock file since last test.
if tf_v1.gfile.Exists(module_dir):
# Lock file will be deleted in the finally-clause.
return module_dir
break # Proceed to downloading the module.
except tf.errors.OpError:
pass
# Wait for lock file to disappear.
_wait_for_lock_to_disappear(handle, lock_file, lock_file_timeout_sec)
# At this point we either deleted a lock or a lock got removed by the
# owner or another process. Perform one more iteration of the while-loop,
# we would either terminate due tf_v1.gfile.Exists(module_dir) or because
# we would obtain a lock ourselves, or wait again for the lock to
# disappear.
# Lock file acquired.
logging.info("Downloading TF-Hub Module '%s'.", handle)
tf_v1.gfile.MakeDirs(tmp_dir)
download_fn(handle, tmp_dir)
# Write module descriptor to capture information about which module was
# downloaded by whom and when. The file stored at the same level as a
# directory in order to keep the content of the 'model_dir' exactly as it
# was define by the module publisher.
#
# Note: The descriptor is written purely to help the end-user to identify
# which directory belongs to which module. The descriptor is not part of the
# module caching protocol and no code in the TF-Hub library reads its
# content.
_write_module_descriptor_file(handle, module_dir)
try:
tf_v1.gfile.Rename(tmp_dir, module_dir)
logging.info("Downloaded TF-Hub Module '%s'.", handle)
except tf.errors.AlreadyExistsError:
logging.warning("Module already exists in %s", module_dir)
finally:
try:
# Temp directory is owned by the current process, remove it.
tf_v1.gfile.DeleteRecursively(tmp_dir)
except tf.errors.NotFoundError:
pass
try:
contents = tf_utils.read_file_to_string(lock_file)
except tf.errors.NotFoundError:
contents = ""
if contents == lock_contents:
# Lock file exists and is owned by this process.
try:
tf_v1.gfile.Remove(lock_file)
except tf.errors.NotFoundError:
pass
return module_dir | [
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receives two arguments, handle and the location of a temporary
directory to download the content into.
module_dir: Directory where to download the module files to.
lock_file_timeout_sec: The amount of time we give the current holder of
the lock to make progress in downloading a module.
If no progress is made, the lock is revoked.
Returns:
A string containing the path to a TF-Hub Module directory.
Raises:
ValueError: if the Module is not found. | [
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30,224 | tensorflow/hub | tensorflow_hub/resolver.py | DownloadManager._print_download_progress_msg | def _print_download_progress_msg(self, msg, flush=False):
"""Prints a message about download progress either to the console or TF log.
Args:
msg: Message to print.
flush: Indicates whether to flush the output (only used in interactive
mode).
"""
if self._interactive_mode():
# Print progress message to console overwriting previous progress
# message.
self._max_prog_str = max(self._max_prog_str, len(msg))
sys.stdout.write("\r%-{}s".format(self._max_prog_str) % msg)
sys.stdout.flush()
if flush:
print("\n")
else:
# Interactive progress tracking is disabled. Print progress to the
# standard TF log.
logging.info(msg) | python | def _print_download_progress_msg(self, msg, flush=False):
"""Prints a message about download progress either to the console or TF log.
Args:
msg: Message to print.
flush: Indicates whether to flush the output (only used in interactive
mode).
"""
if self._interactive_mode():
# Print progress message to console overwriting previous progress
# message.
self._max_prog_str = max(self._max_prog_str, len(msg))
sys.stdout.write("\r%-{}s".format(self._max_prog_str) % msg)
sys.stdout.flush()
if flush:
print("\n")
else:
# Interactive progress tracking is disabled. Print progress to the
# standard TF log.
logging.info(msg) | [
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30,225 | tensorflow/hub | tensorflow_hub/resolver.py | DownloadManager._log_progress | def _log_progress(self, bytes_downloaded):
"""Logs progress information about ongoing module download.
Args:
bytes_downloaded: Number of bytes downloaded.
"""
self._total_bytes_downloaded += bytes_downloaded
now = time.time()
if (self._interactive_mode() or
now - self._last_progress_msg_print_time > 15):
# Print progress message every 15 secs or if interactive progress
# tracking is enabled.
self._print_download_progress_msg(
"Downloading %s: %s" % (self._url,
tf_utils.bytes_to_readable_str(
self._total_bytes_downloaded, True)))
self._last_progress_msg_print_time = now | python | def _log_progress(self, bytes_downloaded):
"""Logs progress information about ongoing module download.
Args:
bytes_downloaded: Number of bytes downloaded.
"""
self._total_bytes_downloaded += bytes_downloaded
now = time.time()
if (self._interactive_mode() or
now - self._last_progress_msg_print_time > 15):
# Print progress message every 15 secs or if interactive progress
# tracking is enabled.
self._print_download_progress_msg(
"Downloading %s: %s" % (self._url,
tf_utils.bytes_to_readable_str(
self._total_bytes_downloaded, True)))
self._last_progress_msg_print_time = now | [
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30,226 | tensorflow/hub | tensorflow_hub/resolver.py | DownloadManager._extract_file | def _extract_file(self, tgz, tarinfo, dst_path, buffer_size=10<<20):
"""Extracts 'tarinfo' from 'tgz' and writes to 'dst_path'."""
src = tgz.extractfile(tarinfo)
dst = tf_v1.gfile.GFile(dst_path, "wb")
while 1:
buf = src.read(buffer_size)
if not buf:
break
dst.write(buf)
self._log_progress(len(buf))
dst.close()
src.close() | python | def _extract_file(self, tgz, tarinfo, dst_path, buffer_size=10<<20):
"""Extracts 'tarinfo' from 'tgz' and writes to 'dst_path'."""
src = tgz.extractfile(tarinfo)
dst = tf_v1.gfile.GFile(dst_path, "wb")
while 1:
buf = src.read(buffer_size)
if not buf:
break
dst.write(buf)
self._log_progress(len(buf))
dst.close()
src.close() | [
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30,227 | tensorflow/hub | tensorflow_hub/resolver.py | DownloadManager.download_and_uncompress | def download_and_uncompress(self, fileobj, dst_path):
"""Streams the content for the 'fileobj' and stores the result in dst_path.
Args:
fileobj: File handle pointing to .tar/.tar.gz content.
dst_path: Absolute path where to store uncompressed data from 'fileobj'.
Raises:
ValueError: Unknown object encountered inside the TAR file.
"""
try:
with tarfile.open(mode="r|*", fileobj=fileobj) as tgz:
for tarinfo in tgz:
abs_target_path = _merge_relative_path(dst_path, tarinfo.name)
if tarinfo.isfile():
self._extract_file(tgz, tarinfo, abs_target_path)
elif tarinfo.isdir():
tf_v1.gfile.MakeDirs(abs_target_path)
else:
# We do not support symlinks and other uncommon objects.
raise ValueError(
"Unexpected object type in tar archive: %s" % tarinfo.type)
total_size_str = tf_utils.bytes_to_readable_str(
self._total_bytes_downloaded, True)
self._print_download_progress_msg(
"Downloaded %s, Total size: %s" % (self._url, total_size_str),
flush=True)
except tarfile.ReadError:
raise IOError("%s does not appear to be a valid module." % self._url) | python | def download_and_uncompress(self, fileobj, dst_path):
"""Streams the content for the 'fileobj' and stores the result in dst_path.
Args:
fileobj: File handle pointing to .tar/.tar.gz content.
dst_path: Absolute path where to store uncompressed data from 'fileobj'.
Raises:
ValueError: Unknown object encountered inside the TAR file.
"""
try:
with tarfile.open(mode="r|*", fileobj=fileobj) as tgz:
for tarinfo in tgz:
abs_target_path = _merge_relative_path(dst_path, tarinfo.name)
if tarinfo.isfile():
self._extract_file(tgz, tarinfo, abs_target_path)
elif tarinfo.isdir():
tf_v1.gfile.MakeDirs(abs_target_path)
else:
# We do not support symlinks and other uncommon objects.
raise ValueError(
"Unexpected object type in tar archive: %s" % tarinfo.type)
total_size_str = tf_utils.bytes_to_readable_str(
self._total_bytes_downloaded, True)
self._print_download_progress_msg(
"Downloaded %s, Total size: %s" % (self._url, total_size_str),
flush=True)
except tarfile.ReadError:
raise IOError("%s does not appear to be a valid module." % self._url) | [
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30,228 | tensorflow/hub | tensorflow_hub/meta_graph_lib.py | prepend_name_scope | def prepend_name_scope(name, import_scope):
"""Prepends name scope to a name."""
# Based on tensorflow/python/framework/ops.py implementation.
if import_scope:
try:
str_to_replace = r"([\^]|loc:@|^)(.*)"
return re.sub(str_to_replace, r"\1" + import_scope + r"/\2",
tf.compat.as_str_any(name))
except TypeError as e:
# If the name is not of a type we can process, simply return it.
logging.warning(e)
return name
else:
return name | python | def prepend_name_scope(name, import_scope):
"""Prepends name scope to a name."""
# Based on tensorflow/python/framework/ops.py implementation.
if import_scope:
try:
str_to_replace = r"([\^]|loc:@|^)(.*)"
return re.sub(str_to_replace, r"\1" + import_scope + r"/\2",
tf.compat.as_str_any(name))
except TypeError as e:
# If the name is not of a type we can process, simply return it.
logging.warning(e)
return name
else:
return name | [
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30,229 | tensorflow/hub | tensorflow_hub/meta_graph_lib.py | prefix_shared_name_attributes | def prefix_shared_name_attributes(meta_graph, absolute_import_scope):
"""In-place prefixes shared_name attributes of nodes."""
shared_name_attr = "shared_name"
for node in meta_graph.graph_def.node:
shared_name_value = node.attr.get(shared_name_attr, None)
if shared_name_value and shared_name_value.HasField("s"):
if shared_name_value.s:
node.attr[shared_name_attr].s = tf.compat.as_bytes(
prepend_name_scope(
shared_name_value.s, import_scope=absolute_import_scope)) | python | def prefix_shared_name_attributes(meta_graph, absolute_import_scope):
"""In-place prefixes shared_name attributes of nodes."""
shared_name_attr = "shared_name"
for node in meta_graph.graph_def.node:
shared_name_value = node.attr.get(shared_name_attr, None)
if shared_name_value and shared_name_value.HasField("s"):
if shared_name_value.s:
node.attr[shared_name_attr].s = tf.compat.as_bytes(
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shared_name_value.s, import_scope=absolute_import_scope)) | [
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30,230 | tensorflow/hub | tensorflow_hub/meta_graph_lib.py | mark_backward | def mark_backward(output_tensor, used_node_names):
"""Function to propagate backwards in the graph and mark nodes as used.
Traverses recursively through the graph from the end tensor, through the op
that generates the tensor, and then to the input tensors that feed the op.
Nodes encountered are stored in used_node_names.
Args:
output_tensor: A Tensor which we start the propagation.
used_node_names: A list of strings, stores the name of nodes we've marked as
visited.
"""
op = output_tensor.op
if op.name in used_node_names:
return
used_node_names.add(op.name)
for input_tensor in op.inputs:
mark_backward(input_tensor, used_node_names)
for control_input_op in op.control_inputs:
used_node_names.add(control_input_op.name)
for input_tensor in control_input_op.inputs:
mark_backward(input_tensor, used_node_names) | python | def mark_backward(output_tensor, used_node_names):
"""Function to propagate backwards in the graph and mark nodes as used.
Traverses recursively through the graph from the end tensor, through the op
that generates the tensor, and then to the input tensors that feed the op.
Nodes encountered are stored in used_node_names.
Args:
output_tensor: A Tensor which we start the propagation.
used_node_names: A list of strings, stores the name of nodes we've marked as
visited.
"""
op = output_tensor.op
if op.name in used_node_names:
return
used_node_names.add(op.name)
for input_tensor in op.inputs:
mark_backward(input_tensor, used_node_names)
for control_input_op in op.control_inputs:
used_node_names.add(control_input_op.name)
for input_tensor in control_input_op.inputs:
mark_backward(input_tensor, used_node_names) | [
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30,231 | tensorflow/hub | tensorflow_hub/meta_graph_lib.py | prune_unused_nodes | def prune_unused_nodes(meta_graph, signature_def):
"""Function to prune unused ops given a signature def.
This function does a graph traversal through from all outputs as
defined in the signature_def to collect all used nodes. Then, any
nodes which are unused can be discarded. This is useful for graph which are
executing eagerly or on TPUs.
Args:
meta_graph: The input/output MetaGraphDef for which we wish to prune.
signature_def: A SignatureDef which specifies the outputs from which we wish
to start graph traversal.
"""
# Instantiate a temporary empty graph so that we have access to Graph API
# and import the meta_graph.
graph = tf_v1.Graph()
with graph.as_default():
tf_v1.train.import_meta_graph(meta_graph, input_map={}, import_scope="")
# Traverse from all outputs and mark all nodes.
used_node_names = set()
for _, tensor_def in signature_def.outputs.items():
output_tensor = graph.get_tensor_by_name(tensor_def.name)
mark_backward(output_tensor, used_node_names)
# Filter out all nodes in the meta_graph that are not used.
node_filter_in_list = []
for node in meta_graph.graph_def.node:
# Make a special exception for VarHandleOp. Removing VarhandleOps
# will make the graph not importable as they often leave nodes hanging.
# These will be disconnected through the feedmap when importing the
# metagraph.
if node.name in used_node_names or node.op == "VarHandleOp":
node_filter_in_list.append(node)
del meta_graph.graph_def.node[:]
meta_graph.graph_def.node.extend(node_filter_in_list)
del graph | python | def prune_unused_nodes(meta_graph, signature_def):
"""Function to prune unused ops given a signature def.
This function does a graph traversal through from all outputs as
defined in the signature_def to collect all used nodes. Then, any
nodes which are unused can be discarded. This is useful for graph which are
executing eagerly or on TPUs.
Args:
meta_graph: The input/output MetaGraphDef for which we wish to prune.
signature_def: A SignatureDef which specifies the outputs from which we wish
to start graph traversal.
"""
# Instantiate a temporary empty graph so that we have access to Graph API
# and import the meta_graph.
graph = tf_v1.Graph()
with graph.as_default():
tf_v1.train.import_meta_graph(meta_graph, input_map={}, import_scope="")
# Traverse from all outputs and mark all nodes.
used_node_names = set()
for _, tensor_def in signature_def.outputs.items():
output_tensor = graph.get_tensor_by_name(tensor_def.name)
mark_backward(output_tensor, used_node_names)
# Filter out all nodes in the meta_graph that are not used.
node_filter_in_list = []
for node in meta_graph.graph_def.node:
# Make a special exception for VarHandleOp. Removing VarhandleOps
# will make the graph not importable as they often leave nodes hanging.
# These will be disconnected through the feedmap when importing the
# metagraph.
if node.name in used_node_names or node.op == "VarHandleOp":
node_filter_in_list.append(node)
del meta_graph.graph_def.node[:]
meta_graph.graph_def.node.extend(node_filter_in_list)
del graph | [
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30,232 | tensorflow/hub | tensorflow_hub/meta_graph_lib.py | prune_feed_map | def prune_feed_map(meta_graph, feed_map):
"""Function to prune the feedmap of nodes which no longer exist."""
node_names = [x.name + ":0" for x in meta_graph.graph_def.node]
keys_to_delete = []
for k, _ in feed_map.items():
if k not in node_names:
keys_to_delete.append(k)
for k in keys_to_delete:
del feed_map[k] | python | def prune_feed_map(meta_graph, feed_map):
"""Function to prune the feedmap of nodes which no longer exist."""
node_names = [x.name + ":0" for x in meta_graph.graph_def.node]
keys_to_delete = []
for k, _ in feed_map.items():
if k not in node_names:
keys_to_delete.append(k)
for k in keys_to_delete:
del feed_map[k] | [
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30,233 | tensorflow/hub | tensorflow_hub/tf_utils.py | atomic_write_string_to_file | def atomic_write_string_to_file(filename, contents, overwrite):
"""Writes to `filename` atomically.
This means that when `filename` appears in the filesystem, it will contain
all of `contents`. With write_string_to_file, it is possible for the file
to appear in the filesystem with `contents` only partially written.
Accomplished by writing to a temp file and then renaming it.
Args:
filename: string, pathname for a file
contents: string, contents that need to be written to the file
overwrite: boolean, if false it's an error for `filename` to be occupied by
an existing file.
"""
temp_pathname = (tf.compat.as_bytes(filename) +
tf.compat.as_bytes(".tmp") +
tf.compat.as_bytes(uuid.uuid4().hex))
with tf_v1.gfile.GFile(temp_pathname, mode="w") as f:
f.write(contents)
try:
tf_v1.gfile.Rename(temp_pathname, filename, overwrite)
except tf.errors.OpError:
tf_v1.gfile.Remove(temp_pathname)
raise | python | def atomic_write_string_to_file(filename, contents, overwrite):
"""Writes to `filename` atomically.
This means that when `filename` appears in the filesystem, it will contain
all of `contents`. With write_string_to_file, it is possible for the file
to appear in the filesystem with `contents` only partially written.
Accomplished by writing to a temp file and then renaming it.
Args:
filename: string, pathname for a file
contents: string, contents that need to be written to the file
overwrite: boolean, if false it's an error for `filename` to be occupied by
an existing file.
"""
temp_pathname = (tf.compat.as_bytes(filename) +
tf.compat.as_bytes(".tmp") +
tf.compat.as_bytes(uuid.uuid4().hex))
with tf_v1.gfile.GFile(temp_pathname, mode="w") as f:
f.write(contents)
try:
tf_v1.gfile.Rename(temp_pathname, filename, overwrite)
except tf.errors.OpError:
tf_v1.gfile.Remove(temp_pathname)
raise | [
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30,234 | tensorflow/hub | tensorflow_hub/tf_utils.py | get_timestamped_export_dir | def get_timestamped_export_dir(export_dir_base):
"""Builds a path to a new subdirectory within the base directory.
Each export is written into a new subdirectory named using the
current time. This guarantees monotonically increasing version
numbers even across multiple runs of the pipeline.
The timestamp used is the number of seconds since epoch UTC.
Args:
export_dir_base: A string containing a directory to write the exported
graph and checkpoints.
Returns:
The full path of the new subdirectory (which is not actually created yet).
Raises:
RuntimeError: if repeated attempts fail to obtain a unique timestamped
directory name.
"""
attempts = 0
while attempts < MAX_DIRECTORY_CREATION_ATTEMPTS:
export_timestamp = int(time.time())
export_dir = os.path.join(
tf.compat.as_bytes(export_dir_base),
tf.compat.as_bytes(str(export_timestamp)))
if not tf_v1.gfile.Exists(export_dir):
# Collisions are still possible (though extremely unlikely): this
# directory is not actually created yet, but it will be almost
# instantly on return from this function.
return export_dir
time.sleep(1)
attempts += 1
logging.warn(
"Export directory %s already exists; retrying (attempt %d/%d)",
export_dir, attempts, MAX_DIRECTORY_CREATION_ATTEMPTS)
raise RuntimeError("Failed to obtain a unique export directory name after "
"%d attempts.".MAX_DIRECTORY_CREATION_ATTEMPTS) | python | def get_timestamped_export_dir(export_dir_base):
"""Builds a path to a new subdirectory within the base directory.
Each export is written into a new subdirectory named using the
current time. This guarantees monotonically increasing version
numbers even across multiple runs of the pipeline.
The timestamp used is the number of seconds since epoch UTC.
Args:
export_dir_base: A string containing a directory to write the exported
graph and checkpoints.
Returns:
The full path of the new subdirectory (which is not actually created yet).
Raises:
RuntimeError: if repeated attempts fail to obtain a unique timestamped
directory name.
"""
attempts = 0
while attempts < MAX_DIRECTORY_CREATION_ATTEMPTS:
export_timestamp = int(time.time())
export_dir = os.path.join(
tf.compat.as_bytes(export_dir_base),
tf.compat.as_bytes(str(export_timestamp)))
if not tf_v1.gfile.Exists(export_dir):
# Collisions are still possible (though extremely unlikely): this
# directory is not actually created yet, but it will be almost
# instantly on return from this function.
return export_dir
time.sleep(1)
attempts += 1
logging.warn(
"Export directory %s already exists; retrying (attempt %d/%d)",
export_dir, attempts, MAX_DIRECTORY_CREATION_ATTEMPTS)
raise RuntimeError("Failed to obtain a unique export directory name after "
"%d attempts.".MAX_DIRECTORY_CREATION_ATTEMPTS) | [
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export_dir_base: A string containing a directory to write the exported
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30,235 | tensorflow/hub | tensorflow_hub/tf_utils.py | get_temp_export_dir | def get_temp_export_dir(timestamped_export_dir):
"""Builds a directory name based on the argument but starting with 'temp-'.
This relies on the fact that TensorFlow Serving ignores subdirectories of
the base directory that can't be parsed as integers.
Args:
timestamped_export_dir: the name of the eventual export directory, e.g.
/foo/bar/<timestamp>
Returns:
A sister directory prefixed with 'temp-', e.g. /foo/bar/temp-<timestamp>.
"""
(dirname, basename) = os.path.split(timestamped_export_dir)
temp_export_dir = os.path.join(
tf.compat.as_bytes(dirname),
tf.compat.as_bytes("temp-{}".format(basename)))
return temp_export_dir | python | def get_temp_export_dir(timestamped_export_dir):
"""Builds a directory name based on the argument but starting with 'temp-'.
This relies on the fact that TensorFlow Serving ignores subdirectories of
the base directory that can't be parsed as integers.
Args:
timestamped_export_dir: the name of the eventual export directory, e.g.
/foo/bar/<timestamp>
Returns:
A sister directory prefixed with 'temp-', e.g. /foo/bar/temp-<timestamp>.
"""
(dirname, basename) = os.path.split(timestamped_export_dir)
temp_export_dir = os.path.join(
tf.compat.as_bytes(dirname),
tf.compat.as_bytes("temp-{}".format(basename)))
return temp_export_dir | [
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timestamped_export_dir: the name of the eventual export directory, e.g.
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30,236 | tensorflow/hub | tensorflow_hub/tf_utils.py | garbage_collect_exports | def garbage_collect_exports(export_dir_base, exports_to_keep):
"""Deletes older exports, retaining only a given number of the most recent.
Export subdirectories are assumed to be named with monotonically increasing
integers; the most recent are taken to be those with the largest values.
Args:
export_dir_base: the base directory under which each export is in a
versioned subdirectory.
exports_to_keep: Number of exports to keep. Older exports will be garbage
collected. Set to None to disable.
"""
if exports_to_keep is None:
return
version_paths = [] # List of tuples (version, path)
for filename in tf_v1.gfile.ListDirectory(export_dir_base):
path = os.path.join(
tf.compat.as_bytes(export_dir_base),
tf.compat.as_bytes(filename))
if len(filename) == 10 and filename.isdigit():
version_paths.append((int(filename), path))
oldest_version_path = sorted(version_paths)[:-exports_to_keep]
for _, path in oldest_version_path:
try:
tf_v1.gfile.DeleteRecursively(path)
except tf.errors.NotFoundError as e:
logging.warn("Can not delete %s recursively: %s", path, e) | python | def garbage_collect_exports(export_dir_base, exports_to_keep):
"""Deletes older exports, retaining only a given number of the most recent.
Export subdirectories are assumed to be named with monotonically increasing
integers; the most recent are taken to be those with the largest values.
Args:
export_dir_base: the base directory under which each export is in a
versioned subdirectory.
exports_to_keep: Number of exports to keep. Older exports will be garbage
collected. Set to None to disable.
"""
if exports_to_keep is None:
return
version_paths = [] # List of tuples (version, path)
for filename in tf_v1.gfile.ListDirectory(export_dir_base):
path = os.path.join(
tf.compat.as_bytes(export_dir_base),
tf.compat.as_bytes(filename))
if len(filename) == 10 and filename.isdigit():
version_paths.append((int(filename), path))
oldest_version_path = sorted(version_paths)[:-exports_to_keep]
for _, path in oldest_version_path:
try:
tf_v1.gfile.DeleteRecursively(path)
except tf.errors.NotFoundError as e:
logging.warn("Can not delete %s recursively: %s", path, e) | [
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Args:
export_dir_base: the base directory under which each export is in a
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30,237 | tensorflow/hub | tensorflow_hub/tf_utils.py | bytes_to_readable_str | def bytes_to_readable_str(num_bytes, include_b=False):
"""Generate a human-readable string representing number of bytes.
The units B, kB, MB and GB are used.
Args:
num_bytes: (`int` or None) Number of bytes.
include_b: (`bool`) Include the letter B at the end of the unit.
Returns:
(`str`) A string representing the number of bytes in a human-readable way,
including a unit at the end.
"""
if num_bytes is None:
return str(num_bytes)
if num_bytes < 1024:
result = "%d" % num_bytes
elif num_bytes < 1048576:
result = "%.2fk" % (num_bytes / float(1 << 10))
elif num_bytes < 1073741824:
result = "%.2fM" % (num_bytes / float(1 << 20))
else:
result = "%.2fG" % (num_bytes / float(1 << 30))
if include_b:
result += "B"
return result | python | def bytes_to_readable_str(num_bytes, include_b=False):
"""Generate a human-readable string representing number of bytes.
The units B, kB, MB and GB are used.
Args:
num_bytes: (`int` or None) Number of bytes.
include_b: (`bool`) Include the letter B at the end of the unit.
Returns:
(`str`) A string representing the number of bytes in a human-readable way,
including a unit at the end.
"""
if num_bytes is None:
return str(num_bytes)
if num_bytes < 1024:
result = "%d" % num_bytes
elif num_bytes < 1048576:
result = "%.2fk" % (num_bytes / float(1 << 10))
elif num_bytes < 1073741824:
result = "%.2fM" % (num_bytes / float(1 << 20))
else:
result = "%.2fG" % (num_bytes / float(1 << 30))
if include_b:
result += "B"
return result | [
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30,238 | pytest-dev/pytest | scripts/release.py | announce | def announce(version):
"""Generates a new release announcement entry in the docs."""
# Get our list of authors
stdout = check_output(["git", "describe", "--abbrev=0", "--tags"])
stdout = stdout.decode("utf-8")
last_version = stdout.strip()
stdout = check_output(
["git", "log", "{}..HEAD".format(last_version), "--format=%aN"]
)
stdout = stdout.decode("utf-8")
contributors = set(stdout.splitlines())
template_name = (
"release.minor.rst" if version.endswith(".0") else "release.patch.rst"
)
template_text = (
Path(__file__).parent.joinpath(template_name).read_text(encoding="UTF-8")
)
contributors_text = (
"\n".join("* {}".format(name) for name in sorted(contributors)) + "\n"
)
text = template_text.format(version=version, contributors=contributors_text)
target = Path(__file__).parent.joinpath(
"../doc/en/announce/release-{}.rst".format(version)
)
target.write_text(text, encoding="UTF-8")
print(f"{Fore.CYAN}[generate.announce] {Fore.RESET}Generated {target.name}")
# Update index with the new release entry
index_path = Path(__file__).parent.joinpath("../doc/en/announce/index.rst")
lines = index_path.read_text(encoding="UTF-8").splitlines()
indent = " "
for index, line in enumerate(lines):
if line.startswith("{}release-".format(indent)):
new_line = indent + target.stem
if line != new_line:
lines.insert(index, new_line)
index_path.write_text("\n".join(lines) + "\n", encoding="UTF-8")
print(
f"{Fore.CYAN}[generate.announce] {Fore.RESET}Updated {index_path.name}"
)
else:
print(
f"{Fore.CYAN}[generate.announce] {Fore.RESET}Skip {index_path.name} (already contains release)"
)
break
check_call(["git", "add", str(target)]) | python | def announce(version):
"""Generates a new release announcement entry in the docs."""
# Get our list of authors
stdout = check_output(["git", "describe", "--abbrev=0", "--tags"])
stdout = stdout.decode("utf-8")
last_version = stdout.strip()
stdout = check_output(
["git", "log", "{}..HEAD".format(last_version), "--format=%aN"]
)
stdout = stdout.decode("utf-8")
contributors = set(stdout.splitlines())
template_name = (
"release.minor.rst" if version.endswith(".0") else "release.patch.rst"
)
template_text = (
Path(__file__).parent.joinpath(template_name).read_text(encoding="UTF-8")
)
contributors_text = (
"\n".join("* {}".format(name) for name in sorted(contributors)) + "\n"
)
text = template_text.format(version=version, contributors=contributors_text)
target = Path(__file__).parent.joinpath(
"../doc/en/announce/release-{}.rst".format(version)
)
target.write_text(text, encoding="UTF-8")
print(f"{Fore.CYAN}[generate.announce] {Fore.RESET}Generated {target.name}")
# Update index with the new release entry
index_path = Path(__file__).parent.joinpath("../doc/en/announce/index.rst")
lines = index_path.read_text(encoding="UTF-8").splitlines()
indent = " "
for index, line in enumerate(lines):
if line.startswith("{}release-".format(indent)):
new_line = indent + target.stem
if line != new_line:
lines.insert(index, new_line)
index_path.write_text("\n".join(lines) + "\n", encoding="UTF-8")
print(
f"{Fore.CYAN}[generate.announce] {Fore.RESET}Updated {index_path.name}"
)
else:
print(
f"{Fore.CYAN}[generate.announce] {Fore.RESET}Skip {index_path.name} (already contains release)"
)
break
check_call(["git", "add", str(target)]) | [
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30,239 | kubernetes-client/python | kubernetes/client/models/v1alpha1_webhook_client_config.py | V1alpha1WebhookClientConfig.ca_bundle | def ca_bundle(self, ca_bundle):
"""
Sets the ca_bundle of this V1alpha1WebhookClientConfig.
`caBundle` is a PEM encoded CA bundle which will be used to validate the webhook's server certificate. If unspecified, system trust roots on the apiserver are used.
:param ca_bundle: The ca_bundle of this V1alpha1WebhookClientConfig.
:type: str
"""
if ca_bundle is not None and not re.search('^(?:[A-Za-z0-9+\/]{4})*(?:[A-Za-z0-9+\/]{2}==|[A-Za-z0-9+\/]{3}=)?$', ca_bundle):
raise ValueError("Invalid value for `ca_bundle`, must be a follow pattern or equal to `/^(?:[A-Za-z0-9+\/]{4})*(?:[A-Za-z0-9+\/]{2}==|[A-Za-z0-9+\/]{3}=)?$/`")
self._ca_bundle = ca_bundle | python | def ca_bundle(self, ca_bundle):
"""
Sets the ca_bundle of this V1alpha1WebhookClientConfig.
`caBundle` is a PEM encoded CA bundle which will be used to validate the webhook's server certificate. If unspecified, system trust roots on the apiserver are used.
:param ca_bundle: The ca_bundle of this V1alpha1WebhookClientConfig.
:type: str
"""
if ca_bundle is not None and not re.search('^(?:[A-Za-z0-9+\/]{4})*(?:[A-Za-z0-9+\/]{2}==|[A-Za-z0-9+\/]{3}=)?$', ca_bundle):
raise ValueError("Invalid value for `ca_bundle`, must be a follow pattern or equal to `/^(?:[A-Za-z0-9+\/]{4})*(?:[A-Za-z0-9+\/]{2}==|[A-Za-z0-9+\/]{3}=)?$/`")
self._ca_bundle = ca_bundle | [
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:param ca_bundle: The ca_bundle of this V1alpha1WebhookClientConfig.
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30,240 | kubernetes-client/python | kubernetes/client/models/runtime_raw_extension.py | RuntimeRawExtension.raw | def raw(self, raw):
"""
Sets the raw of this RuntimeRawExtension.
Raw is the underlying serialization of this object.
:param raw: The raw of this RuntimeRawExtension.
:type: str
"""
if raw is None:
raise ValueError("Invalid value for `raw`, must not be `None`")
if raw is not None and not re.search('^(?:[A-Za-z0-9+\/]{4})*(?:[A-Za-z0-9+\/]{2}==|[A-Za-z0-9+\/]{3}=)?$', raw):
raise ValueError("Invalid value for `raw`, must be a follow pattern or equal to `/^(?:[A-Za-z0-9+\/]{4})*(?:[A-Za-z0-9+\/]{2}==|[A-Za-z0-9+\/]{3}=)?$/`")
self._raw = raw | python | def raw(self, raw):
"""
Sets the raw of this RuntimeRawExtension.
Raw is the underlying serialization of this object.
:param raw: The raw of this RuntimeRawExtension.
:type: str
"""
if raw is None:
raise ValueError("Invalid value for `raw`, must not be `None`")
if raw is not None and not re.search('^(?:[A-Za-z0-9+\/]{4})*(?:[A-Za-z0-9+\/]{2}==|[A-Za-z0-9+\/]{3}=)?$', raw):
raise ValueError("Invalid value for `raw`, must be a follow pattern or equal to `/^(?:[A-Za-z0-9+\/]{4})*(?:[A-Za-z0-9+\/]{2}==|[A-Za-z0-9+\/]{3}=)?$/`")
self._raw = raw | [
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Raw is the underlying serialization of this object.
:param raw: The raw of this RuntimeRawExtension.
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30,241 | kubernetes-client/python | kubernetes/client/api_client.py | ApiClient.pool | def pool(self):
"""Create thread pool on first request
avoids instantiating unused threadpool for blocking clients.
"""
if self._pool is None:
self._pool = ThreadPool(self.pool_threads)
return self._pool | python | def pool(self):
"""Create thread pool on first request
avoids instantiating unused threadpool for blocking clients.
"""
if self._pool is None:
self._pool = ThreadPool(self.pool_threads)
return self._pool | [
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30,242 | kubernetes-client/python | kubernetes/client/configuration.py | Configuration.debug | def debug(self, value):
"""
Sets the debug status.
:param value: The debug status, True or False.
:type: bool
"""
self.__debug = value
if self.__debug:
# if debug status is True, turn on debug logging
for _, logger in iteritems(self.logger):
logger.setLevel(logging.DEBUG)
# turn on httplib debug
httplib.HTTPConnection.debuglevel = 1
else:
# if debug status is False, turn off debug logging,
# setting log level to default `logging.WARNING`
for _, logger in iteritems(self.logger):
logger.setLevel(logging.WARNING)
# turn off httplib debug
httplib.HTTPConnection.debuglevel = 0 | python | def debug(self, value):
"""
Sets the debug status.
:param value: The debug status, True or False.
:type: bool
"""
self.__debug = value
if self.__debug:
# if debug status is True, turn on debug logging
for _, logger in iteritems(self.logger):
logger.setLevel(logging.DEBUG)
# turn on httplib debug
httplib.HTTPConnection.debuglevel = 1
else:
# if debug status is False, turn off debug logging,
# setting log level to default `logging.WARNING`
for _, logger in iteritems(self.logger):
logger.setLevel(logging.WARNING)
# turn off httplib debug
httplib.HTTPConnection.debuglevel = 0 | [
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30,243 | kubernetes-client/python | kubernetes/client/configuration.py | Configuration.logger_format | def logger_format(self, value):
"""
Sets the logger_format.
The logger_formatter will be updated when sets logger_format.
:param value: The format string.
:type: str
"""
self.__logger_format = value
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"""
Sets the logger_format.
The logger_formatter will be updated when sets logger_format.
:param value: The format string.
:type: str
"""
self.__logger_format = value
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30,244 | kubernetes-client/python | kubernetes/client/models/v1beta1_certificate_signing_request_status.py | V1beta1CertificateSigningRequestStatus.certificate | def certificate(self, certificate):
"""
Sets the certificate of this V1beta1CertificateSigningRequestStatus.
If request was approved, the controller will place the issued certificate here.
:param certificate: The certificate of this V1beta1CertificateSigningRequestStatus.
:type: str
"""
if certificate is not None and not re.search('^(?:[A-Za-z0-9+\/]{4})*(?:[A-Za-z0-9+\/]{2}==|[A-Za-z0-9+\/]{3}=)?$', certificate):
raise ValueError("Invalid value for `certificate`, must be a follow pattern or equal to `/^(?:[A-Za-z0-9+\/]{4})*(?:[A-Za-z0-9+\/]{2}==|[A-Za-z0-9+\/]{3}=)?$/`")
self._certificate = certificate | python | def certificate(self, certificate):
"""
Sets the certificate of this V1beta1CertificateSigningRequestStatus.
If request was approved, the controller will place the issued certificate here.
:param certificate: The certificate of this V1beta1CertificateSigningRequestStatus.
:type: str
"""
if certificate is not None and not re.search('^(?:[A-Za-z0-9+\/]{4})*(?:[A-Za-z0-9+\/]{2}==|[A-Za-z0-9+\/]{3}=)?$', certificate):
raise ValueError("Invalid value for `certificate`, must be a follow pattern or equal to `/^(?:[A-Za-z0-9+\/]{4})*(?:[A-Za-z0-9+\/]{2}==|[A-Za-z0-9+\/]{3}=)?$/`")
self._certificate = certificate | [
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30,245 | bokeh/bokeh | bokeh/core/property/wrappers.py | notify_owner | def notify_owner(func):
''' A decorator for mutating methods of property container classes
that notifies owners of the property container about mutating changes.
Args:
func (callable) : the container method to wrap in a notification
Returns:
wrapped method
Examples:
A ``__setitem__`` could be wrapped like this:
.. code-block:: python
# x[i] = y
@notify_owner
def __setitem__(self, i, y):
return super(PropertyValueDict, self).__setitem__(i, y)
The returned wrapped method will have a docstring indicating what
original method it is wrapping.
'''
def wrapper(self, *args, **kwargs):
old = self._saved_copy()
result = func(self, *args, **kwargs)
self._notify_owners(old)
return result
wrapper.__doc__ = "Container method ``%s`` instrumented to notify property owners" % func.__name__
return wrapper | python | def notify_owner(func):
''' A decorator for mutating methods of property container classes
that notifies owners of the property container about mutating changes.
Args:
func (callable) : the container method to wrap in a notification
Returns:
wrapped method
Examples:
A ``__setitem__`` could be wrapped like this:
.. code-block:: python
# x[i] = y
@notify_owner
def __setitem__(self, i, y):
return super(PropertyValueDict, self).__setitem__(i, y)
The returned wrapped method will have a docstring indicating what
original method it is wrapping.
'''
def wrapper(self, *args, **kwargs):
old = self._saved_copy()
result = func(self, *args, **kwargs)
self._notify_owners(old)
return result
wrapper.__doc__ = "Container method ``%s`` instrumented to notify property owners" % func.__name__
return wrapper | [
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Returns:
wrapped method
Examples:
A ``__setitem__`` could be wrapped like this:
.. code-block:: python
# x[i] = y
@notify_owner
def __setitem__(self, i, y):
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30,246 | bokeh/bokeh | bokeh/core/property/wrappers.py | PropertyValueColumnData._stream | def _stream(self, doc, source, new_data, rollover=None, setter=None):
''' Internal implementation to handle special-casing stream events
on ``ColumnDataSource`` columns.
Normally any changes to the ``.data`` dict attribute on a
``ColumnDataSource`` triggers a notification, causing all of the data
to be synchronized between server and clients.
The ``.stream`` method on column data sources exists to provide a
more efficient way to perform streaming (i.e. append-only) updates
to a data source, without having to perform a full synchronization,
which would needlessly re-send all the data.
To accomplish this, this function bypasses the wrapped methods on
``PropertyValueDict`` and uses the unwrapped versions on the dict
superclass directly. It then explicitly makes a notification, adding
a special ``ColumnsStreamedEvent`` hint to the message containing
only the small streamed data that BokehJS needs in order to
efficiently synchronize.
.. warning::
This function assumes the integrity of ``new_data`` has already
been verified.
'''
old = self._saved_copy()
# TODO (bev) Currently this reports old differently for array vs list
# For arrays is reports the actual old value. For lists, the old value
# is actually the already updated value. This is because the method
# self._saved_copy() makes a shallow copy.
for k, v in new_data.items():
if isinstance(self[k], np.ndarray) or isinstance(new_data[k], np.ndarray):
data = np.append(self[k], new_data[k])
if rollover and len(data) > rollover:
data = data[-rollover:]
super(PropertyValueDict, self).__setitem__(k, data)
else:
L = self[k]
L.extend(new_data[k])
if rollover is not None:
del L[:-rollover]
from ...document.events import ColumnsStreamedEvent
self._notify_owners(old,
hint=ColumnsStreamedEvent(doc, source, new_data, rollover, setter)) | python | def _stream(self, doc, source, new_data, rollover=None, setter=None):
''' Internal implementation to handle special-casing stream events
on ``ColumnDataSource`` columns.
Normally any changes to the ``.data`` dict attribute on a
``ColumnDataSource`` triggers a notification, causing all of the data
to be synchronized between server and clients.
The ``.stream`` method on column data sources exists to provide a
more efficient way to perform streaming (i.e. append-only) updates
to a data source, without having to perform a full synchronization,
which would needlessly re-send all the data.
To accomplish this, this function bypasses the wrapped methods on
``PropertyValueDict`` and uses the unwrapped versions on the dict
superclass directly. It then explicitly makes a notification, adding
a special ``ColumnsStreamedEvent`` hint to the message containing
only the small streamed data that BokehJS needs in order to
efficiently synchronize.
.. warning::
This function assumes the integrity of ``new_data`` has already
been verified.
'''
old = self._saved_copy()
# TODO (bev) Currently this reports old differently for array vs list
# For arrays is reports the actual old value. For lists, the old value
# is actually the already updated value. This is because the method
# self._saved_copy() makes a shallow copy.
for k, v in new_data.items():
if isinstance(self[k], np.ndarray) or isinstance(new_data[k], np.ndarray):
data = np.append(self[k], new_data[k])
if rollover and len(data) > rollover:
data = data[-rollover:]
super(PropertyValueDict, self).__setitem__(k, data)
else:
L = self[k]
L.extend(new_data[k])
if rollover is not None:
del L[:-rollover]
from ...document.events import ColumnsStreamedEvent
self._notify_owners(old,
hint=ColumnsStreamedEvent(doc, source, new_data, rollover, setter)) | [
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30,247 | bokeh/bokeh | bokeh/core/property/wrappers.py | PropertyValueColumnData._patch | def _patch(self, doc, source, patches, setter=None):
''' Internal implementation to handle special-casing patch events
on ``ColumnDataSource`` columns.
Normally any changes to the ``.data`` dict attribute on a
``ColumnDataSource`` triggers a notification, causing all of the data
to be synchronized between server and clients.
The ``.patch`` method on column data sources exists to provide a
more efficient way to perform patching (i.e. random access) updates
to a data source, without having to perform a full synchronization,
which would needlessly re-send all the data.
To accomplish this, this function bypasses the wrapped methods on
``PropertyValueDict`` and uses the unwrapped versions on the dict
superclass directly. It then explicitly makes a notification, adding
a special ``ColumnsPatchedEvent`` hint to the message containing
only the small patched data that BokehJS needs in order to efficiently
synchronize.
.. warning::
This function assumes the integrity of ``patches`` has already
been verified.
'''
old = self._saved_copy()
for name, patch in patches.items():
for ind, value in patch:
if isinstance(ind, (int, slice)):
self[name][ind] = value
else:
shape = self[name][ind[0]][tuple(ind[1:])].shape
self[name][ind[0]][tuple(ind[1:])] = np.array(value, copy=False).reshape(shape)
from ...document.events import ColumnsPatchedEvent
self._notify_owners(old,
hint=ColumnsPatchedEvent(doc, source, patches, setter)) | python | def _patch(self, doc, source, patches, setter=None):
''' Internal implementation to handle special-casing patch events
on ``ColumnDataSource`` columns.
Normally any changes to the ``.data`` dict attribute on a
``ColumnDataSource`` triggers a notification, causing all of the data
to be synchronized between server and clients.
The ``.patch`` method on column data sources exists to provide a
more efficient way to perform patching (i.e. random access) updates
to a data source, without having to perform a full synchronization,
which would needlessly re-send all the data.
To accomplish this, this function bypasses the wrapped methods on
``PropertyValueDict`` and uses the unwrapped versions on the dict
superclass directly. It then explicitly makes a notification, adding
a special ``ColumnsPatchedEvent`` hint to the message containing
only the small patched data that BokehJS needs in order to efficiently
synchronize.
.. warning::
This function assumes the integrity of ``patches`` has already
been verified.
'''
old = self._saved_copy()
for name, patch in patches.items():
for ind, value in patch:
if isinstance(ind, (int, slice)):
self[name][ind] = value
else:
shape = self[name][ind[0]][tuple(ind[1:])].shape
self[name][ind[0]][tuple(ind[1:])] = np.array(value, copy=False).reshape(shape)
from ...document.events import ColumnsPatchedEvent
self._notify_owners(old,
hint=ColumnsPatchedEvent(doc, source, patches, setter)) | [
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The ``.patch`` method on column data sources exists to provide a
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This function assumes the integrity of ``patches`` has already
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30,248 | bokeh/bokeh | bokeh/__init__.py | license | def license():
''' Print the Bokeh license to the console.
Returns:
None
'''
from os.path import join
with open(join(__path__[0], 'LICENSE.txt')) as lic:
print(lic.read()) | python | def license():
''' Print the Bokeh license to the console.
Returns:
None
'''
from os.path import join
with open(join(__path__[0], 'LICENSE.txt')) as lic:
print(lic.read()) | [
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30,249 | bokeh/bokeh | bokeh/core/property/bases.py | Property._copy_default | def _copy_default(cls, default):
''' Return a copy of the default, or a new value if the default
is specified by a function.
'''
if not isinstance(default, types.FunctionType):
return copy(default)
else:
return default() | python | def _copy_default(cls, default):
''' Return a copy of the default, or a new value if the default
is specified by a function.
'''
if not isinstance(default, types.FunctionType):
return copy(default)
else:
return default() | [
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30,250 | bokeh/bokeh | bokeh/core/property/bases.py | Property.matches | def matches(self, new, old):
''' Whether two parameters match values.
If either ``new`` or ``old`` is a NumPy array or Pandas Series or Index,
then the result of ``np.array_equal`` will determine if the values match.
Otherwise, the result of standard Python equality will be returned.
Returns:
True, if new and old match, False otherwise
'''
if isinstance(new, np.ndarray) or isinstance(old, np.ndarray):
return np.array_equal(new, old)
if pd:
if isinstance(new, pd.Series) or isinstance(old, pd.Series):
return np.array_equal(new, old)
if isinstance(new, pd.Index) or isinstance(old, pd.Index):
return np.array_equal(new, old)
try:
# this handles the special but common case where there is a dict with array
# or series as values (e.g. the .data property of a ColumnDataSource)
if isinstance(new, dict) and isinstance(old, dict):
if set(new.keys()) != set(old.keys()):
return False
return all(self.matches(new[k], old[k]) for k in new)
return new == old
# if the comparison fails for some reason, just punt and return no-match
except ValueError:
return False | python | def matches(self, new, old):
''' Whether two parameters match values.
If either ``new`` or ``old`` is a NumPy array or Pandas Series or Index,
then the result of ``np.array_equal`` will determine if the values match.
Otherwise, the result of standard Python equality will be returned.
Returns:
True, if new and old match, False otherwise
'''
if isinstance(new, np.ndarray) or isinstance(old, np.ndarray):
return np.array_equal(new, old)
if pd:
if isinstance(new, pd.Series) or isinstance(old, pd.Series):
return np.array_equal(new, old)
if isinstance(new, pd.Index) or isinstance(old, pd.Index):
return np.array_equal(new, old)
try:
# this handles the special but common case where there is a dict with array
# or series as values (e.g. the .data property of a ColumnDataSource)
if isinstance(new, dict) and isinstance(old, dict):
if set(new.keys()) != set(old.keys()):
return False
return all(self.matches(new[k], old[k]) for k in new)
return new == old
# if the comparison fails for some reason, just punt and return no-match
except ValueError:
return False | [
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30,251 | bokeh/bokeh | bokeh/core/property/bases.py | Property.is_valid | def is_valid(self, value):
''' Whether the value passes validation
Args:
value (obj) : the value to validate against this property type
Returns:
True if valid, False otherwise
'''
try:
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''' Whether the value passes validation
Args:
value (obj) : the value to validate against this property type
Returns:
True if valid, False otherwise
'''
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30,252 | bokeh/bokeh | bokeh/core/property/bases.py | Property.accepts | def accepts(self, tp, converter):
''' Declare that other types may be converted to this property type.
Args:
tp (Property) :
A type that may be converted automatically to this property
type.
converter (callable) :
A function accepting ``value`` to perform conversion of the
value to this property type.
Returns:
self
'''
tp = ParameterizedProperty._validate_type_param(tp)
self.alternatives.append((tp, converter))
return self | python | def accepts(self, tp, converter):
''' Declare that other types may be converted to this property type.
Args:
tp (Property) :
A type that may be converted automatically to this property
type.
converter (callable) :
A function accepting ``value`` to perform conversion of the
value to this property type.
Returns:
self
'''
tp = ParameterizedProperty._validate_type_param(tp)
self.alternatives.append((tp, converter))
return self | [
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30,253 | bokeh/bokeh | bokeh/core/property/bases.py | Property.asserts | def asserts(self, fn, msg_or_fn):
''' Assert that prepared values satisfy given conditions.
Assertions are intended in enforce conditions beyond simple value
type validation. For instance, this method can be use to assert that
the columns of a ``ColumnDataSource`` all collectively have the same
length at all times.
Args:
fn (callable) :
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msg_or_fn (str or callable) :
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Returns:
self
'''
self.assertions.append((fn, msg_or_fn))
return self | python | def asserts(self, fn, msg_or_fn):
''' Assert that prepared values satisfy given conditions.
Assertions are intended in enforce conditions beyond simple value
type validation. For instance, this method can be use to assert that
the columns of a ``ColumnDataSource`` all collectively have the same
length at all times.
Args:
fn (callable) :
A function accepting ``(obj, value)`` that returns True if the value
passes the assertion, or False otherwise.
msg_or_fn (str or callable) :
A message to print in case the assertion fails, or a function
accepting ``(obj, name, value)`` to call in in case the assertion
fails.
Returns:
self
'''
self.assertions.append((fn, msg_or_fn))
return self | [
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30,254 | bokeh/bokeh | bokeh/application/handlers/code.py | CodeHandler.url_path | def url_path(self):
''' The last path component for the basename of the configured filename.
'''
if self.failed:
return None
else:
# TODO should fix invalid URL characters
return '/' + os.path.splitext(os.path.basename(self._runner.path))[0] | python | def url_path(self):
''' The last path component for the basename of the configured filename.
'''
if self.failed:
return None
else:
# TODO should fix invalid URL characters
return '/' + os.path.splitext(os.path.basename(self._runner.path))[0] | [
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30,255 | bokeh/bokeh | bokeh/core/property/dataspec.py | UnitsSpec.make_descriptors | def make_descriptors(self, base_name):
''' Return a list of ``PropertyDescriptor`` instances to install on a
class, in order to delegate attribute access to this property.
Unlike simpler property types, ``UnitsSpec`` returns multiple
descriptors to install. In particular, descriptors for the base
property as well as the associated units property are returned.
Args:
name (str) : the name of the property these descriptors are for
Returns:
list[PropertyDescriptor]
The descriptors returned are collected by the ``MetaHasProps``
metaclass and added to ``HasProps`` subclasses during class creation.
'''
units_name = base_name + "_units"
units_props = self._units_type.make_descriptors(units_name)
return units_props + [ UnitsSpecPropertyDescriptor(base_name, self, units_props[0]) ] | python | def make_descriptors(self, base_name):
''' Return a list of ``PropertyDescriptor`` instances to install on a
class, in order to delegate attribute access to this property.
Unlike simpler property types, ``UnitsSpec`` returns multiple
descriptors to install. In particular, descriptors for the base
property as well as the associated units property are returned.
Args:
name (str) : the name of the property these descriptors are for
Returns:
list[PropertyDescriptor]
The descriptors returned are collected by the ``MetaHasProps``
metaclass and added to ``HasProps`` subclasses during class creation.
'''
units_name = base_name + "_units"
units_props = self._units_type.make_descriptors(units_name)
return units_props + [ UnitsSpecPropertyDescriptor(base_name, self, units_props[0]) ] | [
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30,256 | bokeh/bokeh | bokeh/core/property/dataspec.py | ColorSpec.isconst | def isconst(cls, val):
''' Whether the value is a string color literal.
Checks for a well-formed hexadecimal color value or a named color.
Args:
val (str) : the value to check
Returns:
True, if the value is a string color literal
'''
return isinstance(val, string_types) and \
((len(val) == 7 and val[0] == "#") or val in enums.NamedColor) | python | def isconst(cls, val):
''' Whether the value is a string color literal.
Checks for a well-formed hexadecimal color value or a named color.
Args:
val (str) : the value to check
Returns:
True, if the value is a string color literal
'''
return isinstance(val, string_types) and \
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30,257 | bokeh/bokeh | scripts/issues.py | save_object | def save_object(filename, obj):
"""Compresses and pickles given object to the given filename."""
logging.info('saving {}...'.format(filename))
try:
with gzip.GzipFile(filename, 'wb') as f:
f.write(pickle.dumps(obj, 1))
except Exception as e:
logging.error('save failure: {}'.format(e))
raise | python | def save_object(filename, obj):
"""Compresses and pickles given object to the given filename."""
logging.info('saving {}...'.format(filename))
try:
with gzip.GzipFile(filename, 'wb') as f:
f.write(pickle.dumps(obj, 1))
except Exception as e:
logging.error('save failure: {}'.format(e))
raise | [
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30,258 | bokeh/bokeh | scripts/issues.py | load_object | def load_object(filename):
"""Unpickles and decompresses the given filename and returns the created object."""
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buf += data
return pickle.loads(buf)
except Exception as e:
logging.error('load failure: {}'.format(e))
raise | python | def load_object(filename):
"""Unpickles and decompresses the given filename and returns the created object."""
logging.info('loading {}...'.format(filename))
try:
with gzip.GzipFile(filename, 'rb') as f:
buf = ''
while True:
data = f.read()
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buf += data
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logging.error('load failure: {}'.format(e))
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30,259 | bokeh/bokeh | scripts/issues.py | issue_section | def issue_section(issue):
"""Returns the section heading for the issue, or None if this issue should be ignored."""
labels = issue.get('labels', [])
for label in labels:
if not label['name'].startswith('type: '):
continue
if label['name'] in LOG_SECTION:
return LOG_SECTION[label['name']]
elif label['name'] in IGNORE_ISSUE_TYPE:
return None
else:
logging.warning('unknown issue type: "{}" for: {}'.format(label['name'], issue_line(issue)))
return None | python | def issue_section(issue):
"""Returns the section heading for the issue, or None if this issue should be ignored."""
labels = issue.get('labels', [])
for label in labels:
if not label['name'].startswith('type: '):
continue
if label['name'] in LOG_SECTION:
return LOG_SECTION[label['name']]
elif label['name'] in IGNORE_ISSUE_TYPE:
return None
else:
logging.warning('unknown issue type: "{}" for: {}'.format(label['name'], issue_line(issue)))
return None | [
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30,260 | bokeh/bokeh | scripts/issues.py | issue_tags | def issue_tags(issue):
"""Returns list of tags for this issue."""
labels = issue.get('labels', [])
return [label['name'].replace('tag: ', '') for label in labels if label['name'].startswith('tag: ')] | python | def issue_tags(issue):
"""Returns list of tags for this issue."""
labels = issue.get('labels', [])
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30,261 | bokeh/bokeh | scripts/issues.py | closed_issue | def closed_issue(issue, after=None):
"""Returns True iff this issue was closed after given date. If after not given, only checks if issue is closed."""
if issue['state'] == 'closed':
if after is None or parse_timestamp(issue['closed_at']) > after:
return True
return False | python | def closed_issue(issue, after=None):
"""Returns True iff this issue was closed after given date. If after not given, only checks if issue is closed."""
if issue['state'] == 'closed':
if after is None or parse_timestamp(issue['closed_at']) > after:
return True
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30,262 | bokeh/bokeh | scripts/issues.py | relevent_issue | def relevent_issue(issue, after):
"""Returns True iff this issue is something we should show in the changelog."""
return (closed_issue(issue, after) and
issue_completed(issue) and
issue_section(issue)) | python | def relevent_issue(issue, after):
"""Returns True iff this issue is something we should show in the changelog."""
return (closed_issue(issue, after) and
issue_completed(issue) and
issue_section(issue)) | [
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30,263 | bokeh/bokeh | scripts/issues.py | all_issues | def all_issues(issues):
"""Yields unique set of issues given a list of issues."""
logging.info('finding issues...')
seen = set()
for issue in issues:
if issue['title'] not in seen:
seen.add(issue['title'])
yield issue | python | def all_issues(issues):
"""Yields unique set of issues given a list of issues."""
logging.info('finding issues...')
seen = set()
for issue in issues:
if issue['title'] not in seen:
seen.add(issue['title'])
yield issue | [
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30,264 | bokeh/bokeh | scripts/issues.py | get_issues_url | def get_issues_url(page, after):
"""Returns github API URL for querying tags."""
template = '{base_url}/{owner}/{repo}/issues?state=closed&per_page=100&page={page}&since={after}'
return template.format(page=page, after=after.isoformat(), **API_PARAMS) | python | def get_issues_url(page, after):
"""Returns github API URL for querying tags."""
template = '{base_url}/{owner}/{repo}/issues?state=closed&per_page=100&page={page}&since={after}'
return template.format(page=page, after=after.isoformat(), **API_PARAMS) | [
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30,265 | bokeh/bokeh | scripts/issues.py | parse_timestamp | def parse_timestamp(timestamp):
"""Parse ISO8601 timestamps given by github API."""
dt = dateutil.parser.parse(timestamp)
return dt.astimezone(dateutil.tz.tzutc()) | python | def parse_timestamp(timestamp):
"""Parse ISO8601 timestamps given by github API."""
dt = dateutil.parser.parse(timestamp)
return dt.astimezone(dateutil.tz.tzutc()) | [
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30,266 | bokeh/bokeh | scripts/issues.py | read_url | def read_url(url):
"""Reads given URL as JSON and returns data as loaded python object."""
logging.debug('reading {url} ...'.format(url=url))
token = os.environ.get("BOKEH_GITHUB_API_TOKEN")
headers = {}
if token:
headers['Authorization'] = 'token %s' % token
request = Request(url, headers=headers)
response = urlopen(request).read()
return json.loads(response.decode("UTF-8")) | python | def read_url(url):
"""Reads given URL as JSON and returns data as loaded python object."""
logging.debug('reading {url} ...'.format(url=url))
token = os.environ.get("BOKEH_GITHUB_API_TOKEN")
headers = {}
if token:
headers['Authorization'] = 'token %s' % token
request = Request(url, headers=headers)
response = urlopen(request).read()
return json.loads(response.decode("UTF-8")) | [
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30,267 | bokeh/bokeh | scripts/issues.py | query_all_issues | def query_all_issues(after):
"""Hits the github API for all closed issues after the given date, returns the data."""
page = count(1)
data = []
while True:
page_data = query_issues(next(page), after)
if not page_data:
break
data.extend(page_data)
return data | python | def query_all_issues(after):
"""Hits the github API for all closed issues after the given date, returns the data."""
page = count(1)
data = []
while True:
page_data = query_issues(next(page), after)
if not page_data:
break
data.extend(page_data)
return data | [
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30,268 | bokeh/bokeh | scripts/issues.py | dateof | def dateof(tag_name, tags):
"""Given a list of tags, returns the datetime of the tag with the given name; Otherwise None."""
for tag in tags:
if tag['name'] == tag_name:
commit = read_url(tag['commit']['url'])
return parse_timestamp(commit['commit']['committer']['date'])
return None | python | def dateof(tag_name, tags):
"""Given a list of tags, returns the datetime of the tag with the given name; Otherwise None."""
for tag in tags:
if tag['name'] == tag_name:
commit = read_url(tag['commit']['url'])
return parse_timestamp(commit['commit']['committer']['date'])
return None | [
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30,269 | bokeh/bokeh | scripts/issues.py | get_data | def get_data(query_func, load_data=False, save_data=False):
"""Gets data from query_func, optionally saving that data to a file; or loads data from a file."""
if hasattr(query_func, '__name__'):
func_name = query_func.__name__
elif hasattr(query_func, 'func'):
func_name = query_func.func.__name__
pickle_file = '{}.pickle'.format(func_name)
if load_data:
data = load_object(pickle_file)
else:
data = query_func()
if save_data:
save_object(pickle_file, data)
return data | python | def get_data(query_func, load_data=False, save_data=False):
"""Gets data from query_func, optionally saving that data to a file; or loads data from a file."""
if hasattr(query_func, '__name__'):
func_name = query_func.__name__
elif hasattr(query_func, 'func'):
func_name = query_func.func.__name__
pickle_file = '{}.pickle'.format(func_name)
if load_data:
data = load_object(pickle_file)
else:
data = query_func()
if save_data:
save_object(pickle_file, data)
return data | [
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30,270 | bokeh/bokeh | scripts/issues.py | check_issues | def check_issues(issues, after=None):
"""Checks issues for BEP 1 compliance."""
issues = closed_issues(issues, after) if after else all_issues(issues)
issues = sorted(issues, key=ISSUES_SORT_KEY)
have_warnings = False
for section, issue_group in groupby(issues, key=ISSUES_BY_SECTION):
for issue in issue_group:
have_warnings |= check_issue(issue, after)
return have_warnings | python | def check_issues(issues, after=None):
"""Checks issues for BEP 1 compliance."""
issues = closed_issues(issues, after) if after else all_issues(issues)
issues = sorted(issues, key=ISSUES_SORT_KEY)
have_warnings = False
for section, issue_group in groupby(issues, key=ISSUES_BY_SECTION):
for issue in issue_group:
have_warnings |= check_issue(issue, after)
return have_warnings | [
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30,271 | bokeh/bokeh | scripts/issues.py | issue_line | def issue_line(issue):
"""Returns log line for given issue."""
template = '#{number} {tags}{title}'
tags = issue_tags(issue)
params = {
'title': issue['title'].capitalize().rstrip('.'),
'number': issue['number'],
'tags': ' '.join('[{}]'.format(tag) for tag in tags) + (' ' if tags else '')
}
return template.format(**params) | python | def issue_line(issue):
"""Returns log line for given issue."""
template = '#{number} {tags}{title}'
tags = issue_tags(issue)
params = {
'title': issue['title'].capitalize().rstrip('.'),
'number': issue['number'],
'tags': ' '.join('[{}]'.format(tag) for tag in tags) + (' ' if tags else '')
}
return template.format(**params) | [
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30,272 | bokeh/bokeh | scripts/issues.py | generate_changelog | def generate_changelog(issues, after, heading, rtag=False):
"""Prints out changelog."""
relevent = relevant_issues(issues, after)
relevent = sorted(relevent, key=ISSUES_BY_SECTION)
def write(func, endofline="", append=""):
func(heading + '\n' + '-' * 20 + endofline)
for section, issue_group in groupby(relevent, key=ISSUES_BY_SECTION):
func(' * {}:'.format(section) + endofline)
for issue in reversed(list(issue_group)):
func(' - {}'.format(issue_line(issue)) + endofline)
func(endofline + append)
if rtag is not False:
with open("../CHANGELOG", "r+") as f:
content = f.read()
f.seek(0)
write(f.write, '\n', content)
else:
write(print) | python | def generate_changelog(issues, after, heading, rtag=False):
"""Prints out changelog."""
relevent = relevant_issues(issues, after)
relevent = sorted(relevent, key=ISSUES_BY_SECTION)
def write(func, endofline="", append=""):
func(heading + '\n' + '-' * 20 + endofline)
for section, issue_group in groupby(relevent, key=ISSUES_BY_SECTION):
func(' * {}:'.format(section) + endofline)
for issue in reversed(list(issue_group)):
func(' - {}'.format(issue_line(issue)) + endofline)
func(endofline + append)
if rtag is not False:
with open("../CHANGELOG", "r+") as f:
content = f.read()
f.seek(0)
write(f.write, '\n', content)
else:
write(print) | [
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30,273 | bokeh/bokeh | bokeh/colors/rgb.py | RGB.to_css | def to_css(self):
''' Generate the CSS representation of this RGB color.
Returns:
str, ``"rgb(...)"`` or ``"rgba(...)"``
'''
if self.a == 1.0:
return "rgb(%d, %d, %d)" % (self.r, self.g, self.b)
else:
return "rgba(%d, %d, %d, %s)" % (self.r, self.g, self.b, self.a) | python | def to_css(self):
''' Generate the CSS representation of this RGB color.
Returns:
str, ``"rgb(...)"`` or ``"rgba(...)"``
'''
if self.a == 1.0:
return "rgb(%d, %d, %d)" % (self.r, self.g, self.b)
else:
return "rgba(%d, %d, %d, %s)" % (self.r, self.g, self.b, self.a) | [
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30,274 | bokeh/bokeh | bokeh/colors/rgb.py | RGB.to_hsl | def to_hsl(self):
''' Return a corresponding HSL color for this RGB color.
Returns:
:class:`~bokeh.colors.rgb.RGB`
'''
from .hsl import HSL # prevent circular import
h, l, s = colorsys.rgb_to_hls(float(self.r)/255, float(self.g)/255, float(self.b)/255)
return HSL(round(h*360), s, l, self.a) | python | def to_hsl(self):
''' Return a corresponding HSL color for this RGB color.
Returns:
:class:`~bokeh.colors.rgb.RGB`
'''
from .hsl import HSL # prevent circular import
h, l, s = colorsys.rgb_to_hls(float(self.r)/255, float(self.g)/255, float(self.b)/255)
return HSL(round(h*360), s, l, self.a) | [
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30,275 | bokeh/bokeh | bokeh/util/tornado.py | yield_for_all_futures | def yield_for_all_futures(result):
""" Converts result into a Future by collapsing any futures inside result.
If result is a Future we yield until it's done, then if the value inside
the Future is another Future we yield until it's done as well, and so on.
"""
while True:
# This is needed for Tornado >= 4.5 where convert_yielded will no
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break
try:
future = gen.convert_yielded(result)
except gen.BadYieldError:
# result is not a yieldable thing, we are done
break
else:
result = yield future
raise gen.Return(result) | python | def yield_for_all_futures(result):
""" Converts result into a Future by collapsing any futures inside result.
If result is a Future we yield until it's done, then if the value inside
the Future is another Future we yield until it's done as well, and so on.
"""
while True:
# This is needed for Tornado >= 4.5 where convert_yielded will no
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if result is None:
break
try:
future = gen.convert_yielded(result)
except gen.BadYieldError:
# result is not a yieldable thing, we are done
break
else:
result = yield future
raise gen.Return(result) | [
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30,276 | bokeh/bokeh | bokeh/util/tornado.py | _CallbackGroup.remove_all_callbacks | def remove_all_callbacks(self):
""" Removes all registered callbacks."""
for cb_id in list(self._next_tick_callback_removers.keys()):
self.remove_next_tick_callback(cb_id)
for cb_id in list(self._timeout_callback_removers.keys()):
self.remove_timeout_callback(cb_id)
for cb_id in list(self._periodic_callback_removers.keys()):
self.remove_periodic_callback(cb_id) | python | def remove_all_callbacks(self):
""" Removes all registered callbacks."""
for cb_id in list(self._next_tick_callback_removers.keys()):
self.remove_next_tick_callback(cb_id)
for cb_id in list(self._timeout_callback_removers.keys()):
self.remove_timeout_callback(cb_id)
for cb_id in list(self._periodic_callback_removers.keys()):
self.remove_periodic_callback(cb_id) | [
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30,277 | bokeh/bokeh | bokeh/util/tornado.py | _CallbackGroup.add_next_tick_callback | def add_next_tick_callback(self, callback, callback_id=None):
""" Adds a callback to be run on the next tick.
Returns an ID that can be used with remove_next_tick_callback."""
def wrapper(*args, **kwargs):
# this 'removed' flag is a hack because Tornado has no way
# to remove a "next tick" callback added with
# IOLoop.add_callback. So instead we make our wrapper skip
# invoking the callback.
if not wrapper.removed:
self.remove_next_tick_callback(callback_id)
return callback(*args, **kwargs)
else:
return None
wrapper.removed = False
def remover():
wrapper.removed = True
callback_id = self._assign_remover(callback, callback_id, self._next_tick_callback_removers, remover)
self._loop.add_callback(wrapper)
return callback_id | python | def add_next_tick_callback(self, callback, callback_id=None):
""" Adds a callback to be run on the next tick.
Returns an ID that can be used with remove_next_tick_callback."""
def wrapper(*args, **kwargs):
# this 'removed' flag is a hack because Tornado has no way
# to remove a "next tick" callback added with
# IOLoop.add_callback. So instead we make our wrapper skip
# invoking the callback.
if not wrapper.removed:
self.remove_next_tick_callback(callback_id)
return callback(*args, **kwargs)
else:
return None
wrapper.removed = False
def remover():
wrapper.removed = True
callback_id = self._assign_remover(callback, callback_id, self._next_tick_callback_removers, remover)
self._loop.add_callback(wrapper)
return callback_id | [
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30,278 | bokeh/bokeh | bokeh/util/tornado.py | _CallbackGroup.add_timeout_callback | def add_timeout_callback(self, callback, timeout_milliseconds, callback_id=None):
""" Adds a callback to be run once after timeout_milliseconds.
Returns an ID that can be used with remove_timeout_callback."""
def wrapper(*args, **kwargs):
self.remove_timeout_callback(callback_id)
return callback(*args, **kwargs)
handle = None
def remover():
if handle is not None:
self._loop.remove_timeout(handle)
callback_id = self._assign_remover(callback, callback_id, self._timeout_callback_removers, remover)
handle = self._loop.call_later(timeout_milliseconds / 1000.0, wrapper)
return callback_id | python | def add_timeout_callback(self, callback, timeout_milliseconds, callback_id=None):
""" Adds a callback to be run once after timeout_milliseconds.
Returns an ID that can be used with remove_timeout_callback."""
def wrapper(*args, **kwargs):
self.remove_timeout_callback(callback_id)
return callback(*args, **kwargs)
handle = None
def remover():
if handle is not None:
self._loop.remove_timeout(handle)
callback_id = self._assign_remover(callback, callback_id, self._timeout_callback_removers, remover)
handle = self._loop.call_later(timeout_milliseconds / 1000.0, wrapper)
return callback_id | [
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30,279 | bokeh/bokeh | bokeh/util/tornado.py | _CallbackGroup.add_periodic_callback | def add_periodic_callback(self, callback, period_milliseconds, callback_id=None):
""" Adds a callback to be run every period_milliseconds until it is removed.
Returns an ID that can be used with remove_periodic_callback."""
cb = _AsyncPeriodic(callback, period_milliseconds, io_loop=self._loop)
callback_id = self._assign_remover(callback, callback_id, self._periodic_callback_removers, cb.stop)
cb.start()
return callback_id | python | def add_periodic_callback(self, callback, period_milliseconds, callback_id=None):
""" Adds a callback to be run every period_milliseconds until it is removed.
Returns an ID that can be used with remove_periodic_callback."""
cb = _AsyncPeriodic(callback, period_milliseconds, io_loop=self._loop)
callback_id = self._assign_remover(callback, callback_id, self._periodic_callback_removers, cb.stop)
cb.start()
return callback_id | [
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30,280 | bokeh/bokeh | bokeh/sphinxext/bokeh_github.py | bokeh_tree | def bokeh_tree(name, rawtext, text, lineno, inliner, options=None, content=None):
''' Link to a URL in the Bokeh GitHub tree, pointing to appropriate tags
for releases, or to master otherwise.
The link text is simply the URL path supplied, so typical usage might
look like:
.. code-block:: none
All of the examples are located in the :bokeh-tree:`examples`
subdirectory of your Bokeh checkout.
Returns 2 part tuple containing list of nodes to insert into the
document and a list of system messages. Both are allowed to be
empty.
'''
app = inliner.document.settings.env.app
tag = app.env.config['version']
if '-' in tag:
tag = 'master'
url = "%s/tree/%s/%s" % (_BOKEH_GH, tag, text)
options = options or {}
set_classes(options)
node = nodes.reference(rawtext, text, refuri=url, **options)
return [node], [] | python | def bokeh_tree(name, rawtext, text, lineno, inliner, options=None, content=None):
''' Link to a URL in the Bokeh GitHub tree, pointing to appropriate tags
for releases, or to master otherwise.
The link text is simply the URL path supplied, so typical usage might
look like:
.. code-block:: none
All of the examples are located in the :bokeh-tree:`examples`
subdirectory of your Bokeh checkout.
Returns 2 part tuple containing list of nodes to insert into the
document and a list of system messages. Both are allowed to be
empty.
'''
app = inliner.document.settings.env.app
tag = app.env.config['version']
if '-' in tag:
tag = 'master'
url = "%s/tree/%s/%s" % (_BOKEH_GH, tag, text)
options = options or {}
set_classes(options)
node = nodes.reference(rawtext, text, refuri=url, **options)
return [node], [] | [
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.. code-block:: none
All of the examples are located in the :bokeh-tree:`examples`
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Returns 2 part tuple containing list of nodes to insert into the
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30,281 | bokeh/bokeh | bokeh/sphinxext/bokeh_github.py | _make_gh_link_node | def _make_gh_link_node(app, rawtext, role, kind, api_type, id, options=None):
''' Return a link to a Bokeh Github resource.
Args:
app (Sphinx app) : current app
rawtext (str) : text being replaced with link node.
role (str) : role name
kind (str) : resource type (issue, pull, etc.)
api_type (str) : type for api link
id : (str) : id of the resource to link to
options (dict) : options dictionary passed to role function
'''
url = "%s/%s/%s" % (_BOKEH_GH, api_type, id)
options = options or {}
set_classes(options)
node = nodes.reference(
rawtext, kind + utils.unescape(id), refuri=url, **options)
return node | python | def _make_gh_link_node(app, rawtext, role, kind, api_type, id, options=None):
''' Return a link to a Bokeh Github resource.
Args:
app (Sphinx app) : current app
rawtext (str) : text being replaced with link node.
role (str) : role name
kind (str) : resource type (issue, pull, etc.)
api_type (str) : type for api link
id : (str) : id of the resource to link to
options (dict) : options dictionary passed to role function
'''
url = "%s/%s/%s" % (_BOKEH_GH, api_type, id)
options = options or {}
set_classes(options)
node = nodes.reference(
rawtext, kind + utils.unescape(id), refuri=url, **options)
return node | [
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30,282 | bokeh/bokeh | _setup_support.py | show_bokehjs | def show_bokehjs(bokehjs_action, develop=False):
''' Print a useful report after setuptools output describing where and how
BokehJS is installed.
Args:
bokehjs_action (str) : one of 'built', 'installed', or 'packaged'
how (or if) BokehJS was installed into the python source tree
develop (bool, optional) :
whether the command was for "develop" mode (default: False)
Returns:
None
'''
print()
if develop:
print("Installed Bokeh for DEVELOPMENT:")
else:
print("Installed Bokeh:")
if bokehjs_action in ['built', 'installed']:
print(" - using %s built BokehJS from bokehjs/build\n" % (bright(yellow("NEWLY")) if bokehjs_action=='built' else bright(yellow("PREVIOUSLY"))))
else:
print(" - using %s BokehJS, located in 'bokeh.server.static'\n" % bright(yellow("PACKAGED")))
print() | python | def show_bokehjs(bokehjs_action, develop=False):
''' Print a useful report after setuptools output describing where and how
BokehJS is installed.
Args:
bokehjs_action (str) : one of 'built', 'installed', or 'packaged'
how (or if) BokehJS was installed into the python source tree
develop (bool, optional) :
whether the command was for "develop" mode (default: False)
Returns:
None
'''
print()
if develop:
print("Installed Bokeh for DEVELOPMENT:")
else:
print("Installed Bokeh:")
if bokehjs_action in ['built', 'installed']:
print(" - using %s built BokehJS from bokehjs/build\n" % (bright(yellow("NEWLY")) if bokehjs_action=='built' else bright(yellow("PREVIOUSLY"))))
else:
print(" - using %s BokehJS, located in 'bokeh.server.static'\n" % bright(yellow("PACKAGED")))
print() | [
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30,283 | bokeh/bokeh | _setup_support.py | show_help | def show_help(bokehjs_action):
''' Print information about extra Bokeh-specific command line options.
Args:
bokehjs_action (str) : one of 'built', 'installed', or 'packaged'
how (or if) BokehJS was installed into the python source tree
Returns:
None
'''
print()
if bokehjs_action in ['built', 'installed']:
print("Bokeh-specific options available with 'install' or 'develop':")
print()
print(" --build-js build and install a fresh BokehJS")
print(" --install-js install only last previously built BokehJS")
else:
print("Bokeh is using PACKAGED BokehJS, located in 'bokeh.server.static'")
print()
print("No extra Bokeh-specific options are available.")
print() | python | def show_help(bokehjs_action):
''' Print information about extra Bokeh-specific command line options.
Args:
bokehjs_action (str) : one of 'built', 'installed', or 'packaged'
how (or if) BokehJS was installed into the python source tree
Returns:
None
'''
print()
if bokehjs_action in ['built', 'installed']:
print("Bokeh-specific options available with 'install' or 'develop':")
print()
print(" --build-js build and install a fresh BokehJS")
print(" --install-js install only last previously built BokehJS")
else:
print("Bokeh is using PACKAGED BokehJS, located in 'bokeh.server.static'")
print()
print("No extra Bokeh-specific options are available.")
print() | [
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30,284 | bokeh/bokeh | _setup_support.py | fixup_building_sdist | def fixup_building_sdist():
''' Check for 'sdist' and ensure we always build BokehJS when packaging
Source distributions do not ship with BokehJS source code, but must ship
with a pre-built BokehJS library. This function modifies ``sys.argv`` as
necessary so that ``--build-js`` IS present, and ``--install-js` is NOT.
Returns:
None
'''
if "sdist" in sys.argv:
if "--install-js" in sys.argv:
print("Removing '--install-js' incompatible with 'sdist'")
sys.argv.remove('--install-js')
if "--build-js" not in sys.argv:
print("Adding '--build-js' required for 'sdist'")
sys.argv.append('--build-js') | python | def fixup_building_sdist():
''' Check for 'sdist' and ensure we always build BokehJS when packaging
Source distributions do not ship with BokehJS source code, but must ship
with a pre-built BokehJS library. This function modifies ``sys.argv`` as
necessary so that ``--build-js`` IS present, and ``--install-js` is NOT.
Returns:
None
'''
if "sdist" in sys.argv:
if "--install-js" in sys.argv:
print("Removing '--install-js' incompatible with 'sdist'")
sys.argv.remove('--install-js')
if "--build-js" not in sys.argv:
print("Adding '--build-js' required for 'sdist'")
sys.argv.append('--build-js') | [
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30,285 | bokeh/bokeh | _setup_support.py | fixup_for_packaged | def fixup_for_packaged():
''' If we are installing FROM an sdist, then a pre-built BokehJS is
already installed in the python source tree.
The command line options ``--build-js`` or ``--install-js`` are
removed from ``sys.argv``, with a warning.
Also adds ``--existing-js`` to ``sys.argv`` to signal that BokehJS is
already packaged.
Returns:
None
'''
if exists(join(ROOT, 'PKG-INFO')):
if "--build-js" in sys.argv or "--install-js" in sys.argv:
print(SDIST_BUILD_WARNING)
if "--build-js" in sys.argv:
sys.argv.remove('--build-js')
if "--install-js" in sys.argv:
sys.argv.remove('--install-js')
if "--existing-js" not in sys.argv:
sys.argv.append('--existing-js') | python | def fixup_for_packaged():
''' If we are installing FROM an sdist, then a pre-built BokehJS is
already installed in the python source tree.
The command line options ``--build-js`` or ``--install-js`` are
removed from ``sys.argv``, with a warning.
Also adds ``--existing-js`` to ``sys.argv`` to signal that BokehJS is
already packaged.
Returns:
None
'''
if exists(join(ROOT, 'PKG-INFO')):
if "--build-js" in sys.argv or "--install-js" in sys.argv:
print(SDIST_BUILD_WARNING)
if "--build-js" in sys.argv:
sys.argv.remove('--build-js')
if "--install-js" in sys.argv:
sys.argv.remove('--install-js')
if "--existing-js" not in sys.argv:
sys.argv.append('--existing-js') | [
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30,286 | bokeh/bokeh | _setup_support.py | get_cmdclass | def get_cmdclass():
''' A ``cmdclass`` that works around a setuptools deficiency.
There is no need to build wheels when installing a package, however some
versions of setuptools seem to mandate this. This is a hacky workaround
that modifies the ``cmdclass`` returned by versioneer so that not having
wheel installed is not a fatal error.
'''
cmdclass = versioneer.get_cmdclass()
try:
from wheel.bdist_wheel import bdist_wheel
except ImportError:
# pip is not claiming for bdist_wheel when wheel is not installed
bdist_wheel = None
if bdist_wheel is not None:
cmdclass["bdist_wheel"] = bdist_wheel
return cmdclass | python | def get_cmdclass():
''' A ``cmdclass`` that works around a setuptools deficiency.
There is no need to build wheels when installing a package, however some
versions of setuptools seem to mandate this. This is a hacky workaround
that modifies the ``cmdclass`` returned by versioneer so that not having
wheel installed is not a fatal error.
'''
cmdclass = versioneer.get_cmdclass()
try:
from wheel.bdist_wheel import bdist_wheel
except ImportError:
# pip is not claiming for bdist_wheel when wheel is not installed
bdist_wheel = None
if bdist_wheel is not None:
cmdclass["bdist_wheel"] = bdist_wheel
return cmdclass | [
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30,287 | bokeh/bokeh | _setup_support.py | jsbuild_prompt | def jsbuild_prompt():
''' Prompt users whether to build a new BokehJS or install an existing one.
Returns:
bool : True, if a new build is requested, False otherwise
'''
print(BOKEHJS_BUILD_PROMPT)
mapping = {"1": True, "2": False}
value = input("Choice? ")
while value not in mapping:
print("Input '%s' not understood. Valid choices: 1, 2\n" % value)
value = input("Choice? ")
return mapping[value] | python | def jsbuild_prompt():
''' Prompt users whether to build a new BokehJS or install an existing one.
Returns:
bool : True, if a new build is requested, False otherwise
'''
print(BOKEHJS_BUILD_PROMPT)
mapping = {"1": True, "2": False}
value = input("Choice? ")
while value not in mapping:
print("Input '%s' not understood. Valid choices: 1, 2\n" % value)
value = input("Choice? ")
return mapping[value] | [
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30,288 | bokeh/bokeh | _setup_support.py | install_js | def install_js():
''' Copy built BokehJS files into the Python source tree.
Returns:
None
'''
target_jsdir = join(SERVER, 'static', 'js')
target_cssdir = join(SERVER, 'static', 'css')
target_tslibdir = join(SERVER, 'static', 'lib')
STATIC_ASSETS = [
join(JS, 'bokeh.js'),
join(JS, 'bokeh.min.js'),
join(CSS, 'bokeh.css'),
join(CSS, 'bokeh.min.css'),
]
if not all(exists(a) for a in STATIC_ASSETS):
print(BOKEHJS_INSTALL_FAIL)
sys.exit(1)
if exists(target_jsdir):
shutil.rmtree(target_jsdir)
shutil.copytree(JS, target_jsdir)
if exists(target_cssdir):
shutil.rmtree(target_cssdir)
shutil.copytree(CSS, target_cssdir)
if exists(target_tslibdir):
shutil.rmtree(target_tslibdir)
if exists(TSLIB):
# keep in sync with bokehjs/src/compiler/compile.ts
lib = {
"lib.es5.d.ts",
"lib.dom.d.ts",
"lib.es2015.core.d.ts",
"lib.es2015.promise.d.ts",
"lib.es2015.symbol.d.ts",
"lib.es2015.iterable.d.ts",
}
shutil.copytree(TSLIB, target_tslibdir, ignore=lambda _, files: [ f for f in files if f not in lib ]) | python | def install_js():
''' Copy built BokehJS files into the Python source tree.
Returns:
None
'''
target_jsdir = join(SERVER, 'static', 'js')
target_cssdir = join(SERVER, 'static', 'css')
target_tslibdir = join(SERVER, 'static', 'lib')
STATIC_ASSETS = [
join(JS, 'bokeh.js'),
join(JS, 'bokeh.min.js'),
join(CSS, 'bokeh.css'),
join(CSS, 'bokeh.min.css'),
]
if not all(exists(a) for a in STATIC_ASSETS):
print(BOKEHJS_INSTALL_FAIL)
sys.exit(1)
if exists(target_jsdir):
shutil.rmtree(target_jsdir)
shutil.copytree(JS, target_jsdir)
if exists(target_cssdir):
shutil.rmtree(target_cssdir)
shutil.copytree(CSS, target_cssdir)
if exists(target_tslibdir):
shutil.rmtree(target_tslibdir)
if exists(TSLIB):
# keep in sync with bokehjs/src/compiler/compile.ts
lib = {
"lib.es5.d.ts",
"lib.dom.d.ts",
"lib.es2015.core.d.ts",
"lib.es2015.promise.d.ts",
"lib.es2015.symbol.d.ts",
"lib.es2015.iterable.d.ts",
}
shutil.copytree(TSLIB, target_tslibdir, ignore=lambda _, files: [ f for f in files if f not in lib ]) | [
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30,289 | bokeh/bokeh | bokeh/util/hex.py | hexbin | def hexbin(x, y, size, orientation="pointytop", aspect_scale=1):
''' Perform an equal-weight binning of data points into hexagonal tiles.
For more sophisticated use cases, e.g. weighted binning or scaling
individual tiles proportional to some other quantity, consider using
HoloViews.
Args:
x (array[float]) :
A NumPy array of x-coordinates for binning
y (array[float]) :
A NumPy array of y-coordinates for binning
size (float) :
The size of the hexagonal tiling.
The size is defined as the distance from the center of a hexagon
to the top corner for "pointytop" orientation, or from the center
to a side corner for "flattop" orientation.
orientation (str, optional) :
Whether the hex tile orientation should be "pointytop" or
"flattop". (default: "pointytop")
aspect_scale (float, optional) :
Match a plot's aspect ratio scaling.
When working with a plot with ``aspect_scale != 1``, this
parameter can be set to match the plot, in order to draw
regular hexagons (instead of "stretched" ones).
This is roughly equivalent to binning in "screen space", and
it may be better to use axis-aligned rectangular bins when
plot aspect scales are not one.
Returns:
DataFrame
The resulting DataFrame will have columns *q* and *r* that specify
hexagon tile locations in axial coordinates, and a column *counts* that
provides the count for each tile.
.. warning::
Hex binning only functions on linear scales, i.e. not on log plots.
'''
pd = import_required('pandas','hexbin requires pandas to be installed')
q, r = cartesian_to_axial(x, y, size, orientation, aspect_scale=aspect_scale)
df = pd.DataFrame(dict(r=r, q=q))
return df.groupby(['q', 'r']).size().reset_index(name='counts') | python | def hexbin(x, y, size, orientation="pointytop", aspect_scale=1):
''' Perform an equal-weight binning of data points into hexagonal tiles.
For more sophisticated use cases, e.g. weighted binning or scaling
individual tiles proportional to some other quantity, consider using
HoloViews.
Args:
x (array[float]) :
A NumPy array of x-coordinates for binning
y (array[float]) :
A NumPy array of y-coordinates for binning
size (float) :
The size of the hexagonal tiling.
The size is defined as the distance from the center of a hexagon
to the top corner for "pointytop" orientation, or from the center
to a side corner for "flattop" orientation.
orientation (str, optional) :
Whether the hex tile orientation should be "pointytop" or
"flattop". (default: "pointytop")
aspect_scale (float, optional) :
Match a plot's aspect ratio scaling.
When working with a plot with ``aspect_scale != 1``, this
parameter can be set to match the plot, in order to draw
regular hexagons (instead of "stretched" ones).
This is roughly equivalent to binning in "screen space", and
it may be better to use axis-aligned rectangular bins when
plot aspect scales are not one.
Returns:
DataFrame
The resulting DataFrame will have columns *q* and *r* that specify
hexagon tile locations in axial coordinates, and a column *counts* that
provides the count for each tile.
.. warning::
Hex binning only functions on linear scales, i.e. not on log plots.
'''
pd = import_required('pandas','hexbin requires pandas to be installed')
q, r = cartesian_to_axial(x, y, size, orientation, aspect_scale=aspect_scale)
df = pd.DataFrame(dict(r=r, q=q))
return df.groupby(['q', 'r']).size().reset_index(name='counts') | [
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Args:
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A NumPy array of x-coordinates for binning
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The size of the hexagonal tiling.
The size is defined as the distance from the center of a hexagon
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orientation (str, optional) :
Whether the hex tile orientation should be "pointytop" or
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aspect_scale (float, optional) :
Match a plot's aspect ratio scaling.
When working with a plot with ``aspect_scale != 1``, this
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This is roughly equivalent to binning in "screen space", and
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Returns:
DataFrame
The resulting DataFrame will have columns *q* and *r* that specify
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.. warning::
Hex binning only functions on linear scales, i.e. not on log plots. | [
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30,290 | bokeh/bokeh | bokeh/core/property/container.py | ColumnData.from_json | def from_json(self, json, models=None):
''' Decodes column source data encoded as lists or base64 strings.
'''
if json is None:
return None
elif not isinstance(json, dict):
raise DeserializationError("%s expected a dict or None, got %s" % (self, json))
new_data = {}
for key, value in json.items():
key = self.keys_type.from_json(key, models)
if isinstance(value, dict) and '__ndarray__' in value:
new_data[key] = decode_base64_dict(value)
elif isinstance(value, list) and any(isinstance(el, dict) and '__ndarray__' in el for el in value):
new_list = []
for el in value:
if isinstance(el, dict) and '__ndarray__' in el:
el = decode_base64_dict(el)
elif isinstance(el, list):
el = self.values_type.from_json(el)
new_list.append(el)
new_data[key] = new_list
else:
new_data[key] = self.values_type.from_json(value, models)
return new_data | python | def from_json(self, json, models=None):
''' Decodes column source data encoded as lists or base64 strings.
'''
if json is None:
return None
elif not isinstance(json, dict):
raise DeserializationError("%s expected a dict or None, got %s" % (self, json))
new_data = {}
for key, value in json.items():
key = self.keys_type.from_json(key, models)
if isinstance(value, dict) and '__ndarray__' in value:
new_data[key] = decode_base64_dict(value)
elif isinstance(value, list) and any(isinstance(el, dict) and '__ndarray__' in el for el in value):
new_list = []
for el in value:
if isinstance(el, dict) and '__ndarray__' in el:
el = decode_base64_dict(el)
elif isinstance(el, list):
el = self.values_type.from_json(el)
new_list.append(el)
new_data[key] = new_list
else:
new_data[key] = self.values_type.from_json(value, models)
return new_data | [
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30,291 | bokeh/bokeh | bokeh/util/serialization.py | convert_timedelta_type | def convert_timedelta_type(obj):
''' Convert any recognized timedelta value to floating point absolute
milliseconds.
Arg:
obj (object) : the object to convert
Returns:
float : milliseconds
'''
if isinstance(obj, dt.timedelta):
return obj.total_seconds() * 1000.
elif isinstance(obj, np.timedelta64):
return (obj / NP_MS_DELTA) | python | def convert_timedelta_type(obj):
''' Convert any recognized timedelta value to floating point absolute
milliseconds.
Arg:
obj (object) : the object to convert
Returns:
float : milliseconds
'''
if isinstance(obj, dt.timedelta):
return obj.total_seconds() * 1000.
elif isinstance(obj, np.timedelta64):
return (obj / NP_MS_DELTA) | [
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30,292 | bokeh/bokeh | bokeh/util/serialization.py | convert_datetime_type | def convert_datetime_type(obj):
''' Convert any recognized date, time, or datetime value to floating point
milliseconds since epoch.
Arg:
obj (object) : the object to convert
Returns:
float : milliseconds
'''
# Pandas NaT
if pd and obj is pd.NaT:
return np.nan
# Pandas Period
if pd and isinstance(obj, pd.Period):
return obj.to_timestamp().value / 10**6.0
# Pandas Timestamp
if pd and isinstance(obj, _pd_timestamp): return obj.value / 10**6.0
# Pandas Timedelta
elif pd and isinstance(obj, pd.Timedelta): return obj.value / 10**6.0
# Datetime (datetime is a subclass of date)
elif isinstance(obj, dt.datetime):
diff = obj.replace(tzinfo=None) - DT_EPOCH
return diff.total_seconds() * 1000.
# Date
elif isinstance(obj, dt.date):
return (dt.datetime(*obj.timetuple()[:6]) - DT_EPOCH).total_seconds() * 1000
# NumPy datetime64
elif isinstance(obj, np.datetime64):
epoch_delta = obj - NP_EPOCH
return (epoch_delta / NP_MS_DELTA)
# Time
elif isinstance(obj, dt.time):
return (obj.hour * 3600 + obj.minute * 60 + obj.second) * 1000 + obj.microsecond / 1000. | python | def convert_datetime_type(obj):
''' Convert any recognized date, time, or datetime value to floating point
milliseconds since epoch.
Arg:
obj (object) : the object to convert
Returns:
float : milliseconds
'''
# Pandas NaT
if pd and obj is pd.NaT:
return np.nan
# Pandas Period
if pd and isinstance(obj, pd.Period):
return obj.to_timestamp().value / 10**6.0
# Pandas Timestamp
if pd and isinstance(obj, _pd_timestamp): return obj.value / 10**6.0
# Pandas Timedelta
elif pd and isinstance(obj, pd.Timedelta): return obj.value / 10**6.0
# Datetime (datetime is a subclass of date)
elif isinstance(obj, dt.datetime):
diff = obj.replace(tzinfo=None) - DT_EPOCH
return diff.total_seconds() * 1000.
# Date
elif isinstance(obj, dt.date):
return (dt.datetime(*obj.timetuple()[:6]) - DT_EPOCH).total_seconds() * 1000
# NumPy datetime64
elif isinstance(obj, np.datetime64):
epoch_delta = obj - NP_EPOCH
return (epoch_delta / NP_MS_DELTA)
# Time
elif isinstance(obj, dt.time):
return (obj.hour * 3600 + obj.minute * 60 + obj.second) * 1000 + obj.microsecond / 1000. | [
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30,293 | bokeh/bokeh | bokeh/util/serialization.py | convert_datetime_array | def convert_datetime_array(array):
''' Convert NumPy datetime arrays to arrays to milliseconds since epoch.
Args:
array : (obj)
A NumPy array of datetime to convert
If the value passed in is not a NumPy array, it will be returned as-is.
Returns:
array
'''
if not isinstance(array, np.ndarray):
return array
try:
dt2001 = np.datetime64('2001')
legacy_datetime64 = (dt2001.astype('int64') ==
dt2001.astype('datetime64[ms]').astype('int64'))
except AttributeError as e:
if e.args == ("'module' object has no attribute 'datetime64'",):
# for compatibility with PyPy that doesn't have datetime64
if 'PyPy' in sys.version:
legacy_datetime64 = False
pass
else:
raise e
else:
raise e
# not quite correct, truncates to ms..
if array.dtype.kind == 'M':
if legacy_datetime64:
if array.dtype == np.dtype('datetime64[ns]'):
array = array.astype('int64') / 10**6.0
else:
array = array.astype('datetime64[us]').astype('int64') / 1000.
elif array.dtype.kind == 'm':
array = array.astype('timedelta64[us]').astype('int64') / 1000.
return array | python | def convert_datetime_array(array):
''' Convert NumPy datetime arrays to arrays to milliseconds since epoch.
Args:
array : (obj)
A NumPy array of datetime to convert
If the value passed in is not a NumPy array, it will be returned as-is.
Returns:
array
'''
if not isinstance(array, np.ndarray):
return array
try:
dt2001 = np.datetime64('2001')
legacy_datetime64 = (dt2001.astype('int64') ==
dt2001.astype('datetime64[ms]').astype('int64'))
except AttributeError as e:
if e.args == ("'module' object has no attribute 'datetime64'",):
# for compatibility with PyPy that doesn't have datetime64
if 'PyPy' in sys.version:
legacy_datetime64 = False
pass
else:
raise e
else:
raise e
# not quite correct, truncates to ms..
if array.dtype.kind == 'M':
if legacy_datetime64:
if array.dtype == np.dtype('datetime64[ns]'):
array = array.astype('int64') / 10**6.0
else:
array = array.astype('datetime64[us]').astype('int64') / 1000.
elif array.dtype.kind == 'm':
array = array.astype('timedelta64[us]').astype('int64') / 1000.
return array | [
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30,294 | bokeh/bokeh | bokeh/util/serialization.py | make_id | def make_id():
''' Return a new unique ID for a Bokeh object.
Normally this function will return simple monotonically increasing integer
IDs (as strings) for identifying Bokeh objects within a Document. However,
if it is desirable to have globally unique for every object, this behavior
can be overridden by setting the environment variable ``BOKEH_SIMPLE_IDS=no``.
Returns:
str
'''
global _simple_id
if settings.simple_ids(True):
with _simple_id_lock:
_simple_id += 1
return str(_simple_id)
else:
return make_globally_unique_id() | python | def make_id():
''' Return a new unique ID for a Bokeh object.
Normally this function will return simple monotonically increasing integer
IDs (as strings) for identifying Bokeh objects within a Document. However,
if it is desirable to have globally unique for every object, this behavior
can be overridden by setting the environment variable ``BOKEH_SIMPLE_IDS=no``.
Returns:
str
'''
global _simple_id
if settings.simple_ids(True):
with _simple_id_lock:
_simple_id += 1
return str(_simple_id)
else:
return make_globally_unique_id() | [
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30,295 | bokeh/bokeh | bokeh/util/serialization.py | transform_array | def transform_array(array, force_list=False, buffers=None):
''' Transform a NumPy arrays into serialized format
Converts un-serializable dtypes and returns JSON serializable
format
Args:
array (np.ndarray) : a NumPy array to be transformed
force_list (bool, optional) : whether to only output to standard lists
This function can encode some dtypes using a binary encoding, but
setting this argument to True will override that and cause only
standard Python lists to be emitted. (default: False)
buffers (set, optional) :
If binary buffers are desired, the buffers parameter may be
provided, and any columns that may be sent as binary buffers
will be added to the set. If None, then only base64 encoding
will be used (default: None)
If force_list is True, then this value will be ignored, and
no buffers will be generated.
**This is an "out" parameter**. The values it contains will be
modified in-place.
Returns:
JSON
'''
array = convert_datetime_array(array)
return serialize_array(array, force_list=force_list, buffers=buffers) | python | def transform_array(array, force_list=False, buffers=None):
''' Transform a NumPy arrays into serialized format
Converts un-serializable dtypes and returns JSON serializable
format
Args:
array (np.ndarray) : a NumPy array to be transformed
force_list (bool, optional) : whether to only output to standard lists
This function can encode some dtypes using a binary encoding, but
setting this argument to True will override that and cause only
standard Python lists to be emitted. (default: False)
buffers (set, optional) :
If binary buffers are desired, the buffers parameter may be
provided, and any columns that may be sent as binary buffers
will be added to the set. If None, then only base64 encoding
will be used (default: None)
If force_list is True, then this value will be ignored, and
no buffers will be generated.
**This is an "out" parameter**. The values it contains will be
modified in-place.
Returns:
JSON
'''
array = convert_datetime_array(array)
return serialize_array(array, force_list=force_list, buffers=buffers) | [
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30,296 | bokeh/bokeh | bokeh/util/serialization.py | transform_array_to_list | def transform_array_to_list(array):
''' Transforms a NumPy array into a list of values
Args:
array (np.nadarray) : the NumPy array series to transform
Returns:
list or dict
'''
if (array.dtype.kind in ('u', 'i', 'f') and (~np.isfinite(array)).any()):
transformed = array.astype('object')
transformed[np.isnan(array)] = 'NaN'
transformed[np.isposinf(array)] = 'Infinity'
transformed[np.isneginf(array)] = '-Infinity'
return transformed.tolist()
elif (array.dtype.kind == 'O' and pd and pd.isnull(array).any()):
transformed = array.astype('object')
transformed[pd.isnull(array)] = 'NaN'
return transformed.tolist()
return array.tolist() | python | def transform_array_to_list(array):
''' Transforms a NumPy array into a list of values
Args:
array (np.nadarray) : the NumPy array series to transform
Returns:
list or dict
'''
if (array.dtype.kind in ('u', 'i', 'f') and (~np.isfinite(array)).any()):
transformed = array.astype('object')
transformed[np.isnan(array)] = 'NaN'
transformed[np.isposinf(array)] = 'Infinity'
transformed[np.isneginf(array)] = '-Infinity'
return transformed.tolist()
elif (array.dtype.kind == 'O' and pd and pd.isnull(array).any()):
transformed = array.astype('object')
transformed[pd.isnull(array)] = 'NaN'
return transformed.tolist()
return array.tolist() | [
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30,297 | bokeh/bokeh | bokeh/util/serialization.py | transform_series | def transform_series(series, force_list=False, buffers=None):
''' Transforms a Pandas series into serialized form
Args:
series (pd.Series) : the Pandas series to transform
force_list (bool, optional) : whether to only output to standard lists
This function can encode some dtypes using a binary encoding, but
setting this argument to True will override that and cause only
standard Python lists to be emitted. (default: False)
buffers (set, optional) :
If binary buffers are desired, the buffers parameter may be
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If force_list is True, then this value will be ignored, and
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**This is an "out" parameter**. The values it contains will be
modified in-place.
Returns:
list or dict
'''
# not checking for pd here, this function should only be called if it
# is already known that series is a Pandas Series type
if isinstance(series, pd.PeriodIndex):
vals = series.to_timestamp().values
else:
vals = series.values
return transform_array(vals, force_list=force_list, buffers=buffers) | python | def transform_series(series, force_list=False, buffers=None):
''' Transforms a Pandas series into serialized form
Args:
series (pd.Series) : the Pandas series to transform
force_list (bool, optional) : whether to only output to standard lists
This function can encode some dtypes using a binary encoding, but
setting this argument to True will override that and cause only
standard Python lists to be emitted. (default: False)
buffers (set, optional) :
If binary buffers are desired, the buffers parameter may be
provided, and any columns that may be sent as binary buffers
will be added to the set. If None, then only base64 encoding
will be used (default: None)
If force_list is True, then this value will be ignored, and
no buffers will be generated.
**This is an "out" parameter**. The values it contains will be
modified in-place.
Returns:
list or dict
'''
# not checking for pd here, this function should only be called if it
# is already known that series is a Pandas Series type
if isinstance(series, pd.PeriodIndex):
vals = series.to_timestamp().values
else:
vals = series.values
return transform_array(vals, force_list=force_list, buffers=buffers) | [
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30,298 | bokeh/bokeh | bokeh/util/serialization.py | serialize_array | def serialize_array(array, force_list=False, buffers=None):
''' Transforms a NumPy array into serialized form.
Args:
array (np.ndarray) : the NumPy array to transform
force_list (bool, optional) : whether to only output to standard lists
This function can encode some dtypes using a binary encoding, but
setting this argument to True will override that and cause only
standard Python lists to be emitted. (default: False)
buffers (set, optional) :
If binary buffers are desired, the buffers parameter may be
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If force_list is True, then this value will be ignored, and
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**This is an "out" parameter**. The values it contains will be
modified in-place.
Returns:
list or dict
'''
if isinstance(array, np.ma.MaskedArray):
array = array.filled(np.nan) # Set masked values to nan
if (array_encoding_disabled(array) or force_list):
return transform_array_to_list(array)
if not array.flags['C_CONTIGUOUS']:
array = np.ascontiguousarray(array)
if buffers is None:
return encode_base64_dict(array)
else:
return encode_binary_dict(array, buffers) | python | def serialize_array(array, force_list=False, buffers=None):
''' Transforms a NumPy array into serialized form.
Args:
array (np.ndarray) : the NumPy array to transform
force_list (bool, optional) : whether to only output to standard lists
This function can encode some dtypes using a binary encoding, but
setting this argument to True will override that and cause only
standard Python lists to be emitted. (default: False)
buffers (set, optional) :
If binary buffers are desired, the buffers parameter may be
provided, and any columns that may be sent as binary buffers
will be added to the set. If None, then only base64 encoding
will be used (default: None)
If force_list is True, then this value will be ignored, and
no buffers will be generated.
**This is an "out" parameter**. The values it contains will be
modified in-place.
Returns:
list or dict
'''
if isinstance(array, np.ma.MaskedArray):
array = array.filled(np.nan) # Set masked values to nan
if (array_encoding_disabled(array) or force_list):
return transform_array_to_list(array)
if not array.flags['C_CONTIGUOUS']:
array = np.ascontiguousarray(array)
if buffers is None:
return encode_base64_dict(array)
else:
return encode_binary_dict(array, buffers) | [
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30,299 | bokeh/bokeh | bokeh/util/serialization.py | traverse_data | def traverse_data(obj, use_numpy=True, buffers=None):
''' Recursively traverse an object until a flat list is found.
If NumPy is available, the flat list is converted to a numpy array
and passed to transform_array() to handle ``nan``, ``inf``, and
``-inf``.
Otherwise, iterate through all items, converting non-JSON items
Args:
obj (list) : a list of values or lists
use_numpy (bool, optional) toggle NumPy as a dependency for testing
This argument is only useful for testing (default: True)
'''
if use_numpy and all(isinstance(el, np.ndarray) for el in obj):
return [transform_array(el, buffers=buffers) for el in obj]
obj_copy = []
for item in obj:
# Check the base/common case first for performance reasons
# Also use type(x) is float because it's faster than isinstance
if type(item) is float:
if math.isnan(item):
item = 'NaN'
elif math.isinf(item):
if item > 0:
item = 'Infinity'
else:
item = '-Infinity'
obj_copy.append(item)
elif isinstance(item, (list, tuple)): # check less common type second
obj_copy.append(traverse_data(item))
else:
obj_copy.append(item)
return obj_copy | python | def traverse_data(obj, use_numpy=True, buffers=None):
''' Recursively traverse an object until a flat list is found.
If NumPy is available, the flat list is converted to a numpy array
and passed to transform_array() to handle ``nan``, ``inf``, and
``-inf``.
Otherwise, iterate through all items, converting non-JSON items
Args:
obj (list) : a list of values or lists
use_numpy (bool, optional) toggle NumPy as a dependency for testing
This argument is only useful for testing (default: True)
'''
if use_numpy and all(isinstance(el, np.ndarray) for el in obj):
return [transform_array(el, buffers=buffers) for el in obj]
obj_copy = []
for item in obj:
# Check the base/common case first for performance reasons
# Also use type(x) is float because it's faster than isinstance
if type(item) is float:
if math.isnan(item):
item = 'NaN'
elif math.isinf(item):
if item > 0:
item = 'Infinity'
else:
item = '-Infinity'
obj_copy.append(item)
elif isinstance(item, (list, tuple)): # check less common type second
obj_copy.append(traverse_data(item))
else:
obj_copy.append(item)
return obj_copy | [
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