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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 o... | 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 o... | [
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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)
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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)
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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 need... | 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.
"""
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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."""
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if set(meta_graph.meta_info_def.tags) == set(tags or [])]
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"""Returns the matching MetaGraphDef or raises KeyError."""
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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-sp... | python | def _convert_dict_inputs(inputs, tensor_info_map):
"""Converts from inputs into dict of input tensors.
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- putting inputs into a dict, per _prepare_dict_inputs(),
- converting all input values into tensors compatible with the
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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 w... | 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
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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... | 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.
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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 ... | 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.
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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 e... | 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
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of variables.
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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:
result.append(value)
return result | [
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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`
throu... | 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
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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)).... | 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
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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
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"""Uses a Module to get a dense 1-D representation from the pixels of images.
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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):
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Args:
module_spec: A `_ModuleSpec` to test.
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ValueError: if `module_spec` default signature is not compatible with
mappingan "images" input to a Tensor(float32, shape... | python | def _check_module_is_image_embedding(module_spec):
"""Raises ValueError if `module_spec` is not usable as image embedding.
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module_spec: A `_ModuleSpec` to test.
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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
... | 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
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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\nDow... | 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 = (
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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 t... | 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
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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 tha... | 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 tha... | [
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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():
... | 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():
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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()
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now - self._last_progre... | 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()
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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)
... | 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)
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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: Unkn... | 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'.
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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_... | 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",
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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.HasFie... | 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.HasFie... | [
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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_... | 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
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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):
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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 = []
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... | python | def prune_feed_map(meta_graph, feed_map):
"""Function to prune the feedmap of nodes which no longer exist."""
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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 wri... | python | def atomic_write_string_to_file(filename, contents, overwrite):
"""Writes to `filename` atomically.
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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
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The timestamp us... | python | def get_timestamped_export_dir(export_dir_base):
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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
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"""Builds a directory name based on the argument but starting with 'temp-'.
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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_b... | python | def garbage_collect_exports(export_dir_base, exports_to_keep):
"""Deletes older exports, retaining only a given number of the most recent.
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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 strin... | 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.
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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... | 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(
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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_bundl... | 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.
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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 b... | 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
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30,241 | kubernetes-client/python | kubernetes/client/api_client.py | ApiClient.pool | def pool(self):
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if self._pool is None:
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return self._pool | python | def pool(self):
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avoids instantiating unused threadpool for blocking clients.
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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
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"""
Sets the debug status.
:param value: The debug status, True or False.
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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
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Sets the logger_format.
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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.
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"""
Sets the certificate of this V1beta1CertificateSigningRequestStatus.
If request was approved, the controller will place the issued certificate here.
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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 ``_... | 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
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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 o... | 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
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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
... | 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
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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.
... | 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.
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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:
if validation_on():
self.validate(value,... | python | 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:
if validation_on():
self.validate(value,... | [
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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 accep... | 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 accep... | [
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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 ... | 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 ... | [
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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 fo... | python | def make_descriptors(self, base_name):
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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 isi... | 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
'''
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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: {}'.f... | 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: {}'.f... | [
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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."""
logging.info('loading {}...'.format(filename))
try:
with gzip.GzipFile(filename, 'rb') as f:
buf = ''
while True:
data = f.read()
if ... | python | def load_object(filename):
"""Unpickles and decompresses the given filename and returns the created object."""
logging.info('loading {}...'.format(filename))
try:
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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_S... | 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_S... | [
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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', [])
return [label['name'].replace('tag: ', '') for label in labels if label['name'].startswith('tag: ')] | [
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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
return False | [
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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, heade... | 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, heade... | [
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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'])
retu... | 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'])
retu... | [
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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.__n... | 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.__n... | [
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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 ... | 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 ... | [
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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... | 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... | [
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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_g... | 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_g... | [
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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, ... | 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, ... | [
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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)
retur... | 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)
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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 ... | python | def yield_for_all_futures(result):
""" Converts result into a Future by collapsing any futures inside 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)
... | 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)
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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."""
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# this 'removed' flag is a hack because Tornado has no way
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Returns an ID that can be used with remove_next_tick_callback."""
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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):
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Returns an ID that can be used with remove_timeout_callback."""
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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)
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""" 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)
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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
... | 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
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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, et... | 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, et... | [
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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
... | 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
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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 ... | 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()
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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 ``--inst... | 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
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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 signa... | 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
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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
... | 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
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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 mappin... | 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? ")
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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'),
... | 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'),
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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:
... | 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:
... | [
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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... | 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))
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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.
eli... | 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.
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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
... | 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
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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
'''
... | 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
'''
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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 overr... | 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 overr... | [
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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 ... | 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 ... | [
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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.as... | 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.as... | [
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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 usi... | 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
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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 ... | 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 ... | [
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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, co... | 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, co... | [
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