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yongtang/tensorflow | tensorflow/python/training/monitored_session.py | 7 | 58340 | # pylint: disable=g-bad-file-header
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A wrapper of Session API which runs hooks."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import os
import sys
import six
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.distribute import distribute_coordinator_context
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import lookup_ops
from tensorflow.python.ops import resources
from tensorflow.python.ops import variables
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.summary import summary
from tensorflow.python.training import basic_session_run_hooks
from tensorflow.python.training import coordinator
from tensorflow.python.training import queue_runner
from tensorflow.python.training import saver as training_saver
from tensorflow.python.training import session_manager as sm
from tensorflow.python.training import session_run_hook
from tensorflow.python.training.tracking import graph_view
from tensorflow.python.training.tracking import util as trackable_util
from tensorflow.python.util import function_utils
from tensorflow.python.util.tf_export import tf_export
# The list of exceptions that we should recover from. Exceptions not in this
# list may terminate the job.
_PREEMPTION_ERRORS = (errors.AbortedError, errors.UnavailableError)
# Value that indicates no value was provided.
USE_DEFAULT = object()
@tf_export(v1=['train.Scaffold'])
class Scaffold(object):
"""Structure to create or gather pieces commonly needed to train a model.
When you build a model for training you usually need ops to initialize
variables, a `Saver` to checkpoint them, an op to collect summaries for
the visualizer, and so on.
Various libraries built on top of the core TensorFlow library take care of
creating some or all of these pieces and storing them in well known
collections in the graph. The `Scaffold` class helps pick these pieces from
the graph collections, creating and adding them to the collections if needed.
If you call the scaffold constructor without any arguments, it will pick
pieces from the collections, creating default ones if needed when
`scaffold.finalize()` is called. You can pass arguments to the constructor to
provide your own pieces. Pieces that you pass to the constructor are not
added to the graph collections.
The following pieces are directly accessible as attributes of the `Scaffold`
object:
* `saver`: A `tf.compat.v1.train.Saver` object taking care of saving the
variables.
Picked from and stored into the `SAVERS` collection in the graph by default.
* `init_op`: An op to run to initialize the variables. Picked from and
stored into the `INIT_OP` collection in the graph by default.
* `ready_op`: An op to verify that the variables are initialized. Picked
from and stored into the `READY_OP` collection in the graph by default.
* `ready_for_local_init_op`: An op to verify that global state has been
initialized and it is alright to run `local_init_op`. Picked from and
stored into the `READY_FOR_LOCAL_INIT_OP` collection in the graph by
default. This is needed when the initialization of local variables depends
on the values of global variables.
* `local_init_op`: An op to initialize the local variables. Picked
from and stored into the `LOCAL_INIT_OP` collection in the graph by default.
* `summary_op`: An op to run and merge the summaries in the graph. Picked
from and stored into the `SUMMARY_OP` collection in the graph by default.
You can also pass the following additional pieces to the constructor:
* `init_feed_dict`: A session feed dictionary that should be used when
running the init op.
* `init_fn`: A callable to run after the init op to perform additional
initializations. The callable will be called as
`init_fn(scaffold, session)`.
"""
def __init__(self,
init_op=None,
init_feed_dict=None,
init_fn=None,
ready_op=None,
ready_for_local_init_op=None,
local_init_op=None,
summary_op=None,
saver=None,
copy_from_scaffold=None,
local_init_feed_dict=None):
"""Create a scaffold.
Args:
init_op: Optional op for initializing variables.
init_feed_dict: Optional session feed dictionary to use when running the
init_op.
init_fn: Optional function to use to initialize the model after running
the init_op. Will be called as `init_fn(scaffold, session)`.
ready_op: Optional op to verify that the variables are initialized. Must
return an empty 1D string tensor when the variables are initialized, or
a non-empty 1D string tensor listing the names of the non-initialized
variables.
ready_for_local_init_op: Optional op to verify that the global variables
are initialized and `local_init_op` can be run. Must return an empty 1D
string tensor when the global variables are initialized, or a non-empty
1D string tensor listing the names of the non-initialized global
variables.
local_init_op: Optional op to initialize local variables.
summary_op: Optional op to gather all summaries. Must return a scalar
string tensor containing a serialized `Summary` proto.
saver: Optional `tf.compat.v1.train.Saver` object to use to save and
restore variables. May also be a `tf.train.Checkpoint` object, in which
case object-based checkpoints are saved. This will also load some
object-based checkpoints saved from elsewhere, but that loading may be
fragile since it uses fixed keys rather than performing a full
graph-based match. For example if a variable has two paths from the
`Checkpoint` object because two `Model` objects share the `Layer` object
that owns it, removing one `Model` may change the keys and break
checkpoint loading through this API, whereas a graph-based match would
match the variable through the other `Model`.
copy_from_scaffold: Optional scaffold object to copy fields from. Its
fields will be overwritten by the provided fields in this function.
local_init_feed_dict: Optional session feed dictionary to use when running
the local_init_op.
"""
if copy_from_scaffold is not None:
if not isinstance(copy_from_scaffold, Scaffold):
raise TypeError('copy_from_scaffold is not a Scaffold instance.')
# We need _coalesce since Tensor is not converted to bool automatically,
# so the common idiom of (a or b) does not work.
coalesce = lambda a, b: a if a is not None else b
init_op = coalesce(init_op, copy_from_scaffold.init_op)
init_feed_dict = coalesce(init_feed_dict,
copy_from_scaffold.init_feed_dict)
# Use the original init_fn provided by the user to init the new Scaffold.
init_fn = coalesce(init_fn, copy_from_scaffold._user_init_fn) # pylint: disable=protected-access
ready_op = coalesce(ready_op, copy_from_scaffold.ready_op)
ready_for_local_init_op = coalesce(
ready_for_local_init_op, copy_from_scaffold.ready_for_local_init_op)
local_init_op = coalesce(local_init_op, copy_from_scaffold.local_init_op)
local_init_feed_dict = coalesce(local_init_feed_dict,
copy_from_scaffold.local_init_feed_dict)
summary_op = coalesce(summary_op, copy_from_scaffold.summary_op)
saver = coalesce(saver, copy_from_scaffold.saver)
# NOTE(touts): modifying the init function to be passed the scaffold is a
# hack to make it easy to find the saver. Is there a better way?
self._user_init_fn = init_fn
if init_fn:
self._init_fn = lambda sess: init_fn(self, sess)
else:
self._init_fn = None
self._init_op = init_op
self._init_feed_dict = init_feed_dict
self._ready_op = ready_op
self._ready_for_local_init_op = ready_for_local_init_op
self._local_init_op = local_init_op
self._local_init_feed_dict = local_init_feed_dict
self._summary_op = summary_op
self._saver = saver
def finalize(self):
"""Creates operations if needed and finalizes the graph."""
if self._init_op is None:
def default_init_op():
return control_flow_ops.group(
variables.global_variables_initializer(),
resources.initialize_resources(resources.shared_resources()),
ops.get_collection('saved_model_initializers'))
self._init_op = Scaffold.get_or_default('init_op', ops.GraphKeys.INIT_OP,
default_init_op)
if self._ready_op is None:
def default_ready_op():
return array_ops.concat([
variables.report_uninitialized_variables(),
resources.report_uninitialized_resources()
], 0)
self._ready_op = Scaffold.get_or_default('ready_op',
ops.GraphKeys.READY_OP,
default_ready_op)
if self._ready_for_local_init_op is None:
def default_ready_for_local_init_op():
return array_ops.concat([
variables.report_uninitialized_variables(
variables.global_variables()),
resources.report_uninitialized_resources(
resources.shared_resources())
], 0)
self._ready_for_local_init_op = Scaffold.get_or_default(
'ready_for_local_init_op', ops.GraphKeys.READY_FOR_LOCAL_INIT_OP,
default_ready_for_local_init_op)
if self._local_init_op is None:
self._local_init_op = Scaffold.get_or_default(
'local_init_op', ops.GraphKeys.LOCAL_INIT_OP,
Scaffold.default_local_init_op)
if self._summary_op is None:
self._summary_op = Scaffold.get_or_default('summary_op',
ops.GraphKeys.SUMMARY_OP,
summary.merge_all)
# pylint: disable=g-long-lambda
if self._saver is None:
self._saver = training_saver._get_saver_or_default() # pylint: disable=protected-access
# pylint: enable=g-long-lambda
if isinstance(self._saver, trackable_util.Checkpoint):
self._saver = training_saver.Saver(
var_list=graph_view.ObjectGraphView(
self._saver).frozen_saveable_objects(),
sharded=True)
else:
self._saver.build()
ops.get_default_graph().finalize()
logging.info('Graph was finalized.')
return self
@property
def init_fn(self):
return self._init_fn
@property
def init_op(self):
return self._init_op
@property
def ready_op(self):
return self._ready_op
@property
def ready_for_local_init_op(self):
return self._ready_for_local_init_op
@property
def local_init_op(self):
return self._local_init_op
@property
def local_init_feed_dict(self):
return self._local_init_feed_dict
@property
def summary_op(self):
return self._summary_op
@property
def saver(self):
return self._saver
@property
def init_feed_dict(self):
return self._init_feed_dict
@staticmethod
def get_or_default(arg_name, collection_key, default_constructor):
"""Get from cache or create a default operation."""
elements = ops.get_collection(collection_key)
if elements:
if len(elements) > 1:
raise RuntimeError(
'More than one item in the collection "%s". '
'Please indicate which one to use by passing it to '
'the tf.Scaffold constructor as: '
'tf.Scaffold(%s=item to use)', collection_key, arg_name)
return elements[0]
op = default_constructor()
if op is not None:
ops.add_to_collection(collection_key, op)
return op
@staticmethod
def default_local_init_op():
"""Returns an op that groups the default local init ops.
This op is used during session initialization when a Scaffold is
initialized without specifying the local_init_op arg. It includes
`tf.compat.v1.local_variables_initializer`,
`tf.compat.v1.tables_initializer`, and also
initializes local session resources.
Returns:
The default Scaffold local init op.
"""
return control_flow_ops.group(
variables.local_variables_initializer(),
lookup_ops.tables_initializer(),
resources.initialize_resources(resources.local_resources()))
def _create_monitored_session_with_worker_context(
worker_context, # pylint: disable=missing-docstring
scaffold,
checkpoint_dir=None,
hooks=None,
chief_only_hooks=None,
save_checkpoint_secs=None,
save_summaries_steps=None,
save_summaries_secs=None,
config=None,
stop_grace_period_secs=120,
log_step_count_steps=100,
max_wait_secs=7200,
save_checkpoint_steps=None,
summary_dir=None,
save_graph_def=True):
all_hooks = []
if hooks:
all_hooks.extend(hooks)
if chief_only_hooks and worker_context.is_chief:
all_hooks.extend(chief_only_hooks)
# We need to call save or summary ops on all workers since these ops may
# contain collective ops, only running save ops on some workers would make
# collective ops hang. Therefore on those workers that don't need to actually
# write checkpoints or summaries, we let them write to a temp directory.
# pylint: disable=protected-access
if type(
worker_context._strategy).__name__ in ('CollectiveAllReduceStrategy',
'CollectiveAllReduceStrategyV1',
'MultiWorkerMirroredStrategy'):
if worker_context.task_type:
tmpdir = 'tmp_%s_%d' % (worker_context.task_type, worker_context.task_id)
else:
tmpdir = 'tmp'
if save_checkpoint_secs:
logging.warning('Collective ops may deadlock with '
'`save_checkpoints_secs` please use '
'`save_checkpoint_steps` instead. Clearing '
'`save_checkpoint_secs` and setting '
'`save_checkpoint_steps` to 1000 now.')
save_checkpoint_secs = None
save_checkpoint_steps = 1000
if save_summaries_secs:
logging.warning('Collective ops may run out of sync with'
'`save_summaries_secs`, please use '
'`save_summaries_steps` instead.')
else:
tmpdir = None
summary_dir = summary_dir or checkpoint_dir
if summary_dir and log_step_count_steps and log_step_count_steps > 0:
if worker_context.should_save_summary:
all_hooks.append(
basic_session_run_hooks.StepCounterHook(
output_dir=summary_dir, every_n_steps=log_step_count_steps))
elif tmpdir:
all_hooks.append(
basic_session_run_hooks.StepCounterHook(
output_dir=os.path.join(summary_dir, tmpdir),
every_n_steps=log_step_count_steps))
if (((save_summaries_steps and save_summaries_steps > 0) or
(save_summaries_secs and save_summaries_secs > 0)) and summary_dir):
if worker_context.should_save_summary:
all_hooks.append(
basic_session_run_hooks.SummarySaverHook(
scaffold=scaffold,
save_steps=save_summaries_steps,
save_secs=save_summaries_secs,
output_dir=summary_dir))
elif tmpdir:
all_hooks.append(
basic_session_run_hooks.SummarySaverHook(
scaffold=scaffold,
save_steps=save_summaries_steps,
save_secs=save_summaries_secs,
output_dir=os.path.join(summary_dir, tmpdir)))
if (((save_checkpoint_secs and save_checkpoint_secs > 0) or
(save_checkpoint_steps and save_checkpoint_steps > 0)) and
checkpoint_dir):
if worker_context.should_checkpoint:
all_hooks.append(
basic_session_run_hooks.CheckpointSaverHook(
checkpoint_dir,
save_steps=save_checkpoint_steps,
save_secs=save_checkpoint_secs,
scaffold=scaffold,
save_graph_def=save_graph_def))
elif tmpdir:
all_hooks.append(
basic_session_run_hooks.CheckpointSaverHook(
os.path.join(checkpoint_dir, tmpdir),
save_steps=save_checkpoint_steps,
save_secs=save_checkpoint_secs,
scaffold=scaffold,
save_graph_def=save_graph_def))
logging.info('all_hooks %r', all_hooks)
session_creator = worker_context.session_creator(
scaffold,
config=config,
checkpoint_dir=checkpoint_dir,
max_wait_secs=max_wait_secs)
return MonitoredSession(
session_creator=session_creator,
hooks=all_hooks,
stop_grace_period_secs=stop_grace_period_secs)
@tf_export(v1=['train.MonitoredTrainingSession'])
def MonitoredTrainingSession(
master='', # pylint: disable=invalid-name
is_chief=True,
checkpoint_dir=None,
scaffold=None,
hooks=None,
chief_only_hooks=None,
save_checkpoint_secs=USE_DEFAULT,
save_summaries_steps=USE_DEFAULT,
save_summaries_secs=USE_DEFAULT,
config=None,
stop_grace_period_secs=120,
log_step_count_steps=100,
max_wait_secs=7200,
save_checkpoint_steps=USE_DEFAULT,
summary_dir=None,
save_graph_def=True):
"""Creates a `MonitoredSession` for training.
For a chief, this utility sets proper session initializer/restorer. It also
creates hooks related to checkpoint and summary saving. For workers, this
utility sets proper session creator which waits for the chief to
initialize/restore. Please check `tf.compat.v1.train.MonitoredSession` for
more
information.
Args:
master: `String` the TensorFlow master to use.
is_chief: If `True`, it will take care of initialization and recovery the
underlying TensorFlow session. If `False`, it will wait on a chief to
initialize or recover the TensorFlow session.
checkpoint_dir: A string. Optional path to a directory where to restore
variables.
scaffold: A `Scaffold` used for gathering or building supportive ops. If not
specified, a default one is created. It's used to finalize the graph.
hooks: Optional list of `SessionRunHook` objects.
chief_only_hooks: list of `SessionRunHook` objects. Activate these hooks if
`is_chief==True`, ignore otherwise.
save_checkpoint_secs: The frequency, in seconds, that a checkpoint is saved
using a default checkpoint saver. If both `save_checkpoint_steps` and
`save_checkpoint_secs` are set to `None`, then the default checkpoint
saver isn't used. If both are provided, then only `save_checkpoint_secs`
is used. Default 600.
save_summaries_steps: The frequency, in number of global steps, that the
summaries are written to disk using a default summary saver. If both
`save_summaries_steps` and `save_summaries_secs` are set to `None`, then
the default summary saver isn't used. Default 100.
save_summaries_secs: The frequency, in secs, that the summaries are written
to disk using a default summary saver. If both `save_summaries_steps` and
`save_summaries_secs` are set to `None`, then the default summary saver
isn't used. Default not enabled.
config: an instance of `tf.compat.v1.ConfigProto` proto used to configure
the session. It's the `config` argument of constructor of
`tf.compat.v1.Session`.
stop_grace_period_secs: Number of seconds given to threads to stop after
`close()` has been called.
log_step_count_steps: The frequency, in number of global steps, that the
global step/sec is logged.
max_wait_secs: Maximum time workers should wait for the session to become
available. This should be kept relatively short to help detect incorrect
code, but sometimes may need to be increased if the chief takes a while to
start up.
save_checkpoint_steps: The frequency, in number of global steps, that a
checkpoint is saved using a default checkpoint saver. If both
`save_checkpoint_steps` and `save_checkpoint_secs` are set to `None`, then
the default checkpoint saver isn't used. If both are provided, then only
`save_checkpoint_secs` is used. Default not enabled.
summary_dir: A string. Optional path to a directory where to save
summaries. If None, checkpoint_dir is used instead.
save_graph_def: Whether to save the GraphDef and MetaGraphDef to
`checkpoint_dir`. The GraphDef is saved after the session is created as
`graph.pbtxt`. MetaGraphDefs are saved out for every checkpoint as
`model.ckpt-*.meta`.
Returns:
A `MonitoredSession` object.
"""
if save_summaries_steps == USE_DEFAULT and save_summaries_secs == USE_DEFAULT:
save_summaries_steps = 100
save_summaries_secs = None
elif save_summaries_secs == USE_DEFAULT:
save_summaries_secs = None
elif save_summaries_steps == USE_DEFAULT:
save_summaries_steps = None
if (save_checkpoint_steps == USE_DEFAULT and
save_checkpoint_secs == USE_DEFAULT):
save_checkpoint_steps = None
save_checkpoint_secs = 600
elif save_checkpoint_secs == USE_DEFAULT:
save_checkpoint_secs = None
elif save_checkpoint_steps == USE_DEFAULT:
save_checkpoint_steps = None
scaffold = scaffold or Scaffold()
worker_context = distribute_coordinator_context.get_current_worker_context()
if worker_context:
return _create_monitored_session_with_worker_context(
worker_context,
scaffold,
checkpoint_dir=checkpoint_dir,
hooks=hooks,
chief_only_hooks=chief_only_hooks,
save_checkpoint_secs=save_checkpoint_secs,
save_summaries_steps=save_summaries_steps,
save_summaries_secs=save_summaries_secs,
config=config,
stop_grace_period_secs=stop_grace_period_secs,
log_step_count_steps=log_step_count_steps,
max_wait_secs=max_wait_secs,
save_checkpoint_steps=save_checkpoint_steps,
summary_dir=summary_dir,
save_graph_def=save_graph_def)
if not is_chief:
session_creator = WorkerSessionCreator(
scaffold=scaffold,
master=master,
config=config,
max_wait_secs=max_wait_secs)
return MonitoredSession(
session_creator=session_creator,
hooks=hooks or [],
stop_grace_period_secs=stop_grace_period_secs)
all_hooks = []
if chief_only_hooks:
all_hooks.extend(chief_only_hooks)
session_creator = ChiefSessionCreator(
scaffold=scaffold,
checkpoint_dir=checkpoint_dir,
master=master,
config=config)
summary_dir = summary_dir or checkpoint_dir
if summary_dir:
if log_step_count_steps and log_step_count_steps > 0:
all_hooks.append(
basic_session_run_hooks.StepCounterHook(
output_dir=summary_dir, every_n_steps=log_step_count_steps))
if (save_summaries_steps and
save_summaries_steps > 0) or (save_summaries_secs and
save_summaries_secs > 0):
all_hooks.append(
basic_session_run_hooks.SummarySaverHook(
scaffold=scaffold,
save_steps=save_summaries_steps,
save_secs=save_summaries_secs,
output_dir=summary_dir))
if checkpoint_dir:
if (save_checkpoint_secs and
save_checkpoint_secs > 0) or (save_checkpoint_steps and
save_checkpoint_steps > 0):
all_hooks.append(
basic_session_run_hooks.CheckpointSaverHook(
checkpoint_dir,
save_steps=save_checkpoint_steps,
save_secs=save_checkpoint_secs,
scaffold=scaffold,
save_graph_def=save_graph_def))
if hooks:
all_hooks.extend(hooks)
return MonitoredSession(
session_creator=session_creator,
hooks=all_hooks,
stop_grace_period_secs=stop_grace_period_secs)
@tf_export(v1=['train.SessionCreator'])
@six.add_metaclass(abc.ABCMeta)
class SessionCreator(object):
"""A factory for tf.Session."""
@abc.abstractmethod
def create_session(self):
raise NotImplementedError(
'create_session is not implemented for {}.'.format(self))
@tf_export(v1=['train.ChiefSessionCreator'])
class ChiefSessionCreator(SessionCreator):
"""Creates a tf.compat.v1.Session for a chief."""
def __init__(self,
scaffold=None,
master='',
config=None,
checkpoint_dir=None,
checkpoint_filename_with_path=None):
"""Initializes a chief session creator.
Args:
scaffold: A `Scaffold` used for gathering or building supportive ops. If
not specified a default one is created. It's used to finalize the graph.
master: `String` representation of the TensorFlow master to use.
config: `ConfigProto` proto used to configure the session.
checkpoint_dir: A string. Optional path to a directory where to restore
variables.
checkpoint_filename_with_path: Full file name path to the checkpoint file.
"""
self._checkpoint_dir = checkpoint_dir
self._checkpoint_filename_with_path = checkpoint_filename_with_path
self._scaffold = scaffold or Scaffold()
self._session_manager = None
self._master = master
self._config = config
def _get_session_manager(self):
"""Gets or creates a SessionManager."""
if self._session_manager:
return self._session_manager
self._session_manager = sm.SessionManager(
local_init_op=self._scaffold.local_init_op,
local_init_feed_dict=self._scaffold.local_init_feed_dict,
ready_op=self._scaffold.ready_op,
ready_for_local_init_op=self._scaffold.ready_for_local_init_op,
graph=ops.get_default_graph())
return self._session_manager
def create_session(self):
self._scaffold.finalize()
return self._get_session_manager().prepare_session(
self._master,
saver=self._scaffold.saver,
checkpoint_dir=self._checkpoint_dir,
checkpoint_filename_with_path=self._checkpoint_filename_with_path,
config=self._config,
init_op=self._scaffold.init_op,
init_feed_dict=self._scaffold.init_feed_dict,
init_fn=self._scaffold.init_fn)
@tf_export(v1=['train.WorkerSessionCreator'])
class WorkerSessionCreator(SessionCreator):
"""Creates a tf.compat.v1.Session for a worker."""
def __init__(self,
scaffold=None,
master='',
config=None,
max_wait_secs=30 * 60):
"""Initializes a worker session creator.
Args:
scaffold: A `Scaffold` used for gathering or building supportive ops. If
not specified a default one is created. It's used to finalize the graph.
master: `String` representation of the TensorFlow master to use.
config: `ConfigProto` proto used to configure the session.
max_wait_secs: Maximum time to wait for the session to become available.
"""
self._scaffold = scaffold or Scaffold()
self._session_manager = None
self._master = master
self._config = config
self._max_wait_secs = max_wait_secs
def _get_session_manager(self):
"""Gets or creates a SessionManager."""
if self._session_manager:
return self._session_manager
self._session_manager = sm.SessionManager(
local_init_op=self._scaffold.local_init_op,
local_init_feed_dict=self._scaffold.local_init_feed_dict,
ready_op=self._scaffold.ready_op,
ready_for_local_init_op=self._scaffold.ready_for_local_init_op,
graph=ops.get_default_graph())
return self._session_manager
def create_session(self):
self._scaffold.finalize()
return self._get_session_manager().wait_for_session(
self._master, config=self._config, max_wait_secs=self._max_wait_secs)
class _MonitoredSession(object):
"""See `MonitoredSession` or `SingularMonitoredSession`."""
def __init__(self,
session_creator,
hooks,
should_recover,
stop_grace_period_secs=120):
"""Sets up a Monitored or Hooked Session.
Args:
session_creator: A factory object to create session. Typically a
`ChiefSessionCreator` or a `WorkerSessionCreator`.
hooks: An iterable of `SessionRunHook' objects.
should_recover: A bool. Indicates whether to recover from `AbortedError`
and `UnavailableError` or not.
stop_grace_period_secs: Number of seconds given to threads to stop after
`close()` has been called.
"""
self._graph_was_finalized = ops.get_default_graph().finalized
self._hooks = hooks or []
for h in self._hooks:
h.begin()
worker_context = distribute_coordinator_context.get_current_worker_context()
if not session_creator and worker_context:
session_creator = worker_context.session_creator()
# Create the session.
self._coordinated_creator = self._CoordinatedSessionCreator(
session_creator=session_creator or ChiefSessionCreator(),
hooks=self._hooks,
stop_grace_period_secs=stop_grace_period_secs)
if should_recover:
self._sess = _RecoverableSession(self._coordinated_creator)
else:
self._sess = self._coordinated_creator.create_session()
@property
def graph(self):
"""The graph that was launched in this session."""
if self._tf_sess() is None:
return None
return self._tf_sess().graph
def run(self, fetches, feed_dict=None, options=None, run_metadata=None):
"""Run ops in the monitored session.
This method is completely compatible with the `tf.Session.run()` method.
Args:
fetches: Same as `tf.Session.run()`.
feed_dict: Same as `tf.Session.run()`.
options: Same as `tf.Session.run()`.
run_metadata: Same as `tf.Session.run()`.
Returns:
Same as `tf.Session.run()`.
"""
return self._sess.run(
fetches,
feed_dict=feed_dict,
options=options,
run_metadata=run_metadata)
def run_step_fn(self, step_fn):
"""Run ops using a step function.
Args:
step_fn: A function or a method with a single argument of type
`StepContext`. The function may use methods of the argument to perform
computations with access to a raw session. The returned value of the
`step_fn` will be returned from `run_step_fn`, unless a stop is
requested. In that case, the next `should_stop` call will return True.
Example usage:
```python
with tf.Graph().as_default():
c = tf.compat.v1.placeholder(dtypes.float32)
v = tf.add(c, 4.0)
w = tf.add(c, 0.5)
def step_fn(step_context):
a = step_context.session.run(fetches=v, feed_dict={c: 0.5})
if a <= 4.5:
step_context.request_stop()
return step_context.run_with_hooks(fetches=w,
feed_dict={c: 0.1})
with tf.MonitoredSession() as session:
while not session.should_stop():
a = session.run_step_fn(step_fn)
```
Hooks interact with the `run_with_hooks()` call inside the
`step_fn` as they do with a `MonitoredSession.run` call.
Returns:
Returns the returned value of `step_fn`.
Raises:
StopIteration: if `step_fn` has called `request_stop()`. It may be
caught by `with tf.MonitoredSession()` to close the session.
ValueError: if `step_fn` doesn't have a single argument called
`step_context`. It may also optionally have `self` for cases when it
belongs to an object.
"""
step_fn_arguments = function_utils.fn_args(step_fn)
if step_fn_arguments != ('step_context',) and step_fn_arguments != (
'self',
'step_context',
):
raise ValueError(
'`step_fn` may either have one `step_context` argument, or'
' `self` and `step_context` arguments if it\'s an instance'
' method. Got {} instead.'.format(step_fn_arguments))
# `self._sess` is either `_RecoverableSession` or a `_CoordinatedSession`.
# Setting `run_with_hooks` to `None` will cause `run_with_hooks` to be
# `_CoordinatedSession.run` downstream in either case. This allows
# `_PREEMPTION_ERRORS` to propage from within `step_fn` to
# `_RecoverableSession.run_step_fn`.
return self._sess.run_step_fn(step_fn, self._tf_sess(), run_with_hooks=None)
class StepContext(object):
"""Control flow instrument for the `step_fn` from `run_step_fn()`.
Users of `step_fn` may perform `run()` calls without running hooks
by accessing the `session`. A `run()` call with hooks may be performed
using `run_with_hooks()`. Computation flow can be interrupted using
`request_stop()`.
"""
def __init__(self, session, run_with_hooks_fn):
"""Initializes the `step_context` argument for a `step_fn` invocation.
Args:
session: An instance of `tf.compat.v1.Session`.
run_with_hooks_fn: A function for running fetches and hooks.
"""
self._session = session
self._run_with_hooks_fn = run_with_hooks_fn
@property
def session(self):
return self._session
def run_with_hooks(self, *args, **kwargs):
"""Same as `MonitoredSession.run`. Accepts the same arguments."""
return self._run_with_hooks_fn(*args, **kwargs)
def request_stop(self):
"""Exit the training loop by causing `should_stop()` to return `True`.
Causes `step_fn` to exit by raising an exception.
Raises:
StopIteration
"""
raise StopIteration('step_fn has requested the iterations to stop.')
def should_stop(self):
return self._sess is None or self._sess.should_stop()
def close(self):
self._close_internal()
def __enter__(self):
return self
def __exit__(self, exception_type, exception_value, traceback):
if exception_type in [errors.OutOfRangeError, StopIteration]:
exception_type = None
self._close_internal(exception_type)
# __exit__ should return True to suppress an exception.
return exception_type is None
class _CoordinatedSessionCreator(SessionCreator):
"""Factory for _CoordinatedSession."""
def __init__(self, session_creator, hooks, stop_grace_period_secs):
self._session_creator = session_creator
self._hooks = hooks
self.coord = None
self.tf_sess = None
self._stop_grace_period_secs = stop_grace_period_secs
def create_session(self):
"""Creates a coordinated session."""
# Keep the tf_sess for unit testing.
self.tf_sess = self._session_creator.create_session()
# We don't want coordinator to suppress any exception.
self.coord = coordinator.Coordinator(clean_stop_exception_types=[])
if ops.get_collection(ops.GraphKeys.QUEUE_RUNNERS):
queue_runner.start_queue_runners(sess=self.tf_sess, coord=self.coord)
# Inform the hooks that a new session has been created.
for hook in self._hooks:
hook.after_create_session(self.tf_sess, self.coord)
return _CoordinatedSession(
_HookedSession(self.tf_sess, self._hooks), self.coord,
self._stop_grace_period_secs)
def _close_internal(self, exception_type=None):
try:
if not exception_type:
for h in self._hooks:
h.end(self._coordinated_creator.tf_sess)
finally:
try:
if self._sess is None:
raise RuntimeError('Session is already closed.')
self._sess.close()
finally:
self._sess = None
self._coordinated_creator.tf_sess = None
self._coordinated_creator.coord = None
if not self._graph_was_finalized:
ops.get_default_graph()._unsafe_unfinalize() # pylint: disable=protected-access
def _is_closed(self):
"""Return True if the monitored session is closed.
For tests only.
Returns:
A boolean.
"""
return self._coordinated_creator.tf_sess is None
def _tf_sess(self):
"""Return underlying tf.compat.v1.Session object.
Warning: accessing the returned object in user code is likely to cause races
or "flaky tests".
Returns:
A tf.compat.v1.Session object.
"""
return self._coordinated_creator.tf_sess
@tf_export(v1=['train.MonitoredSession'])
class MonitoredSession(_MonitoredSession):
"""Session-like object that handles initialization, recovery and hooks.
Example usage:
```python
saver_hook = CheckpointSaverHook(...)
summary_hook = SummarySaverHook(...)
with MonitoredSession(session_creator=ChiefSessionCreator(...),
hooks=[saver_hook, summary_hook]) as sess:
while not sess.should_stop():
sess.run(train_op)
```
Initialization: At creation time the monitored session does following things
in given order:
* calls `hook.begin()` for each given hook
* finalizes the graph via `scaffold.finalize()`
* create session
* initializes the model via initialization ops provided by `Scaffold`
* restores variables if a checkpoint exists
* launches queue runners
* calls `hook.after_create_session()`
Run: When `run()` is called, the monitored session does following things:
* calls `hook.before_run()`
* calls TensorFlow `session.run()` with merged fetches and feed_dict
* calls `hook.after_run()`
* returns result of `session.run()` asked by user
* if `AbortedError` or `UnavailableError` occurs, it recovers or
reinitializes the session before executing the run() call again
Exit: At the `close()`, the monitored session does following things in order:
* calls `hook.end()`
* closes the queue runners and the session
* suppresses `OutOfRange` error which indicates that all inputs have been
processed if the monitored_session is used as a context
How to set `tf.compat.v1.Session` arguments:
* In most cases you can set session arguments as follows:
```python
MonitoredSession(
session_creator=ChiefSessionCreator(master=..., config=...))
```
* In distributed setting for a non-chief worker, you can use following:
```python
MonitoredSession(
session_creator=WorkerSessionCreator(master=..., config=...))
```
See `MonitoredTrainingSession` for an example usage based on chief or worker.
Note: This is not a `tf.compat.v1.Session`. For example, it cannot do
following:
* it cannot be set as default session.
* it cannot be sent to saver.save.
* it cannot be sent to tf.train.start_queue_runners.
Args:
session_creator: A factory object to create session. Typically a
`ChiefSessionCreator` which is the default one.
hooks: An iterable of `SessionRunHook' objects.
Returns:
A MonitoredSession object.
"""
def __init__(self,
session_creator=None,
hooks=None,
stop_grace_period_secs=120):
super(MonitoredSession, self).__init__(
session_creator,
hooks,
should_recover=True,
stop_grace_period_secs=stop_grace_period_secs)
@tf_export(v1=['train.SingularMonitoredSession'])
class SingularMonitoredSession(_MonitoredSession):
"""Session-like object that handles initialization, restoring, and hooks.
Please note that this utility is not recommended for distributed settings.
For distributed settings, please use `tf.compat.v1.train.MonitoredSession`.
The
differences between `MonitoredSession` and `SingularMonitoredSession` are:
* `MonitoredSession` handles `AbortedError` and `UnavailableError` for
distributed settings, but `SingularMonitoredSession` does not.
* `MonitoredSession` can be created in `chief` or `worker` modes.
`SingularMonitoredSession` is always created as `chief`.
* You can access the raw `tf.compat.v1.Session` object used by
`SingularMonitoredSession`, whereas in MonitoredSession the raw session is
private. This can be used:
- To `run` without hooks.
- To save and restore.
* All other functionality is identical.
Example usage:
```python
saver_hook = CheckpointSaverHook(...)
summary_hook = SummarySaverHook(...)
with SingularMonitoredSession(hooks=[saver_hook, summary_hook]) as sess:
while not sess.should_stop():
sess.run(train_op)
```
Initialization: At creation time the hooked session does following things
in given order:
* calls `hook.begin()` for each given hook
* finalizes the graph via `scaffold.finalize()`
* create session
* initializes the model via initialization ops provided by `Scaffold`
* restores variables if a checkpoint exists
* launches queue runners
Run: When `run()` is called, the hooked session does following things:
* calls `hook.before_run()`
* calls TensorFlow `session.run()` with merged fetches and feed_dict
* calls `hook.after_run()`
* returns result of `session.run()` asked by user
Exit: At the `close()`, the hooked session does following things in order:
* calls `hook.end()`
* closes the queue runners and the session
* suppresses `OutOfRange` error which indicates that all inputs have been
processed if the `SingularMonitoredSession` is used as a context.
"""
def __init__(self,
hooks=None,
scaffold=None,
master='',
config=None,
checkpoint_dir=None,
stop_grace_period_secs=120,
checkpoint_filename_with_path=None):
"""Creates a SingularMonitoredSession.
Args:
hooks: An iterable of `SessionRunHook' objects.
scaffold: A `Scaffold` used for gathering or building supportive ops. If
not specified a default one is created. It's used to finalize the graph.
master: `String` representation of the TensorFlow master to use.
config: `ConfigProto` proto used to configure the session.
checkpoint_dir: A string. Optional path to a directory where to restore
variables.
stop_grace_period_secs: Number of seconds given to threads to stop after
`close()` has been called.
checkpoint_filename_with_path: A string. Optional path to a checkpoint
file from which to restore variables.
"""
session_creator = ChiefSessionCreator(
scaffold=scaffold,
master=master,
config=config,
checkpoint_dir=checkpoint_dir,
checkpoint_filename_with_path=checkpoint_filename_with_path)
super(SingularMonitoredSession, self).__init__(
session_creator,
hooks,
should_recover=False,
stop_grace_period_secs=stop_grace_period_secs)
def raw_session(self):
"""Returns underlying `TensorFlow.Session` object."""
return self._tf_sess()
class _WrappedSession(object):
"""Wrapper around a `tf.compat.v1.Session`.
This wrapper is used as a base class for various session wrappers
that provide additional functionality such as monitoring, coordination,
and recovery.
In addition to the methods exported by `SessionInterface` the wrapper
provides a method to check for stop and never raises exceptions from
calls to `close()`.
"""
def __init__(self, sess):
"""Creates a `_WrappedSession`.
Args:
sess: A `tf.compat.v1.Session` or `_WrappedSession` object. The wrapped
session.
"""
self._sess = sess
self._wrapped_is_stoppable = isinstance(self._sess, _WrappedSession)
@property
def graph(self):
return self._sess.graph
@property
def sess_str(self):
return self._sess.sess_str
def should_stop(self):
"""Return true if this session should not be used anymore.
Always return True if the session was closed.
Returns:
True if the session should stop, False otherwise.
"""
if self._check_stop():
return True
if self._sess:
return self._wrapped_is_stoppable and self._sess.should_stop()
return True
def _check_stop(self):
"""Hook for subclasses to provide their own stop condition.
Returns:
True if the session should stop, False otherwise.
"""
return False
def close(self):
if self._sess:
try:
self._sess.close()
except _PREEMPTION_ERRORS as e:
logging.error(
'An error occurred when attempting to close the '
'session. This may be due to a preemption in a '
'connected worker or parameter server. Error: %s', e)
finally:
self._sess = None
def run(self, *args, **kwargs):
return self._sess.run(*args, **kwargs)
def run_step_fn(self, step_fn, raw_session, run_with_hooks):
# `_RecoverableSession` sets `run_with_hooks` to `_CoordinatedSession.run`.
# It is `None` when called from `_CoordinatedSession`. In that case
# `self.run` is `_CoordinatedSession.run`.
run_with_hooks = run_with_hooks or self.run
return step_fn(_MonitoredSession.StepContext(raw_session, run_with_hooks))
class _RecoverableSession(_WrappedSession):
"""A wrapped session that recreates a session upon certain kinds of errors.
The constructor is passed a SessionCreator object, not a session.
Calls to `run()` are delegated to the wrapped session. If a call raises the
exception `tf.errors.AbortedError` or `tf.errors.UnavailableError`, the
wrapped session is closed, and a new one is created by calling the factory
again.
"""
def __init__(self, sess_creator):
"""Create a new `_RecoverableSession`.
The value returned by calling `sess_creator.create_session()` will be the
session wrapped by this recoverable session.
Args:
sess_creator: A 'SessionCreator' to be wrapped by recoverable.
"""
self._sess_creator = sess_creator
_WrappedSession.__init__(self, self._create_session())
def _create_session(self):
while True:
try:
return self._sess_creator.create_session()
except _PREEMPTION_ERRORS as e:
logging.info(
'An error was raised while a session was being created. '
'This may be due to a preemption of a connected worker '
'or parameter server. A new session will be created. '
'This error may also occur due to a gRPC failure caused '
'by high memory or network bandwidth usage in the '
'parameter servers. If this error occurs repeatedly, try '
'increasing the number of parameter servers assigned to '
'the job. Error: %s', e)
def _check_stop(self):
try:
if self._sess:
return self._sess._check_stop() # pylint: disable=protected-access
else:
return True
except _PREEMPTION_ERRORS as e:
logging.info(
'An error was raised while considering whether the '
'session is complete. This may be due to a preemption in '
'a connected worker or parameter server. The current '
'session will be closed and a new session will be '
'created. This error may also occur due to a gRPC failure '
'caused by high memory or network bandwidth usage in the '
'parameter servers. If this error occurs repeatedly, try '
'increasing the number of parameter servers assigned to '
'the job. Error: %s', e)
self.close()
self._sess = self._create_session()
# Since we have just recreated the session, the overall computation should
# not stop:
return False
except Exception: # pylint: disable=broad-except
# `should_stop` should return True instead of raising an exception.
return True
def run(self, fetches, feed_dict=None, options=None, run_metadata=None):
while True:
try:
if not self._sess:
self._sess = self._create_session()
return self._sess.run(
fetches,
feed_dict=feed_dict,
options=options,
run_metadata=run_metadata)
except _PREEMPTION_ERRORS as e:
logging.info(
'An error was raised. This may be due to a preemption in '
'a connected worker or parameter server. The current '
'session will be closed and a new session will be '
'created. This error may also occur due to a gRPC failure '
'caused by high memory or network bandwidth usage in the '
'parameter servers. If this error occurs repeatedly, try '
'increasing the number of parameter servers assigned to '
'the job. Error: %s', e)
self.close()
self._sess = None
def run_step_fn(self, step_fn, raw_session, run_with_hooks):
while True:
try:
if not self._sess:
self._sess = self._create_session()
run_with_hooks = self._sess.run
return self._sess.run_step_fn(step_fn, raw_session, run_with_hooks)
except _PREEMPTION_ERRORS as e:
logging.info(
'An error was raised. This may be due to a preemption in '
'a connected worker or parameter server. The current '
'session will be closed and a new session will be '
'created. This error may also occur due to a gRPC failure '
'caused by high memory or network bandwidth usage in the '
'parameter servers. If this error occurs repeatedly, try '
'increasing the number of parameter servers assigned to '
'the job. Error: %s', e)
self.close()
self._sess = None
class _CoordinatedSession(_WrappedSession):
"""A wrapped session that works with a `tf.Coordinator`.
Calls to `run()` are delegated to the wrapped session. If a call
raises an exception, the exception is reported to the coordinator.
In addition, after each call to `run()` this session ask the coordinator if
the session should stop. In that case it will join all the threads
registered with the coordinator before returning.
If the coordinator was requested to stop with an exception, that exception
will be re-raised from the call to `run()`.
"""
def __init__(self, sess, coord, stop_grace_period_secs=120):
"""Create a new `_CoordinatedSession`.
Args:
sess: A `tf.compat.v1.Session` object. The wrapped session.
coord: A `tf.train.Coordinator` object.
stop_grace_period_secs: Number of seconds given to threads to stop after
`close()` has been called.
"""
_WrappedSession.__init__(self, sess)
self._coord = coord
self._stop_grace_period_secs = stop_grace_period_secs
def _check_stop(self):
# If the coordinator was asked to stop due to an exception, then it needs
# to be propagated to this stack.
self._coord.raise_requested_exception()
# At this point, no exceptions are recorded in the coordinator.
return self._coord.should_stop()
def close(self):
self._coord.request_stop()
try:
self._coord.join(
stop_grace_period_secs=self._stop_grace_period_secs,
ignore_live_threads=True)
finally:
try:
_WrappedSession.close(self)
except Exception: # pylint: disable=broad-except
# We intentionally suppress exceptions from the close() here since
# useful exceptions are already reported by join().
pass
def run(self, *args, **kwargs):
try:
return self._sess.run(*args, **kwargs)
except _PREEMPTION_ERRORS:
raise
except Exception: # pylint: disable=broad-except
# A non-preemption error could have been caused by a preemption error
# in the coordinator. If this is the case, raise that exception instead,
# since it's the root cause. Otherwise, stick to the `original_exc_info`.
original_exc_info = sys.exc_info()
try:
self._coord.raise_requested_exception()
except _PREEMPTION_ERRORS:
raise
except Exception: # pylint: disable=broad-except
raise six.reraise(*original_exc_info)
else:
raise six.reraise(*original_exc_info)
class _HookedSession(_WrappedSession):
"""A _WrappedSession that calls hooks during calls to run().
The list of hooks to call is passed in the constructor. Before each call
to `run()` the session calls the `before_run()` method of the hooks, which
can return additional ops or tensors to run. These are added to the arguments
of the call to `run()`.
When the `run()` call finishes, the session calls the `after_run()` methods of
the hooks, passing the values returned by the `run()` call corresponding to
the ops and tensors that each hook requested.
If any call to the hooks, requests stop via run_context the session will be
marked as needing to stop and its `should_stop()` method will now return
`True`.
"""
def __init__(self, sess, hooks):
"""Initializes a _HookedSession object.
Args:
sess: A `tf.compat.v1.Session` or a `_WrappedSession` object.
hooks: An iterable of `SessionRunHook' objects.
"""
_WrappedSession.__init__(self, sess)
self._hooks = hooks
self._should_stop = False
def _check_stop(self):
"""See base class."""
return self._should_stop
def run(self, fetches, feed_dict=None, options=None, run_metadata=None):
"""See base class."""
if self.should_stop():
raise RuntimeError('Run called even after should_stop requested.')
actual_fetches = {'caller': fetches}
run_context = session_run_hook.SessionRunContext(
original_args=session_run_hook.SessionRunArgs(fetches, feed_dict),
session=self._sess)
options = options or config_pb2.RunOptions()
feed_dict = self._call_hook_before_run(run_context, actual_fetches,
feed_dict, options)
# Do session run.
run_metadata = run_metadata or config_pb2.RunMetadata()
outputs = _WrappedSession.run(
self,
fetches=actual_fetches,
feed_dict=feed_dict,
options=options,
run_metadata=run_metadata)
for hook in self._hooks:
hook.after_run(
run_context,
session_run_hook.SessionRunValues(
results=outputs[hook] if hook in outputs else None,
options=options,
run_metadata=run_metadata))
self._should_stop = self._should_stop or run_context.stop_requested
return outputs['caller']
def _call_hook_before_run(self, run_context, fetch_dict, user_feed_dict,
options):
"""Calls hooks.before_run and handles requests from hooks."""
hook_feeds = {}
for hook in self._hooks:
request = hook.before_run(run_context)
if request is not None:
if request.fetches is not None:
fetch_dict[hook] = request.fetches
if request.feed_dict:
self._raise_if_feeds_intersects(hook_feeds, request.feed_dict,
'Same tensor is fed by two hooks.')
hook_feeds.update(request.feed_dict)
if request.options:
self._merge_run_options(options, request.options)
if not hook_feeds:
return user_feed_dict
if not user_feed_dict:
return hook_feeds
self._raise_if_feeds_intersects(
user_feed_dict, hook_feeds,
'Same tensor is fed by a SessionRunHook and user.')
hook_feeds.update(user_feed_dict)
return hook_feeds
def _raise_if_feeds_intersects(self, feeds1, feeds2, message):
intersection = set(feeds1.keys()) & set(feeds2.keys())
if intersection:
raise RuntimeError(message + ' Conflict(s): ' + str(list(intersection)))
def _merge_run_options(self, options, incoming_options):
"""Merge two instances of RunOptions into the first one.
During the merger, the numerical fields including trace_level,
timeout_in_ms, inter_op_thread_pool are set to the larger one of the two.
The boolean value is set to the logical OR of the two.
debug_tensor_watch_opts of the original options is extended with that from
the incoming one.
Args:
options: The options to merge into.
incoming_options: The options to be merged into the first argument.
"""
options.trace_level = max(options.trace_level, incoming_options.trace_level)
options.timeout_in_ms = max(options.timeout_in_ms,
incoming_options.timeout_in_ms)
options.inter_op_thread_pool = max(options.inter_op_thread_pool,
incoming_options.inter_op_thread_pool)
options.output_partition_graphs = max(
options.output_partition_graphs,
incoming_options.output_partition_graphs)
options.debug_options.debug_tensor_watch_opts.extend(
incoming_options.debug_options.debug_tensor_watch_opts)
options.debug_options.reset_disk_byte_usage = (
options.debug_options.reset_disk_byte_usage or
incoming_options.debug_options.reset_disk_byte_usage)
options.report_tensor_allocations_upon_oom = (
options.report_tensor_allocations_upon_oom or
incoming_options.report_tensor_allocations_upon_oom)
| apache-2.0 |
mattsanf/legallydistinctpocketmonsters | node_modules/utf8/tests/generate-test-data.py | 1788 | 1435 | #!/usr/bin/env python
import re
import json
# https://mathiasbynens.be/notes/javascript-encoding#surrogate-formulae
# http://stackoverflow.com/a/13436167/96656
def unisymbol(codePoint):
if codePoint >= 0x0000 and codePoint <= 0xFFFF:
return unichr(codePoint)
elif codePoint >= 0x010000 and codePoint <= 0x10FFFF:
highSurrogate = int((codePoint - 0x10000) / 0x400) + 0xD800
lowSurrogate = int((codePoint - 0x10000) % 0x400) + 0xDC00
return unichr(highSurrogate) + unichr(lowSurrogate)
else:
return 'Error'
def hexify(codePoint):
return 'U+' + hex(codePoint)[2:].upper().zfill(6)
def writeFile(filename, contents):
print filename
with open(filename, 'w') as f:
f.write(contents.strip() + '\n')
data = []
for codePoint in range(0x000000, 0x10FFFF + 1):
# Skip non-scalar values.
if codePoint >= 0xD800 and codePoint <= 0xDFFF:
continue
symbol = unisymbol(codePoint)
# http://stackoverflow.com/a/17199950/96656
bytes = symbol.encode('utf8').decode('latin1')
data.append({
'codePoint': codePoint,
'decoded': symbol,
'encoded': bytes
});
jsonData = json.dumps(data, sort_keys=False, indent=2, separators=(',', ': '))
# Use tabs instead of double spaces for indentation
jsonData = jsonData.replace(' ', '\t')
# Escape hexadecimal digits in escape sequences
jsonData = re.sub(
r'\\u([a-fA-F0-9]{4})',
lambda match: r'\u{}'.format(match.group(1).upper()),
jsonData
)
writeFile('data.json', jsonData)
| gpl-3.0 |
marcuskelly/recover | Lib/site-packages/passlib/utils/decor.py | 5 | 7651 | """
passlib.utils.decor -- helper decorators & properties
"""
#=============================================================================
# imports
#=============================================================================
# core
from __future__ import absolute_import, division, print_function
import logging
log = logging.getLogger(__name__)
from functools import wraps, update_wrapper
import types
from warnings import warn
# site
# pkg
from passlib.utils.compat import PY3
# local
__all__ = [
"classproperty",
"hybrid_method",
"memoize_single_value",
"memoized_property",
"deprecated_function",
"deprecated_method",
]
#=============================================================================
# class-level decorators
#=============================================================================
class classproperty(object):
"""Function decorator which acts like a combination of classmethod+property (limited to read-only properties)"""
def __init__(self, func):
self.im_func = func
def __get__(self, obj, cls):
return self.im_func(cls)
@property
def __func__(self):
"""py3 compatible alias"""
return self.im_func
class hybrid_method(object):
"""
decorator which invokes function with class if called as class method,
and with object if called at instance level.
"""
def __init__(self, func):
self.func = func
update_wrapper(self, func)
def __get__(self, obj, cls):
if obj is None:
obj = cls
if PY3:
return types.MethodType(self.func, obj)
else:
return types.MethodType(self.func, obj, cls)
#=============================================================================
# memoization
#=============================================================================
def memoize_single_value(func):
"""
decorator for function which takes no args,
and memoizes result. exposes a ``.clear_cache`` method
to clear the cached value.
"""
cache = {}
@wraps(func)
def wrapper():
try:
return cache[True]
except KeyError:
pass
value = cache[True] = func()
return value
def clear_cache():
cache.pop(True, None)
wrapper.clear_cache = clear_cache
return wrapper
class memoized_property(object):
"""
decorator which invokes method once, then replaces attr with result
"""
def __init__(self, func):
self.__func__ = func
self.__name__ = func.__name__
self.__doc__ = func.__doc__
def __get__(self, obj, cls):
if obj is None:
return self
value = self.__func__(obj)
setattr(obj, self.__name__, value)
return value
if not PY3:
@property
def im_func(self):
"""py2 alias"""
return self.__func__
def clear_cache(self, obj):
"""
class-level helper to clear stored value (if any).
usage: :samp:`type(self).{attr}.clear_cache(self)`
"""
obj.__dict__.pop(self.__name__, None)
def peek_cache(self, obj, default=None):
"""
class-level helper to peek at stored value
usage: :samp:`value = type(self).{attr}.clear_cache(self)`
"""
return obj.__dict__.get(self.__name__, default)
# works but not used
##class memoized_class_property(object):
## """function decorator which calls function as classmethod,
## and replaces itself with result for current and all future invocations.
## """
## def __init__(self, func):
## self.im_func = func
##
## def __get__(self, obj, cls):
## func = self.im_func
## value = func(cls)
## setattr(cls, func.__name__, value)
## return value
##
## @property
## def __func__(self):
## "py3 compatible alias"
#=============================================================================
# deprecation
#=============================================================================
def deprecated_function(msg=None, deprecated=None, removed=None, updoc=True,
replacement=None, _is_method=False,
func_module=None):
"""decorator to deprecate a function.
:arg msg: optional msg, default chosen if omitted
:kwd deprecated: version when function was first deprecated
:kwd removed: version when function will be removed
:kwd replacement: alternate name / instructions for replacing this function.
:kwd updoc: add notice to docstring (default ``True``)
"""
if msg is None:
if _is_method:
msg = "the method %(mod)s.%(klass)s.%(name)s() is deprecated"
else:
msg = "the function %(mod)s.%(name)s() is deprecated"
if deprecated:
msg += " as of Passlib %(deprecated)s"
if removed:
msg += ", and will be removed in Passlib %(removed)s"
if replacement:
msg += ", use %s instead" % replacement
msg += "."
def build(func):
is_classmethod = _is_method and isinstance(func, classmethod)
if is_classmethod:
# NOTE: PY26 doesn't support "classmethod().__func__" directly...
func = func.__get__(None, type).__func__
opts = dict(
mod=func_module or func.__module__,
name=func.__name__,
deprecated=deprecated,
removed=removed,
)
if _is_method:
def wrapper(*args, **kwds):
tmp = opts.copy()
klass = args[0] if is_classmethod else args[0].__class__
tmp.update(klass=klass.__name__, mod=klass.__module__)
warn(msg % tmp, DeprecationWarning, stacklevel=2)
return func(*args, **kwds)
else:
text = msg % opts
def wrapper(*args, **kwds):
warn(text, DeprecationWarning, stacklevel=2)
return func(*args, **kwds)
update_wrapper(wrapper, func)
if updoc and (deprecated or removed) and \
wrapper.__doc__ and ".. deprecated::" not in wrapper.__doc__:
txt = deprecated or ''
if removed or replacement:
txt += "\n "
if removed:
txt += "and will be removed in version %s" % (removed,)
if replacement:
if removed:
txt += ", "
txt += "use %s instead" % replacement
txt += "."
if not wrapper.__doc__.strip(" ").endswith("\n"):
wrapper.__doc__ += "\n"
wrapper.__doc__ += "\n.. deprecated:: %s\n" % (txt,)
if is_classmethod:
wrapper = classmethod(wrapper)
return wrapper
return build
def deprecated_method(msg=None, deprecated=None, removed=None, updoc=True,
replacement=None):
"""decorator to deprecate a method.
:arg msg: optional msg, default chosen if omitted
:kwd deprecated: version when method was first deprecated
:kwd removed: version when method will be removed
:kwd replacement: alternate name / instructions for replacing this method.
:kwd updoc: add notice to docstring (default ``True``)
"""
return deprecated_function(msg, deprecated, removed, updoc, replacement,
_is_method=True)
#=============================================================================
# eof
#=============================================================================
| bsd-2-clause |
bendikro/deluge-yarss-plugin | yarss2/lib/requests/packages/urllib3/util/url.py | 553 | 5836 | from collections import namedtuple
from ..exceptions import LocationParseError
url_attrs = ['scheme', 'auth', 'host', 'port', 'path', 'query', 'fragment']
class Url(namedtuple('Url', url_attrs)):
"""
Datastructure for representing an HTTP URL. Used as a return value for
:func:`parse_url`.
"""
slots = ()
def __new__(cls, scheme=None, auth=None, host=None, port=None, path=None,
query=None, fragment=None):
if path and not path.startswith('/'):
path = '/' + path
return super(Url, cls).__new__(cls, scheme, auth, host, port, path,
query, fragment)
@property
def hostname(self):
"""For backwards-compatibility with urlparse. We're nice like that."""
return self.host
@property
def request_uri(self):
"""Absolute path including the query string."""
uri = self.path or '/'
if self.query is not None:
uri += '?' + self.query
return uri
@property
def netloc(self):
"""Network location including host and port"""
if self.port:
return '%s:%d' % (self.host, self.port)
return self.host
@property
def url(self):
"""
Convert self into a url
This function should more or less round-trip with :func:`.parse_url`. The
returned url may not be exactly the same as the url inputted to
:func:`.parse_url`, but it should be equivalent by the RFC (e.g., urls
with a blank port will have : removed).
Example: ::
>>> U = parse_url('http://google.com/mail/')
>>> U.url
'http://google.com/mail/'
>>> Url('http', 'username:password', 'host.com', 80,
... '/path', 'query', 'fragment').url
'http://username:password@host.com:80/path?query#fragment'
"""
scheme, auth, host, port, path, query, fragment = self
url = ''
# We use "is not None" we want things to happen with empty strings (or 0 port)
if scheme is not None:
url += scheme + '://'
if auth is not None:
url += auth + '@'
if host is not None:
url += host
if port is not None:
url += ':' + str(port)
if path is not None:
url += path
if query is not None:
url += '?' + query
if fragment is not None:
url += '#' + fragment
return url
def __str__(self):
return self.url
def split_first(s, delims):
"""
Given a string and an iterable of delimiters, split on the first found
delimiter. Return two split parts and the matched delimiter.
If not found, then the first part is the full input string.
Example::
>>> split_first('foo/bar?baz', '?/=')
('foo', 'bar?baz', '/')
>>> split_first('foo/bar?baz', '123')
('foo/bar?baz', '', None)
Scales linearly with number of delims. Not ideal for large number of delims.
"""
min_idx = None
min_delim = None
for d in delims:
idx = s.find(d)
if idx < 0:
continue
if min_idx is None or idx < min_idx:
min_idx = idx
min_delim = d
if min_idx is None or min_idx < 0:
return s, '', None
return s[:min_idx], s[min_idx+1:], min_delim
def parse_url(url):
"""
Given a url, return a parsed :class:`.Url` namedtuple. Best-effort is
performed to parse incomplete urls. Fields not provided will be None.
Partly backwards-compatible with :mod:`urlparse`.
Example::
>>> parse_url('http://google.com/mail/')
Url(scheme='http', host='google.com', port=None, path='/mail/', ...)
>>> parse_url('google.com:80')
Url(scheme=None, host='google.com', port=80, path=None, ...)
>>> parse_url('/foo?bar')
Url(scheme=None, host=None, port=None, path='/foo', query='bar', ...)
"""
# While this code has overlap with stdlib's urlparse, it is much
# simplified for our needs and less annoying.
# Additionally, this implementations does silly things to be optimal
# on CPython.
if not url:
# Empty
return Url()
scheme = None
auth = None
host = None
port = None
path = None
fragment = None
query = None
# Scheme
if '://' in url:
scheme, url = url.split('://', 1)
# Find the earliest Authority Terminator
# (http://tools.ietf.org/html/rfc3986#section-3.2)
url, path_, delim = split_first(url, ['/', '?', '#'])
if delim:
# Reassemble the path
path = delim + path_
# Auth
if '@' in url:
# Last '@' denotes end of auth part
auth, url = url.rsplit('@', 1)
# IPv6
if url and url[0] == '[':
host, url = url.split(']', 1)
host += ']'
# Port
if ':' in url:
_host, port = url.split(':', 1)
if not host:
host = _host
if port:
# If given, ports must be integers.
if not port.isdigit():
raise LocationParseError(url)
port = int(port)
else:
# Blank ports are cool, too. (rfc3986#section-3.2.3)
port = None
elif not host and url:
host = url
if not path:
return Url(scheme, auth, host, port, path, query, fragment)
# Fragment
if '#' in path:
path, fragment = path.split('#', 1)
# Query
if '?' in path:
path, query = path.split('?', 1)
return Url(scheme, auth, host, port, path, query, fragment)
def get_host(url):
"""
Deprecated. Use :func:`.parse_url` instead.
"""
p = parse_url(url)
return p.scheme or 'http', p.hostname, p.port
| gpl-3.0 |
mancoast/CPythonPyc_test | cpython/263_test_codecencodings_cn.py | 64 | 2054 | #!/usr/bin/env python
#
# test_codecencodings_cn.py
# Codec encoding tests for PRC encodings.
#
from test import test_support
from test import test_multibytecodec_support
import unittest
class Test_GB2312(test_multibytecodec_support.TestBase, unittest.TestCase):
encoding = 'gb2312'
tstring = test_multibytecodec_support.load_teststring('gb2312')
codectests = (
# invalid bytes
("abc\x81\x81\xc1\xc4", "strict", None),
("abc\xc8", "strict", None),
("abc\x81\x81\xc1\xc4", "replace", u"abc\ufffd\u804a"),
("abc\x81\x81\xc1\xc4\xc8", "replace", u"abc\ufffd\u804a\ufffd"),
("abc\x81\x81\xc1\xc4", "ignore", u"abc\u804a"),
("\xc1\x64", "strict", None),
)
class Test_GBK(test_multibytecodec_support.TestBase, unittest.TestCase):
encoding = 'gbk'
tstring = test_multibytecodec_support.load_teststring('gbk')
codectests = (
# invalid bytes
("abc\x80\x80\xc1\xc4", "strict", None),
("abc\xc8", "strict", None),
("abc\x80\x80\xc1\xc4", "replace", u"abc\ufffd\u804a"),
("abc\x80\x80\xc1\xc4\xc8", "replace", u"abc\ufffd\u804a\ufffd"),
("abc\x80\x80\xc1\xc4", "ignore", u"abc\u804a"),
("\x83\x34\x83\x31", "strict", None),
(u"\u30fb", "strict", None),
)
class Test_GB18030(test_multibytecodec_support.TestBase, unittest.TestCase):
encoding = 'gb18030'
tstring = test_multibytecodec_support.load_teststring('gb18030')
codectests = (
# invalid bytes
("abc\x80\x80\xc1\xc4", "strict", None),
("abc\xc8", "strict", None),
("abc\x80\x80\xc1\xc4", "replace", u"abc\ufffd\u804a"),
("abc\x80\x80\xc1\xc4\xc8", "replace", u"abc\ufffd\u804a\ufffd"),
("abc\x80\x80\xc1\xc4", "ignore", u"abc\u804a"),
("abc\x84\x39\x84\x39\xc1\xc4", "replace", u"abc\ufffd\u804a"),
(u"\u30fb", "strict", "\x819\xa79"),
)
has_iso10646 = True
def test_main():
test_support.run_unittest(__name__)
if __name__ == "__main__":
test_main()
| gpl-3.0 |
qskycolor/viewfinder | backend/logs/itunes_trends.py | 13 | 11557 | # Copyright 2012 Viewfinder Inc. All Rights Reserved
"""Interface to the iTunes Connect sales and trend.
Fetches daily download stats from iTunes Connect and saves them to the metrics table.
Specify the apple user to log in with (eg: --user=marc to use marc@emailscrubbed.com). We get the user's
apple password from the secret 'itunes_connect_${user}'.
Default user is 'itunes_viewer', a special user with "sales" data access only.
Run with:
$ python -m viewfinder.backend.logs.itunes_trends --start_date=2013-01-20
Appropriate for cron: Detect start date, update metrics table. Don't run if last run was less than 6h ago.
$ python -m viewfinder.backend.logs.itunes_trends --dry_run=False --smart_scan=True --hours_between_runs=6
ExceptionOptions:
- user: default=itunes_viewer: apple user. We expand this to ${user}@emailscrubbed.com
- vendor_id: default=<our ID>: the vendor ID, from the iTunes Connect dashboard.
- dry_run: default=True: display stats only, do not update Metrics table or write job summary.
- start_date: default=None: look up stats from that date until yesterday. format: YYYY-MM-DD.
- smart_scan: default=False: determine the start date from previous successful run.
- require_lock: default=True: grab the job:itunes_trends lock for the duration of the job.
- hours_between_runs: default=0: don't run if the last successful run was less than this many hours ago.
"""
import gzip
import cStringIO
import getpass
import json
import logging
import os
import re
import sys
import time
import traceback
import urllib
import urllib2
from tornado import gen, options
from urlparse import urljoin
from viewfinder.backend.base import constants, main, secrets, util
from viewfinder.backend.base.dotdict import DotDict
from viewfinder.backend.db import db_client, metric
from viewfinder.backend.db.job import Job
from viewfinder.backend.logs import logs_util
from viewfinder.backend.services import itunes_trends_codes
from viewfinder.backend.storage.object_store import ObjectStore
options.define('user', default='itunes_viewer', help='User for iTunesConnect. Will expand to user@emailscrubbed.com and '
'lookup the itunes password in secrets/itunes_connect_${user}')
# vendor_id comes from the iTunes Connect dashboard.
options.define('vendor_id', default='85575078', help='iTunes vendor ID')
options.define('dry_run', default=True, help='Print output only, do not write to metrics table.')
options.define('start_date', default=None, help='Lookup stats from date onwards (YYYY-MM-DD)')
options.define('smart_scan', type=bool, default=False,
help='determine start_date from previous successful runs. Overrides start_date.')
options.define('require_lock', type=bool, default=True,
help='attempt to grab the job:itunes_trends lock before running. Exit if acquire fails.')
options.define('hours_between_runs', type=int, default=0,
help='minimum time since start of last successful run (with dry_run=False)')
options.define('download_from_s3', type=bool, default=False,
help='Fetch raw gzipped files from S3 (if false, fetch from iTunesConnect)')
kITCBaseURL = 'https://reportingitc.apple.com/autoingestion.tft'
kS3Bucket = ObjectStore.SERVER_DATA
kS3Base = 'itunes-trends/'
class ITunesTrends(object):
def __init__(self, apple_id, password, vendor_id, html_retries=3):
self._apple_id = apple_id
self._password = password
self._vendor_id = vendor_id
self._html_retries=3
self._form_fields = None
self._available_days = None
self._available_weeks = None
self._object_store = ObjectStore.GetInstance(kS3Bucket)
def _Fetch(self, url, data=None):
"""Attempt to fetch 'url' with optional 'data'. We retry self._retry times, regardless of the error."""
retries = 0
while True:
logging.info('fetching (%d) %s' % (retries, url))
request = urllib2.Request(url, data)
handle = urllib2.urlopen(request)
logging.info('fetch reply headers: %s' % handle.info())
return handle.read()
try:
pass
except Exception:
if retries >= self._html_retries:
raise
time.sleep(2**retries)
retries += 1
@gen.engine
def FetchOneDay(self, day, callback):
"""Fetch a single day's worth of data. Exception could be due to http errors, unavailable date, or failed parsing.
TODO(marc): handle these cases separately.
"""
s3_filename = os.path.join(kS3Base, '%s.gz' % day)
def DownloadFromiTunes():
# We use our own date format in the entire tool. Only now do we convert to iTuneConnect's YYYYMMDD.
y, m, d = day.split('-')
itunes_date = '%s%s%s' % (y, m, d)
data = urllib.urlencode({'USERNAME': self._apple_id,
'PASSWORD': self._password,
'VNDNUMBER': self._vendor_id,
'TYPEOFREPORT': 'Sales',
'DATETYPE': 'Daily',
'REPORTTYPE': 'Summary',
'REPORTDATE': itunes_date })
buf = self._Fetch(kITCBaseURL, data)
return buf
def ParseContents(contents):
result = DotDict()
skipped_lines = []
for line in contents.splitlines():
tokens = line.split('\t')
if tokens[0] == 'Provider':
# Skip header line.
skipped_lines.append(line)
continue
# Replace dots with underscores as we'll be using the version in a DotDict.
version = tokens[5].replace('.', '_')
if not version or version == ' ':
# subscriptions do not have a version, use 'all'.
version = 'all'
type_id = tokens[6]
# Use the type id if we don't have a name for it.
type_name = itunes_trends_codes.PRODUCT_TYPE_IDENTIFIER.get(type_id, type_id)
units = int(tokens[7])
# Ignore proceeds, it does not reflect in-app purchases.
store = tokens[12]
result['itunes.%s.%s.%s' % (type_name, version, store)] = units
assert len(skipped_lines) <= 1, 'Skipped too many lines: %r' % skipped_lines
return result
# Failures in any of Get/Download/Put will interrupt this day's processing.
if options.options.download_from_s3:
logging.info('S3 get %s' % s3_filename)
buf = yield gen.Task(self._object_store.Get, s3_filename)
else:
buf = DownloadFromiTunes()
logging.info('S3 put %s' % s3_filename)
yield gen.Task(self._object_store.Put, s3_filename, buf)
iobuffer = cStringIO.StringIO(buf)
gzipIO = gzip.GzipFile('rb', fileobj=iobuffer)
contents = gzipIO.read()
iobuffer.close()
logging.info('Contents: %s' % contents)
callback(ParseContents(contents))
@gen.engine
def DetermineStartDate(client, job, callback):
"""If smart_scan is true, lookup the start date from previous job summaries, otherwise use --start_date.
--start_date and job summary days are of the form YYYY-MM-DD.
"""
start_date = options.options.start_date
# Lookup previous runs started in the last week.
if options.options.smart_scan:
# Search for successful full-scan run in the last week.
last_run = yield gen.Task(job.FindLastSuccess, with_payload_key='stats.last_day')
if last_run is None:
logging.info('No previous successful scan found, rerun with --start_date')
callback(None)
return
last_run_start = last_run['start_time']
if (last_run_start + options.options.hours_between_runs * constants.SECONDS_PER_HOUR > time.time()):
logging.info('Last successful run started at %s, less than %d hours ago; skipping.' %
(time.asctime(time.localtime(last_run_start)), options.options.hours_between_runs))
callback(None)
return
# Start start_date to the last processed day + 1.
last_day = last_run['stats.last_day']
start_time = util.ISO8601ToUTCTimestamp(last_day) + constants.SECONDS_PER_DAY
start_date = util.TimestampUTCToISO8601(start_time)
logging.info('Last successful run (%s) scanned up to %s, setting start date to %s' %
(time.asctime(time.localtime(last_run_start)), last_day, start_date))
callback(start_date)
@gen.engine
def RunOnce(client, job, apple_id, password, callback):
start_date = yield gen.Task(DetermineStartDate, client, job)
if start_date is None:
logging.info('Start date not specified, last run too recent, or smart_scan could not determine a date; exiting.')
callback(None)
return
query_dates = []
start_time = util.ISO8601ToUTCTimestamp(start_date)
today = util.NowUTCToISO8601()
while start_time < time.time():
date = util.TimestampUTCToISO8601(start_time)
if date != today:
query_dates.append(date)
start_time += constants.SECONDS_PER_DAY
logging.info('fetching data for dates: %r' % query_dates)
results = {}
itc = ITunesTrends(apple_id, password, options.options.vendor_id)
failed = False
for day in query_dates:
if failed:
break
try:
result = yield gen.Task(itc.FetchOneDay, day)
if not result:
# We don't get an exception when iTunesConnect has no data. We also don't want to
# fail as there's no way it will have this data later.
logging.warning('No data for day %s' % day)
else:
results[day] = result
except Exception:
msg = traceback.format_exc()
logging.warning('Error fetching iTunes Connect data for day %s: %s', day, msg)
failed = True
if len(results) == 0:
callback(None)
else:
# Replace the entire 'itunes' category of previous metrics. This is so we can fix any processing errors we
# may have had.
hms = logs_util.kDailyMetricsTimeByLogType['itunes_trends']
yield gen.Task(logs_util.UpdateMetrics, client, results, prefix_to_erase='itunes',
dry_run=options.options.dry_run, hms_tuple=hms)
callback(sorted(results.keys())[-1])
@gen.engine
def _Start(callback):
"""Grab a lock on job:itunes_trends and call RunOnce. If we get a return value, write it to the job summary."""
assert options.options.user is not None and options.options.vendor_id is not None
apple_id = '%s@emailscrubbed.com' % options.options.user
# Attempt to lookup iTunes Connect password from secrets.
password = secrets.GetSecret('itunes_connect_%s' % options.options.user)
assert password
client = db_client.DBClient.Instance()
job = Job(client, 'itunes_trends')
if options.options.require_lock:
got_lock = yield gen.Task(job.AcquireLock)
if got_lock == False:
logging.warning('Failed to acquire job lock: exiting.')
callback()
return
result = None
job.Start()
try:
result = yield gen.Task(RunOnce, client, job, apple_id, password)
except:
# Failure: log run summary with trace.
msg = traceback.format_exc()
logging.info('Registering failed run with message: %s' % msg)
yield gen.Task(job.RegisterRun, Job.STATUS_FAILURE, failure_msg=msg)
else:
if result is not None and not options.options.dry_run:
# Successful run with data processed and not in dry-run mode: write run summary.
stats = DotDict()
stats['last_day'] = result
logging.info('Registering successful run with stats: %r' % stats)
yield gen.Task(job.RegisterRun, Job.STATUS_SUCCESS, stats=stats)
finally:
yield gen.Task(job.ReleaseLock)
callback()
if __name__ == '__main__':
sys.exit(main.InitAndRun(_Start))
| apache-2.0 |
sonnyhu/scikit-learn | examples/svm/plot_separating_hyperplane_unbalanced.py | 329 | 1850 | """
=================================================
SVM: Separating hyperplane for unbalanced classes
=================================================
Find the optimal separating hyperplane using an SVC for classes that
are unbalanced.
We first find the separating plane with a plain SVC and then plot
(dashed) the separating hyperplane with automatically correction for
unbalanced classes.
.. currentmodule:: sklearn.linear_model
.. note::
This example will also work by replacing ``SVC(kernel="linear")``
with ``SGDClassifier(loss="hinge")``. Setting the ``loss`` parameter
of the :class:`SGDClassifier` equal to ``hinge`` will yield behaviour
such as that of a SVC with a linear kernel.
For example try instead of the ``SVC``::
clf = SGDClassifier(n_iter=100, alpha=0.01)
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
#from sklearn.linear_model import SGDClassifier
# we create 40 separable points
rng = np.random.RandomState(0)
n_samples_1 = 1000
n_samples_2 = 100
X = np.r_[1.5 * rng.randn(n_samples_1, 2),
0.5 * rng.randn(n_samples_2, 2) + [2, 2]]
y = [0] * (n_samples_1) + [1] * (n_samples_2)
# fit the model and get the separating hyperplane
clf = svm.SVC(kernel='linear', C=1.0)
clf.fit(X, y)
w = clf.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(-5, 5)
yy = a * xx - clf.intercept_[0] / w[1]
# get the separating hyperplane using weighted classes
wclf = svm.SVC(kernel='linear', class_weight={1: 10})
wclf.fit(X, y)
ww = wclf.coef_[0]
wa = -ww[0] / ww[1]
wyy = wa * xx - wclf.intercept_[0] / ww[1]
# plot separating hyperplanes and samples
h0 = plt.plot(xx, yy, 'k-', label='no weights')
h1 = plt.plot(xx, wyy, 'k--', label='with weights')
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
plt.legend()
plt.axis('tight')
plt.show()
| bsd-3-clause |
fhaoquan/kbengine | kbe/res/scripts/common/Lib/test/test_json/test_dump.py | 109 | 1626 | from io import StringIO
from test.test_json import PyTest, CTest
from test.support import bigmemtest, _1G
class TestDump:
def test_dump(self):
sio = StringIO()
self.json.dump({}, sio)
self.assertEqual(sio.getvalue(), '{}')
def test_dumps(self):
self.assertEqual(self.dumps({}), '{}')
def test_encode_truefalse(self):
self.assertEqual(self.dumps(
{True: False, False: True}, sort_keys=True),
'{"false": true, "true": false}')
self.assertEqual(self.dumps(
{2: 3.0, 4.0: 5, False: 1, 6: True}, sort_keys=True),
'{"false": 1, "2": 3.0, "4.0": 5, "6": true}')
# Issue 16228: Crash on encoding resized list
def test_encode_mutated(self):
a = [object()] * 10
def crasher(obj):
del a[-1]
self.assertEqual(self.dumps(a, default=crasher),
'[null, null, null, null, null]')
class TestPyDump(TestDump, PyTest): pass
class TestCDump(TestDump, CTest):
# The size requirement here is hopefully over-estimated (actual
# memory consumption depending on implementation details, and also
# system memory management, since this may allocate a lot of
# small objects).
@bigmemtest(size=_1G, memuse=1)
def test_large_list(self, size):
N = int(30 * 1024 * 1024 * (size / _1G))
l = [1] * N
encoded = self.dumps(l)
self.assertEqual(len(encoded), N * 3)
self.assertEqual(encoded[:1], "[")
self.assertEqual(encoded[-2:], "1]")
self.assertEqual(encoded[1:-2], "1, " * (N - 1))
| lgpl-3.0 |
maloL/nao-fsm | tracking_and_storing.py | 1 | 11706 | # object tracking algorithm with trajectory points plotting on
# video stream window
# after taking 20 points it stores them in txt files
from naoqi import ALProxy, ALBroker, ALModule
import time
from vision_definitions import kVGA, kBGRColorSpace
import cv2 as opencv
import numpy as np
import random
from ghmm import *
import ConfigParser, argparse
import training
global ObjectTracker
# object tracking module
class ObjectTrackerModule(ALModule):
def __init__(self, name):
ALModule.__init__(self, name)
self.data = 0
self.behaviors = []
self.exists = []
self.kindNames = []
self.waiting = []
self.tts = ALProxy("ALTextToSpeech")
self.gestureProxy = ALProxy("NAOObjectGesture", myBroker)
self.motionProxy = ALProxy("ALMotion", myBroker)
self.memProxy = ALProxy("ALMemory", myBroker)
self.motionProxy.setStiffnesses("Head", 1.0)
self.gestureProxy.startTracker(15, 0)
#self.log = open("temp.txt", "w") ############################################################
def startTracker(self, camId):
self.gestureProxy.startTracker(15, camId)
#self.gestureProxy.focusObject(-1)
def stopTracker(self):
self.gestureProxy.stopTracker()
self.gestureProxy.stopFocus()
def load(self, path, name):
self.gestureProxy.loadDataset(path)
self.kindNames.append(name)
self.exists.append(False)
self.behaviors.append([])
self.waiting.append(None)
self.gestureProxy.trackObject(name, -len(self.kindNames))
self.memProxy.subscribeToMicroEvent(name, "ObjectTracker", name, "storeData")
def getIdx(self, name):
if (name in self.kindNames):
return self.kindNames.index(name)
else:
return None
def getBehaviors(self, name):
idx = self.getIdx(name)
if idx!=None:
return self.behaviors[idx]
else:
return None
def getExist(self, name):
idx = self.getIdx(name)
if idx!=None:
return self.exists[idx]
else:
return None
def getWaiting(self, name):
idx = self.getIdx(name)
if idx!=None:
return self.waiting[idx]
else:
return None
def clearWaiting(self):
for i in range(len(self.waiting)):
self.waiting[i] = None
def waitForBehavior(self, name, behavior):
idx = self.getIdx(name)
self.gestureProxy.clearEventTraj(name)
print('Waiting for behavior: ' + str(behavior))
if idx!=None:
if behavior == "Frog":
self.waiting[idx] = ["FrogL", "FrogR"]
else:
if behavior == "Plane":
self.waiting[idx] = ["PlaneL", "PlaneR"]
else:
self.waiting[idx] = [behavior]
else:
return None
def onObjGet(self, key, value, message):
id = -1
if (key in self.kindNames):
id = self.kindNames.index(key)
else:
return
if (value != None):
if (value[0] != 0):
self.exists[id]=True
if (value[5]!=None):
print (value[5])
self.behaviors[id] = value[5]
if (self.waiting[id]!= None):
for tmp in self.waiting[id]:
if tmp in value[5]:
self.waiting[id] = None
break
else:
self.exists[id]=False
if (value[1]!=None):
print (value[1])
self.behaviors[id] = value[1]
if (self.waiting[id]!= None):
for tmp in self.waiting[id]:
if tmp in value[1]:
self.waiting[id] = None
break
def storeData(self, key, value, message):
if value:
if value[0]:
print("I see the cup")
#self.log.write(str(value[3][0])+", "+str(value[3][1])+"\n") ########################################
self.data = value[3]
else:
self.data = 0
print("I don't see the cup")
def unload(self):
self.gestureProxy.stopTracker()
#self.log.close()
for i in range(0, len(self.exists)):
self.gestureProxy.removeObjectKind(0)
self.gestureProxy.removeEvent(self.kindNames[i])
# class with functions for Kalman filter
class KalmanFilter(object):
def __init__(self, process_variance, estimated_measurement_variance):
self.process_variance = process_variance
self.estimated_measurement_variance = estimated_measurement_variance
self.posteri_estimate = 0.0
self.posteri_error_estimate = 1.0
def input_latest_noisy_measurement(self, measurement):
priori_estimate = self.posteri_estimate
priori_error_estimate = self.posteri_error_estimate + self.process_variance
blending_factor = priori_error_estimate / (priori_error_estimate + self.estimated_measurement_variance)
self.posteri_estimate = priori_estimate + blending_factor * (measurement - priori_estimate)
self.posteri_error_estimate = (1 - blending_factor) * priori_error_estimate
def get_latest_estimated_measurement(self):
return self.posteri_estimate
# function for getting video stream from nao camera
def nao_image_getter(alvideoproxy, video):
alimg = alvideoproxy.getImageRemote(video)
imgheader = opencv.cv.CreateImageHeader((alimg[0], alimg[1]), opencv.cv.IPL_DEPTH_8U, 3)
opencv.cv.SetData(imgheader, alimg[6])
img = np.asarray(imgheader[:, :])
return img
if __name__ == '__main__':
# initializing proxies and other required parameters
IP = "192.168.1.105"
PORT = 9559
myBroker = ALBroker("myBroker", "0.0.0.0", 0, IP, PORT)
#opencv.namedWindow("Robot camera feed")
# get sample image to detect size
alvideoproxy = ALProxy("ALVideoDevice", IP, PORT)
video = alvideoproxy.subscribeCamera("video", 0, kVGA, kBGRColorSpace, 30)
motionproxy=ALProxy('ALMotion', myBroker)
motionproxy.killAll()
tts = ALProxy('ALTextToSpeech', myBroker)
behaveproxy = ALProxy('ALBehaviorManager', myBroker)
postureproxy = ALProxy('ALRobotPosture', myBroker)
navigationProxy = ALProxy('ALNavigation', myBroker)
sound = ALProxy('ALAudioDevice', myBroker)
memory = ALProxy('ALMemory', myBroker)
memory.insertData('ObjectGrabber', int(0))
camProxy = ALProxy("ALVideoDevice", IP, PORT)
postureproxy.goToPosture("StandInit", 0.8)
motionproxy.setAngles('HeadPitch', 0, 0.5)
time.sleep(0.5)
motionproxy.setAngles('HeadYaw', 0, 0.5)
time.sleep(0.5)
motionproxy.setStiffnesses("Head", 1.0)
cfile = "Config.ini"
config = ConfigParser.ConfigParser()
config.read(cfile)
set_num = config.get("Grab settings", "dataset")
volume = config.get('Grab settings', 'Volume')
volume = int(float(volume))
sound.setOutputVolume(volume)
new_set = int(set_num) + 1
filename = '/home/luka/Documents/FER_projekt/Diplomski_rad/Temp_set/Pos/gest' + str(new_set)
config.set("Grab settings", "Dataset", str(new_set))
with open(cfile, 'wb') as configfile:
config.write(configfile)
# try object tracking
try:
# kalman filter preparations
iteration_count = 500
measurement_standard_deviation = np.std([random.random() * 2.0 - 1.0 for j in xrange(iteration_count)])
process_variance = 1e-1 # greater = faster, worse estimation, lower = slower, better estimation
estimated_measurement_variance = measurement_standard_deviation ** 2 # 0.05 ** 2
kalman_filter = KalmanFilter(process_variance, estimated_measurement_variance)
posteri_estimate_graph = []
# initilazing tracking
ObjectTracker = ObjectTrackerModule("ObjectTracker")
ObjectTracker.load("/home/nao/ImageSets/cup", 'Cup')
ObjectTracker.gestureProxy.stopTracker()
time.sleep(2)
#tts.say("Now you repeat the gesture")
time.sleep(2)
print ('Starting tracker...')
ObjectTracker.startTracker(0)
image_position = np.zeros(shape=2)
pos_vec = np.zeros(shape=2)
i = 0
log = open(filename + ".txt", "w") ####################################################################################
estimation = np.zeros(shape=(1, 2))
# while loop where tracking is executed
tts.say("Now you repeat the gesture")
time.sleep(0.5)
while len(estimation) < 30:
# if object is detected do data analysis
image = nao_image_getter(alvideoproxy, video)
if ObjectTracker.data:
# angular position data from micro event
pos_data = np.asarray(ObjectTracker.data)
print "data: "
print ObjectTracker.data
# calculating image position based on angular position of object
image_position = camProxy.getImagePositionFromAngularPosition(0, [pos_data[0], pos_data[1]])
image_position = np.asarray(image_position)
print image_position
# applying kalman filter on image position data
kalman_filter.input_latest_noisy_measurement(image_position)
posteri_estimate_graph.append(kalman_filter.get_latest_estimated_measurement())
# separating estimated values for easier plotting
estimation = np.zeros(shape=(len(posteri_estimate_graph), 2))
for i in range(0, len(posteri_estimate_graph)):
temp2 = posteri_estimate_graph[i]
estimation[i, 0] = temp2[0]
estimation[i, 1] = temp2[1]
# video frame size
height, width = image.shape[:2]
opencv.ellipse(image, (int(estimation[-1, 0] * width), int(estimation[-1, 1] * height + 15)),
(70, 90), -180, 0, 360, (255, 0, 0), 2)
# plotting trajectory points
for j in range(2, len(estimation)):
opencv.circle(image, (int(estimation[j, 0] * width), int(estimation[j, 1] * height + 15)), 5, (0, 0, 255), -1)
opencv.putText(image, "Object", (10, 70), opencv.FONT_HERSHEY_SIMPLEX, 3, (0, 255, 0), 5)
opencv.putText(image, "tracking", (10, 140), opencv.FONT_HERSHEY_SIMPLEX, 3, (0, 255, 0), 5)
#opencv.putText(image, "Object tracking", (100, 100), opencv.FONT_HERSHEY_DUPLEX, 2.0, (0, 0, 255))
opencv.imshow("Robot camera feed", image)
#opencv.imwrite("Slike/Tracking/image" + str(len(estimation)) + ".png", image)
if opencv.waitKey(10) == 27:
break
# if try doesn't work for any reason program breaks and stops after
# stopping video subscribe and other things
finally:
n = len(estimation)
for i in range(0, n):
log.write(str(estimation[i, 0])+", "+str(estimation[i, 1])+"\n")
log.close()
ObjectTracker.gestureProxy.stopTracker()
print('Ending tracking...')
time.sleep(1)
alvideoproxy.unsubscribe(video)
opencv.destroyAllWindows()
ObjectTracker.unload()
behaveproxy.stopAllBehaviors()
time.sleep(1.0)
motionproxy.killAll()
myBroker.shutdown() | lgpl-3.0 |
ChanderG/scipy | doc/source/conf.py | 40 | 10928 | # -*- coding: utf-8 -*-
import sys, os, re
# Check Sphinx version
import sphinx
if sphinx.__version__ < "1.1":
raise RuntimeError("Sphinx 1.1 or newer required")
needs_sphinx = '1.1'
# -----------------------------------------------------------------------------
# General configuration
# -----------------------------------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
sys.path.insert(0, os.path.abspath('../sphinxext'))
sys.path.insert(0, os.path.abspath(os.path.dirname(__file__)))
extensions = ['sphinx.ext.autodoc', 'sphinx.ext.mathjax', 'numpydoc',
'sphinx.ext.intersphinx', 'sphinx.ext.coverage',
'sphinx.ext.autosummary', 'scipyoptdoc']
# Determine if the matplotlib has a recent enough version of the
# plot_directive.
try:
from matplotlib.sphinxext import plot_directive
except ImportError:
use_matplotlib_plot_directive = False
else:
try:
use_matplotlib_plot_directive = (plot_directive.__version__ >= 2)
except AttributeError:
use_matplotlib_plot_directive = False
if use_matplotlib_plot_directive:
extensions.append('matplotlib.sphinxext.plot_directive')
else:
raise RuntimeError("You need a recent enough version of matplotlib")
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix of source filenames.
source_suffix = '.rst'
# The master toctree document.
master_doc = 'index'
# General substitutions.
project = 'SciPy'
copyright = '2008-2014, The Scipy community'
# The default replacements for |version| and |release|, also used in various
# other places throughout the built documents.
import scipy
version = re.sub(r'\.dev-.*$', r'.dev', scipy.__version__)
release = scipy.__version__
print "Scipy (VERSION %s)" % (version,)
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
today_fmt = '%B %d, %Y'
# List of documents that shouldn't be included in the build.
#unused_docs = []
# The reST default role (used for this markup: `text`) to use for all documents.
default_role = "autolink"
# List of directories, relative to source directories, that shouldn't be searched
# for source files.
exclude_dirs = []
# If true, '()' will be appended to :func: etc. cross-reference text.
add_function_parentheses = False
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# -----------------------------------------------------------------------------
# HTML output
# -----------------------------------------------------------------------------
themedir = os.path.join(os.pardir, 'scipy-sphinx-theme', '_theme')
if os.path.isdir(themedir):
html_theme = 'scipy'
html_theme_path = [themedir]
if 'scipyorg' in tags:
# Build for the scipy.org website
html_theme_options = {
"edit_link": True,
"sidebar": "right",
"scipy_org_logo": True,
"rootlinks": [("http://scipy.org/", "Scipy.org"),
("http://docs.scipy.org/", "Docs")]
}
else:
# Default build
html_theme_options = {
"edit_link": False,
"sidebar": "left",
"scipy_org_logo": False,
"rootlinks": []
}
html_logo = '_static/scipyshiny_small.png'
html_sidebars = {'index': 'indexsidebar.html'}
else:
# Build without scipy.org sphinx theme present
if 'scipyorg' in tags:
raise RuntimeError("Get the scipy-sphinx-theme first, "
"via git submodule init & update")
else:
html_style = 'scipy_fallback.css'
html_logo = '_static/scipyshiny_small.png'
html_sidebars = {'index': 'indexsidebar.html'}
html_title = "%s v%s Reference Guide" % (project, version)
html_static_path = ['_static']
html_last_updated_fmt = '%b %d, %Y'
html_additional_pages = {}
html_use_modindex = True
html_copy_source = False
html_file_suffix = '.html'
htmlhelp_basename = 'scipy'
pngmath_use_preview = True
pngmath_dvipng_args = ['-gamma', '1.5', '-D', '96', '-bg', 'Transparent']
# -----------------------------------------------------------------------------
# LaTeX output
# -----------------------------------------------------------------------------
# The paper size ('letter' or 'a4').
#latex_paper_size = 'letter'
# The font size ('10pt', '11pt' or '12pt').
#latex_font_size = '10pt'
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, document class [howto/manual]).
_stdauthor = 'Written by the SciPy community'
latex_documents = [
('index', 'scipy-ref.tex', 'SciPy Reference Guide', _stdauthor, 'manual'),
# ('user/index', 'scipy-user.tex', 'SciPy User Guide',
# _stdauthor, 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# Additional stuff for the LaTeX preamble.
latex_preamble = r'''
\usepackage{amsmath}
\DeclareUnicodeCharacter{00A0}{\nobreakspace}
% In the parameters etc. sections, align uniformly, and adjust label emphasis
\usepackage{expdlist}
\let\latexdescription=\description
\let\endlatexdescription=\enddescription
\renewenvironment{description}%
{\begin{latexdescription}[\setleftmargin{60pt}\breaklabel\setlabelstyle{\bfseries\itshape}]}%
{\end{latexdescription}}
% Make Examples/etc section headers smaller and more compact
\makeatletter
\titleformat{\paragraph}{\normalsize\normalfont\bfseries\itshape}%
{\py@NormalColor}{0em}{\py@NormalColor}{\py@NormalColor}
\titlespacing*{\paragraph}{0pt}{1ex}{0pt}
\makeatother
% Save vertical space in parameter lists and elsewhere
\makeatletter
\renewenvironment{quote}%
{\list{}{\topsep=0pt%
\parsep \z@ \@plus\p@}%
\item\relax}%
{\endlist}
\makeatother
% Fix footer/header
\renewcommand{\chaptermark}[1]{\markboth{\MakeUppercase{\thechapter.\ #1}}{}}
\renewcommand{\sectionmark}[1]{\markright{\MakeUppercase{\thesection.\ #1}}}
'''
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
latex_use_modindex = False
# -----------------------------------------------------------------------------
# Intersphinx configuration
# -----------------------------------------------------------------------------
intersphinx_mapping = {
'http://docs.python.org/dev': None,
'http://docs.scipy.org/doc/numpy': None,
}
# -----------------------------------------------------------------------------
# Numpy extensions
# -----------------------------------------------------------------------------
# If we want to do a phantom import from an XML file for all autodocs
phantom_import_file = 'dump.xml'
# Generate plots for example sections
numpydoc_use_plots = True
# -----------------------------------------------------------------------------
# Autosummary
# -----------------------------------------------------------------------------
if sphinx.__version__ >= "0.7":
import glob
autosummary_generate = glob.glob("*.rst")
# -----------------------------------------------------------------------------
# Coverage checker
# -----------------------------------------------------------------------------
coverage_ignore_modules = r"""
""".split()
coverage_ignore_functions = r"""
test($|_) (some|all)true bitwise_not cumproduct pkgload
generic\.
""".split()
coverage_ignore_classes = r"""
""".split()
coverage_c_path = []
coverage_c_regexes = {}
coverage_ignore_c_items = {}
#------------------------------------------------------------------------------
# Plot
#------------------------------------------------------------------------------
plot_pre_code = """
import numpy as np
np.random.seed(123)
"""
plot_include_source = True
plot_formats = [('png', 96), 'pdf']
plot_html_show_formats = False
import math
phi = (math.sqrt(5) + 1)/2
font_size = 13*72/96.0 # 13 px
plot_rcparams = {
'font.size': font_size,
'axes.titlesize': font_size,
'axes.labelsize': font_size,
'xtick.labelsize': font_size,
'ytick.labelsize': font_size,
'legend.fontsize': font_size,
'figure.figsize': (3*phi, 3),
'figure.subplot.bottom': 0.2,
'figure.subplot.left': 0.2,
'figure.subplot.right': 0.9,
'figure.subplot.top': 0.85,
'figure.subplot.wspace': 0.4,
'text.usetex': False,
}
if not use_matplotlib_plot_directive:
import matplotlib
matplotlib.rcParams.update(plot_rcparams)
# -----------------------------------------------------------------------------
# Source code links
# -----------------------------------------------------------------------------
import inspect
from os.path import relpath, dirname
for name in ['sphinx.ext.linkcode', 'linkcode', 'numpydoc.linkcode']:
try:
__import__(name)
extensions.append(name)
break
except ImportError:
pass
else:
print "NOTE: linkcode extension not found -- no links to source generated"
def linkcode_resolve(domain, info):
"""
Determine the URL corresponding to Python object
"""
if domain != 'py':
return None
modname = info['module']
fullname = info['fullname']
submod = sys.modules.get(modname)
if submod is None:
return None
obj = submod
for part in fullname.split('.'):
try:
obj = getattr(obj, part)
except:
return None
try:
fn = inspect.getsourcefile(obj)
except:
fn = None
if not fn:
try:
fn = inspect.getsourcefile(sys.modules[obj.__module__])
except:
fn = None
if not fn:
return None
try:
source, lineno = inspect.getsourcelines(obj)
except:
lineno = None
if lineno:
linespec = "#L%d-L%d" % (lineno, lineno + len(source) - 1)
else:
linespec = ""
fn = relpath(fn, start=dirname(scipy.__file__))
if 'dev' in scipy.__version__:
return "http://github.com/scipy/scipy/blob/master/scipy/%s%s" % (
fn, linespec)
else:
return "http://github.com/scipy/scipy/blob/v%s/scipy/%s%s" % (
scipy.__version__, fn, linespec)
| bsd-3-clause |
vperron/sentry | src/sentry/migrations/0006_auto.py | 36 | 3873 | # encoding: utf-8
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
pass
def backwards(self, orm):
pass
models = {
'sentry.filtervalue': {
'Meta': {'unique_together': "(('key', 'value'),)", 'object_name': 'FilterValue'},
'id': ('sentry.db.models.fields.bounded.BoundedBigAutoField', [], {'primary_key': 'True'}),
'key': ('django.db.models.fields.CharField', [], {'max_length': '32'}),
'value': ('django.db.models.fields.CharField', [], {'max_length': '255'})
},
'sentry.groupedmessage': {
'Meta': {'unique_together': "(('logger', 'view', 'checksum'),)", 'object_name': 'GroupedMessage'},
'checksum': ('django.db.models.fields.CharField', [], {'max_length': '32'}),
'class_name': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '128', 'null': 'True', 'blank': 'True'}),
'first_seen': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now', 'db_index': 'True'}),
'id': ('sentry.db.models.fields.bounded.BoundedBigAutoField', [], {'primary_key': 'True'}),
'last_seen': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now', 'db_index': 'True'}),
'level': ('django.db.models.fields.PositiveIntegerField', [], {'default': '40', 'db_index': 'True', 'blank': 'True'}),
'logger': ('django.db.models.fields.CharField', [], {'default': "'root'", 'max_length': '64', 'db_index': 'True', 'blank': 'True'}),
'message': ('django.db.models.fields.TextField', [], {}),
'status': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0', 'db_index': 'True'}),
'times_seen': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}),
'traceback': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'view': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'})
},
'sentry.message': {
'Meta': {'object_name': 'Message'},
'checksum': ('django.db.models.fields.CharField', [], {'max_length': '32'}),
'class_name': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '128', 'null': 'True', 'blank': 'True'}),
'data': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'datetime': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now', 'db_index': 'True'}),
'group': ('sentry.db.models.fields.FlexibleForeignKey', [], {'blank': 'True', 'related_name': "'message_set'", 'null': 'True', 'to': "orm['sentry.GroupedMessage']"}),
'id': ('sentry.db.models.fields.bounded.BoundedBigAutoField', [], {'primary_key': 'True'}),
'level': ('django.db.models.fields.PositiveIntegerField', [], {'default': '40', 'db_index': 'True', 'blank': 'True'}),
'logger': ('django.db.models.fields.CharField', [], {'default': "'root'", 'max_length': '64', 'db_index': 'True', 'blank': 'True'}),
'message': ('django.db.models.fields.TextField', [], {}),
'server_name': ('django.db.models.fields.CharField', [], {'max_length': '128', 'db_index': 'True'}),
'traceback': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'view': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'})
}
}
complete_apps = ['sentry']
| bsd-3-clause |
isazi/Transpose | analysis/manage.py | 1 | 2009 | #!/usr/bin/env python
# Copyright 2014 Alessio Sclocco <a.sclocco@vu.nl>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
def get_tables(queue):
"""Get a list of the tables"""
queue.execute("SHOW TABLES")
return queue.fetchall()
def create_table(queue, table):
"""Create a table to store auto-tuning results for transpose."""
queue.execute("CREATE table " + table + "(id INTEGER NOT NULL PRIMARY KEY AUTO_INCREMENT, M INTEGER NOT NULL, N INTEGER NOT NULL, itemsPerBlock INTEGER NOT NULL, GBs FLOAT UNSIGNED NOT NULL, time FLOAT UNSIGNED NOT NULL, time_err FLOAT UNSIGNED NOT NULL, cov FLOAT UNSIGNED NOT NULL)")
def delete_table(queue, table):
"""Delete table."""
queue.execute("DROP table " + table)
def load_file(queue, table, input_file):
"""Load input_file into a table in the database."""
for line in input_file:
if (line[0] != "#") and (line[0] != "\n"):
items = line.split(sep=" ")
queue.execute("INSERT INTO " + table + " VALUES (NULL, " + items[0] + ", " + items[1] + ", " + items[2] + ", " + items[3] + ", " + items[4] + ", " + items[5] + ", " + items[6].rstrip("\n") + ")")
def print_results(confs):
"""Print the result tuples."""
for conf in confs:
for item in conf:
print(item, end=" ")
print()
def get_M_range(queue, table, N):
"""Return the M in the scenario."""
queue.execute("SELECT DISTINCT M FROM " + table + " WHERE (N = " + N + ") ORDER BY M")
return queue.fetchall()
| apache-2.0 |
defionscode/ansible-modules-core | commands/raw.py | 49 | 3161 | # this is a virtual module that is entirely implemented server side
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
DOCUMENTATION = '''
---
module: raw
short_description: Executes a low-down and dirty SSH command
version_added: historical
options:
free_form:
description:
- the raw module takes a free form command to run
required: true
executable:
description:
- change the shell used to execute the command. Should be an absolute path to the executable.
- when using privilege escalation (C(become)), a default shell will be assigned if one is not provided
as privilege escalation requires a shell.
required: false
version_added: "1.0"
description:
- Executes a low-down and dirty SSH command, not going through the module
subsystem. This is useful and should only be done in two cases. The
first case is installing C(python-simplejson) on older (Python 2.4 and
before) hosts that need it as a dependency to run modules, since nearly
all core modules require it. Another is speaking to any devices such as
routers that do not have any Python installed. In any other case, using
the M(shell) or M(command) module is much more appropriate. Arguments
given to M(raw) are run directly through the configured remote shell.
Standard output, error output and return code are returned when
available. There is no change handler support for this module.
- This module does not require python on the remote system, much like
the M(script) module.
notes:
- "If using raw from a playbook, you may need to disable fact gathering
using C(gather_facts: no) if you're using C(raw) to bootstrap python
onto the machine."
- If you want to execute a command securely and predictably, it may be
better to use the M(command) or M(shell) modules instead.
- the C(environment) keyword does not work with raw normally, it requires a shell
which means it only works if C(executable) is set or using the module
with privilege escalation (C(become)).
author:
- Ansible Core Team
- Michael DeHaan
'''
EXAMPLES = '''
# Bootstrap a legacy python 2.4 host
- raw: yum -y install python-simplejson
# Bootstrap a host without python2 installed
- raw: dnf install -y python2 python2-dnf libselinux-python
# Run a command that uses non-posix shell-isms (in this example /bin/sh
# doesn't handle redirection and wildcards together but bash does)
- raw: cat < /tmp/*txt
args:
executable: /bin/bash
'''
| gpl-3.0 |
ojengwa/oh-mainline | vendor/packages/html5lib/html5lib/filters/formfiller.py | 135 | 5839 | #
# The goal is to finally have a form filler where you pass data for
# each form, using the algorithm for "Seeding a form with initial values"
# See http://www.whatwg.org/specs/web-forms/current-work/#seeding
#
import _base
from html5lib.constants import spaceCharacters
spaceCharacters = u"".join(spaceCharacters)
class SimpleFilter(_base.Filter):
def __init__(self, source, fieldStorage):
_base.Filter.__init__(self, source)
self.fieldStorage = fieldStorage
def __iter__(self):
field_indices = {}
state = None
field_name = None
for token in _base.Filter.__iter__(self):
type = token["type"]
if type in ("StartTag", "EmptyTag"):
name = token["name"].lower()
if name == "input":
field_name = None
field_type = None
input_value_index = -1
input_checked_index = -1
for i,(n,v) in enumerate(token["data"]):
n = n.lower()
if n == u"name":
field_name = v.strip(spaceCharacters)
elif n == u"type":
field_type = v.strip(spaceCharacters)
elif n == u"checked":
input_checked_index = i
elif n == u"value":
input_value_index = i
value_list = self.fieldStorage.getlist(field_name)
field_index = field_indices.setdefault(field_name, 0)
if field_index < len(value_list):
value = value_list[field_index]
else:
value = ""
if field_type in (u"checkbox", u"radio"):
if value_list:
if token["data"][input_value_index][1] == value:
if input_checked_index < 0:
token["data"].append((u"checked", u""))
field_indices[field_name] = field_index + 1
elif input_checked_index >= 0:
del token["data"][input_checked_index]
elif field_type not in (u"button", u"submit", u"reset"):
if input_value_index >= 0:
token["data"][input_value_index] = (u"value", value)
else:
token["data"].append((u"value", value))
field_indices[field_name] = field_index + 1
field_type = None
field_name = None
elif name == "textarea":
field_type = "textarea"
field_name = dict((token["data"])[::-1])["name"]
elif name == "select":
field_type = "select"
attributes = dict(token["data"][::-1])
field_name = attributes.get("name")
is_select_multiple = "multiple" in attributes
is_selected_option_found = False
elif field_type == "select" and field_name and name == "option":
option_selected_index = -1
option_value = None
for i,(n,v) in enumerate(token["data"]):
n = n.lower()
if n == "selected":
option_selected_index = i
elif n == "value":
option_value = v.strip(spaceCharacters)
if option_value is None:
raise NotImplementedError("<option>s without a value= attribute")
else:
value_list = self.fieldStorage.getlist(field_name)
if value_list:
field_index = field_indices.setdefault(field_name, 0)
if field_index < len(value_list):
value = value_list[field_index]
else:
value = ""
if (is_select_multiple or not is_selected_option_found) and option_value == value:
if option_selected_index < 0:
token["data"].append((u"selected", u""))
field_indices[field_name] = field_index + 1
is_selected_option_found = True
elif option_selected_index >= 0:
del token["data"][option_selected_index]
elif field_type is not None and field_name and type == "EndTag":
name = token["name"].lower()
if name == field_type:
if name == "textarea":
value_list = self.fieldStorage.getlist(field_name)
if value_list:
field_index = field_indices.setdefault(field_name, 0)
if field_index < len(value_list):
value = value_list[field_index]
else:
value = ""
yield {"type": "Characters", "data": value}
field_indices[field_name] = field_index + 1
field_name = None
elif name == "option" and field_type == "select":
pass # TODO: part of "option without value= attribute" processing
elif field_type == "textarea":
continue # ignore token
yield token
| agpl-3.0 |
Asuka52/jubatus | unittest_gtest.py | 3 | 175890 | #!/usr/bin/env python
# encoding: ISO8859-1
"""
Copyright (c)2011, Hideyuki Tanaka
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials provided
with the distribution.
* Neither the name of Hideyuki Tanaka nor the names of other
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
import os, subprocess, sys
from waflib.TaskGen import before, after, feature
from waflib import Options, Task, Utils, Logs, Errors
C1 = '#XXX'.encode()
C2 = '#YYY'.encode()
UNPACK_DIR = '.unittest-gtest'
GTEST_DIR = 'gtest-1.7.0/fused-src'
def cleanup():
import shutil
try: shutil.rmtree(UNPACK_DIR)
except OSError: pass
def unpack_gtest(conf):
cwd = os.getcwd()
fname = __file__
if fname.endswith('.pyc'):
fname = fname[0:-1]
f = open(fname, 'rb')
while 1:
line = f.readline()
if not line:
Logs.error('not contain gtest archive')
sys.exit(1)
if line == '#==>\n'.encode():
txt = f.readline()
if not txt:
Logs.error('corrupt archive')
if f.readline() != '#<==\n'.encode():
Logs.error('corrupt archive')
break
txt = txt[1:-1].replace(C1, '\n'.encode()).replace(C2, '\r'.encode())
cleanup()
tmp = 't.tar.bz2'
os.makedirs(UNPACK_DIR)
os.chdir(UNPACK_DIR)
t = open(tmp, 'wb')
t.write(txt)
t.close()
def check_call(args):
if subprocess.call(args):
raise
try:
check_call(['tar', 'xf', tmp])
check_call(['mkdir', GTEST_DIR + '/gtest/gtest'])
check_call(['cp', GTEST_DIR + '/gtest/gtest.h', GTEST_DIR + '/gtest/gtest/gtest.h'])
except:
os.chdir(cwd)
cleanup()
Logs.error('gtest cannot be unpacked.')
os.unlink(tmp)
conf.env.UNITTEST_GTEST_PATH = os.path.abspath(os.getcwd())
os.chdir(cwd)
def configure(conf):
try:
unpack_gtest(conf)
conf.msg('Unpacking gtest', 'yes')
except:
conf.msg('Unpacking gtest', 'no')
Logs.error(sys.exc_info()[1])
conf.check_cxx(lib = 'pthread', uselib_store = 'GTEST_PTHREAD')
def options(opt):
opt.add_option('--check', action = 'store_true', default = False,
help = 'Execute unit tests')
opt.add_option('--checkall', action = 'store_true', default = False,
help = 'Execute all unit tests')
opt.add_option('--checkone', action = 'store', default = False,
help = 'Execute specified unit test')
opt.add_option('--checkfilter', action = 'store', default = False,
help = 'Execute unit tests sprcified by pattern')
def match_filter(filt, targ):
if isinstance(filt, str):
(pat, _, _) = filt.partition('.')
if pat == '*':
return True
return pat == targ
return False
@feature('testt', 'gtest')
@before('process_rule')
def test_remover(self):
if not Options.options.check and not Options.options.checkall and self.target != Options.options.checkone and not match_filter(Options.options.checkfilter, self.target):
self.meths[:] = []
@feature('gtest')
@before('process_source')
def gtest_attach(self):
if not hasattr(self.bld, 'def_gtest_objects'):
self.bld.objects(
source = [UNPACK_DIR + '/' + GTEST_DIR + '/gtest/gtest-all.cc',
UNPACK_DIR + '/' + GTEST_DIR + '/gtest/gtest_main.cc'],
target = 'GTEST_OBJECTS'
)
self.bld.def_gtest_objects = True
DIR = self.env.UNITTEST_GTEST_PATH + '/' + GTEST_DIR
self.includes = self.to_list(getattr(self, 'includes', [])) + [DIR]
self.use = self.to_list(getattr(self, 'use', [])) + ['GTEST_PTHREAD', 'GTEST_OBJECTS']
@feature('testt', 'gtest')
@after('apply_link')
def make_test(self):
if not 'cprogram' in self.features and not 'cxxprogram' in self.features:
Logs.error('test cannot be executed %s'%self)
return
self.default_install_path = None
self.create_task('utest', self.link_task.outputs)
import threading
testlock = threading.Lock()
class utest(Task.Task):
"""
Execute a unit test
"""
color = 'PINK'
after = ['vnum','inst']
ext_in = ['.bin']
vars = []
def runnable_status(self):
stat = super(utest, self).runnable_status()
if stat != Task.SKIP_ME:
return stat
if Options.options.checkall:
return Task.RUN_ME
if Options.options.checkone == self.generator.name:
return Task.RUN_ME
if isinstance(Options.options.checkfilter, str):
if match_filter(Options.options.checkfilter, self.generator.name):
return Task.RUN_ME
return stat
def run(self):
"""
Execute the test. The execution is always successful, but the results
are stored on ``self.generator.bld.utest_results`` for postprocessing.
"""
status = 0
filename = self.inputs[0].abspath()
self.ut_exec = getattr(self, 'ut_exec', [filename])
if getattr(self.generator, 'ut_fun', None):
self.generator.ut_fun(self)
try:
fu = getattr(self.generator.bld, 'all_test_paths')
except AttributeError:
fu = os.environ.copy()
lst = []
for g in self.generator.bld.groups:
for tg in g:
if getattr(tg, 'link_task', None):
lst.append(tg.link_task.outputs[0].parent.abspath())
def add_path(dct, path, var):
dct[var] = os.pathsep.join(Utils.to_list(path) + [os.environ.get(var, '')])
if sys.platform == 'win32':
add_path(fu, lst, 'PATH')
elif sys.platform == 'darwin':
add_path(fu, lst, 'DYLD_LIBRARY_PATH')
add_path(fu, lst, 'LD_LIBRARY_PATH')
else:
add_path(fu, lst, 'LD_LIBRARY_PATH')
self.generator.bld.all_test_paths = fu
if isinstance(Options.options.checkfilter, str):
(_, _, filt) = Options.options.checkfilter.partition('.')
if filt != "":
self.ut_exec += ['--gtest_filter=' + filt]
cwd = getattr(self.generator, 'ut_cwd', '') or self.inputs[0].parent.abspath()
proc = Utils.subprocess.Popen(self.ut_exec, cwd=cwd, env=fu, stderr=Utils.subprocess.PIPE, stdout=Utils.subprocess.PIPE)
(stdout, stderr) = proc.communicate()
tup = (filename, proc.returncode, stdout, stderr)
self.generator.utest_result = tup
testlock.acquire()
try:
bld = self.generator.bld
Logs.debug("ut: %r", tup)
try:
bld.utest_results.append(tup)
except AttributeError:
bld.utest_results = [tup]
a = getattr(self.generator.bld, 'added_post_fun', False)
if not a:
self.generator.bld.add_post_fun(summary)
self.generator.bld.added_post_fun = True
finally:
testlock.release()
def summary(bld):
lst = getattr(bld, 'utest_results', [])
if not lst: return
total = len(lst)
fail = len([x for x in lst if x[1]])
Logs.pprint('CYAN', 'test summary')
Logs.pprint('CYAN', ' tests that pass %d/%d' % (total-fail, total))
for (f, code, out, err) in lst:
if not code:
Logs.pprint('GREEN', ' %s' % f)
if isinstance(Options.options.checkfilter, str):
print(out)
if fail>0:
Logs.pprint('RED', ' tests that fail %d/%d' % (fail, total))
for (f, code, out, err) in lst:
if code:
Logs.pprint('RED', ' %s' % f)
print(out.decode('utf-8'))
if err:
print(err.decode('utf-8'))
raise Errors.WafError('test failed')
#==>
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