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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _GoogleCloudStorageInputReader.next | def next(self):
"""Returns the next input from this input reader, a block of bytes.
Non existent files will be logged and skipped. The file might have been
removed after input splitting.
Returns:
The next input from this input reader in the form of a cloudstorage
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"""Returns the next input from this input reader, a block of bytes.
Non existent files will be logged and skipped. The file might have been
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _GoogleCloudStorageRecordInputReader.next | def next(self):
"""Returns the next input from this input reader, a record.
Returns:
The next input from this input reader in the form of a record read from
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Raises:
StopIteration: The ordered set records has been exhausted.
"""
while True:
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"""Returns the next input from this input reader, a record.
Returns:
The next input from this input reader in the form of a record read from
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _ReducerReader.to_json | def to_json(self):
"""Returns an input shard state for the remaining inputs.
Returns:
A json-izable version of the remaining InputReader.
"""
result = super(_ReducerReader, self).to_json()
result["current_key"] = self.encode_data(self.current_key)
result["current_values"] = self.encode_da... | python | def to_json(self):
"""Returns an input shard state for the remaining inputs.
Returns:
A json-izable version of the remaining InputReader.
"""
result = super(_ReducerReader, self).to_json()
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/input_readers.py | _ReducerReader.from_json | def from_json(cls, json):
"""Creates an instance of the InputReader for the given input shard state.
Args:
json: The InputReader state as a dict-like object.
Returns:
An instance of the InputReader configured using the values of json.
"""
result = super(_ReducerReader, cls).from_json(j... | python | def from_json(cls, json):
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Args:
json: The InputReader state as a dict-like object.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/map_job_context.py | ShardContext.incr | def incr(self, counter_name, delta=1):
"""Changes counter by delta.
Args:
counter_name: the name of the counter to change. str.
delta: int.
"""
self._state.counters_map.increment(counter_name, delta) | python | def incr(self, counter_name, delta=1):
"""Changes counter by delta.
Args:
counter_name: the name of the counter to change. str.
delta: int.
"""
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/map_job_context.py | ShardContext.counter | def counter(self, counter_name, default=0):
"""Get the current counter value.
Args:
counter_name: name of the counter in string.
default: default value in int if one doesn't exist.
Returns:
Current value of the counter.
"""
return self._state.counters_map.get(counter_name, defaul... | python | def counter(self, counter_name, default=0):
"""Get the current counter value.
Args:
counter_name: name of the counter in string.
default: default value in int if one doesn't exist.
Returns:
Current value of the counter.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/map_job_context.py | SliceContext.emit | def emit(self, value):
"""Emits a value to output writer.
Args:
value: a value of type expected by the output writer.
"""
if not self._tstate.output_writer:
logging.error("emit is called, but no output writer is set.")
return
self._tstate.output_writer.write(value) | python | def emit(self, value):
"""Emits a value to output writer.
Args:
value: a value of type expected by the output writer.
"""
if not self._tstate.output_writer:
logging.error("emit is called, but no output writer is set.")
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/lib/input_reader/_gcs.py | GCSInputReader.validate | def validate(cls, job_config):
"""Validate mapper specification.
Args:
job_config: map_job.JobConfig.
Raises:
BadReaderParamsError: if the specification is invalid for any reason such
as missing the bucket name or providing an invalid bucket name.
"""
reader_params = job_config... | python | def validate(cls, job_config):
"""Validate mapper specification.
Args:
job_config: map_job.JobConfig.
Raises:
BadReaderParamsError: if the specification is invalid for any reason such
as missing the bucket name or providing an invalid bucket name.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/lib/input_reader/_gcs.py | GCSInputReader.split_input | def split_input(cls, job_config):
"""Returns a list of input readers.
An equal number of input files are assigned to each shard (+/- 1). If there
are fewer files than shards, fewer than the requested number of shards will
be used. Input files are currently never split (although for some formats
cou... | python | def split_input(cls, job_config):
"""Returns a list of input readers.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/lib/input_reader/_gcs.py | GCSInputReader.next | def next(self):
"""Returns a handler to the next file.
Non existent files will be logged and skipped. The file might have been
removed after input splitting.
Returns:
The next input from this input reader in the form of a cloudstorage
ReadBuffer that supports a File-like interface (read, r... | python | def next(self):
"""Returns a handler to the next file.
Non existent files will be logged and skipped. The file might have been
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Returns:
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/lib/input_reader/_gcs.py | GCSInputReader.params_to_json | def params_to_json(cls, params):
"""Inherit docs."""
params_cp = dict(params)
if cls.PATH_FILTER_PARAM in params_cp:
path_filter = params_cp[cls.PATH_FILTER_PARAM]
params_cp[cls.PATH_FILTER_PARAM] = pickle.dumps(path_filter)
return params_cp | python | def params_to_json(cls, params):
"""Inherit docs."""
params_cp = dict(params)
if cls.PATH_FILTER_PARAM in params_cp:
path_filter = params_cp[cls.PATH_FILTER_PARAM]
params_cp[cls.PATH_FILTER_PARAM] = pickle.dumps(path_filter)
return params_cp | [
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/lib/input_reader/_gcs.py | GCSRecordInputReader.next | def next(self):
"""Returns the next input from this input reader, a record.
Returns:
The next input from this input reader in the form of a record read from
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Raises:
StopIteration: The ordered set records has been exhausted.
"""
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Returns:
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/abstract_datastore_input_reader.py | AbstractDatastoreInputReader._get_query_spec | def _get_query_spec(cls, params):
"""Construct a model.QuerySpec from model.MapperSpec."""
entity_kind = params[cls.ENTITY_KIND_PARAM]
filters = params.get(cls.FILTERS_PARAM)
app = params.get(cls._APP_PARAM)
ns = params.get(cls.NAMESPACE_PARAM)
return model.QuerySpec(
entity_kind=cls._g... | python | def _get_query_spec(cls, params):
"""Construct a model.QuerySpec from model.MapperSpec."""
entity_kind = params[cls.ENTITY_KIND_PARAM]
filters = params.get(cls.FILTERS_PARAM)
app = params.get(cls._APP_PARAM)
ns = params.get(cls.NAMESPACE_PARAM)
return model.QuerySpec(
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/abstract_datastore_input_reader.py | AbstractDatastoreInputReader.split_input | def split_input(cls, job_config):
"""Inherit doc."""
shard_count = job_config.shard_count
params = job_config.input_reader_params
query_spec = cls._get_query_spec(params)
namespaces = None
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k_ranges = cls._to_key_ranges_by_shard(
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"""Inherit doc."""
shard_count = job_config.shard_count
params = job_config.input_reader_params
query_spec = cls._get_query_spec(params)
namespaces = None
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/abstract_datastore_input_reader.py | AbstractDatastoreInputReader._to_key_ranges_by_shard | def _to_key_ranges_by_shard(cls, app, namespaces, shard_count, query_spec):
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app: app_id in str.
namespaces: a list ... | python | def _to_key_ranges_by_shard(cls, app, namespaces, shard_count, query_spec):
"""Get a list of key_ranges.KeyRanges objects, one for each shard.
This method uses scatter index to split each namespace into pieces
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app: app_id in str.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/abstract_datastore_input_reader.py | AbstractDatastoreInputReader._split_ns_by_scatter | def _split_ns_by_scatter(cls,
shard_count,
namespace,
raw_entity_kind,
app):
"""Split a namespace by scatter index into key_range.KeyRange.
TODO(user): Power this with key_range.KeyRange.compute_split_po... | python | def _split_ns_by_scatter(cls,
shard_count,
namespace,
raw_entity_kind,
app):
"""Split a namespace by scatter index into key_range.KeyRange.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/abstract_datastore_input_reader.py | AbstractDatastoreInputReader.validate | def validate(cls, job_config):
"""Inherit docs."""
super(AbstractDatastoreInputReader, cls).validate(job_config)
params = job_config.input_reader_params
# Check for the required entity kind parameter.
if cls.ENTITY_KIND_PARAM not in params:
raise errors.BadReaderParamsError("Missing input rea... | python | def validate(cls, job_config):
"""Inherit docs."""
super(AbstractDatastoreInputReader, cls).validate(job_config)
params = job_config.input_reader_params
# Check for the required entity kind parameter.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/namespace_range.py | _setup_constants | def _setup_constants(alphabet=NAMESPACE_CHARACTERS,
max_length=MAX_NAMESPACE_LENGTH,
batch_size=NAMESPACE_BATCH_SIZE):
"""Calculate derived constant values. Only useful for testing."""
global NAMESPACE_CHARACTERS
global MAX_NAMESPACE_LENGTH
# pylint: disable=global-var... | python | def _setup_constants(alphabet=NAMESPACE_CHARACTERS,
max_length=MAX_NAMESPACE_LENGTH,
batch_size=NAMESPACE_BATCH_SIZE):
"""Calculate derived constant values. Only useful for testing."""
global NAMESPACE_CHARACTERS
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/namespace_range.py | _ord_to_namespace | def _ord_to_namespace(n, _max_length=None):
"""Convert a namespace ordinal to a namespace string.
Converts an int, representing the sequence number of a namespace ordered
lexographically, into a namespace string.
>>> _ord_to_namespace(0)
''
>>> _ord_to_namespace(1)
'-'
>>> _ord_to_namespace(2)
'--'
... | python | def _ord_to_namespace(n, _max_length=None):
"""Convert a namespace ordinal to a namespace string.
Converts an int, representing the sequence number of a namespace ordered
lexographically, into a namespace string.
>>> _ord_to_namespace(0)
''
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'-'
>>> _ord_to_namespace(2)
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/namespace_range.py | _namespace_to_ord | def _namespace_to_ord(namespace):
"""Converts a namespace string into an int representing its lexographic order.
>>> _namespace_to_ord('')
''
>>> _namespace_to_ord('_')
1
>>> _namespace_to_ord('__')
2
Args:
namespace: A namespace string.
Returns:
An int representing the lexographical order ... | python | def _namespace_to_ord(namespace):
"""Converts a namespace string into an int representing its lexographic order.
>>> _namespace_to_ord('')
''
>>> _namespace_to_ord('_')
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>>> _namespace_to_ord('__')
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/namespace_range.py | _key_for_namespace | def _key_for_namespace(namespace, app):
"""Return the __namespace__ key for a namespace.
Args:
namespace: The namespace whose key is requested.
app: The id of the application that the key belongs to.
Returns:
A db.Key representing the namespace.
"""
if namespace:
return db.Key.from_path(meta... | python | def _key_for_namespace(namespace, app):
"""Return the __namespace__ key for a namespace.
Args:
namespace: The namespace whose key is requested.
app: The id of the application that the key belongs to.
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A db.Key representing the namespace.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/namespace_range.py | get_namespace_keys | def get_namespace_keys(app, limit):
"""Get namespace keys."""
ns_query = datastore.Query('__namespace__', keys_only=True, _app=app)
return list(ns_query.Run(limit=limit, batch_size=limit)) | python | def get_namespace_keys(app, limit):
"""Get namespace keys."""
ns_query = datastore.Query('__namespace__', keys_only=True, _app=app)
return list(ns_query.Run(limit=limit, batch_size=limit)) | [
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/namespace_range.py | NamespaceRange.split_range | def split_range(self):
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Returns:
If this NamespaceRange contains a single namespace then a list containing
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two NamespaceRanges whose total range is identica... | python | def split_range(self):
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/namespace_range.py | NamespaceRange.with_start_after | def with_start_after(self, after_namespace):
"""Returns a copy of this NamespaceName with a new namespace_start.
Args:
after_namespace: A namespace string.
Returns:
A NamespaceRange object whose namespace_start is the lexographically next
namespace after the given namespace string.
... | python | def with_start_after(self, after_namespace):
"""Returns a copy of this NamespaceName with a new namespace_start.
Args:
after_namespace: A namespace string.
Returns:
A NamespaceRange object whose namespace_start is the lexographically next
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/namespace_range.py | NamespaceRange.make_datastore_query | def make_datastore_query(self, cursor=None):
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Args:
cursor: start cursor for the query.
Returns:
A datastore.Query instance that generates db.Keys for each namespace in
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filters = {}
... | python | def make_datastore_query(self, cursor=None):
"""Returns a datastore.Query that generates all namespaces in the range.
Args:
cursor: start cursor for the query.
Returns:
A datastore.Query instance that generates db.Keys for each namespace in
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/namespace_range.py | NamespaceRange.normalized_start | def normalized_start(self):
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Returns:
A copy of this NamespaceRange whose namespace_start is adjusted to exclude
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datastore. None is returned if the Names... | python | def normalized_start(self):
"""Returns a NamespaceRange with leading non-existant namespaces removed.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/namespace_range.py | NamespaceRange.to_json_object | def to_json_object(self):
"""Returns a dict representation that can be serialized to JSON."""
obj_dict = dict(namespace_start=self.namespace_start,
namespace_end=self.namespace_end)
if self.app is not None:
obj_dict['app'] = self.app
return obj_dict | python | def to_json_object(self):
"""Returns a dict representation that can be serialized to JSON."""
obj_dict = dict(namespace_start=self.namespace_start,
namespace_end=self.namespace_end)
if self.app is not None:
obj_dict['app'] = self.app
return obj_dict | [
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/namespace_range.py | NamespaceRange.split | def split(cls,
n,
contiguous,
can_query=itertools.chain(itertools.repeat(True, 50),
itertools.repeat(False)).next,
_app=None):
# pylint: disable=g-doc-args
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data_length = len(data)
if self._size + data_length > self._flush_size:
self.flush()
if not self._exclusive and data_length > _FILE_POOL_MAX_SIZE:
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"""Append data to a file."""
data_length = len(data)
if self._size + data_length > self._flush_size:
self.flush()
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/output_writers.py | _RecordsPoolBase.flush | def flush(self):
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/output_writers.py | GCSRecordsPool._write | def _write(self, str_buf):
"""Uses the filehandle to the file in GCS to write to it."""
self._filehandle.write(str_buf)
self._buf_size += len(str_buf) | python | def _write(self, str_buf):
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self._filehandle.write(str_buf)
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/output_writers.py | _GoogleCloudStorageBase._get_tmp_gcs_bucket | def _get_tmp_gcs_bucket(cls, writer_spec):
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/output_writers.py | _GoogleCloudStorageBase._get_tmp_account_id | def _get_tmp_account_id(cls, writer_spec):
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/output_writers.py | _GoogleCloudStorageOutputWriterBase._generate_filename | def _generate_filename(cls, writer_spec, name, job_id, num,
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writer_spec: specification dictionary for the output writer.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/output_writers.py | _GoogleCloudStorageOutputWriterBase.validate | def validate(cls, mapper_spec):
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Args:
mapper_spec: an instance of model.MapperSpec.
Raises:
BadWriterParamsError: if the specification is invalid for any reason such
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"""Validate mapper specification.
Args:
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/output_writers.py | _GoogleCloudStorageOutputWriterBase._open_file | def _open_file(cls, writer_spec, filename_suffix, use_tmp_bucket=False):
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if use_tmp_bucket:
bucket = cls._get_tmp_gcs_bucket(writer_spec)
account_id = cls._get_tmp_account_id(writer_spec)
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bucket = cls._get_gcs_bucket(writer_spec)
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"""Opens a new gcs file for writing."""
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bucket = cls._get_tmp_gcs_bucket(writer_spec)
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/output_writers.py | _GoogleCloudStorageOutputWriterBase.write | def write(self, data):
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data: string containing the data to be written.
"""
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Args:
data: string containing the data to be written.
"""
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/output_writers.py | _GoogleCloudStorageOutputWriter.validate | def validate(cls, mapper_spec):
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/output_writers.py | _GoogleCloudStorageOutputWriter.create | def create(cls, mr_spec, shard_number, shard_attempt, _writer_state=None):
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seg_index = None
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"""Inherit docs."""
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seg_index = None
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/output_writers.py | _GoogleCloudStorageOutputWriter._create | def _create(cls, writer_spec, filename_suffix):
"""Helper method that actually creates the file in cloud storage."""
writer = cls._open_file(writer_spec, filename_suffix)
return cls(writer, writer_spec=writer_spec) | python | def _create(cls, writer_spec, filename_suffix):
"""Helper method that actually creates the file in cloud storage."""
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/output_writers.py | GoogleCloudStorageConsistentOutputWriter.create | def create(cls, mr_spec, shard_number, shard_attempt, _writer_state=None):
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/output_writers.py | GoogleCloudStorageConsistentOutputWriter._rewrite_tmpfile | def _rewrite_tmpfile(self, mainfile, tmpfile, writer_spec):
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# can happen when finalize fails
return
account_id = self._get_tmp_account_id(writer_spec)
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/output_writers.py | GoogleCloudStorageConsistentOutputWriter._try_to_clean_garbage | def _try_to_clean_garbage(self, writer_spec, exclude_list=()):
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writer_spec: writer_spec for the MR.
exclude_list: A list of filenames (strings) that should not be
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writer_spec: writer_spec for the MR.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/sample_input_reader.py | SampleInputReader.from_json | def from_json(cls, state):
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shard_count = job_config.shard_count
count_per_shard = count // shard_count
mr_input_readers = [... | python | def split_input(cls, job_config):
"""Inherit docs."""
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count = params[cls.COUNT]
string_length = params.get(cls.STRING_LENGTH, cls._DEFAULT_STRING_LENGTH)
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/sample_input_reader.py | SampleInputReader.validate | def validate(cls, job_config):
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/property_range.py | _get_weights | def _get_weights(max_length):
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max_length: max length of the strings.
Returns:
A list of ints as weights.
Example:
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/property_range.py | _str_to_ord | def _str_to_ord(content, weights):
"""Converts a string to its lexicographical order.
Args:
content: the string to convert. Of type str.
weights: weights from _get_weights.
Returns:
an int or long that represents the order of this string. "" has order 0.
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ordinal = 0
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"""Converts a string to its lexicographical order.
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content: the string to convert. Of type str.
weights: weights from _get_weights.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/property_range.py | _ord_to_str | def _ord_to_str(ordinal, weights):
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ordinal -= 1
index, ordinal = divmod(ordinal, weight)
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"""Reverse function of _str_to_ord."""
chars = []
for weight in weights:
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/property_range.py | PropertyRange._get_range_from_filters | def _get_range_from_filters(cls, filters, model_class):
"""Get property range from filters user provided.
This method also validates there is one and only one closed range on a
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Args:
filters: user supplied filters. Each filter should be a list or tuple of
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/property_range.py | PropertyRange.split | def split(self, n):
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Args:
n: number of splits.
Returns:
a list of contiguous, non overlapping sub PropertyRanges. Maybe less than
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Args:
n: number of splits.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/property_range.py | PropertyRange.make_query | def make_query(self, ns):
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Args:
ns: namespace of this query.
Returns:
a db.Query or ndb.Query, depends on the model class's type.
"""
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"""Make a query of entities within this range.
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ns: namespace of this query.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/output_writer.py | OutputWriter.validate | def validate(cls, job_config):
"""Validates relevant parameters.
This method can validate fields which it deems relevant.
Args:
job_config: an instance of map_job.JobConfig.
Raises:
errors.BadWriterParamsError: required parameters are missing or invalid.
"""
if job_config.output_w... | python | def validate(cls, job_config):
"""Validates relevant parameters.
This method can validate fields which it deems relevant.
Args:
job_config: an instance of map_job.JobConfig.
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/output_writer.py | OutputWriter.commit_output | def commit_output(cls, shard_ctx, iterator):
"""Saves output references when a shard finishes.
Inside end_shard(), an output writer can optionally use this method
to persist some references to the outputs from this shard
(e.g a list of filenames)
Args:
shard_ctx: map_job_context.ShardContext... | python | def commit_output(cls, shard_ctx, iterator):
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/main.py | create_handlers_map | def create_handlers_map():
"""Create new handlers map.
Returns:
list of (regexp, handler) pairs for WSGIApplication constructor.
"""
pipeline_handlers_map = []
if pipeline:
pipeline_handlers_map = pipeline.create_handlers_map(prefix=".*/pipeline")
return pipeline_handlers_map + [
# Task que... | python | def create_handlers_map():
"""Create new handlers map.
Returns:
list of (regexp, handler) pairs for WSGIApplication constructor.
"""
pipeline_handlers_map = []
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GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/key_ranges.py | KeyRangesFactory.from_json | def from_json(cls, json):
"""Deserialize from json.
Args:
json: a dict of json compatible fields.
Returns:
a KeyRanges object.
Raises:
ValueError: if the json is invalid.
"""
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"""Deserialize from json.
Args:
json: a dict of json compatible fields.
Returns:
a KeyRanges object.
Raises:
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GoogleCloudPlatform/appengine-mapreduce | python/demo/main.py | split_into_sentences | def split_into_sentences(s):
"""Split text into list of sentences."""
s = re.sub(r"\s+", " ", s)
s = re.sub(r"[\\.\\?\\!]", "\n", s)
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"""Split text into list of sentences."""
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GoogleCloudPlatform/appengine-mapreduce | python/demo/main.py | split_into_words | def split_into_words(s):
"""Split a sentence into list of words."""
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s = re.sub(r"[_0-9]+", " ", s)
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GoogleCloudPlatform/appengine-mapreduce | python/demo/main.py | index_map | def index_map(data):
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"""Index demo map function."""
(entry, text_fn) = data
text = text_fn()
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GoogleCloudPlatform/appengine-mapreduce | python/demo/main.py | phrases_map | def phrases_map(data):
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"""Phrases demo map function."""
(entry, text_fn) = data
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GoogleCloudPlatform/appengine-mapreduce | python/demo/main.py | phrases_reduce | def phrases_reduce(key, values):
"""Phrases demo reduce function."""
if len(values) < 10:
return
counts = {}
for filename in values:
counts[filename] = counts.get(filename, 0) + 1
words = re.sub(r":", " ", key)
threshold = len(values) / 2
for filename, count in counts.items():
if count > thre... | python | def phrases_reduce(key, values):
"""Phrases demo reduce function."""
if len(values) < 10:
return
counts = {}
for filename in values:
counts[filename] = counts.get(filename, 0) + 1
words = re.sub(r":", " ", key)
threshold = len(values) / 2
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materialsproject/custodian | custodian/custodian.py | Custodian.from_spec | def from_spec(cls, spec):
"""
Load a Custodian instance where the jobs are specified from a
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materialsproject/custodian | custodian/custodian.py | Custodian.run | def run(self):
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"""
Runs all jobs.
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materialsproject/custodian | custodian/custodian.py | Custodian._run_job | def _run_job(self, job_n, job):
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job_n: job number (1 index)
job: Custodian job
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ValidationError: if a job fails validation
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materialsproject/custodian | custodian/custodian.py | Custodian.run_interrupted | def run_interrupted(self):
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Returns:
number of remaining jobs
Raises:
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materialsproject/custodian | custodian/custodian.py | Custodian._do_check | def _do_check(self, handlers, terminate_func=None):
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materialsproject/custodian | custodian/feff/interpreter.py | FeffModder.apply_actions | def apply_actions(self, actions):
"""
Applies a list of actions to the FEFF Input Set and rewrites modified
files.
Args:
actions [dict]: A list of actions of the form {'file': filename,
'action': moddermodification} or {'dict': feffinput_key,
... | python | def apply_actions(self, actions):
"""
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materialsproject/custodian | custodian/ansible/actions.py | FileActions.file_create | def file_create(filename, settings):
"""
Creates a file.
Args:
filename (str): Filename.
settings (dict): Must be {"content": actual_content}
"""
if len(settings) != 1:
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Creates a file.
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materialsproject/custodian | custodian/ansible/actions.py | FileActions.file_move | def file_move(filename, settings):
"""
Moves a file. {'_file_move': {'dest': 'new_file_name'}}
Args:
filename (str): Filename.
settings (dict): Must be {"dest": path of new file}
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"""
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materialsproject/custodian | custodian/ansible/actions.py | FileActions.file_delete | def file_delete(filename, settings):
"""
Deletes a file. {'_file_delete': {'mode': "actual"}}
Args:
filename (str): Filename.
settings (dict): Must be {"mode": actual/simulated}. Simulated
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"""
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settings (dict): Must be {"mode": actual/simulated}. Simulated
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materialsproject/custodian | custodian/ansible/actions.py | FileActions.file_copy | def file_copy(filename, settings):
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filename (str): Filename.
settings (dict): Must be {"dest": path of new file}
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filename (str): Filename.
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filename (str): Filename.
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materialsproject/custodian | custodian/feff/jobs.py | FeffJob.setup | def setup(self):
"""
Performs initial setup for FeffJob, do backing up.
Returns:
"""
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"""
Performs initial setup for FeffJob, do backing up.
Returns:
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materialsproject/custodian | custodian/feff/jobs.py | FeffJob.run | def run(self):
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Performs the actual FEFF run
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(subprocess.Popen) Used for monitoring.
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materialsproject/custodian | custodian/ansible/interpreter.py | Modder.modify | def modify(self, modification, obj):
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materialsproject/custodian | custodian/vasp/interpreter.py | VaspModder.apply_actions | def apply_actions(self, actions):
"""
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"""
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materialsproject/custodian | custodian/utils.py | backup | def backup(filenames, prefix="error"):
"""
Backup files to a tar.gz file. Used, for example, in backing up the
files of an errored run before performing corrections.
Args:
filenames ([str]): List of files to backup. Supports wildcards, e.g.,
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prefix (str): prefix to the ... | python | def backup(filenames, prefix="error"):
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Backup files to a tar.gz file. Used, for example, in backing up the
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materialsproject/custodian | custodian/utils.py | get_execution_host_info | def get_execution_host_info():
"""
Tries to return a tuple describing the execution host.
Doesn't work for all queueing systems
Returns:
(HOSTNAME, CLUSTER_NAME)
"""
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cluster = os.environ.get('SGE_O_HOST', None)
if host is None:
try... | python | def get_execution_host_info():
"""
Tries to return a tuple describing the execution host.
Doesn't work for all queueing systems
Returns:
(HOSTNAME, CLUSTER_NAME)
"""
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materialsproject/custodian | custodian/qchem/jobs.py | QCJob.run | def run(self):
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Returns:
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"""
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Perform the actual QChem run.
Returns:
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materialsproject/custodian | custodian/qchem/jobs.py | QCJob.opt_with_frequency_flattener | def opt_with_frequency_flattener(cls,
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output_file="mol.qout",
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input_file="mol.qin",
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qclog_file="mol.... | [
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materialsproject/custodian | custodian/vasp/jobs.py | VaspJob.setup | def setup(self):
"""
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"""
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materialsproject/custodian | custodian/vasp/jobs.py | VaspJob.run | def run(self):
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Perform the actual VASP run.
Returns:
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materialsproject/custodian | custodian/vasp/jobs.py | VaspJob.postprocess | def postprocess(self):
"""
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materialsproject/custodian | custodian/vasp/jobs.py | VaspJob.double_relaxation_run | def double_relaxation_run(cls, vasp_cmd, auto_npar=True, ediffg=-0.05,
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"""
Returns a list of two jobs corresponding to an AFLOW style double
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Args:
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materialsproject/custodian | custodian/vasp/jobs.py | VaspJob.metagga_opt_run | def metagga_opt_run(cls, vasp_cmd, auto_npar=True, ediffg=-0.05,
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materialsproject/custodian | custodian/vasp/jobs.py | VaspJob.full_opt_run | def full_opt_run(cls, vasp_cmd, vol_change_tol=0.02,
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materialsproject/custodian | custodian/vasp/jobs.py | VaspJob.constrained_opt_run | def constrained_opt_run(cls, vasp_cmd, lattice_direction, initial_strain,
atom_relax=True, max_steps=20, algo="bfgs",
**vasp_job_kwargs):
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materialsproject/custodian | custodian/vasp/jobs.py | VaspNEBJob.setup | def setup(self):
"""
Performs initial setup for VaspNEBJob, including overriding any settings
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"""
neb_dirs = self.neb_dirs
if self.backup:
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shutil.copy(... | python | def setup(self):
"""
Performs initial setup for VaspNEBJob, including overriding any settings
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"""
neb_dirs = self.neb_dirs
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materialsproject/custodian | custodian/vasp/jobs.py | VaspNEBJob.postprocess | def postprocess(self):
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"""
# Add suffix to all sub_dir/{items}
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for f in VASP_NEB_OUTPUT_SUB_FILES:
f = os.path.join(path, f)
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"""
Postprocessing includes renaming and gzipping where necessary.
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materialsproject/custodian | custodian/nwchem/jobs.py | NwchemJob.setup | def setup(self):
"""
Performs backup if necessary.
"""
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shutil.copy(self.input_file, "{}.orig".format(self.input_file)) | python | def setup(self):
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Performs backup if necessary.
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materialsproject/custodian | custodian/nwchem/jobs.py | NwchemJob.run | def run(self):
"""
Performs actual nwchem run.
"""
with zopen(self.output_file, 'w') as fout:
return subprocess.Popen(self.nwchem_cmd + [self.input_file],
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Performs actual nwchem run.
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wdecoster/nanofilt | nanofilt/NanoFilt.py | valid_GC | def valid_GC(x):
"""type function for argparse to check GC values.
Check if the supplied value for minGC and maxGC is a valid input, being between 0 and 1
"""
x = float(x)
if x < 0.0 or x > 1.0:
raise ArgumentTypeError("{} not in range [0.0, 1.0]".format(x))
return x | python | def valid_GC(x):
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"""
x = float(x)
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wdecoster/nanofilt | nanofilt/NanoFilt.py | filter_stream | def filter_stream(fq, args):
"""Filter a fastq file on stdin.
Print fastq record to stdout if it passes
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- length filter (optional)
- min/maxGC filter (optional)
Optionally trim a number of nucleotides from beginning and end.
Record has to be longer than args.leng... | python | def filter_stream(fq, args):
"""Filter a fastq file on stdin.
Print fastq record to stdout if it passes
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- length filter (optional)
- min/maxGC filter (optional)
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wdecoster/nanofilt | nanofilt/NanoFilt.py | filter_using_summary | def filter_using_summary(fq, args):
"""Use quality scores from albacore summary file for filtering
Use the summary file from albacore for more accurate quality estimate
Get the dataframe from nanoget, convert to dictionary
"""
data = {entry[0]: entry[1] for entry in process_summary(
summary... | python | def filter_using_summary(fq, args):
"""Use quality scores from albacore summary file for filtering
Use the summary file from albacore for more accurate quality estimate
Get the dataframe from nanoget, convert to dictionary
"""
data = {entry[0]: entry[1] for entry in process_summary(
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milesrichardson/ParsePy | parse_rest/connection.py | master_key_required | def master_key_required(func):
'''decorator describing methods that require the master key'''
def ret(obj, *args, **kw):
conn = ACCESS_KEYS
if not (conn and conn.get('master_key')):
message = '%s requires the master key' % func.__name__
raise core.ParseError(message)
... | python | def master_key_required(func):
'''decorator describing methods that require the master key'''
def ret(obj, *args, **kw):
conn = ACCESS_KEYS
if not (conn and conn.get('master_key')):
message = '%s requires the master key' % func.__name__
raise core.ParseError(message)
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milesrichardson/ParsePy | parse_rest/connection.py | ParseBase.execute | def execute(cls, uri, http_verb, extra_headers=None, batch=False, _body=None, **kw):
"""
if batch == False, execute a command with the given parameters and
return the response JSON.
If batch == True, return the dictionary that would be used in a batch
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"""
... | python | def execute(cls, uri, http_verb, extra_headers=None, batch=False, _body=None, **kw):
"""
if batch == False, execute a command with the given parameters and
return the response JSON.
If batch == True, return the dictionary that would be used in a batch
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