id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
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220,700 | istresearch/scrapy-cluster | redis-monitor/plugins/stop_monitor.py | StopMonitor._mini_purge | def _mini_purge(self, spiderid, appid, crawlid):
'''
Actually purges the crawlid from the queue
@param spiderid: the spider id
@param appid: the app id
@param crawlid: the crawl id
@return: The number of requests purged
'''
total_purged = 0
match... | python | def _mini_purge(self, spiderid, appid, crawlid):
'''
Actually purges the crawlid from the queue
@param spiderid: the spider id
@param appid: the app id
@param crawlid: the crawl id
@return: The number of requests purged
'''
total_purged = 0
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220,701 | istresearch/scrapy-cluster | rest/rest_service.py | log_call | def log_call(call_name):
"""Log the API call to the logger."""
def decorator(f):
@wraps(f)
def wrapper(*args, **kw):
instance = args[0]
instance.logger.info(call_name, {"content": request.get_json()})
return f(*args, **kw)
return wrapper
return dec... | python | def log_call(call_name):
"""Log the API call to the logger."""
def decorator(f):
@wraps(f)
def wrapper(*args, **kw):
instance = args[0]
instance.logger.info(call_name, {"content": request.get_json()})
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220,702 | istresearch/scrapy-cluster | rest/rest_service.py | error_catch | def error_catch(f):
"""Handle unexpected errors within the rest function."""
@wraps(f)
def wrapper(*args, **kw):
instance = args[0]
try:
result = f(*args, **kw)
if isinstance(result, tuple):
return jsonify(result[0]), result[1]
else:
... | python | def error_catch(f):
"""Handle unexpected errors within the rest function."""
@wraps(f)
def wrapper(*args, **kw):
instance = args[0]
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result = f(*args, **kw)
if isinstance(result, tuple):
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220,703 | istresearch/scrapy-cluster | rest/rest_service.py | validate_json | def validate_json(f):
"""Validate that the call is JSON."""
@wraps(f)
def wrapper(*args, **kw):
instance = args[0]
try:
if request.get_json() is None:
ret_dict = instance._create_ret_object(instance.FAILURE,
N... | python | def validate_json(f):
"""Validate that the call is JSON."""
@wraps(f)
def wrapper(*args, **kw):
instance = args[0]
try:
if request.get_json() is None:
ret_dict = instance._create_ret_object(instance.FAILURE,
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220,704 | istresearch/scrapy-cluster | rest/rest_service.py | validate_schema | def validate_schema(schema_name):
"""Validate the JSON against a required schema_name."""
def decorator(f):
@wraps(f)
def wrapper(*args, **kw):
instance = args[0]
try:
instance.validator(instance.schemas[schema_name]).validate(request.get_json())
... | python | def validate_schema(schema_name):
"""Validate the JSON against a required schema_name."""
def decorator(f):
@wraps(f)
def wrapper(*args, **kw):
instance = args[0]
try:
instance.validator(instance.schemas[schema_name]).validate(request.get_json())
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220,705 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._load_schemas | def _load_schemas(self):
"""Loads any schemas for JSON validation"""
for filename in os.listdir(self.settings['SCHEMA_DIR']):
if filename[-4:] == 'json':
name = filename[:-5]
with open(self.settings['SCHEMA_DIR'] + filename) as the_file:
se... | python | def _load_schemas(self):
"""Loads any schemas for JSON validation"""
for filename in os.listdir(self.settings['SCHEMA_DIR']):
if filename[-4:] == 'json':
name = filename[:-5]
with open(self.settings['SCHEMA_DIR'] + filename) as the_file:
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220,706 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._spawn_redis_connection_thread | def _spawn_redis_connection_thread(self):
"""Spawns a redis connection thread"""
self.logger.debug("Spawn redis connection thread")
self.redis_connected = False
self._redis_thread = Thread(target=self._setup_redis)
self._redis_thread.setDaemon(True)
self._redis_thread.sta... | python | def _spawn_redis_connection_thread(self):
"""Spawns a redis connection thread"""
self.logger.debug("Spawn redis connection thread")
self.redis_connected = False
self._redis_thread = Thread(target=self._setup_redis)
self._redis_thread.setDaemon(True)
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220,707 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._spawn_kafka_connection_thread | def _spawn_kafka_connection_thread(self):
"""Spawns a kafka connection thread"""
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self.kafka_connected = False
self._kafka_thread = Thread(target=self._setup_kafka)
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self._kafka_thread.sta... | python | def _spawn_kafka_connection_thread(self):
"""Spawns a kafka connection thread"""
self.logger.debug("Spawn kafka connection thread")
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self._kafka_thread = Thread(target=self._setup_kafka)
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220,708 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._consumer_loop | def _consumer_loop(self):
"""The main consumer loop"""
self.logger.debug("running main consumer thread")
while not self.closed:
if self.kafka_connected:
self._process_messages()
time.sleep(self.settings['KAFKA_CONSUMER_SLEEP_TIME']) | python | def _consumer_loop(self):
"""The main consumer loop"""
self.logger.debug("running main consumer thread")
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220,709 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._process_messages | def _process_messages(self):
"""Processes messages received from kafka"""
try:
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self.logger.debug("no message")
break
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"""Processes messages received from kafka"""
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220,710 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._send_result_to_redis | def _send_result_to_redis(self, result):
"""Sends the result of a poll to redis to be used potentially by
another process
@param result: the result retrieved from kafka"""
if self.redis_connected:
self.logger.debug("Sending result to redis")
try:
... | python | def _send_result_to_redis(self, result):
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@param result: the result retrieved from kafka"""
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self.logger.debug("Sending result to redis")
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220,711 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._check_kafka_disconnect | def _check_kafka_disconnect(self):
"""Checks the kafka connection is still valid"""
for node_id in self.consumer._client._conns:
conn = self.consumer._client._conns[node_id]
if conn.state == ConnectionStates.DISCONNECTED or \
conn.state == ConnectionStates.DIS... | python | def _check_kafka_disconnect(self):
"""Checks the kafka connection is still valid"""
for node_id in self.consumer._client._conns:
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220,712 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._heartbeat_loop | def _heartbeat_loop(self):
"""A main run loop thread to do work"""
self.logger.debug("running main heartbeat thread")
while not self.closed:
time.sleep(self.settings['SLEEP_TIME'])
self._report_self() | python | def _heartbeat_loop(self):
"""A main run loop thread to do work"""
self.logger.debug("running main heartbeat thread")
while not self.closed:
time.sleep(self.settings['SLEEP_TIME'])
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220,713 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._setup_redis | def _setup_redis(self):
"""Returns a Redis Client"""
if not self.closed:
try:
self.logger.debug("Creating redis connection to host " +
str(self.settings['REDIS_HOST']))
self.redis_conn = redis.StrictRedis(host=self.settings['R... | python | def _setup_redis(self):
"""Returns a Redis Client"""
if not self.closed:
try:
self.logger.debug("Creating redis connection to host " +
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self.redis_conn = redis.StrictRedis(host=self.settings['R... | [
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220,714 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._setup_kafka | def _setup_kafka(self):
"""
Sets up kafka connections
"""
# close older connections
if self.consumer is not None:
self.logger.debug("Closing existing kafka consumer")
self.consumer.close()
self.consumer = None
if self.producer is not No... | python | def _setup_kafka(self):
"""
Sets up kafka connections
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# close older connections
if self.consumer is not None:
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220,715 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._create_consumer | def _create_consumer(self):
"""Tries to establing the Kafka consumer connection"""
if not self.closed:
try:
self.logger.debug("Creating new kafka consumer using brokers: " +
str(self.settings['KAFKA_HOSTS']) + ' and topic ' +
... | python | def _create_consumer(self):
"""Tries to establing the Kafka consumer connection"""
if not self.closed:
try:
self.logger.debug("Creating new kafka consumer using brokers: " +
str(self.settings['KAFKA_HOSTS']) + ' and topic ' +
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220,716 | istresearch/scrapy-cluster | rest/rest_service.py | RestService.run | def run(self):
"""Main flask run loop"""
self.logger.info("Running main flask method on port " +
str(self.settings['FLASK_PORT']))
self.app.run(host='0.0.0.0', port=self.settings['FLASK_PORT']) | python | def run(self):
"""Main flask run loop"""
self.logger.info("Running main flask method on port " +
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220,717 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._create_ret_object | def _create_ret_object(self, status=SUCCESS, data=None, error=False,
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220,718 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._close_thread | def _close_thread(self, thread, thread_name):
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@param thread: the thread to close
@param thread_name: a human readable name of the thread
"""
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@param thread_name: a human readable name of the thread
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220,719 | istresearch/scrapy-cluster | rest/rest_service.py | RestService.close | def close(self):
"""
Cleans up anything from the process
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self.logger.info("Closing Rest Service")
self.closed = True
# close threads
self._close_thread(self._redis_thread, "Redis setup")
self._close_thread(self._heartbeat_thread, "Heartbeat")
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"""
Cleans up anything from the process
"""
self.logger.info("Closing Rest Service")
self.closed = True
# close threads
self._close_thread(self._redis_thread, "Redis setup")
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220,720 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._calculate_health | def _calculate_health(self):
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"""
if self.redis_connected and self.kafka_connected:
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220,721 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._feed_to_kafka | def _feed_to_kafka(self, json_item):
"""Sends a request to Kafka
:param json_item: The json item to send
:returns: A boolean indicating whther the data was sent successfully or not
"""
@MethodTimer.timeout(self.settings['KAFKA_FEED_TIMEOUT'], False)
def _feed(json_item):... | python | def _feed_to_kafka(self, json_item):
"""Sends a request to Kafka
:param json_item: The json item to send
:returns: A boolean indicating whther the data was sent successfully or not
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@MethodTimer.timeout(self.settings['KAFKA_FEED_TIMEOUT'], False)
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220,722 | istresearch/scrapy-cluster | rest/rest_service.py | RestService._decorate_routes | def _decorate_routes(self):
"""
Decorates the routes to use within the flask app
"""
self.logger.debug("Decorating routes")
# self.app.add_url_rule('/', 'catch', self.catch, methods=['GET'],
# defaults={'path': ''})
self.app.add_url_rule('/<... | python | def _decorate_routes(self):
"""
Decorates the routes to use within the flask app
"""
self.logger.debug("Decorating routes")
# self.app.add_url_rule('/', 'catch', self.catch, methods=['GET'],
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220,723 | istresearch/scrapy-cluster | rest/rest_service.py | RestService.poll | def poll(self):
"""Retrieves older requests that may not make it back quick
enough"""
if self.redis_connected:
json_item = request.get_json()
result = None
try:
key = "rest:poll:{u}".format(u=json_item['poll_id'])
result = self.... | python | def poll(self):
"""Retrieves older requests that may not make it back quick
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if self.redis_connected:
json_item = request.get_json()
result = None
try:
key = "rest:poll:{u}".format(u=json_item['poll_id'])
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220,724 | vimalloc/flask-jwt-extended | flask_jwt_extended/utils.py | create_refresh_token | def create_refresh_token(identity, expires_delta=None, user_claims=None):
"""
Creates a new refresh token.
:param identity: The identity of this token, which can be any data that is
json serializable. It can also be a python object, in which
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Creates a new refresh token.
:param identity: The identity of this token, which can be any data that is
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220,725 | vimalloc/flask-jwt-extended | examples/database_blacklist/blacklist_helpers.py | is_token_revoked | def is_token_revoked(decoded_token):
"""
Checks if the given token is revoked or not. Because we are adding all the
tokens that we create into this database, if the token is not present
in the database we are going to consider it revoked, as we don't know where
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"""
jti = deco... | python | def is_token_revoked(decoded_token):
"""
Checks if the given token is revoked or not. Because we are adding all the
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220,726 | vimalloc/flask-jwt-extended | examples/database_blacklist/blacklist_helpers.py | revoke_token | def revoke_token(token_id, user):
"""
Revokes the given token. Raises a TokenNotFound error if the token does
not exist in the database
"""
try:
token = TokenBlacklist.query.filter_by(id=token_id, user_identity=user).one()
token.revoked = True
db.session.commit()
except N... | python | def revoke_token(token_id, user):
"""
Revokes the given token. Raises a TokenNotFound error if the token does
not exist in the database
"""
try:
token = TokenBlacklist.query.filter_by(id=token_id, user_identity=user).one()
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220,727 | vimalloc/flask-jwt-extended | examples/database_blacklist/blacklist_helpers.py | prune_database | def prune_database():
"""
Delete tokens that have expired from the database.
How (and if) you call this is entirely up you. You could expose it to an
endpoint that only administrators could call, you could run it as a cron,
set it up with flask cli, etc.
"""
now = datetime.now()
expired... | python | def prune_database():
"""
Delete tokens that have expired from the database.
How (and if) you call this is entirely up you. You could expose it to an
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220,728 | vimalloc/flask-jwt-extended | flask_jwt_extended/view_decorators.py | verify_jwt_in_request | def verify_jwt_in_request():
"""
Ensure that the requester has a valid access token. This does not check the
freshness of the access token. Raises an appropiate exception there is
no token or if the token is invalid.
"""
if request.method not in config.exempt_methods:
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"""
Ensure that the requester has a valid access token. This does not check the
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220,729 | vimalloc/flask-jwt-extended | flask_jwt_extended/view_decorators.py | verify_fresh_jwt_in_request | def verify_fresh_jwt_in_request():
"""
Ensure that the requester has a valid and fresh access token. Raises an
appropiate exception if there is no token, the token is invalid, or the
token is not marked as fresh.
"""
if request.method not in config.exempt_methods:
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220,730 | vimalloc/flask-jwt-extended | flask_jwt_extended/view_decorators.py | jwt_optional | def jwt_optional(fn):
"""
A decorator to optionally protect a Flask endpoint
If an access token in present in the request, this will call the endpoint
with :func:`~flask_jwt_extended.get_jwt_identity` having the identity
of the access token. If no access token is present in the request,
this en... | python | def jwt_optional(fn):
"""
A decorator to optionally protect a Flask endpoint
If an access token in present in the request, this will call the endpoint
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220,731 | MycroftAI/mycroft-precise | precise/model.py | load_precise_model | def load_precise_model(model_name: str) -> Any:
"""Loads a Keras model from file, handling custom loss function"""
if not model_name.endswith('.net'):
print('Warning: Unknown model type, ', model_name)
inject_params(model_name)
return load_keras().models.load_model(model_name) | python | def load_precise_model(model_name: str) -> Any:
"""Loads a Keras model from file, handling custom loss function"""
if not model_name.endswith('.net'):
print('Warning: Unknown model type, ', model_name)
inject_params(model_name)
return load_keras().models.load_model(model_name) | [
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220,732 | MycroftAI/mycroft-precise | precise/model.py | create_model | def create_model(model_name: Optional[str], params: ModelParams) -> 'Sequential':
"""
Load or create a precise model
Args:
model_name: Name of model
params: Parameters used to create the model
Returns:
model: Loaded Keras model
"""
if model_name and isfile(model_name):
... | python | def create_model(model_name: Optional[str], params: ModelParams) -> 'Sequential':
"""
Load or create a precise model
Args:
model_name: Name of model
params: Parameters used to create the model
Returns:
model: Loaded Keras model
"""
if model_name and isfile(model_name):
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220,733 | MycroftAI/mycroft-precise | precise/scripts/train_generated.py | GeneratedTrainer.layer_with | def layer_with(self, sample: np.ndarray, value: int) -> np.ndarray:
"""Create an identical 2d array where the second row is filled with value"""
b = np.full((2, len(sample)), value, dtype=float)
b[0] = sample
return b | python | def layer_with(self, sample: np.ndarray, value: int) -> np.ndarray:
"""Create an identical 2d array where the second row is filled with value"""
b = np.full((2, len(sample)), value, dtype=float)
b[0] = sample
return b | [
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220,734 | MycroftAI/mycroft-precise | precise/scripts/train_generated.py | GeneratedTrainer.generate_wakeword_pieces | def generate_wakeword_pieces(self, volume):
"""Generates chunks of audio that represent the wakeword stream"""
while True:
target = 1 if random() > 0.5 else 0
it = self.pos_files_it if target else self.neg_files_it
sample_file = next(it)
yield self.layer_w... | python | def generate_wakeword_pieces(self, volume):
"""Generates chunks of audio that represent the wakeword stream"""
while True:
target = 1 if random() > 0.5 else 0
it = self.pos_files_it if target else self.neg_files_it
sample_file = next(it)
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220,735 | MycroftAI/mycroft-precise | precise/scripts/train_generated.py | GeneratedTrainer.chunk_audio_pieces | def chunk_audio_pieces(self, pieces, chunk_size):
"""Convert chunks of audio into a series of equally sized pieces"""
left_over = np.array([])
for piece in pieces:
if left_over.size == 0:
combined = piece
else:
combined = np.concatenate([le... | python | def chunk_audio_pieces(self, pieces, chunk_size):
"""Convert chunks of audio into a series of equally sized pieces"""
left_over = np.array([])
for piece in pieces:
if left_over.size == 0:
combined = piece
else:
combined = np.concatenate([le... | [
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220,736 | MycroftAI/mycroft-precise | precise/scripts/train_generated.py | GeneratedTrainer.calc_volume | def calc_volume(self, sample: np.ndarray):
"""Find the RMS of the audio"""
return sqrt(np.mean(np.square(sample))) | python | def calc_volume(self, sample: np.ndarray):
"""Find the RMS of the audio"""
return sqrt(np.mean(np.square(sample))) | [
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220,737 | MycroftAI/mycroft-precise | precise/scripts/train_generated.py | GeneratedTrainer.max_run_length | def max_run_length(x: np.ndarray, val: int):
"""Finds the maximum continuous length of the given value in the sequence"""
if x.size == 0:
return 0
else:
y = np.array(x[1:] != x[:-1])
i = np.append(np.where(y), len(x) - 1)
run_lengths = np.diff(np.a... | python | def max_run_length(x: np.ndarray, val: int):
"""Finds the maximum continuous length of the given value in the sequence"""
if x.size == 0:
return 0
else:
y = np.array(x[1:] != x[:-1])
i = np.append(np.where(y), len(x) - 1)
run_lengths = np.diff(np.a... | [
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220,738 | MycroftAI/mycroft-precise | precise/scripts/train_generated.py | GeneratedTrainer.samples_to_batches | def samples_to_batches(samples: Iterable, batch_size: int):
"""Chunk a series of network inputs and outputs into larger batches"""
it = iter(samples)
while True:
with suppress(StopIteration):
batch_in, batch_out = [], []
for i in range(batch_size):
... | python | def samples_to_batches(samples: Iterable, batch_size: int):
"""Chunk a series of network inputs and outputs into larger batches"""
it = iter(samples)
while True:
with suppress(StopIteration):
batch_in, batch_out = [], []
for i in range(batch_size):
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220,739 | MycroftAI/mycroft-precise | precise/scripts/train_generated.py | GeneratedTrainer.run | def run(self):
"""Train the model on randomly generated batches"""
_, test_data = self.data.load(train=False, test=True)
try:
self.model.fit_generator(
self.samples_to_batches(self.generate_samples(), self.args.batch_size), steps_per_epoch=self.args.steps_per_epoch,
... | python | def run(self):
"""Train the model on randomly generated batches"""
_, test_data = self.data.load(train=False, test=True)
try:
self.model.fit_generator(
self.samples_to_batches(self.generate_samples(), self.args.batch_size), steps_per_epoch=self.args.steps_per_epoch,
... | [
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220,740 | MycroftAI/mycroft-precise | precise/train_data.py | TrainData.from_both | def from_both(cls, tags_file: str, tags_folder: str, folder: str) -> 'TrainData':
"""Load data from both a database and a structured folder"""
return cls.from_tags(tags_file, tags_folder) + cls.from_folder(folder) | python | def from_both(cls, tags_file: str, tags_folder: str, folder: str) -> 'TrainData':
"""Load data from both a database and a structured folder"""
return cls.from_tags(tags_file, tags_folder) + cls.from_folder(folder) | [
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220,741 | MycroftAI/mycroft-precise | precise/train_data.py | TrainData.load_inhibit | def load_inhibit(self, train=True, test=True) -> tuple:
"""Generate data with inhibitory inputs created from wake word samples"""
def loader(kws: list, nkws: list):
from precise.params import pr
inputs = np.empty((0, pr.n_features, pr.feature_size))
outputs = np.zero... | python | def load_inhibit(self, train=True, test=True) -> tuple:
"""Generate data with inhibitory inputs created from wake word samples"""
def loader(kws: list, nkws: list):
from precise.params import pr
inputs = np.empty((0, pr.n_features, pr.feature_size))
outputs = np.zero... | [
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220,742 | MycroftAI/mycroft-precise | precise/train_data.py | TrainData.parse_args | def parse_args(parser: ArgumentParser) -> Any:
"""Return parsed args from parser, adding options for train data inputs"""
extra_usage = '''
:folder str
Folder to load wav files from
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Specify a different f... | python | def parse_args(parser: ArgumentParser) -> Any:
"""Return parsed args from parser, adding options for train data inputs"""
extra_usage = '''
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220,743 | MycroftAI/mycroft-precise | precise/vectorization.py | vectorize_raw | def vectorize_raw(audio: np.ndarray) -> np.ndarray:
"""Turns audio into feature vectors, without clipping for length"""
if len(audio) == 0:
raise InvalidAudio('Cannot vectorize empty audio!')
return vectorizers[pr.vectorizer](audio) | python | def vectorize_raw(audio: np.ndarray) -> np.ndarray:
"""Turns audio into feature vectors, without clipping for length"""
if len(audio) == 0:
raise InvalidAudio('Cannot vectorize empty audio!')
return vectorizers[pr.vectorizer](audio) | [
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220,744 | MycroftAI/mycroft-precise | precise/vectorization.py | vectorize_inhibit | def vectorize_inhibit(audio: np.ndarray) -> np.ndarray:
"""
Returns an array of inputs generated from the
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"""
def samp(x):
return int(pr.sample_rate * x)
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"""
Returns an array of inputs generated from the
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def samp(x):
return int(pr.sample_rate * x)
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220,745 | MycroftAI/mycroft-precise | runner/precise_runner/runner.py | TriggerDetector.update | def update(self, prob):
# type: (float) -> bool
"""Returns whether the new prediction caused an activation"""
chunk_activated = prob > 1.0 - self.sensitivity
if chunk_activated or self.activation < 0:
self.activation += 1
has_activated = self.activation > self.tr... | python | def update(self, prob):
# type: (float) -> bool
"""Returns whether the new prediction caused an activation"""
chunk_activated = prob > 1.0 - self.sensitivity
if chunk_activated or self.activation < 0:
self.activation += 1
has_activated = self.activation > self.tr... | [
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220,746 | MycroftAI/mycroft-precise | runner/precise_runner/runner.py | PreciseRunner.start | def start(self):
"""Start listening from stream"""
if self.stream is None:
from pyaudio import PyAudio, paInt16
self.pa = PyAudio()
self.stream = self.pa.open(
16000, 1, paInt16, True, frames_per_buffer=self.chunk_size
)
self._wrap... | python | def start(self):
"""Start listening from stream"""
if self.stream is None:
from pyaudio import PyAudio, paInt16
self.pa = PyAudio()
self.stream = self.pa.open(
16000, 1, paInt16, True, frames_per_buffer=self.chunk_size
)
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220,747 | MycroftAI/mycroft-precise | runner/precise_runner/runner.py | PreciseRunner.stop | def stop(self):
"""Stop listening and close stream"""
if self.thread:
self.running = False
if isinstance(self.stream, ReadWriteStream):
self.stream.write(b'\0' * self.chunk_size)
self.thread.join()
self.thread = None
self.engine.st... | python | def stop(self):
"""Stop listening and close stream"""
if self.thread:
self.running = False
if isinstance(self.stream, ReadWriteStream):
self.stream.write(b'\0' * self.chunk_size)
self.thread.join()
self.thread = None
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220,748 | MycroftAI/mycroft-precise | runner/precise_runner/runner.py | PreciseRunner._handle_predictions | def _handle_predictions(self):
"""Continuously check Precise process output"""
while self.running:
chunk = self.stream.read(self.chunk_size)
if self.is_paused:
continue
prob = self.engine.get_prediction(chunk)
self.on_prediction(prob)
... | python | def _handle_predictions(self):
"""Continuously check Precise process output"""
while self.running:
chunk = self.stream.read(self.chunk_size)
if self.is_paused:
continue
prob = self.engine.get_prediction(chunk)
self.on_prediction(prob)
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220,749 | MycroftAI/mycroft-precise | precise/stats.py | Stats.calc_filenames | def calc_filenames(self, is_correct: bool, actual_output: bool, threshold=0.5) -> list:
"""Find a list of files with the given classification"""
return [
filename
for output, target, filename in zip(self.outputs, self.targets, self.filenames)
if ((output > threshold) ... | python | def calc_filenames(self, is_correct: bool, actual_output: bool, threshold=0.5) -> list:
"""Find a list of files with the given classification"""
return [
filename
for output, target, filename in zip(self.outputs, self.targets, self.filenames)
if ((output > threshold) ... | [
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220,750 | MycroftAI/mycroft-precise | precise/scripts/graph.py | get_thresholds | def get_thresholds(points=100, power=3) -> list:
"""Run a function with a series of thresholds between 0 and 1"""
return [(i / (points + 1)) ** power for i in range(1, points + 1)] | python | def get_thresholds(points=100, power=3) -> list:
"""Run a function with a series of thresholds between 0 and 1"""
return [(i / (points + 1)) ** power for i in range(1, points + 1)] | [
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220,751 | MycroftAI/mycroft-precise | precise/scripts/graph.py | CachedDataLoader.load_for | def load_for(self, model: str) -> Tuple[list, list]:
"""Injects the model parameters, reloading if they changed, and returning the data"""
inject_params(model)
if self.prev_cache != pr.vectorization_md5_hash():
self.prev_cache = pr.vectorization_md5_hash()
self.data = sel... | python | def load_for(self, model: str) -> Tuple[list, list]:
"""Injects the model parameters, reloading if they changed, and returning the data"""
inject_params(model)
if self.prev_cache != pr.vectorization_md5_hash():
self.prev_cache = pr.vectorization_md5_hash()
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220,752 | MycroftAI/mycroft-precise | precise/util.py | buffer_to_audio | def buffer_to_audio(buffer: bytes) -> np.ndarray:
"""Convert a raw mono audio byte string to numpy array of floats"""
return np.fromstring(buffer, dtype='<i2').astype(np.float32, order='C') / 32768.0 | python | def buffer_to_audio(buffer: bytes) -> np.ndarray:
"""Convert a raw mono audio byte string to numpy array of floats"""
return np.fromstring(buffer, dtype='<i2').astype(np.float32, order='C') / 32768.0 | [
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220,753 | MycroftAI/mycroft-precise | precise/util.py | find_wavs | def find_wavs(folder: str) -> Tuple[List[str], List[str]]:
"""Finds wake-word and not-wake-word wavs in folder"""
return (glob_all(join(folder, 'wake-word'), '*.wav'),
glob_all(join(folder, 'not-wake-word'), '*.wav')) | python | def find_wavs(folder: str) -> Tuple[List[str], List[str]]:
"""Finds wake-word and not-wake-word wavs in folder"""
return (glob_all(join(folder, 'wake-word'), '*.wav'),
glob_all(join(folder, 'not-wake-word'), '*.wav')) | [
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220,754 | MycroftAI/mycroft-precise | precise/scripts/convert.py | convert | def convert(model_path: str, out_file: str):
"""
Converts an HD5F file from Keras to a .pb for use with TensorFlow
Args:
model_path: location of Keras model
out_file: location to write protobuf
"""
print('Converting', model_path, 'to', out_file, '...')
import tensorflow as tf
... | python | def convert(model_path: str, out_file: str):
"""
Converts an HD5F file from Keras to a .pb for use with TensorFlow
Args:
model_path: location of Keras model
out_file: location to write protobuf
"""
print('Converting', model_path, 'to', out_file, '...')
import tensorflow as tf
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220,755 | MycroftAI/mycroft-precise | precise/params.py | inject_params | def inject_params(model_name: str) -> ListenerParams:
"""Set the global listener params to a saved model"""
params_file = model_name + '.params'
try:
with open(params_file) as f:
pr.__dict__.update(compatibility_params, **json.load(f))
except (OSError, ValueError, TypeError):
... | python | def inject_params(model_name: str) -> ListenerParams:
"""Set the global listener params to a saved model"""
params_file = model_name + '.params'
try:
with open(params_file) as f:
pr.__dict__.update(compatibility_params, **json.load(f))
except (OSError, ValueError, TypeError):
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220,756 | MycroftAI/mycroft-precise | precise/params.py | save_params | def save_params(model_name: str):
"""Save current global listener params to a file"""
with open(model_name + '.params', 'w') as f:
json.dump(pr.__dict__, f) | python | def save_params(model_name: str):
"""Save current global listener params to a file"""
with open(model_name + '.params', 'w') as f:
json.dump(pr.__dict__, f) | [
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220,757 | MycroftAI/mycroft-precise | precise/scripts/train_incremental.py | IncrementalTrainer.retrain | def retrain(self):
"""Train for a session, pulling in any new data from the filesystem"""
folder = TrainData.from_folder(self.args.folder)
train_data, test_data = folder.load(True, not self.args.no_validation)
train_data = TrainData.merge(train_data, self.sampled_data)
test_data... | python | def retrain(self):
"""Train for a session, pulling in any new data from the filesystem"""
folder = TrainData.from_folder(self.args.folder)
train_data, test_data = folder.load(True, not self.args.no_validation)
train_data = TrainData.merge(train_data, self.sampled_data)
test_data... | [
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220,758 | MycroftAI/mycroft-precise | precise/scripts/train_incremental.py | IncrementalTrainer.train_on_audio | def train_on_audio(self, fn: str):
"""Run through a single audio file"""
save_test = random() > 0.8
audio = load_audio(fn)
num_chunks = len(audio) // self.args.chunk_size
self.listener.clear()
for i, chunk in enumerate(chunk_audio(audio, self.args.chunk_size)):
... | python | def train_on_audio(self, fn: str):
"""Run through a single audio file"""
save_test = random() > 0.8
audio = load_audio(fn)
num_chunks = len(audio) // self.args.chunk_size
self.listener.clear()
for i, chunk in enumerate(chunk_audio(audio, self.args.chunk_size)):
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220,759 | MycroftAI/mycroft-precise | precise/scripts/train_incremental.py | IncrementalTrainer.run | def run(self):
"""
Begin reading through audio files, saving false
activations and retraining when necessary
"""
for fn in glob_all(self.args.random_data_folder, '*.wav'):
if fn in self.trained_fns:
print('Skipping ' + fn + '...')
conti... | python | def run(self):
"""
Begin reading through audio files, saving false
activations and retraining when necessary
"""
for fn in glob_all(self.args.random_data_folder, '*.wav'):
if fn in self.trained_fns:
print('Skipping ' + fn + '...')
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220,760 | MycroftAI/mycroft-precise | precise/network_runner.py | TensorFlowRunner.predict | def predict(self, inputs: np.ndarray) -> np.ndarray:
"""Run on multiple inputs"""
return self.sess.run(self.out_var, {self.inp_var: inputs}) | python | def predict(self, inputs: np.ndarray) -> np.ndarray:
"""Run on multiple inputs"""
return self.sess.run(self.out_var, {self.inp_var: inputs}) | [
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220,761 | ofek/bit | bit/network/rates.py | satoshi_to_currency | def satoshi_to_currency(num, currency):
"""Converts a given number of satoshi to another currency as a formatted
string rounded down to the proper number of decimal places.
:param num: The number of satoshi.
:type num: ``int``
:param currency: One of the :ref:`supported currencies`.
:type curre... | python | def satoshi_to_currency(num, currency):
"""Converts a given number of satoshi to another currency as a formatted
string rounded down to the proper number of decimal places.
:param num: The number of satoshi.
:type num: ``int``
:param currency: One of the :ref:`supported currencies`.
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220,762 | ofek/bit | bit/network/services.py | NetworkAPI.get_balance | def get_balance(cls, address):
"""Gets the balance of an address in satoshi.
:param address: The address in question.
:type address: ``str``
:raises ConnectionError: If all API services fail.
:rtype: ``int``
"""
for api_call in cls.GET_BALANCE_MAIN:
... | python | def get_balance(cls, address):
"""Gets the balance of an address in satoshi.
:param address: The address in question.
:type address: ``str``
:raises ConnectionError: If all API services fail.
:rtype: ``int``
"""
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220,763 | ofek/bit | bit/network/services.py | NetworkAPI.get_transactions | def get_transactions(cls, address):
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:raises ConnectionError: If all API services fail.
:rtype: ``list`` of ``str``
"""
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"""Gets the ID of all transactions related to an address.
:param address: The address in question.
:type address: ``str``
:raises ConnectionError: If all API services fail.
:rtype: ``list`` of ``str``
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220,764 | ofek/bit | bit/network/services.py | NetworkAPI.get_unspent | def get_unspent(cls, address):
"""Gets all unspent transaction outputs belonging to an address.
:param address: The address in question.
:type address: ``str``
:raises ConnectionError: If all API services fail.
:rtype: ``list`` of :class:`~bit.network.meta.Unspent`
"""
... | python | def get_unspent(cls, address):
"""Gets all unspent transaction outputs belonging to an address.
:param address: The address in question.
:type address: ``str``
:raises ConnectionError: If all API services fail.
:rtype: ``list`` of :class:`~bit.network.meta.Unspent`
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220,765 | ofek/bit | bit/network/services.py | NetworkAPI.broadcast_tx | def broadcast_tx(cls, tx_hex): # pragma: no cover
"""Broadcasts a transaction to the blockchain.
:param tx_hex: A signed transaction in hex form.
:type tx_hex: ``str``
:raises ConnectionError: If all API services fail.
"""
success = None
for api_call in cls.BRO... | python | def broadcast_tx(cls, tx_hex): # pragma: no cover
"""Broadcasts a transaction to the blockchain.
:param tx_hex: A signed transaction in hex form.
:type tx_hex: ``str``
:raises ConnectionError: If all API services fail.
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220,766 | ofek/bit | bit/transaction.py | calculate_preimages | def calculate_preimages(tx_obj, inputs_parameters):
"""Calculates preimages for provided transaction structure and input
values.
:param tx_obj: The transaction object used to calculate preimage from using
a transaction digest algorithm, such as BIP-143 for Segwit
input... | python | def calculate_preimages(tx_obj, inputs_parameters):
"""Calculates preimages for provided transaction structure and input
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:param tx_obj: The transaction object used to calculate preimage from using
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220,767 | ofek/bit | bit/wallet.py | BaseKey.verify | def verify(self, signature, data):
"""Verifies some data was signed by this private key.
:param signature: The signature to verify.
:type signature: ``bytes``
:param data: The data that was supposedly signed.
:type data: ``bytes``
:rtype: ``bool``
"""
ret... | python | def verify(self, signature, data):
"""Verifies some data was signed by this private key.
:param signature: The signature to verify.
:type signature: ``bytes``
:param data: The data that was supposedly signed.
:type data: ``bytes``
:rtype: ``bool``
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220,768 | ofek/bit | bit/wallet.py | PrivateKey.get_transactions | def get_transactions(self):
"""Fetches transaction history.
:rtype: ``list`` of ``str`` transaction IDs
"""
self.transactions[:] = NetworkAPI.get_transactions(self.address)
if self.segwit_address:
self.transactions += NetworkAPI.get_transactions(self.segwit_address)
... | python | def get_transactions(self):
"""Fetches transaction history.
:rtype: ``list`` of ``str`` transaction IDs
"""
self.transactions[:] = NetworkAPI.get_transactions(self.address)
if self.segwit_address:
self.transactions += NetworkAPI.get_transactions(self.segwit_address)
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220,769 | ofek/bit | bit/wallet.py | MultiSig.address | def address(self):
"""The public address you share with others to receive funds."""
if self._address is None:
self._address = multisig_to_address(self.public_keys, self.m,
version=self.version)
return self._address | python | def address(self):
"""The public address you share with others to receive funds."""
if self._address is None:
self._address = multisig_to_address(self.public_keys, self.m,
version=self.version)
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220,770 | ofek/bit | bit/base32.py | bech32_polymod | def bech32_polymod(values):
"""Internal function that computes the Bech32 checksum."""
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for value in values:
top = chk >> 25
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"""Internal function that computes the Bech32 checksum."""
generator = [0x3b6a57b2, 0x26508e6d, 0x1ea119fa, 0x3d4233dd, 0x2a1462b3]
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220,771 | ofek/bit | bit/base32.py | bech32_hrp_expand | def bech32_hrp_expand(hrp):
"""Expand the HRP into values for checksum computation."""
return [ord(x) >> 5 for x in hrp] + [0] + [ord(x) & 31 for x in hrp] | python | def bech32_hrp_expand(hrp):
"""Expand the HRP into values for checksum computation."""
return [ord(x) >> 5 for x in hrp] + [0] + [ord(x) & 31 for x in hrp] | [
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220,772 | ofek/bit | bit/base32.py | bech32_create_checksum | def bech32_create_checksum(hrp, data):
"""Compute the checksum values given HRP and data."""
values = bech32_hrp_expand(hrp) + data
polymod = bech32_polymod(values + [0, 0, 0, 0, 0, 0]) ^ 1
return [(polymod >> 5 * (5 - i)) & 31 for i in range(6)] | python | def bech32_create_checksum(hrp, data):
"""Compute the checksum values given HRP and data."""
values = bech32_hrp_expand(hrp) + data
polymod = bech32_polymod(values + [0, 0, 0, 0, 0, 0]) ^ 1
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220,773 | ofek/bit | bit/base32.py | bech32_encode | def bech32_encode(hrp, data):
"""Compute a Bech32 string given HRP and data values."""
combined = data + bech32_create_checksum(hrp, data)
return hrp + '1' + ''.join([CHARSET[d] for d in combined]) | python | def bech32_encode(hrp, data):
"""Compute a Bech32 string given HRP and data values."""
combined = data + bech32_create_checksum(hrp, data)
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220,774 | ofek/bit | bit/base32.py | convertbits | def convertbits(data, frombits, tobits, pad=True):
"""General power-of-2 base conversion."""
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bits = 0
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maxv = (1 << tobits) - 1
max_acc = (1 << (frombits + tobits - 1)) - 1
for value in data:
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return None
acc = (... | python | def convertbits(data, frombits, tobits, pad=True):
"""General power-of-2 base conversion."""
acc = 0
bits = 0
ret = []
maxv = (1 << tobits) - 1
max_acc = (1 << (frombits + tobits - 1)) - 1
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220,775 | ofek/bit | bit/base32.py | decode | def decode(addr):
"""Decode a segwit address."""
hrpgot, data = bech32_decode(addr)
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return (None, None)
decoded = convertbits(data[1:], 5, 8, False)
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"""Decode a segwit address."""
hrpgot, data = bech32_decode(addr)
# if hrpgot != hrp:
if hrpgot not in BECH32_VERSION_SET:
return (None, None)
decoded = convertbits(data[1:], 5, 8, False)
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220,776 | nok/sklearn-porter | sklearn_porter/utils/Shell.py | Shell._run | def _run(method, cmd, cwd=None, shell=True, universal_newlines=True,
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220,777 | nok/sklearn-porter | sklearn_porter/utils/Shell.py | Shell.call | def call(cmd, shell=True, cwd=None, universal_newlines=True, stderr=STDOUT):
"""Just execute a specific command."""
return Shell._run(call, cmd, shell=shell, cwd=cwd, stderr=stderr,
universal_newlines=universal_newlines) | python | def call(cmd, shell=True, cwd=None, universal_newlines=True, stderr=STDOUT):
"""Just execute a specific command."""
return Shell._run(call, cmd, shell=shell, cwd=cwd, stderr=stderr,
universal_newlines=universal_newlines) | [
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220,778 | nok/sklearn-porter | sklearn_porter/utils/Logging.py | Logging.get_logger | def get_logger(name, level=0):
"""Setup a logging instance"""
level = 0 if not isinstance(level, int) else level
level = 0 if level < 0 else level
level = 4 if level > 4 else level
console = logging.StreamHandler()
level = [logging.NOTSET, logging.ERROR, logging.WARN, log... | python | def get_logger(name, level=0):
"""Setup a logging instance"""
level = 0 if not isinstance(level, int) else level
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220,779 | nok/sklearn-porter | sklearn_porter/Template.py | Template.indent | def indent(self, text, n_indents=1, skipping=False):
"""
Indent text with single spaces.
Parameters
----------
:param text : string
The text which get a specific indentation.
:param n_indents : int, default: 1
The number of indentations.
:... | python | def indent(self, text, n_indents=1, skipping=False):
"""
Indent text with single spaces.
Parameters
----------
:param text : string
The text which get a specific indentation.
:param n_indents : int, default: 1
The number of indentations.
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220,780 | nok/sklearn-porter | sklearn_porter/Template.py | Template.temp | def temp(self, name, templates=None, n_indents=None, skipping=False):
"""
Get specific template of chosen
programming language.
Parameters
----------
:param param name : string
The key name of the template.
:param param templates : string, default: No... | python | def temp(self, name, templates=None, n_indents=None, skipping=False):
"""
Get specific template of chosen
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Parameters
----------
:param param name : string
The key name of the template.
:param param templates : string, default: No... | [
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220,781 | nok/sklearn-porter | sklearn_porter/utils/Environment.py | Environment.read_sklearn_version | def read_sklearn_version():
"""Determine the installed version of sklearn"""
from sklearn import __version__ as sklearn_ver
sklearn_ver = str(sklearn_ver).split('.')
sklearn_ver = [int(v) for v in sklearn_ver]
major, minor = sklearn_ver[0], sklearn_ver[1]
patch = sklearn_... | python | def read_sklearn_version():
"""Determine the installed version of sklearn"""
from sklearn import __version__ as sklearn_ver
sklearn_ver = str(sklearn_ver).split('.')
sklearn_ver = [int(v) for v in sklearn_ver]
major, minor = sklearn_ver[0], sklearn_ver[1]
patch = sklearn_... | [
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220,782 | nok/sklearn-porter | sklearn_porter/utils/Environment.py | Environment._platform_is_windows | def _platform_is_windows(platform=sys.platform):
"""Is the current OS a Windows?"""
matched = platform in ('cygwin', 'win32', 'win64')
if matched:
error_msg = "Windows isn't supported yet"
raise OSError(error_msg)
return matched | python | def _platform_is_windows(platform=sys.platform):
"""Is the current OS a Windows?"""
matched = platform in ('cygwin', 'win32', 'win64')
if matched:
error_msg = "Windows isn't supported yet"
raise OSError(error_msg)
return matched | [
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220,783 | nok/sklearn-porter | sklearn_porter/utils/Environment.py | Environment.check_deps | def check_deps(deps):
"""check whether specific requirements are available."""
if not isinstance(deps, list):
deps = [deps]
checks = list(Environment.has_apps(deps))
if not all(checks):
for name, available in list(dict(zip(deps, checks)).items()):
... | python | def check_deps(deps):
"""check whether specific requirements are available."""
if not isinstance(deps, list):
deps = [deps]
checks = list(Environment.has_apps(deps))
if not all(checks):
for name, available in list(dict(zip(deps, checks)).items()):
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220,784 | nok/sklearn-porter | sklearn_porter/estimator/classifier/DecisionTreeClassifier/__init__.py | DecisionTreeClassifier.create_tree | def create_tree(self):
"""
Parse and build the tree branches.
Returns
-------
:return : string
The tree branches as string.
"""
feature_indices = []
for i in self.estimator.tree_.feature:
n_features = self.n_features
if... | python | def create_tree(self):
"""
Parse and build the tree branches.
Returns
-------
:return : string
The tree branches as string.
"""
feature_indices = []
for i in self.estimator.tree_.feature:
n_features = self.n_features
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220,785 | nok/sklearn-porter | sklearn_porter/estimator/classifier/MLPClassifier/__init__.py | MLPClassifier._get_intercepts | def _get_intercepts(self):
"""
Concatenate all intercepts of the classifier.
"""
temp_arr = self.temp('arr')
for layer in self.intercepts:
inter = ', '.join([self.repr(b) for b in layer])
yield temp_arr.format(inter) | python | def _get_intercepts(self):
"""
Concatenate all intercepts of the classifier.
"""
temp_arr = self.temp('arr')
for layer in self.intercepts:
inter = ', '.join([self.repr(b) for b in layer])
yield temp_arr.format(inter) | [
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220,786 | nok/sklearn-porter | sklearn_porter/estimator/classifier/AdaBoostClassifier/__init__.py | AdaBoostClassifier.create_embedded_meth | def create_embedded_meth(self):
"""
Build the estimator methods or functions.
Returns
-------
:return : string
The built methods as merged string.
"""
# Generate related trees:
fns = []
for idx, estimator in enumerate(self.estimators):... | python | def create_embedded_meth(self):
"""
Build the estimator methods or functions.
Returns
-------
:return : string
The built methods as merged string.
"""
# Generate related trees:
fns = []
for idx, estimator in enumerate(self.estimators):... | [
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220,787 | nok/sklearn-porter | sklearn_porter/Porter.py | Porter._classifiers | def _classifiers(self):
"""
Get a set of supported classifiers.
Returns
-------
classifiers : {set}
The set of supported classifiers.
"""
# sklearn version < 0.18.0
classifiers = (
AdaBoostClassifier,
BernoulliNB,
... | python | def _classifiers(self):
"""
Get a set of supported classifiers.
Returns
-------
classifiers : {set}
The set of supported classifiers.
"""
# sklearn version < 0.18.0
classifiers = (
AdaBoostClassifier,
BernoulliNB,
... | [
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220,788 | nok/sklearn-porter | sklearn_porter/Porter.py | Porter._regressors | def _regressors(self):
"""
Get a set of supported regressors.
Returns
-------
regressors : {set}
The set of supported regressors.
"""
# sklearn version < 0.18.0
regressors = ()
# sklearn version >= 0.18.0
if self.sklearn_ver[... | python | def _regressors(self):
"""
Get a set of supported regressors.
Returns
-------
regressors : {set}
The set of supported regressors.
"""
# sklearn version < 0.18.0
regressors = ()
# sklearn version >= 0.18.0
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220,789 | nok/sklearn-porter | sklearn_porter/Porter.py | Porter.predict | def predict(self, X, class_name=None, method_name=None, tnp_dir='tmp',
keep_tmp_dir=False, num_format=lambda x: str(x)):
"""
Predict using the transpiled model.
Parameters
----------
:param X : {array-like}, shape (n_features) or (n_samples, n_features)
... | python | def predict(self, X, class_name=None, method_name=None, tnp_dir='tmp',
keep_tmp_dir=False, num_format=lambda x: str(x)):
"""
Predict using the transpiled model.
Parameters
----------
:param X : {array-like}, shape (n_features) or (n_samples, n_features)
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220,790 | nok/sklearn-porter | sklearn_porter/Porter.py | Porter.integrity_score | def integrity_score(self, X, method='predict', normalize=True,
num_format=lambda x: str(x)):
"""
Compute the accuracy of the ported classifier.
Parameters
----------
:param X : ndarray, shape (n_samples, n_features)
Input data.
:param... | python | def integrity_score(self, X, method='predict', normalize=True,
num_format=lambda x: str(x)):
"""
Compute the accuracy of the ported classifier.
Parameters
----------
:param X : ndarray, shape (n_samples, n_features)
Input data.
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220,791 | nok/sklearn-porter | sklearn_porter/Porter.py | Porter._get_filename | def _get_filename(class_name, language):
"""
Generate the specific filename.
Parameters
----------
:param class_name : str
The used class name.
:param language : {'c', 'go', 'java', 'js', 'php', 'ruby'}
The target programming language.
R... | python | def _get_filename(class_name, language):
"""
Generate the specific filename.
Parameters
----------
:param class_name : str
The used class name.
:param language : {'c', 'go', 'java', 'js', 'php', 'ruby'}
The target programming language.
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220,792 | nok/sklearn-porter | sklearn_porter/Porter.py | Porter._get_commands | def _get_commands(filename, class_name, language):
"""
Generate the related compilation and
execution commands.
Parameters
----------
:param filename : str
The used filename.
:param class_name : str
The used class name.
:param la... | python | def _get_commands(filename, class_name, language):
"""
Generate the related compilation and
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Parameters
----------
:param filename : str
The used filename.
:param class_name : str
The used class name.
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"comp_vars",
... | Generate the related compilation and
execution commands.
Parameters
----------
:param filename : str
The used filename.
:param class_name : str
The used class name.
:param language : {'c', 'go', 'java', 'js', 'php', 'ruby'}
The targe... | [
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220,793 | daviddrysdale/python-phonenumbers | python/phonenumbers/asyoutypeformatter.py | _get_metadata_for_region | def _get_metadata_for_region(region_code):
"""The metadata needed by this class is the same for all regions
sharing the same country calling code. Therefore, we return the
metadata for "main" region for this country calling code."""
country_calling_code = country_code_for_region(region_code)
main_co... | python | def _get_metadata_for_region(region_code):
"""The metadata needed by this class is the same for all regions
sharing the same country calling code. Therefore, we return the
metadata for "main" region for this country calling code."""
country_calling_code = country_code_for_region(region_code)
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220,794 | daviddrysdale/python-phonenumbers | python/phonenumbers/asyoutypeformatter.py | AsYouTypeFormatter._maybe_create_new_template | def _maybe_create_new_template(self):
"""Returns True if a new template is created as opposed to reusing the existing template.
When there are multiple available formats, the formatter uses the
first format where a formatting template could be created.
"""
ii = 0
while i... | python | def _maybe_create_new_template(self):
"""Returns True if a new template is created as opposed to reusing the existing template.
When there are multiple available formats, the formatter uses the
first format where a formatting template could be created.
"""
ii = 0
while i... | [
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220,795 | daviddrysdale/python-phonenumbers | python/phonenumbers/asyoutypeformatter.py | AsYouTypeFormatter._get_formatting_template | def _get_formatting_template(self, number_pattern, number_format):
"""Gets a formatting template which can be used to efficiently
format a partial number where digits are added one by one."""
# Create a phone number consisting only of the digit 9 that matches the
# number_pattern by appl... | python | def _get_formatting_template(self, number_pattern, number_format):
"""Gets a formatting template which can be used to efficiently
format a partial number where digits are added one by one."""
# Create a phone number consisting only of the digit 9 that matches the
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220,796 | daviddrysdale/python-phonenumbers | python/phonenumbers/asyoutypeformatter.py | AsYouTypeFormatter.input_digit | def input_digit(self, next_char, remember_position=False):
"""Formats a phone number on-the-fly as each digit is entered.
If remember_position is set, remembers the position where next_char is
inserted, so that it can be retrieved later by using
get_remembered_position. The remembered p... | python | def input_digit(self, next_char, remember_position=False):
"""Formats a phone number on-the-fly as each digit is entered.
If remember_position is set, remembers the position where next_char is
inserted, so that it can be retrieved later by using
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220,797 | daviddrysdale/python-phonenumbers | python/phonenumbers/asyoutypeformatter.py | AsYouTypeFormatter._attempt_to_format_accrued_digits | def _attempt_to_format_accrued_digits(self):
"""Checks to see if there is an exact pattern match for these digits. If so, we should use this
instead of any other formatting template whose leadingDigitsPattern also matches the input.
"""
for number_format in self._possible_formats:
... | python | def _attempt_to_format_accrued_digits(self):
"""Checks to see if there is an exact pattern match for these digits. If so, we should use this
instead of any other formatting template whose leadingDigitsPattern also matches the input.
"""
for number_format in self._possible_formats:
... | [
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220,798 | daviddrysdale/python-phonenumbers | python/phonenumbers/asyoutypeformatter.py | AsYouTypeFormatter._attempt_to_choose_formatting_pattern | def _attempt_to_choose_formatting_pattern(self):
"""Attempts to set the formatting template and returns a string which
contains the formatted version of the digits entered so far."""
# We start to attempt to format only when at least MIN_LEADING_DIGITS_LENGTH digits of national
# number ... | python | def _attempt_to_choose_formatting_pattern(self):
"""Attempts to set the formatting template and returns a string which
contains the formatted version of the digits entered so far."""
# We start to attempt to format only when at least MIN_LEADING_DIGITS_LENGTH digits of national
# number ... | [
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220,799 | daviddrysdale/python-phonenumbers | python/phonenumbers/asyoutypeformatter.py | AsYouTypeFormatter._input_accrued_national_number | def _input_accrued_national_number(self):
"""Invokes input_digit_helper on each digit of the national number
accrued, and returns a formatted string in the end."""
length_of_national_number = len(self._national_number)
if length_of_national_number > 0:
temp_national_number = ... | python | def _input_accrued_national_number(self):
"""Invokes input_digit_helper on each digit of the national number
accrued, and returns a formatted string in the end."""
length_of_national_number = len(self._national_number)
if length_of_national_number > 0:
temp_national_number = ... | [
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