_id
stringlengths 2
7
| title
stringlengths 1
88
| partition
stringclasses 3
values | text
stringlengths 75
19.8k
| language
stringclasses 1
value | meta_information
dict |
|---|---|---|---|---|---|
q900
|
UriConnection._parse_uri_options
|
train
|
def _parse_uri_options(self, parsed_uri, use_ssl=False, ssl_options=None):
"""Parse the uri options.
:param parsed_uri:
:param bool use_ssl:
:return:
"""
ssl_options = ssl_options or {}
kwargs = urlparse.parse_qs(parsed_uri.query)
vhost = urlparse.unquote(parsed_uri.path[1:]) or DEFAULT_VIRTUAL_HOST
options = {
'ssl': use_ssl,
'virtual_host': vhost,
'heartbeat': int(kwargs.pop('heartbeat',
[DEFAULT_HEARTBEAT_INTERVAL])[0]),
'timeout': int(kwargs.pop('timeout',
[DEFAULT_SOCKET_TIMEOUT])[0])
}
if use_ssl:
if not compatibility.SSL_SUPPORTED:
raise AMQPConnectionError(
'Python not compiled with support '
'for TLSv1 or higher'
)
ssl_options.update(self._parse_ssl_options(kwargs))
options['ssl_options'] = ssl_options
return options
|
python
|
{
"resource": ""
}
|
q901
|
UriConnection._parse_ssl_options
|
train
|
def _parse_ssl_options(self, ssl_kwargs):
"""Parse TLS Options.
:param ssl_kwargs:
:rtype: dict
"""
ssl_options = {}
for key in ssl_kwargs:
if key not in compatibility.SSL_OPTIONS:
LOGGER.warning('invalid option: %s', key)
continue
if 'ssl_version' in key:
value = self._get_ssl_version(ssl_kwargs[key][0])
elif 'cert_reqs' in key:
value = self._get_ssl_validation(ssl_kwargs[key][0])
else:
value = ssl_kwargs[key][0]
ssl_options[key] = value
return ssl_options
|
python
|
{
"resource": ""
}
|
q902
|
UriConnection._get_ssl_version
|
train
|
def _get_ssl_version(self, value):
"""Get the TLS Version.
:param str value:
:return: TLS Version
"""
return self._get_ssl_attribute(value, compatibility.SSL_VERSIONS,
ssl.PROTOCOL_TLSv1,
'ssl_options: ssl_version \'%s\' not '
'found falling back to PROTOCOL_TLSv1.')
|
python
|
{
"resource": ""
}
|
q903
|
UriConnection._get_ssl_validation
|
train
|
def _get_ssl_validation(self, value):
"""Get the TLS Validation option.
:param str value:
:return: TLS Certificate Options
"""
return self._get_ssl_attribute(value, compatibility.SSL_CERT_MAP,
ssl.CERT_NONE,
'ssl_options: cert_reqs \'%s\' not '
'found falling back to CERT_NONE.')
|
python
|
{
"resource": ""
}
|
q904
|
UriConnection._get_ssl_attribute
|
train
|
def _get_ssl_attribute(value, mapping, default_value, warning_message):
"""Get the TLS attribute based on the compatibility mapping.
If no valid attribute can be found, fall-back on default and
display a warning.
:param str value:
:param dict mapping: Dictionary based mapping
:param default_value: Default fall-back value
:param str warning_message: Warning message
:return:
"""
for key in mapping:
if not key.endswith(value.lower()):
continue
return mapping[key]
LOGGER.warning(warning_message, value)
return default_value
|
python
|
{
"resource": ""
}
|
q905
|
ManagementApi.top
|
train
|
def top(self):
"""Top Processes.
:raises ApiError: Raises if the remote server encountered an error.
:raises ApiConnectionError: Raises if there was a connectivity issue.
:rtype: list
"""
nodes = []
for node in self.nodes():
nodes.append(self.http_client.get(API_TOP % node['name']))
return nodes
|
python
|
{
"resource": ""
}
|
q906
|
Basic.qos
|
train
|
def qos(self, prefetch_count=0, prefetch_size=0, global_=False):
"""Specify quality of service.
:param int prefetch_count: Prefetch window in messages
:param int/long prefetch_size: Prefetch window in octets
:param bool global_: Apply to entire connection
:raises AMQPInvalidArgument: Invalid Parameters
:raises AMQPChannelError: Raises if the channel encountered an error.
:raises AMQPConnectionError: Raises if the connection
encountered an error.
:rtype: dict
"""
if not compatibility.is_integer(prefetch_count):
raise AMQPInvalidArgument('prefetch_count should be an integer')
elif not compatibility.is_integer(prefetch_size):
raise AMQPInvalidArgument('prefetch_size should be an integer')
elif not isinstance(global_, bool):
raise AMQPInvalidArgument('global_ should be a boolean')
qos_frame = specification.Basic.Qos(prefetch_count=prefetch_count,
prefetch_size=prefetch_size,
global_=global_)
return self._channel.rpc_request(qos_frame)
|
python
|
{
"resource": ""
}
|
q907
|
Basic.get
|
train
|
def get(self, queue='', no_ack=False, to_dict=False, auto_decode=True):
"""Fetch a single message.
:param str queue: Queue name
:param bool no_ack: No acknowledgement needed
:param bool to_dict: Should incoming messages be converted to a
dictionary before delivery.
:param bool auto_decode: Auto-decode strings when possible.
:raises AMQPInvalidArgument: Invalid Parameters
:raises AMQPChannelError: Raises if the channel encountered an error.
:raises AMQPConnectionError: Raises if the connection
encountered an error.
:returns: Returns a single message, as long as there is a message in
the queue. If no message is available, returns None.
:rtype: dict|Message|None
"""
if not compatibility.is_string(queue):
raise AMQPInvalidArgument('queue should be a string')
elif not isinstance(no_ack, bool):
raise AMQPInvalidArgument('no_ack should be a boolean')
elif self._channel.consumer_tags:
raise AMQPChannelError("Cannot call 'get' when channel is "
"set to consume")
get_frame = specification.Basic.Get(queue=queue,
no_ack=no_ack)
with self._channel.lock and self._channel.rpc.lock:
message = self._get_message(get_frame, auto_decode=auto_decode)
if message and to_dict:
return message.to_dict()
return message
|
python
|
{
"resource": ""
}
|
q908
|
Basic.recover
|
train
|
def recover(self, requeue=False):
"""Redeliver unacknowledged messages.
:param bool requeue: Re-queue the messages
:raises AMQPInvalidArgument: Invalid Parameters
:raises AMQPChannelError: Raises if the channel encountered an error.
:raises AMQPConnectionError: Raises if the connection
encountered an error.
:rtype: dict
"""
if not isinstance(requeue, bool):
raise AMQPInvalidArgument('requeue should be a boolean')
recover_frame = specification.Basic.Recover(requeue=requeue)
return self._channel.rpc_request(recover_frame)
|
python
|
{
"resource": ""
}
|
q909
|
Basic.cancel
|
train
|
def cancel(self, consumer_tag=''):
"""Cancel a queue consumer.
:param str consumer_tag: Consumer tag
:raises AMQPInvalidArgument: Invalid Parameters
:raises AMQPChannelError: Raises if the channel encountered an error.
:raises AMQPConnectionError: Raises if the connection
encountered an error.
:rtype: dict
"""
if not compatibility.is_string(consumer_tag):
raise AMQPInvalidArgument('consumer_tag should be a string')
cancel_frame = specification.Basic.Cancel(consumer_tag=consumer_tag)
result = self._channel.rpc_request(cancel_frame)
self._channel.remove_consumer_tag(consumer_tag)
return result
|
python
|
{
"resource": ""
}
|
q910
|
Basic.reject
|
train
|
def reject(self, delivery_tag=0, requeue=True):
"""Reject Message.
:param int/long delivery_tag: Server-assigned delivery tag
:param bool requeue: Re-queue the message
:raises AMQPInvalidArgument: Invalid Parameters
:raises AMQPChannelError: Raises if the channel encountered an error.
:raises AMQPConnectionError: Raises if the connection
encountered an error.
:return:
"""
if not compatibility.is_integer(delivery_tag):
raise AMQPInvalidArgument('delivery_tag should be an integer')
elif not isinstance(requeue, bool):
raise AMQPInvalidArgument('requeue should be a boolean')
reject_frame = specification.Basic.Reject(delivery_tag=delivery_tag,
requeue=requeue)
self._channel.write_frame(reject_frame)
|
python
|
{
"resource": ""
}
|
q911
|
Basic._consume_add_and_get_tag
|
train
|
def _consume_add_and_get_tag(self, consume_rpc_result):
"""Add the tag to the channel and return it.
:param dict consume_rpc_result:
:rtype: str
"""
consumer_tag = consume_rpc_result['consumer_tag']
self._channel.add_consumer_tag(consumer_tag)
return consumer_tag
|
python
|
{
"resource": ""
}
|
q912
|
Basic._consume_rpc_request
|
train
|
def _consume_rpc_request(self, arguments, consumer_tag, exclusive, no_ack,
no_local, queue):
"""Create a Consume Frame and execute a RPC request.
:param str queue: Queue name
:param str consumer_tag: Consumer tag
:param bool no_local: Do not deliver own messages
:param bool no_ack: No acknowledgement needed
:param bool exclusive: Request exclusive access
:param dict arguments: Consume key/value arguments
:rtype: dict
"""
consume_frame = specification.Basic.Consume(queue=queue,
consumer_tag=consumer_tag,
exclusive=exclusive,
no_local=no_local,
no_ack=no_ack,
arguments=arguments)
return self._channel.rpc_request(consume_frame)
|
python
|
{
"resource": ""
}
|
q913
|
Basic._validate_publish_parameters
|
train
|
def _validate_publish_parameters(body, exchange, immediate, mandatory,
properties, routing_key):
"""Validate Publish Parameters.
:param bytes|str|unicode body: Message payload
:param str routing_key: Message routing key
:param str exchange: The exchange to publish the message to
:param dict properties: Message properties
:param bool mandatory: Requires the message is published
:param bool immediate: Request immediate delivery
:raises AMQPInvalidArgument: Invalid Parameters
:return:
"""
if not compatibility.is_string(body):
raise AMQPInvalidArgument('body should be a string')
elif not compatibility.is_string(routing_key):
raise AMQPInvalidArgument('routing_key should be a string')
elif not compatibility.is_string(exchange):
raise AMQPInvalidArgument('exchange should be a string')
elif properties is not None and not isinstance(properties, dict):
raise AMQPInvalidArgument('properties should be a dict or None')
elif not isinstance(mandatory, bool):
raise AMQPInvalidArgument('mandatory should be a boolean')
elif not isinstance(immediate, bool):
raise AMQPInvalidArgument('immediate should be a boolean')
|
python
|
{
"resource": ""
}
|
q914
|
Basic._handle_utf8_payload
|
train
|
def _handle_utf8_payload(body, properties):
"""Update the Body and Properties to the appropriate encoding.
:param bytes|str|unicode body: Message payload
:param dict properties: Message properties
:return:
"""
if 'content_encoding' not in properties:
properties['content_encoding'] = 'utf-8'
encoding = properties['content_encoding']
if compatibility.is_unicode(body):
body = body.encode(encoding)
elif compatibility.PYTHON3 and isinstance(body, str):
body = bytes(body, encoding=encoding)
return body
|
python
|
{
"resource": ""
}
|
q915
|
Basic._get_message
|
train
|
def _get_message(self, get_frame, auto_decode):
"""Get and return a message using a Basic.Get frame.
:param Basic.Get get_frame:
:param bool auto_decode: Auto-decode strings when possible.
:rtype: Message
"""
message_uuid = self._channel.rpc.register_request(
get_frame.valid_responses + ['ContentHeader', 'ContentBody']
)
try:
self._channel.write_frame(get_frame)
get_ok_frame = self._channel.rpc.get_request(message_uuid,
raw=True,
multiple=True)
if isinstance(get_ok_frame, specification.Basic.GetEmpty):
return None
content_header = self._channel.rpc.get_request(message_uuid,
raw=True,
multiple=True)
body = self._get_content_body(message_uuid,
content_header.body_size)
finally:
self._channel.rpc.remove(message_uuid)
return Message(channel=self._channel,
body=body,
method=dict(get_ok_frame),
properties=dict(content_header.properties),
auto_decode=auto_decode)
|
python
|
{
"resource": ""
}
|
q916
|
Basic._publish_confirm
|
train
|
def _publish_confirm(self, frames_out):
"""Confirm that message was published successfully.
:param list frames_out:
:rtype: bool
"""
confirm_uuid = self._channel.rpc.register_request(['Basic.Ack',
'Basic.Nack'])
self._channel.write_frames(frames_out)
result = self._channel.rpc.get_request(confirm_uuid, raw=True)
self._channel.check_for_errors()
if isinstance(result, specification.Basic.Ack):
return True
return False
|
python
|
{
"resource": ""
}
|
q917
|
Basic._create_content_body
|
train
|
def _create_content_body(self, body):
"""Split body based on the maximum frame size.
This function is based on code from Rabbitpy.
https://github.com/gmr/rabbitpy
:param bytes|str|unicode body: Message payload
:rtype: collections.Iterable
"""
frames = int(math.ceil(len(body) / float(self._max_frame_size)))
for offset in compatibility.RANGE(0, frames):
start_frame = self._max_frame_size * offset
end_frame = start_frame + self._max_frame_size
body_len = len(body)
if end_frame > body_len:
end_frame = body_len
yield pamqp_body.ContentBody(body[start_frame:end_frame])
|
python
|
{
"resource": ""
}
|
q918
|
Basic._get_content_body
|
train
|
def _get_content_body(self, message_uuid, body_size):
"""Get Content Body using RPC requests.
:param str uuid_body: Rpc Identifier.
:param int body_size: Content Size.
:rtype: str
"""
body = bytes()
while len(body) < body_size:
body_piece = self._channel.rpc.get_request(message_uuid, raw=True,
multiple=True)
if not body_piece.value:
break
body += body_piece.value
return body
|
python
|
{
"resource": ""
}
|
q919
|
Channel0.on_frame
|
train
|
def on_frame(self, frame_in):
"""Handle frames sent to Channel0.
:param frame_in: Amqp frame.
:return:
"""
LOGGER.debug('Frame Received: %s', frame_in.name)
if frame_in.name == 'Heartbeat':
return
elif frame_in.name == 'Connection.Close':
self._close_connection(frame_in)
elif frame_in.name == 'Connection.CloseOk':
self._close_connection_ok()
elif frame_in.name == 'Connection.Blocked':
self._blocked_connection(frame_in)
elif frame_in.name == 'Connection.Unblocked':
self._unblocked_connection()
elif frame_in.name == 'Connection.OpenOk':
self._set_connection_state(Stateful.OPEN)
elif frame_in.name == 'Connection.Start':
self.server_properties = frame_in.server_properties
self._send_start_ok(frame_in)
elif frame_in.name == 'Connection.Tune':
self._send_tune_ok(frame_in)
self._send_open_connection()
else:
LOGGER.error('[Channel0] Unhandled Frame: %s', frame_in.name)
|
python
|
{
"resource": ""
}
|
q920
|
Channel0._close_connection
|
train
|
def _close_connection(self, frame_in):
"""Connection Close.
:param specification.Connection.Close frame_in: Amqp frame.
:return:
"""
self._set_connection_state(Stateful.CLOSED)
if frame_in.reply_code != 200:
reply_text = try_utf8_decode(frame_in.reply_text)
message = (
'Connection was closed by remote server: %s' % reply_text
)
exception = AMQPConnectionError(message,
reply_code=frame_in.reply_code)
self._connection.exceptions.append(exception)
|
python
|
{
"resource": ""
}
|
q921
|
Channel0._blocked_connection
|
train
|
def _blocked_connection(self, frame_in):
"""Connection is Blocked.
:param frame_in:
:return:
"""
self.is_blocked = True
LOGGER.warning(
'Connection is blocked by remote server: %s',
try_utf8_decode(frame_in.reason)
)
|
python
|
{
"resource": ""
}
|
q922
|
Channel0._send_start_ok
|
train
|
def _send_start_ok(self, frame_in):
"""Send Start OK frame.
:param specification.Connection.Start frame_in: Amqp frame.
:return:
"""
mechanisms = try_utf8_decode(frame_in.mechanisms)
if 'EXTERNAL' in mechanisms:
mechanism = 'EXTERNAL'
credentials = '\0\0'
elif 'PLAIN' in mechanisms:
mechanism = 'PLAIN'
credentials = self._plain_credentials()
else:
exception = AMQPConnectionError(
'Unsupported Security Mechanism(s): %s' %
frame_in.mechanisms
)
self._connection.exceptions.append(exception)
return
start_ok_frame = specification.Connection.StartOk(
mechanism=mechanism,
client_properties=self._client_properties(),
response=credentials,
locale=LOCALE
)
self._write_frame(start_ok_frame)
|
python
|
{
"resource": ""
}
|
q923
|
Channel0._send_tune_ok
|
train
|
def _send_tune_ok(self, frame_in):
"""Send Tune OK frame.
:param specification.Connection.Tune frame_in: Tune frame.
:return:
"""
self.max_allowed_channels = self._negotiate(frame_in.channel_max,
MAX_CHANNELS)
self.max_frame_size = self._negotiate(frame_in.frame_max,
MAX_FRAME_SIZE)
LOGGER.debug(
'Negotiated max frame size %d, max channels %d',
self.max_frame_size, self.max_allowed_channels
)
tune_ok_frame = specification.Connection.TuneOk(
channel_max=self.max_allowed_channels,
frame_max=self.max_frame_size,
heartbeat=self._heartbeat)
self._write_frame(tune_ok_frame)
|
python
|
{
"resource": ""
}
|
q924
|
Channel0._send_open_connection
|
train
|
def _send_open_connection(self):
"""Send Open Connection frame.
:return:
"""
open_frame = specification.Connection.Open(
virtual_host=self._parameters['virtual_host']
)
self._write_frame(open_frame)
|
python
|
{
"resource": ""
}
|
q925
|
Channel0._write_frame
|
train
|
def _write_frame(self, frame_out):
"""Write a pamqp frame from Channel0.
:param frame_out: Amqp frame.
:return:
"""
self._connection.write_frame(0, frame_out)
LOGGER.debug('Frame Sent: %s', frame_out.name)
|
python
|
{
"resource": ""
}
|
q926
|
Channel0._client_properties
|
train
|
def _client_properties():
"""AMQPStorm Client Properties.
:rtype: dict
"""
return {
'product': 'AMQPStorm',
'platform': 'Python %s (%s)' % (platform.python_version(),
platform.python_implementation()),
'capabilities': {
'basic.nack': True,
'connection.blocked': True,
'publisher_confirms': True,
'consumer_cancel_notify': True,
'authentication_failure_close': True,
},
'information': 'See https://github.com/eandersson/amqpstorm',
'version': __version__
}
|
python
|
{
"resource": ""
}
|
q927
|
Channel.build_inbound_messages
|
train
|
def build_inbound_messages(self, break_on_empty=False, to_tuple=False,
auto_decode=True):
"""Build messages in the inbound queue.
:param bool break_on_empty: Should we break the loop when there are
no more messages in our inbound queue.
This does not guarantee that the upstream
queue is empty, as it's possible that if
messages are consumed faster than
delivered, the inbound queue will then be
emptied and the consumption will be broken.
:param bool to_tuple: Should incoming messages be converted to a
tuple before delivery.
:param bool auto_decode: Auto-decode strings when possible.
:raises AMQPChannelError: Raises if the channel encountered an error.
:raises AMQPConnectionError: Raises if the connection
encountered an error.
:rtype: :py:class:`generator`
"""
self.check_for_errors()
while not self.is_closed:
message = self._build_message(auto_decode=auto_decode)
if not message:
self.check_for_errors()
sleep(IDLE_WAIT)
if break_on_empty and not self._inbound:
break
continue
if to_tuple:
yield message.to_tuple()
continue
yield message
|
python
|
{
"resource": ""
}
|
q928
|
Channel.check_for_errors
|
train
|
def check_for_errors(self):
"""Check connection and channel for errors.
:raises AMQPChannelError: Raises if the channel encountered an error.
:raises AMQPConnectionError: Raises if the connection
encountered an error.
:return:
"""
try:
self._connection.check_for_errors()
except AMQPConnectionError:
self.set_state(self.CLOSED)
raise
if self.exceptions:
exception = self.exceptions[0]
if self.is_open:
self.exceptions.pop(0)
raise exception
if self.is_closed:
raise AMQPChannelError('channel was closed')
|
python
|
{
"resource": ""
}
|
q929
|
Channel.confirm_deliveries
|
train
|
def confirm_deliveries(self):
"""Set the channel to confirm that each message has been
successfully delivered.
:raises AMQPChannelError: Raises if the channel encountered an error.
:raises AMQPConnectionError: Raises if the connection
encountered an error.
:return:
"""
self._confirming_deliveries = True
confirm_frame = specification.Confirm.Select()
return self.rpc_request(confirm_frame)
|
python
|
{
"resource": ""
}
|
q930
|
Channel.on_frame
|
train
|
def on_frame(self, frame_in):
"""Handle frame sent to this specific channel.
:param pamqp.Frame frame_in: Amqp frame.
:return:
"""
if self.rpc.on_frame(frame_in):
return
if frame_in.name in CONTENT_FRAME:
self._inbound.append(frame_in)
elif frame_in.name == 'Basic.Cancel':
self._basic_cancel(frame_in)
elif frame_in.name == 'Basic.CancelOk':
self.remove_consumer_tag(frame_in.consumer_tag)
elif frame_in.name == 'Basic.ConsumeOk':
self.add_consumer_tag(frame_in['consumer_tag'])
elif frame_in.name == 'Basic.Return':
self._basic_return(frame_in)
elif frame_in.name == 'Channel.Close':
self._close_channel(frame_in)
elif frame_in.name == 'Channel.Flow':
self.write_frame(specification.Channel.FlowOk(frame_in.active))
else:
LOGGER.error(
'[Channel%d] Unhandled Frame: %s -- %s',
self.channel_id, frame_in.name, dict(frame_in)
)
|
python
|
{
"resource": ""
}
|
q931
|
Channel.process_data_events
|
train
|
def process_data_events(self, to_tuple=False, auto_decode=True):
"""Consume inbound messages.
:param bool to_tuple: Should incoming messages be converted to a
tuple before delivery.
:param bool auto_decode: Auto-decode strings when possible.
:raises AMQPChannelError: Raises if the channel encountered an error.
:raises AMQPConnectionError: Raises if the connection
encountered an error.
:return:
"""
if not self._consumer_callbacks:
raise AMQPChannelError('no consumer callback defined')
for message in self.build_inbound_messages(break_on_empty=True,
auto_decode=auto_decode):
consumer_tag = message._method.get('consumer_tag')
if to_tuple:
# noinspection PyCallingNonCallable
self._consumer_callbacks[consumer_tag](*message.to_tuple())
continue
# noinspection PyCallingNonCallable
self._consumer_callbacks[consumer_tag](message)
|
python
|
{
"resource": ""
}
|
q932
|
Channel.rpc_request
|
train
|
def rpc_request(self, frame_out, connection_adapter=None):
"""Perform a RPC Request.
:param specification.Frame frame_out: Amqp frame.
:rtype: dict
"""
with self.rpc.lock:
uuid = self.rpc.register_request(frame_out.valid_responses)
self._connection.write_frame(self.channel_id, frame_out)
return self.rpc.get_request(
uuid, connection_adapter=connection_adapter
)
|
python
|
{
"resource": ""
}
|
q933
|
Channel.start_consuming
|
train
|
def start_consuming(self, to_tuple=False, auto_decode=True):
"""Start consuming messages.
:param bool to_tuple: Should incoming messages be converted to a
tuple before delivery.
:param bool auto_decode: Auto-decode strings when possible.
:raises AMQPChannelError: Raises if the channel encountered an error.
:raises AMQPConnectionError: Raises if the connection
encountered an error.
:return:
"""
while not self.is_closed:
self.process_data_events(
to_tuple=to_tuple,
auto_decode=auto_decode
)
if self.consumer_tags:
sleep(IDLE_WAIT)
continue
break
|
python
|
{
"resource": ""
}
|
q934
|
Channel.stop_consuming
|
train
|
def stop_consuming(self):
"""Stop consuming messages.
:raises AMQPChannelError: Raises if the channel encountered an error.
:raises AMQPConnectionError: Raises if the connection
encountered an error.
:return:
"""
if not self.consumer_tags:
return
if not self.is_closed:
for tag in self.consumer_tags:
self.basic.cancel(tag)
self.remove_consumer_tag()
|
python
|
{
"resource": ""
}
|
q935
|
Channel.write_frame
|
train
|
def write_frame(self, frame_out):
"""Write a pamqp frame from the current channel.
:param specification.Frame frame_out: A single pamqp frame.
:return:
"""
self.check_for_errors()
self._connection.write_frame(self.channel_id, frame_out)
|
python
|
{
"resource": ""
}
|
q936
|
Channel.write_frames
|
train
|
def write_frames(self, frames_out):
"""Write multiple pamqp frames from the current channel.
:param list frames_out: A list of pamqp frames.
:return:
"""
self.check_for_errors()
self._connection.write_frames(self.channel_id, frames_out)
|
python
|
{
"resource": ""
}
|
q937
|
Channel._basic_cancel
|
train
|
def _basic_cancel(self, frame_in):
"""Handle a Basic Cancel frame.
:param specification.Basic.Cancel frame_in: Amqp frame.
:return:
"""
LOGGER.warning(
'Received Basic.Cancel on consumer_tag: %s',
try_utf8_decode(frame_in.consumer_tag)
)
self.remove_consumer_tag(frame_in.consumer_tag)
|
python
|
{
"resource": ""
}
|
q938
|
Channel._basic_return
|
train
|
def _basic_return(self, frame_in):
"""Handle a Basic Return Frame and treat it as an error.
:param specification.Basic.Return frame_in: Amqp frame.
:return:
"""
reply_text = try_utf8_decode(frame_in.reply_text)
message = (
"Message not delivered: %s (%s) to queue '%s' from exchange '%s'" %
(
reply_text,
frame_in.reply_code,
frame_in.routing_key,
frame_in.exchange
)
)
exception = AMQPMessageError(message,
reply_code=frame_in.reply_code)
self.exceptions.append(exception)
|
python
|
{
"resource": ""
}
|
q939
|
Channel._build_message
|
train
|
def _build_message(self, auto_decode):
"""Fetch and build a complete Message from the inbound queue.
:param bool auto_decode: Auto-decode strings when possible.
:rtype: Message
"""
with self.lock:
if len(self._inbound) < 2:
return None
headers = self._build_message_headers()
if not headers:
return None
basic_deliver, content_header = headers
body = self._build_message_body(content_header.body_size)
message = Message(channel=self,
body=body,
method=dict(basic_deliver),
properties=dict(content_header.properties),
auto_decode=auto_decode)
return message
|
python
|
{
"resource": ""
}
|
q940
|
Channel._build_message_body
|
train
|
def _build_message_body(self, body_size):
"""Build the Message body from the inbound queue.
:rtype: str
"""
body = bytes()
while len(body) < body_size:
if not self._inbound:
self.check_for_errors()
sleep(IDLE_WAIT)
continue
body_piece = self._inbound.pop(0)
if not body_piece.value:
break
body += body_piece.value
return body
|
python
|
{
"resource": ""
}
|
q941
|
User.create
|
train
|
def create(self, username, password, tags=''):
"""Create User.
:param str username: Username
:param str password: Password
:param str tags: Comma-separate list of tags (e.g. monitoring)
:rtype: None
"""
user_payload = json.dumps({
'password': password,
'tags': tags
})
return self.http_client.put(API_USER % username,
payload=user_payload)
|
python
|
{
"resource": ""
}
|
q942
|
User.get_permission
|
train
|
def get_permission(self, username, virtual_host):
"""Get User permissions for the configured virtual host.
:param str username: Username
:param str virtual_host: Virtual host name
:raises ApiError: Raises if the remote server encountered an error.
:raises ApiConnectionError: Raises if there was a connectivity issue.
:rtype: dict
"""
virtual_host = quote(virtual_host, '')
return self.http_client.get(API_USER_VIRTUAL_HOST_PERMISSIONS %
(
virtual_host,
username
))
|
python
|
{
"resource": ""
}
|
q943
|
User.set_permission
|
train
|
def set_permission(self, username, virtual_host, configure_regex='.*',
write_regex='.*', read_regex='.*'):
"""Set User permissions for the configured virtual host.
:param str username: Username
:param str virtual_host: Virtual host name
:param str configure_regex: Permission pattern for configuration
operations for this user.
:param str write_regex: Permission pattern for write operations
for this user.
:param str read_regex: Permission pattern for read operations
for this user.
:raises ApiError: Raises if the remote server encountered an error.
:raises ApiConnectionError: Raises if there was a connectivity issue.
:rtype: dict
"""
virtual_host = quote(virtual_host, '')
permission_payload = json.dumps({
"configure": configure_regex,
"read": read_regex,
"write": write_regex
})
return self.http_client.put(API_USER_VIRTUAL_HOST_PERMISSIONS %
(
virtual_host,
username
),
payload=permission_payload)
|
python
|
{
"resource": ""
}
|
q944
|
User.delete_permission
|
train
|
def delete_permission(self, username, virtual_host):
"""Delete User permissions for the configured virtual host.
:param str username: Username
:param str virtual_host: Virtual host name
:raises ApiError: Raises if the remote server encountered an error.
:raises ApiConnectionError: Raises if there was a connectivity issue.
:rtype: dict
"""
virtual_host = quote(virtual_host, '')
return self.http_client.delete(
API_USER_VIRTUAL_HOST_PERMISSIONS %
(
virtual_host,
username
))
|
python
|
{
"resource": ""
}
|
q945
|
ScalableRpcServer.start_server
|
train
|
def start_server(self):
"""Start the RPC Server.
:return:
"""
self._stopped.clear()
if not self._connection or self._connection.is_closed:
self._create_connection()
while not self._stopped.is_set():
try:
# Check our connection for errors.
self._connection.check_for_errors()
self._update_consumers()
except amqpstorm.AMQPError as why:
# If an error occurs, re-connect and let update_consumers
# re-open the channels.
LOGGER.warning(why)
self._stop_consumers()
self._create_connection()
time.sleep(1)
|
python
|
{
"resource": ""
}
|
q946
|
ScalableRpcServer._update_consumers
|
train
|
def _update_consumers(self):
"""Update Consumers.
- Add more if requested.
- Make sure the consumers are healthy.
- Remove excess consumers.
:return:
"""
# Do we need to start more consumers.
consumer_to_start = \
min(max(self.number_of_consumers - len(self._consumers), 0), 2)
for _ in range(consumer_to_start):
consumer = Consumer(self.rpc_queue)
self._start_consumer(consumer)
self._consumers.append(consumer)
# Check that all our consumers are active.
for consumer in self._consumers:
if consumer.active:
continue
self._start_consumer(consumer)
break
# Do we have any overflow of consumers.
self._stop_consumers(self.number_of_consumers)
|
python
|
{
"resource": ""
}
|
q947
|
HTTPClient.get
|
train
|
def get(self, path, payload=None, headers=None):
"""HTTP GET operation.
:param path: URI Path
:param payload: HTTP Body
:param headers: HTTP Headers
:raises ApiError: Raises if the remote server encountered an error.
:raises ApiConnectionError: Raises if there was a connectivity issue.
:return: Response
"""
return self._request('get', path, payload, headers)
|
python
|
{
"resource": ""
}
|
q948
|
HTTPClient.post
|
train
|
def post(self, path, payload=None, headers=None):
"""HTTP POST operation.
:param path: URI Path
:param payload: HTTP Body
:param headers: HTTP Headers
:raises ApiError: Raises if the remote server encountered an error.
:raises ApiConnectionError: Raises if there was a connectivity issue.
:return: Response
"""
return self._request('post', path, payload, headers)
|
python
|
{
"resource": ""
}
|
q949
|
HTTPClient.delete
|
train
|
def delete(self, path, payload=None, headers=None):
"""HTTP DELETE operation.
:param path: URI Path
:param payload: HTTP Body
:param headers: HTTP Headers
:raises ApiError: Raises if the remote server encountered an error.
:raises ApiConnectionError: Raises if there was a connectivity issue.
:return: Response
"""
return self._request('delete', path, payload, headers)
|
python
|
{
"resource": ""
}
|
q950
|
HTTPClient.put
|
train
|
def put(self, path, payload=None, headers=None):
"""HTTP PUT operation.
:param path: URI Path
:param payload: HTTP Body
:param headers: HTTP Headers
:raises ApiError: Raises if the remote server encountered an error.
:raises ApiConnectionError: Raises if there was a connectivity issue.
:return: Response
"""
return self._request('put', path, payload, headers)
|
python
|
{
"resource": ""
}
|
q951
|
HTTPClient._request
|
train
|
def _request(self, method, path, payload=None, headers=None):
"""HTTP operation.
:param method: Operation type (e.g. post)
:param path: URI Path
:param payload: HTTP Body
:param headers: HTTP Headers
:raises ApiError: Raises if the remote server encountered an error.
:raises ApiConnectionError: Raises if there was a connectivity issue.
:return: Response
"""
url = urlparse.urljoin(self._base_url, 'api/%s' % path)
headers = headers or {}
headers['content-type'] = 'application/json'
try:
response = requests.request(
method, url,
auth=self._auth,
data=payload,
headers=headers,
cert=self._cert,
verify=self._verify,
timeout=self._timeout
)
except requests.RequestException as why:
raise ApiConnectionError(str(why))
json_response = self._get_json_output(response)
self._check_for_errors(response, json_response)
return json_response
|
python
|
{
"resource": ""
}
|
q952
|
HTTPClient._check_for_errors
|
train
|
def _check_for_errors(response, json_response):
"""Check payload for errors.
:param response: HTTP response
:param json_response: Json response
:raises ApiError: Raises if the remote server encountered an error.
:return:
"""
status_code = response.status_code
try:
response.raise_for_status()
except requests.HTTPError as why:
raise ApiError(str(why), reply_code=status_code)
if isinstance(json_response, dict) and 'error' in json_response:
raise ApiError(json_response['error'], reply_code=status_code)
|
python
|
{
"resource": ""
}
|
q953
|
HealthChecks.get
|
train
|
def get(self, node=None):
"""Run basic healthchecks against the current node, or against a given
node.
Example response:
> {"status":"ok"}
> {"status":"failed","reason":"string"}
:param node: Node name
:raises ApiError: Raises if the remote server encountered an error.
:raises ApiConnectionError: Raises if there was a connectivity issue.
:rtype: dict
"""
if not node:
return self.http_client.get(HEALTHCHECKS)
return self.http_client.get(HEALTHCHECKS_NODE % node)
|
python
|
{
"resource": ""
}
|
q954
|
mean_pairwise_difference
|
train
|
def mean_pairwise_difference(ac, an=None, fill=np.nan):
"""Calculate for each variant the mean number of pairwise differences
between chromosomes sampled from within a single population.
Parameters
----------
ac : array_like, int, shape (n_variants, n_alleles)
Allele counts array.
an : array_like, int, shape (n_variants,), optional
Allele numbers. If not provided, will be calculated from `ac`.
fill : float
Use this value where there are no pairs to compare (e.g.,
all allele calls are missing).
Returns
-------
mpd : ndarray, float, shape (n_variants,)
Notes
-----
The values returned by this function can be summed over a genome
region and divided by the number of accessible bases to estimate
nucleotide diversity, a.k.a. *pi*.
Examples
--------
>>> import allel
>>> h = allel.HaplotypeArray([[0, 0, 0, 0],
... [0, 0, 0, 1],
... [0, 0, 1, 1],
... [0, 1, 1, 1],
... [1, 1, 1, 1],
... [0, 0, 1, 2],
... [0, 1, 1, 2],
... [0, 1, -1, -1]])
>>> ac = h.count_alleles()
>>> allel.mean_pairwise_difference(ac)
array([0. , 0.5 , 0.66666667, 0.5 , 0. ,
0.83333333, 0.83333333, 1. ])
See Also
--------
sequence_diversity, windowed_diversity
"""
# This function calculates the mean number of pairwise differences
# between haplotypes within a single population, generalising to any number
# of alleles.
# check inputs
ac = asarray_ndim(ac, 2)
# total number of haplotypes
if an is None:
an = np.sum(ac, axis=1)
else:
an = asarray_ndim(an, 1)
check_dim0_aligned(ac, an)
# total number of pairwise comparisons for each variant:
# (an choose 2)
n_pairs = an * (an - 1) / 2
# number of pairwise comparisons where there is no difference:
# sum of (ac choose 2) for each allele (i.e., number of ways to
# choose the same allele twice)
n_same = np.sum(ac * (ac - 1) / 2, axis=1)
# number of pairwise differences
n_diff = n_pairs - n_same
# mean number of pairwise differences, accounting for cases where
# there are no pairs
with ignore_invalid():
mpd = np.where(n_pairs > 0, n_diff / n_pairs, fill)
return mpd
|
python
|
{
"resource": ""
}
|
q955
|
mean_pairwise_difference_between
|
train
|
def mean_pairwise_difference_between(ac1, ac2, an1=None, an2=None,
fill=np.nan):
"""Calculate for each variant the mean number of pairwise differences
between chromosomes sampled from two different populations.
Parameters
----------
ac1 : array_like, int, shape (n_variants, n_alleles)
Allele counts array from the first population.
ac2 : array_like, int, shape (n_variants, n_alleles)
Allele counts array from the second population.
an1 : array_like, int, shape (n_variants,), optional
Allele numbers for the first population. If not provided, will be
calculated from `ac1`.
an2 : array_like, int, shape (n_variants,), optional
Allele numbers for the second population. If not provided, will be
calculated from `ac2`.
fill : float
Use this value where there are no pairs to compare (e.g.,
all allele calls are missing).
Returns
-------
mpd : ndarray, float, shape (n_variants,)
Notes
-----
The values returned by this function can be summed over a genome
region and divided by the number of accessible bases to estimate
nucleotide divergence between two populations, a.k.a. *Dxy*.
Examples
--------
>>> import allel
>>> h = allel.HaplotypeArray([[0, 0, 0, 0],
... [0, 0, 0, 1],
... [0, 0, 1, 1],
... [0, 1, 1, 1],
... [1, 1, 1, 1],
... [0, 0, 1, 2],
... [0, 1, 1, 2],
... [0, 1, -1, -1]])
>>> ac1 = h.count_alleles(subpop=[0, 1])
>>> ac2 = h.count_alleles(subpop=[2, 3])
>>> allel.mean_pairwise_difference_between(ac1, ac2)
array([0. , 0.5 , 1. , 0.5 , 0. , 1. , 0.75, nan])
See Also
--------
sequence_divergence, windowed_divergence
"""
# This function calculates the mean number of pairwise differences
# between haplotypes from two different populations, generalising to any
# number of alleles.
# check inputs
ac1 = asarray_ndim(ac1, 2)
ac2 = asarray_ndim(ac2, 2)
check_dim0_aligned(ac1, ac2)
ac1, ac2 = ensure_dim1_aligned(ac1, ac2)
# total number of haplotypes sampled from each population
if an1 is None:
an1 = np.sum(ac1, axis=1)
else:
an1 = asarray_ndim(an1, 1)
check_dim0_aligned(ac1, an1)
if an2 is None:
an2 = np.sum(ac2, axis=1)
else:
an2 = asarray_ndim(an2, 1)
check_dim0_aligned(ac2, an2)
# total number of pairwise comparisons for each variant
n_pairs = an1 * an2
# number of pairwise comparisons where there is no difference:
# sum of (ac1 * ac2) for each allele (i.e., number of ways to
# choose the same allele twice)
n_same = np.sum(ac1 * ac2, axis=1)
# number of pairwise differences
n_diff = n_pairs - n_same
# mean number of pairwise differences, accounting for cases where
# there are no pairs
with ignore_invalid():
mpd = np.where(n_pairs > 0, n_diff / n_pairs, fill)
return mpd
|
python
|
{
"resource": ""
}
|
q956
|
watterson_theta
|
train
|
def watterson_theta(pos, ac, start=None, stop=None,
is_accessible=None):
"""Calculate the value of Watterson's estimator over a given region.
Parameters
----------
pos : array_like, int, shape (n_items,)
Variant positions, using 1-based coordinates, in ascending order.
ac : array_like, int, shape (n_variants, n_alleles)
Allele counts array.
start : int, optional
The position at which to start (1-based). Defaults to the first position.
stop : int, optional
The position at which to stop (1-based). Defaults to the last position.
is_accessible : array_like, bool, shape (len(contig),), optional
Boolean array indicating accessibility status for all positions in the
chromosome/contig.
Returns
-------
theta_hat_w : float
Watterson's estimator (theta hat per base).
Examples
--------
>>> import allel
>>> g = allel.GenotypeArray([[[0, 0], [0, 0]],
... [[0, 0], [0, 1]],
... [[0, 0], [1, 1]],
... [[0, 1], [1, 1]],
... [[1, 1], [1, 1]],
... [[0, 0], [1, 2]],
... [[0, 1], [1, 2]],
... [[0, 1], [-1, -1]],
... [[-1, -1], [-1, -1]]])
>>> ac = g.count_alleles()
>>> pos = [2, 4, 7, 14, 15, 18, 19, 25, 27]
>>> theta_hat_w = allel.watterson_theta(pos, ac, start=1, stop=31)
>>> theta_hat_w
0.10557184750733138
"""
# check inputs
if not isinstance(pos, SortedIndex):
pos = SortedIndex(pos, copy=False)
is_accessible = asarray_ndim(is_accessible, 1, allow_none=True)
if not hasattr(ac, 'count_segregating'):
ac = AlleleCountsArray(ac, copy=False)
# deal with subregion
if start is not None or stop is not None:
loc = pos.locate_range(start, stop)
pos = pos[loc]
ac = ac[loc]
if start is None:
start = pos[0]
if stop is None:
stop = pos[-1]
# count segregating variants
S = ac.count_segregating()
# assume number of chromosomes sampled is constant for all variants
n = ac.sum(axis=1).max()
# (n-1)th harmonic number
a1 = np.sum(1 / np.arange(1, n))
# calculate absolute value
theta_hat_w_abs = S / a1
# calculate value per base
if is_accessible is None:
n_bases = stop - start + 1
else:
n_bases = np.count_nonzero(is_accessible[start-1:stop])
theta_hat_w = theta_hat_w_abs / n_bases
return theta_hat_w
|
python
|
{
"resource": ""
}
|
q957
|
tajima_d
|
train
|
def tajima_d(ac, pos=None, start=None, stop=None, min_sites=3):
"""Calculate the value of Tajima's D over a given region.
Parameters
----------
ac : array_like, int, shape (n_variants, n_alleles)
Allele counts array.
pos : array_like, int, shape (n_items,), optional
Variant positions, using 1-based coordinates, in ascending order.
start : int, optional
The position at which to start (1-based). Defaults to the first position.
stop : int, optional
The position at which to stop (1-based). Defaults to the last position.
min_sites : int, optional
Minimum number of segregating sites for which to calculate a value. If
there are fewer, np.nan is returned. Defaults to 3.
Returns
-------
D : float
Examples
--------
>>> import allel
>>> g = allel.GenotypeArray([[[0, 0], [0, 0]],
... [[0, 0], [0, 1]],
... [[0, 0], [1, 1]],
... [[0, 1], [1, 1]],
... [[1, 1], [1, 1]],
... [[0, 0], [1, 2]],
... [[0, 1], [1, 2]],
... [[0, 1], [-1, -1]],
... [[-1, -1], [-1, -1]]])
>>> ac = g.count_alleles()
>>> allel.tajima_d(ac)
3.1445848780213814
>>> pos = [2, 4, 7, 14, 15, 18, 19, 25, 27]
>>> allel.tajima_d(ac, pos=pos, start=7, stop=25)
3.8779735196179366
"""
# check inputs
if not hasattr(ac, 'count_segregating'):
ac = AlleleCountsArray(ac, copy=False)
# deal with subregion
if pos is not None and (start is not None or stop is not None):
if not isinstance(pos, SortedIndex):
pos = SortedIndex(pos, copy=False)
loc = pos.locate_range(start, stop)
ac = ac[loc]
# count segregating variants
S = ac.count_segregating()
if S < min_sites:
return np.nan
# assume number of chromosomes sampled is constant for all variants
n = ac.sum(axis=1).max()
# (n-1)th harmonic number
a1 = np.sum(1 / np.arange(1, n))
# calculate Watterson's theta (absolute value)
theta_hat_w_abs = S / a1
# calculate mean pairwise difference
mpd = mean_pairwise_difference(ac, fill=0)
# calculate theta_hat pi (sum differences over variants)
theta_hat_pi_abs = np.sum(mpd)
# N.B., both theta estimates are usually divided by the number of
# (accessible) bases but here we want the absolute difference
d = theta_hat_pi_abs - theta_hat_w_abs
# calculate the denominator (standard deviation)
a2 = np.sum(1 / (np.arange(1, n)**2))
b1 = (n + 1) / (3 * (n - 1))
b2 = 2 * (n**2 + n + 3) / (9 * n * (n - 1))
c1 = b1 - (1 / a1)
c2 = b2 - ((n + 2) / (a1 * n)) + (a2 / (a1**2))
e1 = c1 / a1
e2 = c2 / (a1**2 + a2)
d_stdev = np.sqrt((e1 * S) + (e2 * S * (S - 1)))
# finally calculate Tajima's D
D = d / d_stdev
return D
|
python
|
{
"resource": ""
}
|
q958
|
moving_tajima_d
|
train
|
def moving_tajima_d(ac, size, start=0, stop=None, step=None, min_sites=3):
"""Calculate the value of Tajima's D in moving windows of `size` variants.
Parameters
----------
ac : array_like, int, shape (n_variants, n_alleles)
Allele counts array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
step : int, optional
The number of variants between start positions of windows. If not
given, defaults to the window size, i.e., non-overlapping windows.
min_sites : int, optional
Minimum number of segregating sites for which to calculate a value. If
there are fewer, np.nan is returned. Defaults to 3.
Returns
-------
d : ndarray, float, shape (n_windows,)
Tajima's D.
Examples
--------
>>> import allel
>>> g = allel.GenotypeArray([[[0, 0], [0, 0]],
... [[0, 0], [0, 1]],
... [[0, 0], [1, 1]],
... [[0, 1], [1, 1]],
... [[1, 1], [1, 1]],
... [[0, 0], [1, 2]],
... [[0, 1], [1, 2]],
... [[0, 1], [-1, -1]],
... [[-1, -1], [-1, -1]]])
>>> ac = g.count_alleles()
>>> D = allel.moving_tajima_d(ac, size=4, step=2)
>>> D
array([0.1676558 , 2.01186954, 5.70029703])
"""
d = moving_statistic(values=ac, statistic=tajima_d, size=size, start=start, stop=stop,
step=step, min_sites=min_sites)
return d
|
python
|
{
"resource": ""
}
|
q959
|
sfs
|
train
|
def sfs(dac, n=None):
"""Compute the site frequency spectrum given derived allele counts at
a set of biallelic variants.
Parameters
----------
dac : array_like, int, shape (n_variants,)
Array of derived allele counts.
n : int, optional
The total number of chromosomes called.
Returns
-------
sfs : ndarray, int, shape (n_chromosomes,)
Array where the kth element is the number of variant sites with k
derived alleles.
"""
# check input
dac, n = _check_dac_n(dac, n)
# need platform integer for bincount
dac = dac.astype(int, copy=False)
# compute site frequency spectrum
x = n + 1
s = np.bincount(dac, minlength=x)
return s
|
python
|
{
"resource": ""
}
|
q960
|
sfs_folded
|
train
|
def sfs_folded(ac, n=None):
"""Compute the folded site frequency spectrum given reference and
alternate allele counts at a set of biallelic variants.
Parameters
----------
ac : array_like, int, shape (n_variants, 2)
Allele counts array.
n : int, optional
The total number of chromosomes called.
Returns
-------
sfs_folded : ndarray, int, shape (n_chromosomes//2,)
Array where the kth element is the number of variant sites with a
minor allele count of k.
"""
# check input
ac, n = _check_ac_n(ac, n)
# compute minor allele counts
mac = np.amin(ac, axis=1)
# need platform integer for bincount
mac = mac.astype(int, copy=False)
# compute folded site frequency spectrum
x = n//2 + 1
s = np.bincount(mac, minlength=x)
return s
|
python
|
{
"resource": ""
}
|
q961
|
sfs_scaled
|
train
|
def sfs_scaled(dac, n=None):
"""Compute the site frequency spectrum scaled such that a constant value is
expected across the spectrum for neutral variation and constant
population size.
Parameters
----------
dac : array_like, int, shape (n_variants,)
Array of derived allele counts.
n : int, optional
The total number of chromosomes called.
Returns
-------
sfs_scaled : ndarray, int, shape (n_chromosomes,)
An array where the value of the kth element is the number of variants
with k derived alleles, multiplied by k.
"""
# compute site frequency spectrum
s = sfs(dac, n=n)
# apply scaling
s = scale_sfs(s)
return s
|
python
|
{
"resource": ""
}
|
q962
|
scale_sfs
|
train
|
def scale_sfs(s):
"""Scale a site frequency spectrum.
Parameters
----------
s : array_like, int, shape (n_chromosomes,)
Site frequency spectrum.
Returns
-------
sfs_scaled : ndarray, int, shape (n_chromosomes,)
Scaled site frequency spectrum.
"""
k = np.arange(s.size)
out = s * k
return out
|
python
|
{
"resource": ""
}
|
q963
|
sfs_folded_scaled
|
train
|
def sfs_folded_scaled(ac, n=None):
"""Compute the folded site frequency spectrum scaled such that a constant
value is expected across the spectrum for neutral variation and constant
population size.
Parameters
----------
ac : array_like, int, shape (n_variants, 2)
Allele counts array.
n : int, optional
The total number of chromosomes called.
Returns
-------
sfs_folded_scaled : ndarray, int, shape (n_chromosomes//2,)
An array where the value of the kth element is the number of variants
with minor allele count k, multiplied by the scaling factor
(k * (n - k) / n).
"""
# check input
ac, n = _check_ac_n(ac, n)
# compute the site frequency spectrum
s = sfs_folded(ac, n=n)
# apply scaling
s = scale_sfs_folded(s, n)
return s
|
python
|
{
"resource": ""
}
|
q964
|
scale_sfs_folded
|
train
|
def scale_sfs_folded(s, n):
"""Scale a folded site frequency spectrum.
Parameters
----------
s : array_like, int, shape (n_chromosomes//2,)
Folded site frequency spectrum.
n : int
Number of chromosomes called.
Returns
-------
sfs_folded_scaled : ndarray, int, shape (n_chromosomes//2,)
Scaled folded site frequency spectrum.
"""
k = np.arange(s.shape[0])
out = s * k * (n - k) / n
return out
|
python
|
{
"resource": ""
}
|
q965
|
joint_sfs
|
train
|
def joint_sfs(dac1, dac2, n1=None, n2=None):
"""Compute the joint site frequency spectrum between two populations.
Parameters
----------
dac1 : array_like, int, shape (n_variants,)
Derived allele counts for the first population.
dac2 : array_like, int, shape (n_variants,)
Derived allele counts for the second population.
n1, n2 : int, optional
The total number of chromosomes called in each population.
Returns
-------
joint_sfs : ndarray, int, shape (m_chromosomes, n_chromosomes)
Array where the (i, j)th element is the number of variant sites with i
derived alleles in the first population and j derived alleles in the
second population.
"""
# check inputs
dac1, n1 = _check_dac_n(dac1, n1)
dac2, n2 = _check_dac_n(dac2, n2)
# compute site frequency spectrum
x = n1 + 1
y = n2 + 1
# need platform integer for bincount
tmp = (dac1 * y + dac2).astype(int, copy=False)
s = np.bincount(tmp)
s.resize(x, y)
return s
|
python
|
{
"resource": ""
}
|
q966
|
joint_sfs_folded
|
train
|
def joint_sfs_folded(ac1, ac2, n1=None, n2=None):
"""Compute the joint folded site frequency spectrum between two
populations.
Parameters
----------
ac1 : array_like, int, shape (n_variants, 2)
Allele counts for the first population.
ac2 : array_like, int, shape (n_variants, 2)
Allele counts for the second population.
n1, n2 : int, optional
The total number of chromosomes called in each population.
Returns
-------
joint_sfs_folded : ndarray, int, shape (n1//2 + 1, n2//2 + 1)
Array where the (i, j)th element is the number of variant sites with a
minor allele count of i in the first population and j in the second
population.
"""
# check inputs
ac1, n1 = _check_ac_n(ac1, n1)
ac2, n2 = _check_ac_n(ac2, n2)
# compute minor allele counts
mac1 = np.amin(ac1, axis=1)
mac2 = np.amin(ac2, axis=1)
# compute site frequency spectrum
x = n1//2 + 1
y = n2//2 + 1
tmp = (mac1 * y + mac2).astype(int, copy=False)
s = np.bincount(tmp)
s.resize(x, y)
return s
|
python
|
{
"resource": ""
}
|
q967
|
joint_sfs_scaled
|
train
|
def joint_sfs_scaled(dac1, dac2, n1=None, n2=None):
"""Compute the joint site frequency spectrum between two populations,
scaled such that a constant value is expected across the spectrum for
neutral variation, constant population size and unrelated populations.
Parameters
----------
dac1 : array_like, int, shape (n_variants,)
Derived allele counts for the first population.
dac2 : array_like, int, shape (n_variants,)
Derived allele counts for the second population.
n1, n2 : int, optional
The total number of chromosomes called in each population.
Returns
-------
joint_sfs_scaled : ndarray, int, shape (n1 + 1, n2 + 1)
Array where the (i, j)th element is the scaled frequency of variant
sites with i derived alleles in the first population and j derived
alleles in the second population.
"""
# compute site frequency spectrum
s = joint_sfs(dac1, dac2, n1=n1, n2=n2)
# apply scaling
s = scale_joint_sfs(s)
return s
|
python
|
{
"resource": ""
}
|
q968
|
scale_joint_sfs
|
train
|
def scale_joint_sfs(s):
"""Scale a joint site frequency spectrum.
Parameters
----------
s : array_like, int, shape (n1, n2)
Joint site frequency spectrum.
Returns
-------
joint_sfs_scaled : ndarray, int, shape (n1, n2)
Scaled joint site frequency spectrum.
"""
i = np.arange(s.shape[0])[:, None]
j = np.arange(s.shape[1])[None, :]
out = (s * i) * j
return out
|
python
|
{
"resource": ""
}
|
q969
|
joint_sfs_folded_scaled
|
train
|
def joint_sfs_folded_scaled(ac1, ac2, n1=None, n2=None):
"""Compute the joint folded site frequency spectrum between two
populations, scaled such that a constant value is expected across the
spectrum for neutral variation, constant population size and unrelated
populations.
Parameters
----------
ac1 : array_like, int, shape (n_variants, 2)
Allele counts for the first population.
ac2 : array_like, int, shape (n_variants, 2)
Allele counts for the second population.
n1, n2 : int, optional
The total number of chromosomes called in each population.
Returns
-------
joint_sfs_folded_scaled : ndarray, int, shape (n1//2 + 1, n2//2 + 1)
Array where the (i, j)th element is the scaled frequency of variant
sites with a minor allele count of i in the first population and j
in the second population.
""" # noqa
# check inputs
ac1, n1 = _check_ac_n(ac1, n1)
ac2, n2 = _check_ac_n(ac2, n2)
# compute site frequency spectrum
s = joint_sfs_folded(ac1, ac2, n1=n1, n2=n2)
# apply scaling
s = scale_joint_sfs_folded(s, n1, n2)
return s
|
python
|
{
"resource": ""
}
|
q970
|
scale_joint_sfs_folded
|
train
|
def scale_joint_sfs_folded(s, n1, n2):
"""Scale a folded joint site frequency spectrum.
Parameters
----------
s : array_like, int, shape (m_chromosomes//2, n_chromosomes//2)
Folded joint site frequency spectrum.
n1, n2 : int, optional
The total number of chromosomes called in each population.
Returns
-------
joint_sfs_folded_scaled : ndarray, int, shape (m_chromosomes//2, n_chromosomes//2)
Scaled folded joint site frequency spectrum.
""" # noqa
out = np.empty_like(s)
for i in range(s.shape[0]):
for j in range(s.shape[1]):
out[i, j] = s[i, j] * i * j * (n1 - i) * (n2 - j)
return out
|
python
|
{
"resource": ""
}
|
q971
|
fold_sfs
|
train
|
def fold_sfs(s, n):
"""Fold a site frequency spectrum.
Parameters
----------
s : array_like, int, shape (n_chromosomes,)
Site frequency spectrum
n : int
Total number of chromosomes called.
Returns
-------
sfs_folded : ndarray, int
Folded site frequency spectrum
"""
# check inputs
s = asarray_ndim(s, 1)
assert s.shape[0] <= n + 1, 'invalid number of chromosomes'
# need to check s has all entries up to n
if s.shape[0] < n + 1:
sn = np.zeros(n + 1, dtype=s.dtype)
sn[:s.shape[0]] = s
s = sn
# fold
nf = (n + 1) // 2
n = nf * 2
o = s[:nf] + s[nf:n][::-1]
return o
|
python
|
{
"resource": ""
}
|
q972
|
fold_joint_sfs
|
train
|
def fold_joint_sfs(s, n1, n2):
"""Fold a joint site frequency spectrum.
Parameters
----------
s : array_like, int, shape (m_chromosomes, n_chromosomes)
Joint site frequency spectrum.
n1, n2 : int, optional
The total number of chromosomes called in each population.
Returns
-------
joint_sfs_folded : ndarray, int
Folded joint site frequency spectrum.
"""
# check inputs
s = asarray_ndim(s, 2)
assert s.shape[0] <= n1 + 1, 'invalid number of chromosomes'
assert s.shape[1] <= n2 + 1, 'invalid number of chromosomes'
# need to check s has all entries up to m
if s.shape[0] < n1 + 1:
sm = np.zeros((n1 + 1, s.shape[1]), dtype=s.dtype)
sm[:s.shape[0]] = s
s = sm
# need to check s has all entries up to n
if s.shape[1] < n2 + 1:
sn = np.zeros((s.shape[0], n2 + 1), dtype=s.dtype)
sn[:, :s.shape[1]] = s
s = sn
# fold
mf = (n1 + 1) // 2
nf = (n2 + 1) // 2
n1 = mf * 2
n2 = nf * 2
o = (s[:mf, :nf] + # top left
s[mf:n1, :nf][::-1] + # top right
s[:mf, nf:n2][:, ::-1] + # bottom left
s[mf:n1, nf:n2][::-1, ::-1]) # bottom right
return o
|
python
|
{
"resource": ""
}
|
q973
|
plot_sfs
|
train
|
def plot_sfs(s, yscale='log', bins=None, n=None,
clip_endpoints=True, label=None, plot_kwargs=None,
ax=None):
"""Plot a site frequency spectrum.
Parameters
----------
s : array_like, int, shape (n_chromosomes,)
Site frequency spectrum.
yscale : string, optional
Y axis scale.
bins : int or array_like, int, optional
Allele count bins.
n : int, optional
Number of chromosomes sampled. If provided, X axis will be plotted
as allele frequency, otherwise as allele count.
clip_endpoints : bool, optional
If True, do not plot first and last values from frequency spectrum.
label : string, optional
Label for data series in plot.
plot_kwargs : dict-like
Additional keyword arguments, passed through to ax.plot().
ax : axes, optional
Axes on which to draw. If not provided, a new figure will be created.
Returns
-------
ax : axes
The axes on which the plot was drawn.
"""
import matplotlib.pyplot as plt
import scipy
# check inputs
s = asarray_ndim(s, 1)
# setup axes
if ax is None:
fig, ax = plt.subplots()
# setup data
if bins is None:
if clip_endpoints:
x = np.arange(1, s.shape[0]-1)
y = s[1:-1]
else:
x = np.arange(s.shape[0])
y = s
else:
if clip_endpoints:
y, b, _ = scipy.stats.binned_statistic(
np.arange(1, s.shape[0]-1),
values=s[1:-1],
bins=bins,
statistic='sum')
else:
y, b, _ = scipy.stats.binned_statistic(
np.arange(s.shape[0]),
values=s,
bins=bins,
statistic='sum')
# use bin midpoints for plotting
x = (b[:-1] + b[1:]) / 2
if n:
# convert allele counts to allele frequencies
x = x / n
ax.set_xlabel('derived allele frequency')
else:
ax.set_xlabel('derived allele count')
# do plotting
if plot_kwargs is None:
plot_kwargs = dict()
ax.plot(x, y, label=label, **plot_kwargs)
# tidy
ax.set_yscale(yscale)
ax.set_ylabel('site frequency')
ax.autoscale(axis='x', tight=True)
return ax
|
python
|
{
"resource": ""
}
|
q974
|
plot_sfs_folded
|
train
|
def plot_sfs_folded(*args, **kwargs):
"""Plot a folded site frequency spectrum.
Parameters
----------
s : array_like, int, shape (n_chromosomes/2,)
Site frequency spectrum.
yscale : string, optional
Y axis scale.
bins : int or array_like, int, optional
Allele count bins.
n : int, optional
Number of chromosomes sampled. If provided, X axis will be plotted
as allele frequency, otherwise as allele count.
clip_endpoints : bool, optional
If True, do not plot first and last values from frequency spectrum.
label : string, optional
Label for data series in plot.
plot_kwargs : dict-like
Additional keyword arguments, passed through to ax.plot().
ax : axes, optional
Axes on which to draw. If not provided, a new figure will be created.
Returns
-------
ax : axes
The axes on which the plot was drawn.
"""
ax = plot_sfs(*args, **kwargs)
n = kwargs.get('n', None)
if n:
ax.set_xlabel('minor allele frequency')
else:
ax.set_xlabel('minor allele count')
return ax
|
python
|
{
"resource": ""
}
|
q975
|
plot_sfs_scaled
|
train
|
def plot_sfs_scaled(*args, **kwargs):
"""Plot a scaled site frequency spectrum.
Parameters
----------
s : array_like, int, shape (n_chromosomes,)
Site frequency spectrum.
yscale : string, optional
Y axis scale.
bins : int or array_like, int, optional
Allele count bins.
n : int, optional
Number of chromosomes sampled. If provided, X axis will be plotted
as allele frequency, otherwise as allele count.
clip_endpoints : bool, optional
If True, do not plot first and last values from frequency spectrum.
label : string, optional
Label for data series in plot.
plot_kwargs : dict-like
Additional keyword arguments, passed through to ax.plot().
ax : axes, optional
Axes on which to draw. If not provided, a new figure will be created.
Returns
-------
ax : axes
The axes on which the plot was drawn.
"""
kwargs.setdefault('yscale', 'linear')
ax = plot_sfs(*args, **kwargs)
ax.set_ylabel('scaled site frequency')
return ax
|
python
|
{
"resource": ""
}
|
q976
|
plot_sfs_folded_scaled
|
train
|
def plot_sfs_folded_scaled(*args, **kwargs):
"""Plot a folded scaled site frequency spectrum.
Parameters
----------
s : array_like, int, shape (n_chromosomes/2,)
Site frequency spectrum.
yscale : string, optional
Y axis scale.
bins : int or array_like, int, optional
Allele count bins.
n : int, optional
Number of chromosomes sampled. If provided, X axis will be plotted
as allele frequency, otherwise as allele count.
clip_endpoints : bool, optional
If True, do not plot first and last values from frequency spectrum.
label : string, optional
Label for data series in plot.
plot_kwargs : dict-like
Additional keyword arguments, passed through to ax.plot().
ax : axes, optional
Axes on which to draw. If not provided, a new figure will be created.
Returns
-------
ax : axes
The axes on which the plot was drawn.
"""
kwargs.setdefault('yscale', 'linear')
ax = plot_sfs_folded(*args, **kwargs)
ax.set_ylabel('scaled site frequency')
n = kwargs.get('n', None)
if n:
ax.set_xlabel('minor allele frequency')
else:
ax.set_xlabel('minor allele count')
return ax
|
python
|
{
"resource": ""
}
|
q977
|
plot_joint_sfs_scaled
|
train
|
def plot_joint_sfs_scaled(*args, **kwargs):
"""Plot a scaled joint site frequency spectrum.
Parameters
----------
s : array_like, int, shape (n_chromosomes_pop1, n_chromosomes_pop2)
Joint site frequency spectrum.
ax : axes, optional
Axes on which to draw. If not provided, a new figure will be created.
imshow_kwargs : dict-like
Additional keyword arguments, passed through to ax.imshow().
Returns
-------
ax : axes
The axes on which the plot was drawn.
"""
imshow_kwargs = kwargs.get('imshow_kwargs', dict())
imshow_kwargs.setdefault('norm', None)
kwargs['imshow_kwargs'] = imshow_kwargs
ax = plot_joint_sfs(*args, **kwargs)
return ax
|
python
|
{
"resource": ""
}
|
q978
|
plot_joint_sfs_folded_scaled
|
train
|
def plot_joint_sfs_folded_scaled(*args, **kwargs):
"""Plot a scaled folded joint site frequency spectrum.
Parameters
----------
s : array_like, int, shape (n_chromosomes_pop1/2, n_chromosomes_pop2/2)
Joint site frequency spectrum.
ax : axes, optional
Axes on which to draw. If not provided, a new figure will be created.
imshow_kwargs : dict-like
Additional keyword arguments, passed through to ax.imshow().
Returns
-------
ax : axes
The axes on which the plot was drawn.
"""
imshow_kwargs = kwargs.get('imshow_kwargs', dict())
imshow_kwargs.setdefault('norm', None)
kwargs['imshow_kwargs'] = imshow_kwargs
ax = plot_joint_sfs_folded(*args, **kwargs)
ax.set_xlabel('minor allele count (population 1)')
ax.set_ylabel('minor allele count (population 2)')
return ax
|
python
|
{
"resource": ""
}
|
q979
|
_prep_fields_param
|
train
|
def _prep_fields_param(fields):
"""Prepare the `fields` parameter, and determine whether or not to store samples."""
store_samples = False
if fields is None:
# add samples by default
return True, None
if isinstance(fields, str):
fields = [fields]
else:
fields = list(fields)
if 'samples' in fields:
fields.remove('samples')
store_samples = True
elif '*' in fields:
store_samples = True
return store_samples, fields
|
python
|
{
"resource": ""
}
|
q980
|
_chunk_iter_progress
|
train
|
def _chunk_iter_progress(it, log, prefix):
"""Wrap a chunk iterator for progress logging."""
n_variants = 0
before_all = time.time()
before_chunk = before_all
for chunk, chunk_length, chrom, pos in it:
after_chunk = time.time()
elapsed_chunk = after_chunk - before_chunk
elapsed = after_chunk - before_all
n_variants += chunk_length
chrom = text_type(chrom, 'utf8')
message = (
'%s %s rows in %.2fs; chunk in %.2fs (%s rows/s)' %
(prefix, n_variants, elapsed, elapsed_chunk,
int(chunk_length // elapsed_chunk))
)
if chrom:
message += '; %s:%s' % (chrom, pos)
print(message, file=log)
log.flush()
yield chunk, chunk_length, chrom, pos
before_chunk = after_chunk
after_all = time.time()
elapsed = after_all - before_all
print('%s all done (%s rows/s)' %
(prefix, int(n_variants // elapsed)), file=log)
log.flush()
|
python
|
{
"resource": ""
}
|
q981
|
read_vcf
|
train
|
def read_vcf(input,
fields=None,
exclude_fields=None,
rename_fields=None,
types=None,
numbers=None,
alt_number=DEFAULT_ALT_NUMBER,
fills=None,
region=None,
tabix='tabix',
samples=None,
transformers=None,
buffer_size=DEFAULT_BUFFER_SIZE,
chunk_length=DEFAULT_CHUNK_LENGTH,
log=None):
"""Read data from a VCF file into NumPy arrays.
.. versionchanged:: 1.12.0
Now returns None if no variants are found in the VCF file or matching the
requested region.
Parameters
----------
input : string or file-like
{input}
fields : list of strings, optional
{fields}
exclude_fields : list of strings, optional
{exclude_fields}
rename_fields : dict[str -> str], optional
{rename_fields}
types : dict, optional
{types}
numbers : dict, optional
{numbers}
alt_number : int, optional
{alt_number}
fills : dict, optional
{fills}
region : string, optional
{region}
tabix : string, optional
{tabix}
samples : list of strings
{samples}
transformers : list of transformer objects, optional
{transformers}
buffer_size : int, optional
{buffer_size}
chunk_length : int, optional
{chunk_length}
log : file-like, optional
{log}
Returns
-------
data : dict[str, ndarray]
A dictionary holding arrays, or None if no variants were found.
"""
# samples requested?
# noinspection PyTypeChecker
store_samples, fields = _prep_fields_param(fields)
# setup
fields, samples, headers, it = iter_vcf_chunks(
input=input, fields=fields, exclude_fields=exclude_fields, types=types,
numbers=numbers, alt_number=alt_number, buffer_size=buffer_size,
chunk_length=chunk_length, fills=fills, region=region, tabix=tabix,
samples=samples, transformers=transformers
)
# handle field renaming
if rename_fields:
rename_fields, it = _do_rename(it, fields=fields,
rename_fields=rename_fields,
headers=headers)
# setup progress logging
if log is not None:
it = _chunk_iter_progress(it, log, prefix='[read_vcf]')
# read all chunks into a list
chunks = [d[0] for d in it]
if chunks:
# setup output
output = dict()
if len(samples) > 0 and store_samples:
output['samples'] = samples
# find array keys
keys = sorted(chunks[0].keys())
# concatenate chunks
for k in keys:
output[k] = np.concatenate([chunk[k] for chunk in chunks], axis=0)
else:
output = None
return output
|
python
|
{
"resource": ""
}
|
q982
|
vcf_to_npz
|
train
|
def vcf_to_npz(input, output,
compressed=True,
overwrite=False,
fields=None,
exclude_fields=None,
rename_fields=None,
types=None,
numbers=None,
alt_number=DEFAULT_ALT_NUMBER,
fills=None,
region=None,
tabix=True,
samples=None,
transformers=None,
buffer_size=DEFAULT_BUFFER_SIZE,
chunk_length=DEFAULT_CHUNK_LENGTH,
log=None):
"""Read data from a VCF file into NumPy arrays and save as a .npz file.
.. versionchanged:: 1.12.0
Now will not create any output file if no variants are found in the VCF file or
matching the requested region.
Parameters
----------
input : string
{input}
output : string
{output}
compressed : bool, optional
If True (default), save with compression.
overwrite : bool, optional
{overwrite}
fields : list of strings, optional
{fields}
exclude_fields : list of strings, optional
{exclude_fields}
rename_fields : dict[str -> str], optional
{rename_fields}
types : dict, optional
{types}
numbers : dict, optional
{numbers}
alt_number : int, optional
{alt_number}
fills : dict, optional
{fills}
region : string, optional
{region}
tabix : string, optional
{tabix}
samples : list of strings
{samples}
transformers : list of transformer objects, optional
{transformers}
buffer_size : int, optional
{buffer_size}
chunk_length : int, optional
{chunk_length}
log : file-like, optional
{log}
"""
# guard condition
if not overwrite and os.path.exists(output):
raise ValueError('file exists at path %r; use overwrite=True to replace' % output)
# read all data into memory
data = read_vcf(
input=input, fields=fields, exclude_fields=exclude_fields,
rename_fields=rename_fields, types=types, numbers=numbers,
alt_number=alt_number, buffer_size=buffer_size, chunk_length=chunk_length,
log=log, fills=fills, region=region, tabix=tabix, samples=samples,
transformers=transformers
)
if data is None:
# no data, bail out
return
# setup save function
if compressed:
savez = np.savez_compressed
else:
savez = np.savez
# save as npz
savez(output, **data)
|
python
|
{
"resource": ""
}
|
q983
|
vcf_to_hdf5
|
train
|
def vcf_to_hdf5(input, output,
group='/',
compression='gzip',
compression_opts=1,
shuffle=False,
overwrite=False,
vlen=True,
fields=None,
exclude_fields=None,
rename_fields=None,
types=None,
numbers=None,
alt_number=DEFAULT_ALT_NUMBER,
fills=None,
region=None,
tabix='tabix',
samples=None,
transformers=None,
buffer_size=DEFAULT_BUFFER_SIZE,
chunk_length=DEFAULT_CHUNK_LENGTH,
chunk_width=DEFAULT_CHUNK_WIDTH,
log=None):
"""Read data from a VCF file and load into an HDF5 file.
.. versionchanged:: 1.12.0
Now will not create any output file if no variants are found in the VCF file or
matching the requested region.
Parameters
----------
input : string
{input}
output : string
{output}
group : string
Group within destination HDF5 file to store data in.
compression : string
Compression algorithm, e.g., 'gzip' (default).
compression_opts : int
Compression level, e.g., 1 (default).
shuffle : bool
Use byte shuffling, which may improve compression (default is False).
overwrite : bool
{overwrite}
vlen : bool
If True, store variable length strings. Note that there is considerable storage
overhead for variable length strings in HDF5, and leaving this option as True (
default) may lead to large file sizes. If False, all strings will be stored in
the HDF5 file as fixed length strings, even if they are specified as 'object'
type. In this case, the string length for any field with 'object' type will be
determined based on the maximum length of strings found in the first chunk,
and this may cause values to be truncated if longer values are found in later
chunks. To avoid truncation and large file sizes, manually set the type for all
string fields to an explicit fixed length string type, e.g., 'S10' for a field
where you know at most 10 characters are required.
fields : list of strings, optional
{fields}
exclude_fields : list of strings, optional
{exclude_fields}
rename_fields : dict[str -> str], optional
{rename_fields}
types : dict, optional
{types}
numbers : dict, optional
{numbers}
alt_number : int, optional
{alt_number}
fills : dict, optional
{fills}
region : string, optional
{region}
tabix : string, optional
{tabix}
samples : list of strings
{samples}
transformers : list of transformer objects, optional
{transformers}
buffer_size : int, optional
{buffer_size}
chunk_length : int, optional
{chunk_length}
chunk_width : int, optional
{chunk_width}
log : file-like, optional
{log}
"""
import h5py
# samples requested?
# noinspection PyTypeChecker
store_samples, fields = _prep_fields_param(fields)
# setup chunk iterator
fields, samples, headers, it = iter_vcf_chunks(
input, fields=fields, exclude_fields=exclude_fields, types=types,
numbers=numbers, alt_number=alt_number, buffer_size=buffer_size,
chunk_length=chunk_length, fills=fills, region=region, tabix=tabix,
samples=samples, transformers=transformers
)
# handle field renaming
if rename_fields:
rename_fields, it = _do_rename(it, fields=fields,
rename_fields=rename_fields,
headers=headers)
# setup progress logging
if log is not None:
it = _chunk_iter_progress(it, log, prefix='[vcf_to_hdf5]')
# read first chunk
try:
chunk, _, _, _ = next(it)
except StopIteration:
# no data, bail out
return
with h5py.File(output, mode='a') as h5f:
# obtain root group that data will be stored into
root = h5f.require_group(group)
if len(samples) > 0 and store_samples:
# store samples
name = 'samples'
if name in root:
if overwrite:
del root[name]
else:
raise ValueError(
'dataset exists at path %r; use overwrite=True to replace' % name)
if samples.dtype.kind == 'O':
if vlen:
t = h5py.special_dtype(vlen=str)
else:
samples = samples.astype('S')
t = samples.dtype
else:
t = samples.dtype
root.create_dataset(name, data=samples, chunks=None, dtype=t)
# setup datasets
# noinspection PyTypeChecker
keys = _hdf5_setup_datasets(
chunk=chunk, root=root, chunk_length=chunk_length, chunk_width=chunk_width,
compression=compression, compression_opts=compression_opts, shuffle=shuffle,
overwrite=overwrite, headers=headers, vlen=vlen
)
# store first chunk
_hdf5_store_chunk(root, keys, chunk, vlen)
# store remaining chunks
for chunk, _, _, _ in it:
_hdf5_store_chunk(root, keys, chunk, vlen)
|
python
|
{
"resource": ""
}
|
q984
|
vcf_to_zarr
|
train
|
def vcf_to_zarr(input, output,
group='/',
compressor='default',
overwrite=False,
fields=None,
exclude_fields=None,
rename_fields=None,
types=None,
numbers=None,
alt_number=DEFAULT_ALT_NUMBER,
fills=None,
region=None,
tabix='tabix',
samples=None,
transformers=None,
buffer_size=DEFAULT_BUFFER_SIZE,
chunk_length=DEFAULT_CHUNK_LENGTH,
chunk_width=DEFAULT_CHUNK_WIDTH,
log=None):
"""Read data from a VCF file and load into a Zarr on-disk store.
.. versionchanged:: 1.12.0
Now will not create any output files if no variants are found in the VCF file or
matching the requested region.
Parameters
----------
input : string
{input}
output : string
{output}
group : string
Group within destination Zarr hierarchy to store data in.
compressor : compressor
Compression algorithm, e.g., zarr.Blosc(cname='zstd', clevel=1, shuffle=1).
overwrite : bool
{overwrite}
fields : list of strings, optional
{fields}
exclude_fields : list of strings, optional
{exclude_fields}
rename_fields : dict[str -> str], optional
{rename_fields}
types : dict, optional
{types}
numbers : dict, optional
{numbers}
alt_number : int, optional
{alt_number}
fills : dict, optional
{fills}
region : string, optional
{region}
tabix : string, optional
{tabix}
samples : list of strings
{samples}
transformers : list of transformer objects, optional
{transformers}
buffer_size : int, optional
{buffer_size}
chunk_length : int, optional
{chunk_length}
chunk_width : int, optional
{chunk_width}
log : file-like, optional
{log}
"""
import zarr
# samples requested?
# noinspection PyTypeChecker
store_samples, fields = _prep_fields_param(fields)
# setup chunk iterator
fields, samples, headers, it = iter_vcf_chunks(
input, fields=fields, exclude_fields=exclude_fields, types=types,
numbers=numbers, alt_number=alt_number, buffer_size=buffer_size,
chunk_length=chunk_length, fills=fills, region=region, tabix=tabix,
samples=samples, transformers=transformers
)
# handle field renaming
if rename_fields:
rename_fields, it = _do_rename(it, fields=fields,
rename_fields=rename_fields,
headers=headers)
# check for any case-insensitive duplicate fields
# https://github.com/cggh/scikit-allel/issues/215
ci_field_index = defaultdict(list)
for f in fields:
if rename_fields:
f = rename_fields.get(f, f)
ci_field_index[f.lower()].append(f)
for k, v in ci_field_index.items():
if len(v) > 1:
msg = textwrap.fill(
'Found two or more fields with the same name when compared '
'case-insensitive: {!r}; this is not supported because it causes '
'problems on platforms with a case-insensitive file system, which is '
'usually the default on Windows and Mac OS. Please rename fields so they '
'are distinct under a case-insensitive comparison via the '
'rename_fields argument.'.format(v), width=80)
raise ValueError(msg)
# setup progress logging
if log is not None:
it = _chunk_iter_progress(it, log, prefix='[vcf_to_zarr]')
# read first chunk
try:
chunk, _, _, _ = next(it)
except StopIteration:
# no data, bail out
return
# open root group
root = zarr.open_group(output, mode='a', path=group)
if len(samples) > 0 and store_samples:
# store samples
if samples.dtype.kind == 'O':
if PY2:
dtype = 'unicode'
else:
dtype = 'str'
else:
dtype = samples.dtype
root.create_dataset('samples', data=samples, compressor=None, overwrite=overwrite,
dtype=dtype)
# setup datasets
# noinspection PyTypeChecker
keys = _zarr_setup_datasets(
chunk, root=root, chunk_length=chunk_length, chunk_width=chunk_width,
compressor=compressor, overwrite=overwrite, headers=headers
)
# store first chunk
_zarr_store_chunk(root, keys, chunk)
# store remaining chunks
for chunk, _, _, _ in it:
_zarr_store_chunk(root, keys, chunk)
|
python
|
{
"resource": ""
}
|
q985
|
iter_vcf_chunks
|
train
|
def iter_vcf_chunks(input,
fields=None,
exclude_fields=None,
types=None,
numbers=None,
alt_number=DEFAULT_ALT_NUMBER,
fills=None,
region=None,
tabix='tabix',
samples=None,
transformers=None,
buffer_size=DEFAULT_BUFFER_SIZE,
chunk_length=DEFAULT_CHUNK_LENGTH):
"""Iterate over chunks of data from a VCF file as NumPy arrays.
Parameters
----------
input : string
{input}
fields : list of strings, optional
{fields}
exclude_fields : list of strings, optional
{exclude_fields}
types : dict, optional
{types}
numbers : dict, optional
{numbers}
alt_number : int, optional
{alt_number}
fills : dict, optional
{fills}
region : string, optional
{region}
tabix : string, optional
{tabix}
samples : list of strings
{samples}
transformers : list of transformer objects, optional
{transformers}
buffer_size : int, optional
{buffer_size}
chunk_length : int, optional
{chunk_length}
Returns
-------
fields : list of strings
Normalised names of fields that will be extracted.
samples : ndarray
Samples for which data will be extracted.
headers : VCFHeaders
Tuple of metadata extracted from VCF headers.
it : iterator
Chunk iterator.
"""
# setup commmon keyword args
kwds = dict(fields=fields, exclude_fields=exclude_fields, types=types,
numbers=numbers, alt_number=alt_number, chunk_length=chunk_length,
fills=fills, samples=samples, region=region)
# setup input stream
stream = _setup_input_stream(input=input, region=region, tabix=tabix,
buffer_size=buffer_size)
# setup iterator
fields, samples, headers, it = _iter_vcf_stream(stream, **kwds)
# setup transformers
if transformers is not None:
# API flexibility
if not isinstance(transformers, (list, tuple)):
transformers = [transformers]
for trans in transformers:
fields = trans.transform_fields(fields)
it = _chunk_iter_transform(it, transformers)
return fields, samples, headers, it
|
python
|
{
"resource": ""
}
|
q986
|
vcf_to_dataframe
|
train
|
def vcf_to_dataframe(input,
fields=None,
exclude_fields=None,
types=None,
numbers=None,
alt_number=DEFAULT_ALT_NUMBER,
fills=None,
region=None,
tabix='tabix',
transformers=None,
buffer_size=DEFAULT_BUFFER_SIZE,
chunk_length=DEFAULT_CHUNK_LENGTH,
log=None):
"""Read data from a VCF file into a pandas DataFrame.
Parameters
----------
input : string
{input}
fields : list of strings, optional
{fields}
exclude_fields : list of strings, optional
{exclude_fields}
types : dict, optional
{types}
numbers : dict, optional
{numbers}
alt_number : int, optional
{alt_number}
fills : dict, optional
{fills}
region : string, optional
{region}
tabix : string, optional
{tabix}
transformers : list of transformer objects, optional
{transformers}
buffer_size : int, optional
{buffer_size}
chunk_length : int, optional
{chunk_length}
log : file-like, optional
{log}
Returns
-------
df : pandas.DataFrame
"""
import pandas
# samples requested?
# noinspection PyTypeChecker
_, fields = _prep_fields_param(fields)
# setup
fields, _, _, it = iter_vcf_chunks(
input=input, fields=fields, exclude_fields=exclude_fields, types=types,
numbers=numbers, alt_number=alt_number, buffer_size=buffer_size,
chunk_length=chunk_length, fills=fills, region=region, tabix=tabix, samples=[],
transformers=transformers
)
# setup progress logging
if log is not None:
it = _chunk_iter_progress(it, log, prefix='[vcf_to_dataframe]')
# read all chunks into a list
chunks = [d[0] for d in it]
# setup output
output = None
if chunks:
# concatenate chunks
output = pandas.concat([_chunk_to_dataframe(fields, chunk)
for chunk in chunks])
return output
|
python
|
{
"resource": ""
}
|
q987
|
vcf_to_recarray
|
train
|
def vcf_to_recarray(input,
fields=None,
exclude_fields=None,
types=None,
numbers=None,
alt_number=DEFAULT_ALT_NUMBER,
fills=None,
region=None,
tabix='tabix',
transformers=None,
buffer_size=DEFAULT_BUFFER_SIZE,
chunk_length=DEFAULT_CHUNK_LENGTH,
log=None):
"""Read data from a VCF file into a NumPy recarray.
Parameters
----------
input : string
{input}
fields : list of strings, optional
{fields}
exclude_fields : list of strings, optional
{exclude_fields}
types : dict, optional
{types}
numbers : dict, optional
{numbers}
alt_number : int, optional
{alt_number}
fills : dict, optional
{fills}
region : string, optional
{region}
tabix : string, optional
{tabix}
transformers : list of transformer objects, optional
{transformers}
buffer_size : int, optional
{buffer_size}
chunk_length : int, optional
{chunk_length}
log : file-like, optional
{log}
Returns
-------
ra : np.rec.array
"""
# samples requested?
# noinspection PyTypeChecker
_, fields = _prep_fields_param(fields)
# setup chunk iterator
# N.B., set samples to empty list so we don't get any calldata fields
fields, _, _, it = iter_vcf_chunks(
input=input, fields=fields, exclude_fields=exclude_fields, types=types,
numbers=numbers, alt_number=alt_number, buffer_size=buffer_size,
chunk_length=chunk_length, fills=fills, region=region, tabix=tabix, samples=[],
transformers=transformers
)
# setup progress logging
if log is not None:
it = _chunk_iter_progress(it, log, prefix='[vcf_to_recarray]')
# read all chunks into a list
chunks = [d[0] for d in it]
# setup output
output = None
if chunks:
# concatenate chunks
output = np.concatenate([_chunk_to_recarray(fields, chunk) for chunk in chunks])
return output
|
python
|
{
"resource": ""
}
|
q988
|
write_fasta
|
train
|
def write_fasta(path, sequences, names, mode='w', width=80):
"""Write nucleotide sequences stored as numpy arrays to a FASTA file.
Parameters
----------
path : string
File path.
sequences : sequence of arrays
One or more ndarrays of dtype 'S1' containing the sequences.
names : sequence of strings
Names of the sequences.
mode : string, optional
Use 'a' to append to an existing file.
width : int, optional
Maximum line width.
"""
# check inputs
if isinstance(sequences, np.ndarray):
# single sequence
sequences = [sequences]
names = [names]
if len(sequences) != len(names):
raise ValueError('must provide the same number of sequences and names')
for sequence in sequences:
if sequence.dtype != np.dtype('S1'):
raise ValueError('expected S1 dtype, found %r' % sequence.dtype)
# force binary mode
mode = 'ab' if 'a' in mode else 'wb'
# write to file
with open(path, mode=mode) as fasta:
for name, sequence in zip(names, sequences):
# force bytes
if isinstance(name, text_type):
name = name.encode('ascii')
header = b'>' + name + b'\n'
fasta.write(header)
for i in range(0, sequence.size, width):
line = sequence[i:i+width].tostring() + b'\n'
fasta.write(line)
|
python
|
{
"resource": ""
}
|
q989
|
heterozygosity_observed
|
train
|
def heterozygosity_observed(g, fill=np.nan):
"""Calculate the rate of observed heterozygosity for each variant.
Parameters
----------
g : array_like, int, shape (n_variants, n_samples, ploidy)
Genotype array.
fill : float, optional
Use this value for variants where all calls are missing.
Returns
-------
ho : ndarray, float, shape (n_variants,)
Observed heterozygosity
Examples
--------
>>> import allel
>>> g = allel.GenotypeArray([[[0, 0], [0, 0], [0, 0]],
... [[0, 0], [0, 1], [1, 1]],
... [[0, 0], [1, 1], [2, 2]],
... [[1, 1], [1, 2], [-1, -1]]])
>>> allel.heterozygosity_observed(g)
array([0. , 0.33333333, 0. , 0.5 ])
"""
# check inputs
if not hasattr(g, 'count_het') or not hasattr(g, 'count_called'):
g = GenotypeArray(g, copy=False)
# count hets
n_het = np.asarray(g.count_het(axis=1))
n_called = np.asarray(g.count_called(axis=1))
# calculate rate of observed heterozygosity, accounting for variants
# where all calls are missing
with ignore_invalid():
ho = np.where(n_called > 0, n_het / n_called, fill)
return ho
|
python
|
{
"resource": ""
}
|
q990
|
heterozygosity_expected
|
train
|
def heterozygosity_expected(af, ploidy, fill=np.nan):
"""Calculate the expected rate of heterozygosity for each variant
under Hardy-Weinberg equilibrium.
Parameters
----------
af : array_like, float, shape (n_variants, n_alleles)
Allele frequencies array.
ploidy : int
Sample ploidy.
fill : float, optional
Use this value for variants where allele frequencies do not sum to 1.
Returns
-------
he : ndarray, float, shape (n_variants,)
Expected heterozygosity
Examples
--------
>>> import allel
>>> g = allel.GenotypeArray([[[0, 0], [0, 0], [0, 0]],
... [[0, 0], [0, 1], [1, 1]],
... [[0, 0], [1, 1], [2, 2]],
... [[1, 1], [1, 2], [-1, -1]]])
>>> af = g.count_alleles().to_frequencies()
>>> allel.heterozygosity_expected(af, ploidy=2)
array([0. , 0.5 , 0.66666667, 0.375 ])
"""
# check inputs
af = asarray_ndim(af, 2)
# calculate expected heterozygosity
out = 1 - np.sum(np.power(af, ploidy), axis=1)
# fill values where allele frequencies could not be calculated
af_sum = np.sum(af, axis=1)
with ignore_invalid():
out[(af_sum < 1) | np.isnan(af_sum)] = fill
return out
|
python
|
{
"resource": ""
}
|
q991
|
inbreeding_coefficient
|
train
|
def inbreeding_coefficient(g, fill=np.nan):
"""Calculate the inbreeding coefficient for each variant.
Parameters
----------
g : array_like, int, shape (n_variants, n_samples, ploidy)
Genotype array.
fill : float, optional
Use this value for variants where the expected heterozygosity is
zero.
Returns
-------
f : ndarray, float, shape (n_variants,)
Inbreeding coefficient.
Notes
-----
The inbreeding coefficient is calculated as *1 - (Ho/He)* where *Ho* is
the observed heterozygosity and *He* is the expected heterozygosity.
Examples
--------
>>> import allel
>>> g = allel.GenotypeArray([[[0, 0], [0, 0], [0, 0]],
... [[0, 0], [0, 1], [1, 1]],
... [[0, 0], [1, 1], [2, 2]],
... [[1, 1], [1, 2], [-1, -1]]])
>>> allel.inbreeding_coefficient(g)
array([ nan, 0.33333333, 1. , -0.33333333])
"""
# check inputs
if not hasattr(g, 'count_het') or not hasattr(g, 'count_called'):
g = GenotypeArray(g, copy=False)
# calculate observed and expected heterozygosity
ho = heterozygosity_observed(g)
af = g.count_alleles().to_frequencies()
he = heterozygosity_expected(af, ploidy=g.shape[-1], fill=0)
# calculate inbreeding coefficient, accounting for variants with no
# expected heterozygosity
with ignore_invalid():
f = np.where(he > 0, 1 - (ho / he), fill)
return f
|
python
|
{
"resource": ""
}
|
q992
|
mendel_errors
|
train
|
def mendel_errors(parent_genotypes, progeny_genotypes):
"""Locate genotype calls not consistent with Mendelian transmission of
alleles.
Parameters
----------
parent_genotypes : array_like, int, shape (n_variants, 2, 2)
Genotype calls for the two parents.
progeny_genotypes : array_like, int, shape (n_variants, n_progeny, 2)
Genotype calls for the progeny.
Returns
-------
me : ndarray, int, shape (n_variants, n_progeny)
Count of Mendel errors for each progeny genotype call.
Examples
--------
The following are all consistent with Mendelian transmission. Note that a
value of 0 is returned for missing calls::
>>> import allel
>>> import numpy as np
>>> genotypes = np.array([
... # aa x aa -> aa
... [[0, 0], [0, 0], [0, 0], [-1, -1], [-1, -1], [-1, -1]],
... [[1, 1], [1, 1], [1, 1], [-1, -1], [-1, -1], [-1, -1]],
... [[2, 2], [2, 2], [2, 2], [-1, -1], [-1, -1], [-1, -1]],
... # aa x ab -> aa or ab
... [[0, 0], [0, 1], [0, 0], [0, 1], [-1, -1], [-1, -1]],
... [[0, 0], [0, 2], [0, 0], [0, 2], [-1, -1], [-1, -1]],
... [[1, 1], [0, 1], [1, 1], [0, 1], [-1, -1], [-1, -1]],
... # aa x bb -> ab
... [[0, 0], [1, 1], [0, 1], [-1, -1], [-1, -1], [-1, -1]],
... [[0, 0], [2, 2], [0, 2], [-1, -1], [-1, -1], [-1, -1]],
... [[1, 1], [2, 2], [1, 2], [-1, -1], [-1, -1], [-1, -1]],
... # aa x bc -> ab or ac
... [[0, 0], [1, 2], [0, 1], [0, 2], [-1, -1], [-1, -1]],
... [[1, 1], [0, 2], [0, 1], [1, 2], [-1, -1], [-1, -1]],
... # ab x ab -> aa or ab or bb
... [[0, 1], [0, 1], [0, 0], [0, 1], [1, 1], [-1, -1]],
... [[1, 2], [1, 2], [1, 1], [1, 2], [2, 2], [-1, -1]],
... [[0, 2], [0, 2], [0, 0], [0, 2], [2, 2], [-1, -1]],
... # ab x bc -> ab or ac or bb or bc
... [[0, 1], [1, 2], [0, 1], [0, 2], [1, 1], [1, 2]],
... [[0, 1], [0, 2], [0, 0], [0, 1], [0, 1], [1, 2]],
... # ab x cd -> ac or ad or bc or bd
... [[0, 1], [2, 3], [0, 2], [0, 3], [1, 2], [1, 3]],
... ])
>>> me = allel.mendel_errors(genotypes[:, :2], genotypes[:, 2:])
>>> me
array([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
The following are cases of 'non-parental' inheritance where one or two
alleles are found in the progeny that are not present in either parent.
Note that the number of errors may be 1 or 2 depending on the number of
non-parental alleles::
>>> genotypes = np.array([
... # aa x aa -> ab or ac or bb or cc
... [[0, 0], [0, 0], [0, 1], [0, 2], [1, 1], [2, 2]],
... [[1, 1], [1, 1], [0, 1], [1, 2], [0, 0], [2, 2]],
... [[2, 2], [2, 2], [0, 2], [1, 2], [0, 0], [1, 1]],
... # aa x ab -> ac or bc or cc
... [[0, 0], [0, 1], [0, 2], [1, 2], [2, 2], [2, 2]],
... [[0, 0], [0, 2], [0, 1], [1, 2], [1, 1], [1, 1]],
... [[1, 1], [0, 1], [1, 2], [0, 2], [2, 2], [2, 2]],
... # aa x bb -> ac or bc or cc
... [[0, 0], [1, 1], [0, 2], [1, 2], [2, 2], [2, 2]],
... [[0, 0], [2, 2], [0, 1], [1, 2], [1, 1], [1, 1]],
... [[1, 1], [2, 2], [0, 1], [0, 2], [0, 0], [0, 0]],
... # ab x ab -> ac or bc or cc
... [[0, 1], [0, 1], [0, 2], [1, 2], [2, 2], [2, 2]],
... [[0, 2], [0, 2], [0, 1], [1, 2], [1, 1], [1, 1]],
... [[1, 2], [1, 2], [0, 1], [0, 2], [0, 0], [0, 0]],
... # ab x bc -> ad or bd or cd or dd
... [[0, 1], [1, 2], [0, 3], [1, 3], [2, 3], [3, 3]],
... [[0, 1], [0, 2], [0, 3], [1, 3], [2, 3], [3, 3]],
... [[0, 2], [1, 2], [0, 3], [1, 3], [2, 3], [3, 3]],
... # ab x cd -> ae or be or ce or de
... [[0, 1], [2, 3], [0, 4], [1, 4], [2, 4], [3, 4]],
... ])
>>> me = allel.mendel_errors(genotypes[:, :2], genotypes[:, 2:])
>>> me
array([[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 2, 2],
[1, 1, 1, 2],
[1, 1, 1, 2],
[1, 1, 1, 2],
[1, 1, 1, 1]])
The following are cases of 'hemi-parental' inheritance, where progeny
appear to have inherited two copies of an allele found only once in one of
the parents::
>>> genotypes = np.array([
... # aa x ab -> bb
... [[0, 0], [0, 1], [1, 1], [-1, -1]],
... [[0, 0], [0, 2], [2, 2], [-1, -1]],
... [[1, 1], [0, 1], [0, 0], [-1, -1]],
... # ab x bc -> aa or cc
... [[0, 1], [1, 2], [0, 0], [2, 2]],
... [[0, 1], [0, 2], [1, 1], [2, 2]],
... [[0, 2], [1, 2], [0, 0], [1, 1]],
... # ab x cd -> aa or bb or cc or dd
... [[0, 1], [2, 3], [0, 0], [1, 1]],
... [[0, 1], [2, 3], [2, 2], [3, 3]],
... ])
>>> me = allel.mendel_errors(genotypes[:, :2], genotypes[:, 2:])
>>> me
array([[1, 0],
[1, 0],
[1, 0],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1]])
The following are cases of 'uni-parental' inheritance, where progeny
appear to have inherited both alleles from a single parent::
>>> genotypes = np.array([
... # aa x bb -> aa or bb
... [[0, 0], [1, 1], [0, 0], [1, 1]],
... [[0, 0], [2, 2], [0, 0], [2, 2]],
... [[1, 1], [2, 2], [1, 1], [2, 2]],
... # aa x bc -> aa or bc
... [[0, 0], [1, 2], [0, 0], [1, 2]],
... [[1, 1], [0, 2], [1, 1], [0, 2]],
... # ab x cd -> ab or cd
... [[0, 1], [2, 3], [0, 1], [2, 3]],
... ])
>>> me = allel.mendel_errors(genotypes[:, :2], genotypes[:, 2:])
>>> me
array([[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 1]])
"""
# setup
parent_genotypes = GenotypeArray(parent_genotypes)
progeny_genotypes = GenotypeArray(progeny_genotypes)
check_ploidy(parent_genotypes.ploidy, 2)
check_ploidy(progeny_genotypes.ploidy, 2)
# transform into per-call allele counts
max_allele = max(parent_genotypes.max(), progeny_genotypes.max())
parent_gc = parent_genotypes.to_allele_counts(max_allele=max_allele, dtype='i1')
progeny_gc = progeny_genotypes.to_allele_counts(max_allele=max_allele, dtype='i1')
# detect nonparental and hemiparental inheritance by comparing allele
# counts between parents and progeny
max_progeny_gc = parent_gc.clip(max=1).sum(axis=1)
max_progeny_gc = max_progeny_gc[:, np.newaxis, :]
me = (progeny_gc - max_progeny_gc).clip(min=0).sum(axis=2)
# detect uniparental inheritance by finding cases where no alleles are
# shared between parents, then comparing progeny allele counts to each
# parent
p1_gc = parent_gc[:, 0, np.newaxis, :]
p2_gc = parent_gc[:, 1, np.newaxis, :]
# find variants where parents don't share any alleles
is_shared_allele = (p1_gc > 0) & (p2_gc > 0)
no_shared_alleles = ~np.any(is_shared_allele, axis=2)
# find calls where progeny genotype is identical to one or the other parent
me[no_shared_alleles &
(np.all(progeny_gc == p1_gc, axis=2) |
np.all(progeny_gc == p2_gc, axis=2))] = 1
# retrofit where either or both parent has a missing call
me[np.any(parent_genotypes.is_missing(), axis=1)] = 0
return me
|
python
|
{
"resource": ""
}
|
q993
|
paint_transmission
|
train
|
def paint_transmission(parent_haplotypes, progeny_haplotypes):
"""Paint haplotypes inherited from a single diploid parent according to
their allelic inheritance.
Parameters
----------
parent_haplotypes : array_like, int, shape (n_variants, 2)
Both haplotypes from a single diploid parent.
progeny_haplotypes : array_like, int, shape (n_variants, n_progeny)
Haplotypes found in progeny of the given parent, inherited from the
given parent. I.e., haplotypes from gametes of the given parent.
Returns
-------
painting : ndarray, uint8, shape (n_variants, n_progeny)
An array of integers coded as follows: 1 = allele inherited from
first parental haplotype; 2 = allele inherited from second parental
haplotype; 3 = reference allele, also carried by both parental
haplotypes; 4 = non-reference allele, also carried by both parental
haplotypes; 5 = non-parental allele; 6 = either or both parental
alleles missing; 7 = missing allele; 0 = undetermined.
Examples
--------
>>> import allel
>>> haplotypes = allel.HaplotypeArray([
... [0, 0, 0, 1, 2, -1],
... [0, 1, 0, 1, 2, -1],
... [1, 0, 0, 1, 2, -1],
... [1, 1, 0, 1, 2, -1],
... [0, 2, 0, 1, 2, -1],
... [0, -1, 0, 1, 2, -1],
... [-1, 1, 0, 1, 2, -1],
... [-1, -1, 0, 1, 2, -1],
... ], dtype='i1')
>>> painting = allel.paint_transmission(haplotypes[:, :2],
... haplotypes[:, 2:])
>>> painting
array([[3, 5, 5, 7],
[1, 2, 5, 7],
[2, 1, 5, 7],
[5, 4, 5, 7],
[1, 5, 2, 7],
[6, 6, 6, 7],
[6, 6, 6, 7],
[6, 6, 6, 7]], dtype=uint8)
"""
# check inputs
parent_haplotypes = HaplotypeArray(parent_haplotypes)
progeny_haplotypes = HaplotypeArray(progeny_haplotypes)
if parent_haplotypes.n_haplotypes != 2:
raise ValueError('exactly two parental haplotypes should be provided')
# convenience variables
parent1 = parent_haplotypes[:, 0, np.newaxis]
parent2 = parent_haplotypes[:, 1, np.newaxis]
progeny_is_missing = progeny_haplotypes < 0
parent_is_missing = np.any(parent_haplotypes < 0, axis=1)
# need this for broadcasting, but also need to retain original for later
parent_is_missing_bc = parent_is_missing[:, np.newaxis]
parent_diplotype = GenotypeArray(parent_haplotypes[:, np.newaxis, :])
parent_is_hom_ref = parent_diplotype.is_hom_ref()
parent_is_het = parent_diplotype.is_het()
parent_is_hom_alt = parent_diplotype.is_hom_alt()
# identify allele calls where inheritance can be determined
is_callable = ~progeny_is_missing & ~parent_is_missing_bc
is_callable_seg = is_callable & parent_is_het
# main inheritance states
inherit_parent1 = is_callable_seg & (progeny_haplotypes == parent1)
inherit_parent2 = is_callable_seg & (progeny_haplotypes == parent2)
nonseg_ref = (is_callable & parent_is_hom_ref & (progeny_haplotypes == parent1))
nonseg_alt = (is_callable & parent_is_hom_alt & (progeny_haplotypes == parent1))
nonparental = (
is_callable & (progeny_haplotypes != parent1) & (progeny_haplotypes != parent2)
)
# record inheritance states
# N.B., order in which these are set matters
painting = np.zeros(progeny_haplotypes.shape, dtype='u1')
painting[inherit_parent1] = INHERIT_PARENT1
painting[inherit_parent2] = INHERIT_PARENT2
painting[nonseg_ref] = INHERIT_NONSEG_REF
painting[nonseg_alt] = INHERIT_NONSEG_ALT
painting[nonparental] = INHERIT_NONPARENTAL
painting[parent_is_missing] = INHERIT_PARENT_MISSING
painting[progeny_is_missing] = INHERIT_MISSING
return painting
|
python
|
{
"resource": ""
}
|
q994
|
phase_progeny_by_transmission
|
train
|
def phase_progeny_by_transmission(g):
"""Phase progeny genotypes from a trio or cross using Mendelian
transmission.
Parameters
----------
g : array_like, int, shape (n_variants, n_samples, 2)
Genotype array, with parents as first two columns and progeny as
remaining columns.
Returns
-------
g : ndarray, int8, shape (n_variants, n_samples, 2)
Genotype array with progeny phased where possible.
Examples
--------
>>> import allel
>>> g = allel.GenotypeArray([
... [[0, 0], [0, 0], [0, 0]],
... [[1, 1], [1, 1], [1, 1]],
... [[0, 0], [1, 1], [0, 1]],
... [[1, 1], [0, 0], [0, 1]],
... [[0, 0], [0, 1], [0, 0]],
... [[0, 0], [0, 1], [0, 1]],
... [[0, 1], [0, 0], [0, 1]],
... [[0, 1], [0, 1], [0, 1]],
... [[0, 1], [1, 2], [0, 1]],
... [[1, 2], [0, 1], [1, 2]],
... [[0, 1], [2, 3], [0, 2]],
... [[2, 3], [0, 1], [1, 3]],
... [[0, 0], [0, 0], [-1, -1]],
... [[0, 0], [0, 0], [1, 1]],
... ], dtype='i1')
>>> g = allel.phase_progeny_by_transmission(g)
>>> print(g.to_str(row_threshold=None))
0/0 0/0 0|0
1/1 1/1 1|1
0/0 1/1 0|1
1/1 0/0 1|0
0/0 0/1 0|0
0/0 0/1 0|1
0/1 0/0 1|0
0/1 0/1 0/1
0/1 1/2 0|1
1/2 0/1 2|1
0/1 2/3 0|2
2/3 0/1 3|1
0/0 0/0 ./.
0/0 0/0 1/1
>>> g.is_phased
array([[False, False, True],
[False, False, True],
[False, False, True],
[False, False, True],
[False, False, True],
[False, False, True],
[False, False, True],
[False, False, False],
[False, False, True],
[False, False, True],
[False, False, True],
[False, False, True],
[False, False, False],
[False, False, False]])
"""
# setup
g = GenotypeArray(g, dtype='i1', copy=True)
check_ploidy(g.ploidy, 2)
check_min_samples(g.n_samples, 3)
# run the phasing
# N.B., a copy has already been made, so no need to make memoryview safe
is_phased = _opt_phase_progeny_by_transmission(g.values)
g.is_phased = np.asarray(is_phased).view(bool)
# outputs
return g
|
python
|
{
"resource": ""
}
|
q995
|
phase_parents_by_transmission
|
train
|
def phase_parents_by_transmission(g, window_size):
"""Phase parent genotypes from a trio or cross, given progeny genotypes
already phased by Mendelian transmission.
Parameters
----------
g : GenotypeArray
Genotype array, with parents as first two columns and progeny as
remaining columns, where progeny genotypes are already phased.
window_size : int
Number of previous heterozygous sites to include when phasing each
parent. A number somewhere between 10 and 100 may be appropriate,
depending on levels of heterozygosity and quality of data.
Returns
-------
g : GenotypeArray
Genotype array with parents phased where possible.
"""
# setup
check_type(g, GenotypeArray)
check_dtype(g.values, 'i1')
check_ploidy(g.ploidy, 2)
if g.is_phased is None:
raise ValueError('genotype array must first have progeny phased by transmission')
check_min_samples(g.n_samples, 3)
# run the phasing
g._values = memoryview_safe(g.values)
g._is_phased = memoryview_safe(g.is_phased)
_opt_phase_parents_by_transmission(g.values, g.is_phased.view('u1'), window_size)
# outputs
return g
|
python
|
{
"resource": ""
}
|
q996
|
phase_by_transmission
|
train
|
def phase_by_transmission(g, window_size, copy=True):
"""Phase genotypes in a trio or cross where possible using Mendelian
transmission.
Parameters
----------
g : array_like, int, shape (n_variants, n_samples, 2)
Genotype array, with parents as first two columns and progeny as
remaining columns.
window_size : int
Number of previous heterozygous sites to include when phasing each
parent. A number somewhere between 10 and 100 may be appropriate,
depending on levels of heterozygosity and quality of data.
copy : bool, optional
If False, attempt to phase genotypes in-place. Note that this is
only possible if the input array has int8 dtype, otherwise a copy is
always made regardless of this parameter.
Returns
-------
g : GenotypeArray
Genotype array with progeny phased where possible.
"""
# setup
g = np.asarray(g, dtype='i1')
g = GenotypeArray(g, copy=copy)
g._values = memoryview_safe(g.values)
check_ploidy(g.ploidy, 2)
check_min_samples(g.n_samples, 3)
# phase the progeny
is_phased = _opt_phase_progeny_by_transmission(g.values)
g.is_phased = np.asarray(is_phased).view(bool)
# phase the parents
_opt_phase_parents_by_transmission(g.values, is_phased, window_size)
return g
|
python
|
{
"resource": ""
}
|
q997
|
get_blen_array
|
train
|
def get_blen_array(data, blen=None):
"""Try to guess a reasonable block length to use for block-wise iteration
over `data`."""
if blen is None:
if hasattr(data, 'chunklen'):
# bcolz carray
return data.chunklen
elif hasattr(data, 'chunks') and \
hasattr(data, 'shape') and \
hasattr(data.chunks, '__len__') and \
hasattr(data.shape, '__len__') and \
len(data.chunks) == len(data.shape):
# something like h5py dataset
return data.chunks[0]
else:
# fall back to something simple, ~1Mb chunks
row = np.asarray(data[0])
return max(1, (2**20) // row.nbytes)
else:
return blen
|
python
|
{
"resource": ""
}
|
q998
|
h5fmem
|
train
|
def h5fmem(**kwargs):
"""Create an in-memory HDF5 file."""
# need a file name even tho nothing is ever written
fn = tempfile.mktemp()
# file creation args
kwargs['mode'] = 'w'
kwargs['driver'] = 'core'
kwargs['backing_store'] = False
# open HDF5 file
h5f = h5py.File(fn, **kwargs)
return h5f
|
python
|
{
"resource": ""
}
|
q999
|
h5ftmp
|
train
|
def h5ftmp(**kwargs):
"""Create an HDF5 file backed by a temporary file."""
# create temporary file name
suffix = kwargs.pop('suffix', '.h5')
prefix = kwargs.pop('prefix', 'scikit_allel_')
tempdir = kwargs.pop('dir', None)
fn = tempfile.mktemp(suffix=suffix, prefix=prefix, dir=tempdir)
atexit.register(os.remove, fn)
# file creation args
kwargs['mode'] = 'w'
# open HDF5 file
h5f = h5py.File(fn, **kwargs)
return h5f
|
python
|
{
"resource": ""
}
|
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