code stringlengths 75 104k | docstring stringlengths 1 46.9k | text stringlengths 164 112k |
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def put_container(self, container, headers=None, query=None, cdn=False,
body=None):
"""
PUTs the container and returns the results. This is usually
done to create new containers and can also be used to set
X-Container-Meta-xxx headers. Note that if the container
already exists, any existing X-Container-Meta-xxx headers will
remain untouched. To remove an X-Container-Meta-xxx header,
send the header with an empty string as its value.
:param container: The name of the container.
:param headers: Additional headers to send with the request.
:param query: Set to a dict of query values to send on the
query string of the request.
:param cdn: If set True, the CDN management interface will be
used.
:param body: Some container PUT requests, like the
extract-archive bulk upload request, take a body.
:returns: A tuple of (status, reason, headers, contents).
:status: is an int for the HTTP status code.
:reason: is the str for the HTTP status (ex: "Ok").
:headers: is a dict with all lowercase keys of the HTTP
headers; if a header has multiple values, it will be a
list.
:contents: is the str for the HTTP body.
"""
path = self._container_path(container)
return self.request(
'PUT', path, body or '', headers, query=query, cdn=cdn) | PUTs the container and returns the results. This is usually
done to create new containers and can also be used to set
X-Container-Meta-xxx headers. Note that if the container
already exists, any existing X-Container-Meta-xxx headers will
remain untouched. To remove an X-Container-Meta-xxx header,
send the header with an empty string as its value.
:param container: The name of the container.
:param headers: Additional headers to send with the request.
:param query: Set to a dict of query values to send on the
query string of the request.
:param cdn: If set True, the CDN management interface will be
used.
:param body: Some container PUT requests, like the
extract-archive bulk upload request, take a body.
:returns: A tuple of (status, reason, headers, contents).
:status: is an int for the HTTP status code.
:reason: is the str for the HTTP status (ex: "Ok").
:headers: is a dict with all lowercase keys of the HTTP
headers; if a header has multiple values, it will be a
list.
:contents: is the str for the HTTP body. | Below is the the instruction that describes the task:
### Input:
PUTs the container and returns the results. This is usually
done to create new containers and can also be used to set
X-Container-Meta-xxx headers. Note that if the container
already exists, any existing X-Container-Meta-xxx headers will
remain untouched. To remove an X-Container-Meta-xxx header,
send the header with an empty string as its value.
:param container: The name of the container.
:param headers: Additional headers to send with the request.
:param query: Set to a dict of query values to send on the
query string of the request.
:param cdn: If set True, the CDN management interface will be
used.
:param body: Some container PUT requests, like the
extract-archive bulk upload request, take a body.
:returns: A tuple of (status, reason, headers, contents).
:status: is an int for the HTTP status code.
:reason: is the str for the HTTP status (ex: "Ok").
:headers: is a dict with all lowercase keys of the HTTP
headers; if a header has multiple values, it will be a
list.
:contents: is the str for the HTTP body.
### Response:
def put_container(self, container, headers=None, query=None, cdn=False,
body=None):
"""
PUTs the container and returns the results. This is usually
done to create new containers and can also be used to set
X-Container-Meta-xxx headers. Note that if the container
already exists, any existing X-Container-Meta-xxx headers will
remain untouched. To remove an X-Container-Meta-xxx header,
send the header with an empty string as its value.
:param container: The name of the container.
:param headers: Additional headers to send with the request.
:param query: Set to a dict of query values to send on the
query string of the request.
:param cdn: If set True, the CDN management interface will be
used.
:param body: Some container PUT requests, like the
extract-archive bulk upload request, take a body.
:returns: A tuple of (status, reason, headers, contents).
:status: is an int for the HTTP status code.
:reason: is the str for the HTTP status (ex: "Ok").
:headers: is a dict with all lowercase keys of the HTTP
headers; if a header has multiple values, it will be a
list.
:contents: is the str for the HTTP body.
"""
path = self._container_path(container)
return self.request(
'PUT', path, body or '', headers, query=query, cdn=cdn) |
def read_folder(folder):
"""
Parameters
----------
folder : str
Returns
-------
list of HandwrittenData objects
"""
hwr_objects = []
for filepath in natsort.natsorted(glob.glob("%s/*.inkml" % folder)):
tmp = inkml.read(filepath)
for hwr in tmp.to_single_symbol_list():
hwr_objects.append(hwr)
logging.info("Done reading formulas")
save_raw_pickle(hwr_objects)
return hwr_objects | Parameters
----------
folder : str
Returns
-------
list of HandwrittenData objects | Below is the the instruction that describes the task:
### Input:
Parameters
----------
folder : str
Returns
-------
list of HandwrittenData objects
### Response:
def read_folder(folder):
"""
Parameters
----------
folder : str
Returns
-------
list of HandwrittenData objects
"""
hwr_objects = []
for filepath in natsort.natsorted(glob.glob("%s/*.inkml" % folder)):
tmp = inkml.read(filepath)
for hwr in tmp.to_single_symbol_list():
hwr_objects.append(hwr)
logging.info("Done reading formulas")
save_raw_pickle(hwr_objects)
return hwr_objects |
def qos_queue_scheduler_strict_priority_dwrr_traffic_class_last(self, **kwargs):
"""Auto Generated Code
"""
config = ET.Element("config")
qos = ET.SubElement(config, "qos", xmlns="urn:brocade.com:mgmt:brocade-qos")
queue = ET.SubElement(qos, "queue")
scheduler = ET.SubElement(queue, "scheduler")
strict_priority = ET.SubElement(scheduler, "strict-priority")
dwrr_traffic_class_last = ET.SubElement(strict_priority, "dwrr-traffic-class-last")
dwrr_traffic_class_last.text = kwargs.pop('dwrr_traffic_class_last')
callback = kwargs.pop('callback', self._callback)
return callback(config) | Auto Generated Code | Below is the the instruction that describes the task:
### Input:
Auto Generated Code
### Response:
def qos_queue_scheduler_strict_priority_dwrr_traffic_class_last(self, **kwargs):
"""Auto Generated Code
"""
config = ET.Element("config")
qos = ET.SubElement(config, "qos", xmlns="urn:brocade.com:mgmt:brocade-qos")
queue = ET.SubElement(qos, "queue")
scheduler = ET.SubElement(queue, "scheduler")
strict_priority = ET.SubElement(scheduler, "strict-priority")
dwrr_traffic_class_last = ET.SubElement(strict_priority, "dwrr-traffic-class-last")
dwrr_traffic_class_last.text = kwargs.pop('dwrr_traffic_class_last')
callback = kwargs.pop('callback', self._callback)
return callback(config) |
def _summarize_o_mutation_type(model):
"""
This function create the actual mutation io summary corresponding to the model
"""
from nautilus.api.util import summarize_mutation_io
# compute the appropriate name for the object
object_type_name = get_model_string(model)
# return a mutation io object
return summarize_mutation_io(
name=object_type_name,
type=_summarize_object_type(model),
required=False
) | This function create the actual mutation io summary corresponding to the model | Below is the the instruction that describes the task:
### Input:
This function create the actual mutation io summary corresponding to the model
### Response:
def _summarize_o_mutation_type(model):
"""
This function create the actual mutation io summary corresponding to the model
"""
from nautilus.api.util import summarize_mutation_io
# compute the appropriate name for the object
object_type_name = get_model_string(model)
# return a mutation io object
return summarize_mutation_io(
name=object_type_name,
type=_summarize_object_type(model),
required=False
) |
def _can_retry(self, batch, error):
"""
We can retry a send if the error is transient and the number of
attempts taken is fewer than the maximum allowed
"""
return (batch.attempts < self.config['retries']
and getattr(error, 'retriable', False)) | We can retry a send if the error is transient and the number of
attempts taken is fewer than the maximum allowed | Below is the the instruction that describes the task:
### Input:
We can retry a send if the error is transient and the number of
attempts taken is fewer than the maximum allowed
### Response:
def _can_retry(self, batch, error):
"""
We can retry a send if the error is transient and the number of
attempts taken is fewer than the maximum allowed
"""
return (batch.attempts < self.config['retries']
and getattr(error, 'retriable', False)) |
def get_merkle_root(block_representation, coin_symbol='btc', api_key=None):
'''
Takes a block_representation and returns the merkle root
'''
return get_block_overview(block_representation=block_representation,
coin_symbol=coin_symbol, txn_limit=1, api_key=api_key)['mrkl_root'] | Takes a block_representation and returns the merkle root | Below is the the instruction that describes the task:
### Input:
Takes a block_representation and returns the merkle root
### Response:
def get_merkle_root(block_representation, coin_symbol='btc', api_key=None):
'''
Takes a block_representation and returns the merkle root
'''
return get_block_overview(block_representation=block_representation,
coin_symbol=coin_symbol, txn_limit=1, api_key=api_key)['mrkl_root'] |
def save_array(store, arr, **kwargs):
"""Convenience function to save a NumPy array to the local file system, following a
similar API to the NumPy save() function.
Parameters
----------
store : MutableMapping or string
Store or path to directory in file system or name of zip file.
arr : ndarray
NumPy array with data to save.
kwargs
Passed through to :func:`create`, e.g., compressor.
Examples
--------
Save an array to a directory on the file system (uses a :class:`DirectoryStore`)::
>>> import zarr
>>> import numpy as np
>>> arr = np.arange(10000)
>>> zarr.save_array('data/example.zarr', arr)
>>> zarr.load('data/example.zarr')
array([ 0, 1, 2, ..., 9997, 9998, 9999])
Save an array to a single file (uses a :class:`ZipStore`)::
>>> zarr.save_array('data/example.zip', arr)
>>> zarr.load('data/example.zip')
array([ 0, 1, 2, ..., 9997, 9998, 9999])
"""
may_need_closing = isinstance(store, str)
store = normalize_store_arg(store, clobber=True)
try:
_create_array(arr, store=store, overwrite=True, **kwargs)
finally:
if may_need_closing and hasattr(store, 'close'):
# needed to ensure zip file records are written
store.close() | Convenience function to save a NumPy array to the local file system, following a
similar API to the NumPy save() function.
Parameters
----------
store : MutableMapping or string
Store or path to directory in file system or name of zip file.
arr : ndarray
NumPy array with data to save.
kwargs
Passed through to :func:`create`, e.g., compressor.
Examples
--------
Save an array to a directory on the file system (uses a :class:`DirectoryStore`)::
>>> import zarr
>>> import numpy as np
>>> arr = np.arange(10000)
>>> zarr.save_array('data/example.zarr', arr)
>>> zarr.load('data/example.zarr')
array([ 0, 1, 2, ..., 9997, 9998, 9999])
Save an array to a single file (uses a :class:`ZipStore`)::
>>> zarr.save_array('data/example.zip', arr)
>>> zarr.load('data/example.zip')
array([ 0, 1, 2, ..., 9997, 9998, 9999]) | Below is the the instruction that describes the task:
### Input:
Convenience function to save a NumPy array to the local file system, following a
similar API to the NumPy save() function.
Parameters
----------
store : MutableMapping or string
Store or path to directory in file system or name of zip file.
arr : ndarray
NumPy array with data to save.
kwargs
Passed through to :func:`create`, e.g., compressor.
Examples
--------
Save an array to a directory on the file system (uses a :class:`DirectoryStore`)::
>>> import zarr
>>> import numpy as np
>>> arr = np.arange(10000)
>>> zarr.save_array('data/example.zarr', arr)
>>> zarr.load('data/example.zarr')
array([ 0, 1, 2, ..., 9997, 9998, 9999])
Save an array to a single file (uses a :class:`ZipStore`)::
>>> zarr.save_array('data/example.zip', arr)
>>> zarr.load('data/example.zip')
array([ 0, 1, 2, ..., 9997, 9998, 9999])
### Response:
def save_array(store, arr, **kwargs):
"""Convenience function to save a NumPy array to the local file system, following a
similar API to the NumPy save() function.
Parameters
----------
store : MutableMapping or string
Store or path to directory in file system or name of zip file.
arr : ndarray
NumPy array with data to save.
kwargs
Passed through to :func:`create`, e.g., compressor.
Examples
--------
Save an array to a directory on the file system (uses a :class:`DirectoryStore`)::
>>> import zarr
>>> import numpy as np
>>> arr = np.arange(10000)
>>> zarr.save_array('data/example.zarr', arr)
>>> zarr.load('data/example.zarr')
array([ 0, 1, 2, ..., 9997, 9998, 9999])
Save an array to a single file (uses a :class:`ZipStore`)::
>>> zarr.save_array('data/example.zip', arr)
>>> zarr.load('data/example.zip')
array([ 0, 1, 2, ..., 9997, 9998, 9999])
"""
may_need_closing = isinstance(store, str)
store = normalize_store_arg(store, clobber=True)
try:
_create_array(arr, store=store, overwrite=True, **kwargs)
finally:
if may_need_closing and hasattr(store, 'close'):
# needed to ensure zip file records are written
store.close() |
def sign_extend(self, new_length):
"""
Unary operation: SignExtend
:param new_length: New length after sign-extension
:return: A new StridedInterval
"""
msb = self.extract(self.bits - 1, self.bits - 1).eval(2)
if msb == [ 0 ]:
# All positive numbers
return self.zero_extend(new_length)
if msb == [ 1 ]:
# All negative numbers
si = self.copy()
si._bits = new_length
mask = (2 ** new_length - 1) - (2 ** self.bits - 1)
si._lower_bound |= mask
si._upper_bound |= mask
else:
# Both positive numbers and negative numbers
numbers = self._nsplit()
# Since there are both positive and negative numbers, there must be two bounds after nsplit
# assert len(numbers) == 2
all_resulting_intervals = list()
assert len(numbers) > 0
for n in numbers:
a, b = n.lower_bound, n.upper_bound
mask_a = 0
mask_b = 0
mask_n = ((1 << (new_length - n.bits)) - 1) << n.bits
if StridedInterval._get_msb(a, n.bits) == 1:
mask_a = mask_n
if StridedInterval._get_msb(b, n.bits) == 1:
mask_b = mask_n
si_ = StridedInterval(bits=new_length, stride=n.stride, lower_bound=a | mask_a, upper_bound=b | mask_b)
all_resulting_intervals.append(si_)
si = StridedInterval.least_upper_bound(*all_resulting_intervals).normalize()
si.uninitialized = self.uninitialized
return si | Unary operation: SignExtend
:param new_length: New length after sign-extension
:return: A new StridedInterval | Below is the the instruction that describes the task:
### Input:
Unary operation: SignExtend
:param new_length: New length after sign-extension
:return: A new StridedInterval
### Response:
def sign_extend(self, new_length):
"""
Unary operation: SignExtend
:param new_length: New length after sign-extension
:return: A new StridedInterval
"""
msb = self.extract(self.bits - 1, self.bits - 1).eval(2)
if msb == [ 0 ]:
# All positive numbers
return self.zero_extend(new_length)
if msb == [ 1 ]:
# All negative numbers
si = self.copy()
si._bits = new_length
mask = (2 ** new_length - 1) - (2 ** self.bits - 1)
si._lower_bound |= mask
si._upper_bound |= mask
else:
# Both positive numbers and negative numbers
numbers = self._nsplit()
# Since there are both positive and negative numbers, there must be two bounds after nsplit
# assert len(numbers) == 2
all_resulting_intervals = list()
assert len(numbers) > 0
for n in numbers:
a, b = n.lower_bound, n.upper_bound
mask_a = 0
mask_b = 0
mask_n = ((1 << (new_length - n.bits)) - 1) << n.bits
if StridedInterval._get_msb(a, n.bits) == 1:
mask_a = mask_n
if StridedInterval._get_msb(b, n.bits) == 1:
mask_b = mask_n
si_ = StridedInterval(bits=new_length, stride=n.stride, lower_bound=a | mask_a, upper_bound=b | mask_b)
all_resulting_intervals.append(si_)
si = StridedInterval.least_upper_bound(*all_resulting_intervals).normalize()
si.uninitialized = self.uninitialized
return si |
def verify(self, signature):
"""Verifies a signature
:raises InvalidJWSSignature: if the verification fails.
"""
try:
payload = self._payload()
sigin = b'.'.join([self.protected.encode('utf-8'), payload])
self.engine.verify(self.key, sigin, signature)
except Exception as e: # pylint: disable=broad-except
raise InvalidJWSSignature('Verification failed', repr(e))
return True | Verifies a signature
:raises InvalidJWSSignature: if the verification fails. | Below is the the instruction that describes the task:
### Input:
Verifies a signature
:raises InvalidJWSSignature: if the verification fails.
### Response:
def verify(self, signature):
"""Verifies a signature
:raises InvalidJWSSignature: if the verification fails.
"""
try:
payload = self._payload()
sigin = b'.'.join([self.protected.encode('utf-8'), payload])
self.engine.verify(self.key, sigin, signature)
except Exception as e: # pylint: disable=broad-except
raise InvalidJWSSignature('Verification failed', repr(e))
return True |
def main():
"""Handles external calling for this module
Execute this python module and provide the args shown below to
external call this module to send Slack messages with attachments!
:return: None
"""
log = logging.getLogger(mod_logger + '.main')
parser = argparse.ArgumentParser(description='This Python module allows '
'sending Slack messages.')
parser.add_argument('-u', '--url', help='Slack webhook URL', required=True)
parser.add_argument('-t', '--text', help='Text of the message', required=True)
parser.add_argument('-n', '--channel', help='Slack channel', required=True)
parser.add_argument('-i', '--icon', help='URL for the Slack icon', required=False)
parser.add_argument('-c', '--color', help='Color of the Slack post', required=False)
parser.add_argument('-a', '--attachment', help='Text for the Slack Attachment', required=False)
parser.add_argument('-p', '--pretext', help='Pretext for the Slack attachment', required=False)
args = parser.parse_args()
# Create the SlackMessage object
try:
slack_msg = SlackMessage(args.url, channel=args.channel, icon_url=args.icon, text=args.text)
except ValueError as e:
msg = 'Unable to create slack message\n{ex}'.format(ex=e)
log.error(msg)
print(msg)
return
# If provided, create the SlackAttachment object
if args.attachment:
try:
slack_att = SlackAttachment(fallback=args.attachment, color=args.color,
pretext=args.pretext, text=args.attachment)
except ValueError:
_, ex, trace = sys.exc_info()
log.error('Unable to create slack attachment\n{e}'.format(e=str(ex)))
return
slack_msg.add_attachment(slack_att)
# Send Slack message
try:
slack_msg.send()
except(TypeError, ValueError, IOError):
_, ex, trace = sys.exc_info()
log.error('Unable to send Slack message\n{e}'.format(e=str(ex)))
return
log.debug('Your message has been Slacked successfully!') | Handles external calling for this module
Execute this python module and provide the args shown below to
external call this module to send Slack messages with attachments!
:return: None | Below is the the instruction that describes the task:
### Input:
Handles external calling for this module
Execute this python module and provide the args shown below to
external call this module to send Slack messages with attachments!
:return: None
### Response:
def main():
"""Handles external calling for this module
Execute this python module and provide the args shown below to
external call this module to send Slack messages with attachments!
:return: None
"""
log = logging.getLogger(mod_logger + '.main')
parser = argparse.ArgumentParser(description='This Python module allows '
'sending Slack messages.')
parser.add_argument('-u', '--url', help='Slack webhook URL', required=True)
parser.add_argument('-t', '--text', help='Text of the message', required=True)
parser.add_argument('-n', '--channel', help='Slack channel', required=True)
parser.add_argument('-i', '--icon', help='URL for the Slack icon', required=False)
parser.add_argument('-c', '--color', help='Color of the Slack post', required=False)
parser.add_argument('-a', '--attachment', help='Text for the Slack Attachment', required=False)
parser.add_argument('-p', '--pretext', help='Pretext for the Slack attachment', required=False)
args = parser.parse_args()
# Create the SlackMessage object
try:
slack_msg = SlackMessage(args.url, channel=args.channel, icon_url=args.icon, text=args.text)
except ValueError as e:
msg = 'Unable to create slack message\n{ex}'.format(ex=e)
log.error(msg)
print(msg)
return
# If provided, create the SlackAttachment object
if args.attachment:
try:
slack_att = SlackAttachment(fallback=args.attachment, color=args.color,
pretext=args.pretext, text=args.attachment)
except ValueError:
_, ex, trace = sys.exc_info()
log.error('Unable to create slack attachment\n{e}'.format(e=str(ex)))
return
slack_msg.add_attachment(slack_att)
# Send Slack message
try:
slack_msg.send()
except(TypeError, ValueError, IOError):
_, ex, trace = sys.exc_info()
log.error('Unable to send Slack message\n{e}'.format(e=str(ex)))
return
log.debug('Your message has been Slacked successfully!') |
def poweron_refresh(self):
"""Keep requesting all attributes until it works.
Immediately after a power on event (POW1) the AVR is inconsistent with
which attributes can be successfully queried. When we detect that
power has just been turned on, we loop every second making a bulk
query for every known attribute. This continues until we detect that
values have been returned for at least one input name (this seems to
be the laggiest of all the attributes)
"""
if self._poweron_refresh_successful:
return
else:
self.refresh_all()
self._loop.call_later(2, self.poweron_refresh) | Keep requesting all attributes until it works.
Immediately after a power on event (POW1) the AVR is inconsistent with
which attributes can be successfully queried. When we detect that
power has just been turned on, we loop every second making a bulk
query for every known attribute. This continues until we detect that
values have been returned for at least one input name (this seems to
be the laggiest of all the attributes) | Below is the the instruction that describes the task:
### Input:
Keep requesting all attributes until it works.
Immediately after a power on event (POW1) the AVR is inconsistent with
which attributes can be successfully queried. When we detect that
power has just been turned on, we loop every second making a bulk
query for every known attribute. This continues until we detect that
values have been returned for at least one input name (this seems to
be the laggiest of all the attributes)
### Response:
def poweron_refresh(self):
"""Keep requesting all attributes until it works.
Immediately after a power on event (POW1) the AVR is inconsistent with
which attributes can be successfully queried. When we detect that
power has just been turned on, we loop every second making a bulk
query for every known attribute. This continues until we detect that
values have been returned for at least one input name (this seems to
be the laggiest of all the attributes)
"""
if self._poweron_refresh_successful:
return
else:
self.refresh_all()
self._loop.call_later(2, self.poweron_refresh) |
def save_lyrics(self, filename=None, extension='json', verbose=True,
overwrite=None, binary_encoding=False):
"""Allows user to save song lyrics from Song object to a .json or .txt file."""
extension = extension.lstrip(".")
assert (extension == 'json') or (extension == 'txt'), "format_ must be JSON or TXT"
# Determine the filename
if filename:
for ext in ["txt", "TXT", "json", "JSON"]:
filename = filename.replace("." + ext, "")
filename += "." + extension
else:
filename = "Lyrics_{}_{}.{}".format(self.artist.replace(" ", ""),
self.title.replace(" ", ""),
extension).lower()
filename = self._sanitize_filename(filename)
# Check if file already exists
write_file = False
if not os.path.isfile(filename):
write_file = True
elif overwrite:
write_file = True
else:
if input("{} already exists. Overwrite?\n(y/n): ".format(filename)).lower() == 'y':
write_file = True
# Format lyrics as either .txt or .json
if extension == 'json':
lyrics_to_write = {'songs': [], 'artist': self.artist}
lyrics_to_write['songs'].append(self.to_dict())
else:
lyrics_to_write = self.lyrics
if binary_encoding:
lyrics_to_write = lyrics_to_write.encode('utf8')
# Write the lyrics to either a .json or .txt file
if write_file:
with open(filename, 'wb' if binary_encoding else 'w') as lyrics_file:
if extension == 'json':
json.dump(lyrics_to_write, lyrics_file)
else:
lyrics_file.write(lyrics_to_write)
if verbose:
print('Wrote {} to {}.'.format(self.title, filename))
else:
if verbose:
print('Skipping file save.\n')
return lyrics_to_write | Allows user to save song lyrics from Song object to a .json or .txt file. | Below is the the instruction that describes the task:
### Input:
Allows user to save song lyrics from Song object to a .json or .txt file.
### Response:
def save_lyrics(self, filename=None, extension='json', verbose=True,
overwrite=None, binary_encoding=False):
"""Allows user to save song lyrics from Song object to a .json or .txt file."""
extension = extension.lstrip(".")
assert (extension == 'json') or (extension == 'txt'), "format_ must be JSON or TXT"
# Determine the filename
if filename:
for ext in ["txt", "TXT", "json", "JSON"]:
filename = filename.replace("." + ext, "")
filename += "." + extension
else:
filename = "Lyrics_{}_{}.{}".format(self.artist.replace(" ", ""),
self.title.replace(" ", ""),
extension).lower()
filename = self._sanitize_filename(filename)
# Check if file already exists
write_file = False
if not os.path.isfile(filename):
write_file = True
elif overwrite:
write_file = True
else:
if input("{} already exists. Overwrite?\n(y/n): ".format(filename)).lower() == 'y':
write_file = True
# Format lyrics as either .txt or .json
if extension == 'json':
lyrics_to_write = {'songs': [], 'artist': self.artist}
lyrics_to_write['songs'].append(self.to_dict())
else:
lyrics_to_write = self.lyrics
if binary_encoding:
lyrics_to_write = lyrics_to_write.encode('utf8')
# Write the lyrics to either a .json or .txt file
if write_file:
with open(filename, 'wb' if binary_encoding else 'w') as lyrics_file:
if extension == 'json':
json.dump(lyrics_to_write, lyrics_file)
else:
lyrics_file.write(lyrics_to_write)
if verbose:
print('Wrote {} to {}.'.format(self.title, filename))
else:
if verbose:
print('Skipping file save.\n')
return lyrics_to_write |
def get_status(self, instance):
"""Retrives a status of a field from cache. Fields in state 'error' and
'complete' will not retain the status after the call.
"""
status_key, status = self._get_status(instance)
if status['state'] in ['complete', 'error']:
cache.delete(status_key)
return status | Retrives a status of a field from cache. Fields in state 'error' and
'complete' will not retain the status after the call. | Below is the the instruction that describes the task:
### Input:
Retrives a status of a field from cache. Fields in state 'error' and
'complete' will not retain the status after the call.
### Response:
def get_status(self, instance):
"""Retrives a status of a field from cache. Fields in state 'error' and
'complete' will not retain the status after the call.
"""
status_key, status = self._get_status(instance)
if status['state'] in ['complete', 'error']:
cache.delete(status_key)
return status |
def parse_all(self):
"""Parse the __all__ definition in a module."""
assert self.current.value == "__all__"
self.consume(tk.NAME)
if self.current.value != "=":
raise AllError("Could not evaluate contents of __all__. ")
self.consume(tk.OP)
if self.current.value not in "([":
raise AllError("Could not evaluate contents of __all__. ")
self.consume(tk.OP)
self.all = []
all_content = "("
while self.current.kind != tk.OP or self.current.value not in ")]":
if self.current.kind in (tk.NL, tk.COMMENT):
pass
elif self.current.kind == tk.STRING or self.current.value == ",":
all_content += self.current.value
else:
raise AllError(
"Unexpected token kind in __all__: {!r}. ".format(
self.current.kind
)
)
self.stream.move()
self.consume(tk.OP)
all_content += ")"
try:
self.all = eval(all_content, {})
except BaseException as e:
raise AllError(
"Could not evaluate contents of __all__."
"\bThe value was {}. The exception was:\n{}".format(all_content, e)
) | Parse the __all__ definition in a module. | Below is the the instruction that describes the task:
### Input:
Parse the __all__ definition in a module.
### Response:
def parse_all(self):
"""Parse the __all__ definition in a module."""
assert self.current.value == "__all__"
self.consume(tk.NAME)
if self.current.value != "=":
raise AllError("Could not evaluate contents of __all__. ")
self.consume(tk.OP)
if self.current.value not in "([":
raise AllError("Could not evaluate contents of __all__. ")
self.consume(tk.OP)
self.all = []
all_content = "("
while self.current.kind != tk.OP or self.current.value not in ")]":
if self.current.kind in (tk.NL, tk.COMMENT):
pass
elif self.current.kind == tk.STRING or self.current.value == ",":
all_content += self.current.value
else:
raise AllError(
"Unexpected token kind in __all__: {!r}. ".format(
self.current.kind
)
)
self.stream.move()
self.consume(tk.OP)
all_content += ")"
try:
self.all = eval(all_content, {})
except BaseException as e:
raise AllError(
"Could not evaluate contents of __all__."
"\bThe value was {}. The exception was:\n{}".format(all_content, e)
) |
def frame_to_sample(self, frame_index):
"""
Return a tuple containing the indices of the sample which are the first sample and the end (exclusive)
of the frame with the given index.
"""
start = frame_index * self.hop_size
end = start + self.frame_size
return start, end | Return a tuple containing the indices of the sample which are the first sample and the end (exclusive)
of the frame with the given index. | Below is the the instruction that describes the task:
### Input:
Return a tuple containing the indices of the sample which are the first sample and the end (exclusive)
of the frame with the given index.
### Response:
def frame_to_sample(self, frame_index):
"""
Return a tuple containing the indices of the sample which are the first sample and the end (exclusive)
of the frame with the given index.
"""
start = frame_index * self.hop_size
end = start + self.frame_size
return start, end |
def p_simple_command_element(p):
'''simple_command_element : WORD
| ASSIGNMENT_WORD
| redirection'''
if isinstance(p[1], ast.node):
p[0] = [p[1]]
return
parserobj = p.context
p[0] = [_expandword(parserobj, p.slice[1])]
# change the word node to an assignment if necessary
if p.slice[1].ttype == tokenizer.tokentype.ASSIGNMENT_WORD:
p[0][0].kind = 'assignment' | simple_command_element : WORD
| ASSIGNMENT_WORD
| redirection | Below is the the instruction that describes the task:
### Input:
simple_command_element : WORD
| ASSIGNMENT_WORD
| redirection
### Response:
def p_simple_command_element(p):
'''simple_command_element : WORD
| ASSIGNMENT_WORD
| redirection'''
if isinstance(p[1], ast.node):
p[0] = [p[1]]
return
parserobj = p.context
p[0] = [_expandword(parserobj, p.slice[1])]
# change the word node to an assignment if necessary
if p.slice[1].ttype == tokenizer.tokentype.ASSIGNMENT_WORD:
p[0][0].kind = 'assignment' |
def pmag_results_extract(res_file="pmag_results.txt", crit_file="", spec_file="",
age_file="", latex=False, grade=False, WD="."):
"""
Generate tab delimited output file(s) with result data.
Save output files and return True if successful.
Possible output files: Directions, Intensities, SiteNfo, Criteria,
Specimens
Optional Parameters (defaults are used if not specified)
----------
res_file : name of pmag_results file (default is "pmag_results.txt")
crit_file : name of criteria file (default is "pmag_criteria.txt")
spec_file : name of specimen file (default is "pmag_specimens.txt")
age_file : name of age file (default is "er_ages.txt")
latex : boolean argument to output in LaTeX (default is False)
WD : path to directory that contains input files and takes output (default is current directory, '.')
"""
# format outfiles
if latex:
latex = 1
file_type = '.tex'
else:
latex = 0
file_type = '.txt'
dir_path = os.path.realpath(WD)
outfile = os.path.join(dir_path, 'Directions' + file_type)
Ioutfile = os.path.join(dir_path, 'Intensities' + file_type)
Soutfile = os.path.join(dir_path, 'SiteNfo' + file_type)
Specout = os.path.join(dir_path, 'Specimens' + file_type)
Critout = os.path.join(dir_path, 'Criteria' + file_type)
# format infiles
res_file = os.path.join(dir_path, res_file)
if crit_file:
crit_file = os.path.join(dir_path, crit_file)
if spec_file:
spec_file = os.path.join(dir_path, spec_file)
else:
grade = False
# open output files
f = open(outfile, 'w')
sf = open(Soutfile, 'w')
fI = open(Ioutfile, 'w')
if crit_file:
cr = open(Critout, 'w')
# set up column headers
Sites, file_type = pmag.magic_read(res_file)
if crit_file:
Crits, file_type = pmag.magic_read(crit_file)
else:
Crits = []
SiteCols = ["Site", "Location",
"Lat. (N)", "Long. (E)", "Age ", "Age sigma", "Units"]
SiteKeys = ["er_site_names", "average_lat", "average_lon", "average_age",
"average_age_sigma", "average_age_unit"]
DirCols = ["Site", 'Comp.', "perc TC", "Dec.", "Inc.", "Nl", "Np", "k ", "R", "a95",
"PLat", "PLong"]
DirKeys = ["er_site_names", "pole_comp_name", "tilt_correction", "average_dec", "average_inc",
"average_n_lines", "average_n_planes", "average_k", "average_r", "average_alpha95",
"vgp_lat", "vgp_lon"]
IntCols = ["Site", "N", "B (uT)", "sigma",
"sigma perc", "VADM", "VADM sigma"]
IntKeys = ["er_site_names", "average_int_n", "average_int", "average_int_sigma",
'average_int_sigma_perc', "vadm", "vadm_sigma"]
AllowedKeys = ['specimen_frac', 'specimen_scat', 'specimen_gap_max', 'measurement_step_min',
'measurement_step_max', 'measurement_step_unit', 'specimen_polarity',
'specimen_nrm', 'specimen_direction_type', 'specimen_comp_nmb', 'specimen_mad',
'specimen_alpha95', 'specimen_n', 'specimen_int_sigma',
'specimen_int_sigma_perc', 'specimen_int_rel_sigma',
'specimen_int_rel_sigma_perc', 'specimen_int_mad', 'specimen_int_n',
'specimen_w', 'specimen_q', 'specimen_f', 'specimen_fvds', 'specimen_b_sigma',
'specimen_b_beta', 'specimen_g', 'specimen_dang', 'specimen_md',
'specimen_ptrm', 'specimen_drat', 'specimen_drats', 'specimen_rsc',
'specimen_viscosity_index', 'specimen_magn_moment', 'specimen_magn_volume',
'specimen_magn_mass', 'specimen_int_ptrm_n', 'specimen_delta', 'specimen_theta',
'specimen_gamma', 'sample_polarity', 'sample_nrm', 'sample_direction_type',
'sample_comp_nmb', 'sample_sigma', 'sample_alpha95', 'sample_n',
'sample_n_lines', 'sample_n_planes', 'sample_k', 'sample_r',
'sample_tilt_correction', 'sample_int_sigma', 'sample_int_sigma_perc',
'sample_int_rel_sigma', 'sample_int_rel_sigma_perc', 'sample_int_n',
'sample_magn_moment', 'sample_magn_volume', 'sample_magn_mass', 'site_polarity',
'site_nrm', 'site_direction_type', 'site_comp_nmb', 'site_sigma',
'site_alpha95', 'site_n', 'site_n_lines', 'site_n_planes', 'site_k', 'site_r',
'site_tilt_correction', 'site_int_sigma', 'site_int_sigma_perc',
'site_int_rel_sigma', 'site_int_rel_sigma_perc', 'site_int_n',
'site_magn_moment', 'site_magn_volume', 'site_magn_mass', 'average_age_min',
'average_age_max', 'average_age_sigma', 'average_age_unit', 'average_sigma',
'average_alpha95', 'average_n', 'average_nn', 'average_k', 'average_r',
'average_int_sigma', 'average_int_rel_sigma', 'average_int_rel_sigma_perc',
'average_int_n', 'average_int_nn', 'vgp_dp', 'vgp_dm', 'vgp_sigma',
'vgp_alpha95', 'vgp_n', 'vdm_sigma', 'vdm_n', 'vadm_sigma', 'vadm_n']
if crit_file:
crit = Crits[0] # get a list of useful keys
for key in list(crit.keys()):
if key not in AllowedKeys:
del(crit[key])
for key in list(crit.keys()):
if (not crit[key]) or (eval(crit[key]) > 1000) or (eval(crit[key]) == 0):
# get rid of all blank or too big ones or too little ones
del(crit[key])
CritKeys = list(crit.keys())
if spec_file:
Specs, file_type = pmag.magic_read(spec_file)
fsp = open(Specout, 'w') # including specimen intensities if desired
SpecCols = ["Site", "Specimen", "B (uT)", "MAD", "Beta", "N", "Q", "DANG", "f-vds",
"DRATS", "T (C)"]
SpecKeys = ['er_site_name', 'er_specimen_name', 'specimen_int', 'specimen_int_mad',
'specimen_b_beta', 'specimen_int_n', 'specimen_q', 'specimen_dang',
'specimen_fvds', 'specimen_drats', 'trange']
Xtra = ['specimen_frac', 'specimen_scat', 'specimen_gmax']
if grade:
SpecCols.append('Grade')
SpecKeys.append('specimen_grade')
for x in Xtra: # put in the new intensity keys if present
if x in list(Specs[0].keys()):
SpecKeys.append(x)
newkey = ""
for k in x.split('_')[1:]:
newkey = newkey + k + '_'
SpecCols.append(newkey.strip('_'))
SpecCols.append('Corrections')
SpecKeys.append('corrections')
# these should be multiplied by 1e6
Micro = ['specimen_int', 'average_int', 'average_int_sigma']
Zeta = ['vadm', 'vadm_sigma'] # these should be multiplied by 1e21
# write out the header information for each output file
if latex: # write out the latex header stuff
sep = ' & '
end = '\\\\'
f.write('\\documentclass{article}\n')
f.write('\\usepackage[margin=1in]{geometry}\n')
f.write('\\usepackage{longtable}\n')
f.write('\\begin{document}\n')
sf.write('\\documentclass{article}\n')
sf.write('\\usepackage[margin=1in]{geometry}\n')
sf.write('\\usepackage{longtable}\n')
sf.write('\\begin{document}\n')
fI.write('\\documentclass{article}\n')
fI.write('\\usepackage[margin=1in]{geometry}\n')
fI.write('\\usepackage{longtable}\n')
fI.write('\\begin{document}\n')
if crit_file:
cr.write('\\documentclass{article}\n')
cr.write('\\usepackage[margin=1in]{geometry}\n')
cr.write('\\usepackage{longtable}\n')
cr.write('\\begin{document}\n')
if spec_file:
fsp.write('\\documentclass{article}\n')
fsp.write('\\usepackage[margin=1in]{geometry}\n')
fsp.write('\\usepackage{longtable}\n')
fsp.write('\\begin{document}\n')
tabstring = '\\begin{longtable}{'
fstring = tabstring
for k in range(len(SiteCols)):
fstring = fstring + 'r'
sf.write(fstring + '}\n')
sf.write('\hline\n')
fstring = tabstring
for k in range(len(DirCols)):
fstring = fstring + 'r'
f.write(fstring + '}\n')
f.write('\hline\n')
fstring = tabstring
for k in range(len(IntCols)):
fstring = fstring + 'r'
fI.write(fstring + '}\n')
fI.write('\hline\n')
fstring = tabstring
if crit_file:
for k in range(len(CritKeys)):
fstring = fstring + 'r'
cr.write(fstring + '}\n')
cr.write('\hline\n')
if spec_file:
fstring = tabstring
for k in range(len(SpecCols)):
fstring = fstring + 'r'
fsp.write(fstring + '}\n')
fsp.write('\hline\n')
else: # just set the tab and line endings for tab delimited
sep = ' \t '
end = ''
# now write out the actual column headers
Soutstring, Doutstring, Ioutstring, Spoutstring, Croutstring = "", "", "", "", ""
for k in range(len(SiteCols)):
Soutstring = Soutstring + SiteCols[k] + sep
Soutstring = Soutstring.strip(sep)
Soutstring = Soutstring + end + '\n'
sf.write(Soutstring)
for k in range(len(DirCols)):
Doutstring = Doutstring + DirCols[k] + sep
Doutstring = Doutstring.strip(sep)
Doutstring = Doutstring + end + '\n'
f.write(Doutstring)
for k in range(len(IntCols)):
Ioutstring = Ioutstring + IntCols[k] + sep
Ioutstring = Ioutstring.strip(sep)
Ioutstring = Ioutstring + end + '\n'
fI.write(Ioutstring)
if crit_file:
for k in range(len(CritKeys)):
Croutstring = Croutstring + CritKeys[k] + sep
Croutstring = Croutstring.strip(sep)
Croutstring = Croutstring + end + '\n'
cr.write(Croutstring)
if spec_file:
for k in range(len(SpecCols)):
Spoutstring = Spoutstring + SpecCols[k] + sep
Spoutstring = Spoutstring.strip(sep)
Spoutstring = Spoutstring + end + "\n"
fsp.write(Spoutstring)
if latex: # put in a horizontal line in latex file
f.write('\hline\n')
sf.write('\hline\n')
fI.write('\hline\n')
if crit_file:
cr.write('\hline\n')
if spec_file:
fsp.write('\hline\n')
# do criteria
if crit_file:
for crit in Crits:
Croutstring = ""
for key in CritKeys:
Croutstring = Croutstring + crit[key] + sep
Croutstring = Croutstring.strip(sep) + end
cr.write(Croutstring + '\n')
# do directions
# get all results with VGPs
VGPs = pmag.get_dictitem(Sites, 'vgp_lat', '', 'F')
VGPs = pmag.get_dictitem(VGPs, 'data_type', 'i',
'T') # get site level stuff
for site in VGPs:
if len(site['er_site_names'].split(":")) == 1:
if 'er_sample_names' not in list(site.keys()):
site['er_sample_names'] = ''
if 'pole_comp_name' not in list(site.keys()):
site['pole_comp_name'] = "A"
if 'average_nn' not in list(site.keys()) and 'average_n' in list(site.keys()):
site['average_nn'] = site['average_n']
if 'average_n_lines' not in list(site.keys()):
site['average_n_lines'] = site['average_nn']
if 'average_n_planes' not in list(site.keys()):
site['average_n_planes'] = ""
Soutstring, Doutstring = "", ""
for key in SiteKeys:
if key in list(site.keys()):
Soutstring = Soutstring + site[key] + sep
Soutstring = Soutstring.strip(sep) + end
sf.write(Soutstring + '\n')
for key in DirKeys:
if key in list(site.keys()):
Doutstring = Doutstring + site[key] + sep
Doutstring = Doutstring.strip(sep) + end
f.write(Doutstring + '\n')
# now do intensities
VADMs = pmag.get_dictitem(Sites, 'vadm', '', 'F')
VADMs = pmag.get_dictitem(VADMs, 'data_type', 'i', 'T')
for site in VADMs: # do results level stuff
if site not in VGPs:
Soutstring = ""
for key in SiteKeys:
if key in list(site.keys()):
Soutstring = Soutstring + site[key] + sep
else:
Soutstring = Soutstring + " " + sep
Soutstring = Soutstring.strip(sep) + end
sf.write(Soutstring + '\n')
if len(site['er_site_names'].split(":")) == 1 and site['data_type'] == 'i':
if 'average_int_sigma_perc' not in list(site.keys()):
site['average_int_sigma_perc'] = "0"
if site["average_int_sigma"] == "":
site["average_int_sigma"] = "0"
if site["average_int_sigma_perc"] == "":
site["average_int_sigma_perc"] = "0"
if site["vadm"] == "":
site["vadm"] = "0"
if site["vadm_sigma"] == "":
site["vadm_sigma"] = "0"
for key in list(site.keys()): # reformat vadms, intensities
if key in Micro:
site[key] = '%7.1f' % (float(site[key]) * 1e6)
if key in Zeta:
site[key] = '%7.1f' % (float(site[key]) * 1e-21)
outstring = ""
for key in IntKeys:
if key not in list(site.keys()):
site[key] = ""
outstring = outstring + site[key] + sep
outstring = outstring.strip(sep) + end + '\n'
fI.write(outstring)
# VDMs=pmag.get_dictitem(Sites,'vdm','','F') # get non-blank VDMs
# for site in VDMs: # do results level stuff
# if len(site['er_site_names'].split(":"))==1:
# if 'average_int_sigma_perc' not in site.keys():site['average_int_sigma_perc']="0"
# if site["average_int_sigma"]=="":site["average_int_sigma"]="0"
# if site["average_int_sigma_perc"]=="":site["average_int_sigma_perc"]="0"
# if site["vadm"]=="":site["vadm"]="0"
# if site["vadm_sigma"]=="":site["vadm_sigma"]="0"
# for key in site.keys(): # reformat vadms, intensities
# if key in Micro: site[key]='%7.1f'%(float(site[key])*1e6)
# if key in Zeta: site[key]='%7.1f'%(float(site[key])*1e-21)
# outstring=""
# for key in IntKeys:
# outstring=outstring+site[key]+sep
# fI.write(outstring.strip(sep)+'\n')
if spec_file:
SpecsInts = pmag.get_dictitem(Specs, 'specimen_int', '', 'F')
for spec in SpecsInts:
spec['trange'] = '%i' % (int(float(spec['measurement_step_min']) - 273)) + \
'-' + '%i' % (int(float(spec['measurement_step_max']) - 273))
meths = spec['magic_method_codes'].split(':')
corrections = ''
for meth in meths:
if 'DA' in meth:
corrections = corrections + meth[3:] + ':'
corrections = corrections.strip(':')
if corrections.strip() == "":
corrections = "None"
spec['corrections'] = corrections
outstring = ""
for key in SpecKeys:
if key in Micro:
spec[key] = '%7.1f' % (float(spec[key]) * 1e6)
if key in Zeta:
spec[key] = '%7.1f' % (float(spec[key]) * 1e-21)
outstring = outstring + spec[key] + sep
fsp.write(outstring.strip(sep) + end + '\n')
#
if latex: # write out the tail stuff
f.write('\hline\n')
sf.write('\hline\n')
fI.write('\hline\n')
f.write('\end{longtable}\n')
sf.write('\end{longtable}\n')
fI.write('\end{longtable}\n')
f.write('\end{document}\n')
sf.write('\end{document}\n')
fI.write('\end{document}\n')
if spec_file:
fsp.write('\hline\n')
fsp.write('\end{longtable}\n')
fsp.write('\end{document}\n')
if crit_file:
cr.write('\hline\n')
cr.write('\end{longtable}\n')
cr.write('\end{document}\n')
f.close()
sf.close()
fI.close()
print('data saved in: ', outfile, Ioutfile, Soutfile)
outfiles = [outfile, Ioutfile, Soutfile]
if spec_file:
fsp.close()
print('specimen data saved in: ', Specout)
outfiles.append(Specout)
if crit_file:
cr.close()
print('Selection criteria saved in: ', Critout)
outfiles.append(Critout)
return True, outfiles | Generate tab delimited output file(s) with result data.
Save output files and return True if successful.
Possible output files: Directions, Intensities, SiteNfo, Criteria,
Specimens
Optional Parameters (defaults are used if not specified)
----------
res_file : name of pmag_results file (default is "pmag_results.txt")
crit_file : name of criteria file (default is "pmag_criteria.txt")
spec_file : name of specimen file (default is "pmag_specimens.txt")
age_file : name of age file (default is "er_ages.txt")
latex : boolean argument to output in LaTeX (default is False)
WD : path to directory that contains input files and takes output (default is current directory, '.') | Below is the the instruction that describes the task:
### Input:
Generate tab delimited output file(s) with result data.
Save output files and return True if successful.
Possible output files: Directions, Intensities, SiteNfo, Criteria,
Specimens
Optional Parameters (defaults are used if not specified)
----------
res_file : name of pmag_results file (default is "pmag_results.txt")
crit_file : name of criteria file (default is "pmag_criteria.txt")
spec_file : name of specimen file (default is "pmag_specimens.txt")
age_file : name of age file (default is "er_ages.txt")
latex : boolean argument to output in LaTeX (default is False)
WD : path to directory that contains input files and takes output (default is current directory, '.')
### Response:
def pmag_results_extract(res_file="pmag_results.txt", crit_file="", spec_file="",
age_file="", latex=False, grade=False, WD="."):
"""
Generate tab delimited output file(s) with result data.
Save output files and return True if successful.
Possible output files: Directions, Intensities, SiteNfo, Criteria,
Specimens
Optional Parameters (defaults are used if not specified)
----------
res_file : name of pmag_results file (default is "pmag_results.txt")
crit_file : name of criteria file (default is "pmag_criteria.txt")
spec_file : name of specimen file (default is "pmag_specimens.txt")
age_file : name of age file (default is "er_ages.txt")
latex : boolean argument to output in LaTeX (default is False)
WD : path to directory that contains input files and takes output (default is current directory, '.')
"""
# format outfiles
if latex:
latex = 1
file_type = '.tex'
else:
latex = 0
file_type = '.txt'
dir_path = os.path.realpath(WD)
outfile = os.path.join(dir_path, 'Directions' + file_type)
Ioutfile = os.path.join(dir_path, 'Intensities' + file_type)
Soutfile = os.path.join(dir_path, 'SiteNfo' + file_type)
Specout = os.path.join(dir_path, 'Specimens' + file_type)
Critout = os.path.join(dir_path, 'Criteria' + file_type)
# format infiles
res_file = os.path.join(dir_path, res_file)
if crit_file:
crit_file = os.path.join(dir_path, crit_file)
if spec_file:
spec_file = os.path.join(dir_path, spec_file)
else:
grade = False
# open output files
f = open(outfile, 'w')
sf = open(Soutfile, 'w')
fI = open(Ioutfile, 'w')
if crit_file:
cr = open(Critout, 'w')
# set up column headers
Sites, file_type = pmag.magic_read(res_file)
if crit_file:
Crits, file_type = pmag.magic_read(crit_file)
else:
Crits = []
SiteCols = ["Site", "Location",
"Lat. (N)", "Long. (E)", "Age ", "Age sigma", "Units"]
SiteKeys = ["er_site_names", "average_lat", "average_lon", "average_age",
"average_age_sigma", "average_age_unit"]
DirCols = ["Site", 'Comp.', "perc TC", "Dec.", "Inc.", "Nl", "Np", "k ", "R", "a95",
"PLat", "PLong"]
DirKeys = ["er_site_names", "pole_comp_name", "tilt_correction", "average_dec", "average_inc",
"average_n_lines", "average_n_planes", "average_k", "average_r", "average_alpha95",
"vgp_lat", "vgp_lon"]
IntCols = ["Site", "N", "B (uT)", "sigma",
"sigma perc", "VADM", "VADM sigma"]
IntKeys = ["er_site_names", "average_int_n", "average_int", "average_int_sigma",
'average_int_sigma_perc', "vadm", "vadm_sigma"]
AllowedKeys = ['specimen_frac', 'specimen_scat', 'specimen_gap_max', 'measurement_step_min',
'measurement_step_max', 'measurement_step_unit', 'specimen_polarity',
'specimen_nrm', 'specimen_direction_type', 'specimen_comp_nmb', 'specimen_mad',
'specimen_alpha95', 'specimen_n', 'specimen_int_sigma',
'specimen_int_sigma_perc', 'specimen_int_rel_sigma',
'specimen_int_rel_sigma_perc', 'specimen_int_mad', 'specimen_int_n',
'specimen_w', 'specimen_q', 'specimen_f', 'specimen_fvds', 'specimen_b_sigma',
'specimen_b_beta', 'specimen_g', 'specimen_dang', 'specimen_md',
'specimen_ptrm', 'specimen_drat', 'specimen_drats', 'specimen_rsc',
'specimen_viscosity_index', 'specimen_magn_moment', 'specimen_magn_volume',
'specimen_magn_mass', 'specimen_int_ptrm_n', 'specimen_delta', 'specimen_theta',
'specimen_gamma', 'sample_polarity', 'sample_nrm', 'sample_direction_type',
'sample_comp_nmb', 'sample_sigma', 'sample_alpha95', 'sample_n',
'sample_n_lines', 'sample_n_planes', 'sample_k', 'sample_r',
'sample_tilt_correction', 'sample_int_sigma', 'sample_int_sigma_perc',
'sample_int_rel_sigma', 'sample_int_rel_sigma_perc', 'sample_int_n',
'sample_magn_moment', 'sample_magn_volume', 'sample_magn_mass', 'site_polarity',
'site_nrm', 'site_direction_type', 'site_comp_nmb', 'site_sigma',
'site_alpha95', 'site_n', 'site_n_lines', 'site_n_planes', 'site_k', 'site_r',
'site_tilt_correction', 'site_int_sigma', 'site_int_sigma_perc',
'site_int_rel_sigma', 'site_int_rel_sigma_perc', 'site_int_n',
'site_magn_moment', 'site_magn_volume', 'site_magn_mass', 'average_age_min',
'average_age_max', 'average_age_sigma', 'average_age_unit', 'average_sigma',
'average_alpha95', 'average_n', 'average_nn', 'average_k', 'average_r',
'average_int_sigma', 'average_int_rel_sigma', 'average_int_rel_sigma_perc',
'average_int_n', 'average_int_nn', 'vgp_dp', 'vgp_dm', 'vgp_sigma',
'vgp_alpha95', 'vgp_n', 'vdm_sigma', 'vdm_n', 'vadm_sigma', 'vadm_n']
if crit_file:
crit = Crits[0] # get a list of useful keys
for key in list(crit.keys()):
if key not in AllowedKeys:
del(crit[key])
for key in list(crit.keys()):
if (not crit[key]) or (eval(crit[key]) > 1000) or (eval(crit[key]) == 0):
# get rid of all blank or too big ones or too little ones
del(crit[key])
CritKeys = list(crit.keys())
if spec_file:
Specs, file_type = pmag.magic_read(spec_file)
fsp = open(Specout, 'w') # including specimen intensities if desired
SpecCols = ["Site", "Specimen", "B (uT)", "MAD", "Beta", "N", "Q", "DANG", "f-vds",
"DRATS", "T (C)"]
SpecKeys = ['er_site_name', 'er_specimen_name', 'specimen_int', 'specimen_int_mad',
'specimen_b_beta', 'specimen_int_n', 'specimen_q', 'specimen_dang',
'specimen_fvds', 'specimen_drats', 'trange']
Xtra = ['specimen_frac', 'specimen_scat', 'specimen_gmax']
if grade:
SpecCols.append('Grade')
SpecKeys.append('specimen_grade')
for x in Xtra: # put in the new intensity keys if present
if x in list(Specs[0].keys()):
SpecKeys.append(x)
newkey = ""
for k in x.split('_')[1:]:
newkey = newkey + k + '_'
SpecCols.append(newkey.strip('_'))
SpecCols.append('Corrections')
SpecKeys.append('corrections')
# these should be multiplied by 1e6
Micro = ['specimen_int', 'average_int', 'average_int_sigma']
Zeta = ['vadm', 'vadm_sigma'] # these should be multiplied by 1e21
# write out the header information for each output file
if latex: # write out the latex header stuff
sep = ' & '
end = '\\\\'
f.write('\\documentclass{article}\n')
f.write('\\usepackage[margin=1in]{geometry}\n')
f.write('\\usepackage{longtable}\n')
f.write('\\begin{document}\n')
sf.write('\\documentclass{article}\n')
sf.write('\\usepackage[margin=1in]{geometry}\n')
sf.write('\\usepackage{longtable}\n')
sf.write('\\begin{document}\n')
fI.write('\\documentclass{article}\n')
fI.write('\\usepackage[margin=1in]{geometry}\n')
fI.write('\\usepackage{longtable}\n')
fI.write('\\begin{document}\n')
if crit_file:
cr.write('\\documentclass{article}\n')
cr.write('\\usepackage[margin=1in]{geometry}\n')
cr.write('\\usepackage{longtable}\n')
cr.write('\\begin{document}\n')
if spec_file:
fsp.write('\\documentclass{article}\n')
fsp.write('\\usepackage[margin=1in]{geometry}\n')
fsp.write('\\usepackage{longtable}\n')
fsp.write('\\begin{document}\n')
tabstring = '\\begin{longtable}{'
fstring = tabstring
for k in range(len(SiteCols)):
fstring = fstring + 'r'
sf.write(fstring + '}\n')
sf.write('\hline\n')
fstring = tabstring
for k in range(len(DirCols)):
fstring = fstring + 'r'
f.write(fstring + '}\n')
f.write('\hline\n')
fstring = tabstring
for k in range(len(IntCols)):
fstring = fstring + 'r'
fI.write(fstring + '}\n')
fI.write('\hline\n')
fstring = tabstring
if crit_file:
for k in range(len(CritKeys)):
fstring = fstring + 'r'
cr.write(fstring + '}\n')
cr.write('\hline\n')
if spec_file:
fstring = tabstring
for k in range(len(SpecCols)):
fstring = fstring + 'r'
fsp.write(fstring + '}\n')
fsp.write('\hline\n')
else: # just set the tab and line endings for tab delimited
sep = ' \t '
end = ''
# now write out the actual column headers
Soutstring, Doutstring, Ioutstring, Spoutstring, Croutstring = "", "", "", "", ""
for k in range(len(SiteCols)):
Soutstring = Soutstring + SiteCols[k] + sep
Soutstring = Soutstring.strip(sep)
Soutstring = Soutstring + end + '\n'
sf.write(Soutstring)
for k in range(len(DirCols)):
Doutstring = Doutstring + DirCols[k] + sep
Doutstring = Doutstring.strip(sep)
Doutstring = Doutstring + end + '\n'
f.write(Doutstring)
for k in range(len(IntCols)):
Ioutstring = Ioutstring + IntCols[k] + sep
Ioutstring = Ioutstring.strip(sep)
Ioutstring = Ioutstring + end + '\n'
fI.write(Ioutstring)
if crit_file:
for k in range(len(CritKeys)):
Croutstring = Croutstring + CritKeys[k] + sep
Croutstring = Croutstring.strip(sep)
Croutstring = Croutstring + end + '\n'
cr.write(Croutstring)
if spec_file:
for k in range(len(SpecCols)):
Spoutstring = Spoutstring + SpecCols[k] + sep
Spoutstring = Spoutstring.strip(sep)
Spoutstring = Spoutstring + end + "\n"
fsp.write(Spoutstring)
if latex: # put in a horizontal line in latex file
f.write('\hline\n')
sf.write('\hline\n')
fI.write('\hline\n')
if crit_file:
cr.write('\hline\n')
if spec_file:
fsp.write('\hline\n')
# do criteria
if crit_file:
for crit in Crits:
Croutstring = ""
for key in CritKeys:
Croutstring = Croutstring + crit[key] + sep
Croutstring = Croutstring.strip(sep) + end
cr.write(Croutstring + '\n')
# do directions
# get all results with VGPs
VGPs = pmag.get_dictitem(Sites, 'vgp_lat', '', 'F')
VGPs = pmag.get_dictitem(VGPs, 'data_type', 'i',
'T') # get site level stuff
for site in VGPs:
if len(site['er_site_names'].split(":")) == 1:
if 'er_sample_names' not in list(site.keys()):
site['er_sample_names'] = ''
if 'pole_comp_name' not in list(site.keys()):
site['pole_comp_name'] = "A"
if 'average_nn' not in list(site.keys()) and 'average_n' in list(site.keys()):
site['average_nn'] = site['average_n']
if 'average_n_lines' not in list(site.keys()):
site['average_n_lines'] = site['average_nn']
if 'average_n_planes' not in list(site.keys()):
site['average_n_planes'] = ""
Soutstring, Doutstring = "", ""
for key in SiteKeys:
if key in list(site.keys()):
Soutstring = Soutstring + site[key] + sep
Soutstring = Soutstring.strip(sep) + end
sf.write(Soutstring + '\n')
for key in DirKeys:
if key in list(site.keys()):
Doutstring = Doutstring + site[key] + sep
Doutstring = Doutstring.strip(sep) + end
f.write(Doutstring + '\n')
# now do intensities
VADMs = pmag.get_dictitem(Sites, 'vadm', '', 'F')
VADMs = pmag.get_dictitem(VADMs, 'data_type', 'i', 'T')
for site in VADMs: # do results level stuff
if site not in VGPs:
Soutstring = ""
for key in SiteKeys:
if key in list(site.keys()):
Soutstring = Soutstring + site[key] + sep
else:
Soutstring = Soutstring + " " + sep
Soutstring = Soutstring.strip(sep) + end
sf.write(Soutstring + '\n')
if len(site['er_site_names'].split(":")) == 1 and site['data_type'] == 'i':
if 'average_int_sigma_perc' not in list(site.keys()):
site['average_int_sigma_perc'] = "0"
if site["average_int_sigma"] == "":
site["average_int_sigma"] = "0"
if site["average_int_sigma_perc"] == "":
site["average_int_sigma_perc"] = "0"
if site["vadm"] == "":
site["vadm"] = "0"
if site["vadm_sigma"] == "":
site["vadm_sigma"] = "0"
for key in list(site.keys()): # reformat vadms, intensities
if key in Micro:
site[key] = '%7.1f' % (float(site[key]) * 1e6)
if key in Zeta:
site[key] = '%7.1f' % (float(site[key]) * 1e-21)
outstring = ""
for key in IntKeys:
if key not in list(site.keys()):
site[key] = ""
outstring = outstring + site[key] + sep
outstring = outstring.strip(sep) + end + '\n'
fI.write(outstring)
# VDMs=pmag.get_dictitem(Sites,'vdm','','F') # get non-blank VDMs
# for site in VDMs: # do results level stuff
# if len(site['er_site_names'].split(":"))==1:
# if 'average_int_sigma_perc' not in site.keys():site['average_int_sigma_perc']="0"
# if site["average_int_sigma"]=="":site["average_int_sigma"]="0"
# if site["average_int_sigma_perc"]=="":site["average_int_sigma_perc"]="0"
# if site["vadm"]=="":site["vadm"]="0"
# if site["vadm_sigma"]=="":site["vadm_sigma"]="0"
# for key in site.keys(): # reformat vadms, intensities
# if key in Micro: site[key]='%7.1f'%(float(site[key])*1e6)
# if key in Zeta: site[key]='%7.1f'%(float(site[key])*1e-21)
# outstring=""
# for key in IntKeys:
# outstring=outstring+site[key]+sep
# fI.write(outstring.strip(sep)+'\n')
if spec_file:
SpecsInts = pmag.get_dictitem(Specs, 'specimen_int', '', 'F')
for spec in SpecsInts:
spec['trange'] = '%i' % (int(float(spec['measurement_step_min']) - 273)) + \
'-' + '%i' % (int(float(spec['measurement_step_max']) - 273))
meths = spec['magic_method_codes'].split(':')
corrections = ''
for meth in meths:
if 'DA' in meth:
corrections = corrections + meth[3:] + ':'
corrections = corrections.strip(':')
if corrections.strip() == "":
corrections = "None"
spec['corrections'] = corrections
outstring = ""
for key in SpecKeys:
if key in Micro:
spec[key] = '%7.1f' % (float(spec[key]) * 1e6)
if key in Zeta:
spec[key] = '%7.1f' % (float(spec[key]) * 1e-21)
outstring = outstring + spec[key] + sep
fsp.write(outstring.strip(sep) + end + '\n')
#
if latex: # write out the tail stuff
f.write('\hline\n')
sf.write('\hline\n')
fI.write('\hline\n')
f.write('\end{longtable}\n')
sf.write('\end{longtable}\n')
fI.write('\end{longtable}\n')
f.write('\end{document}\n')
sf.write('\end{document}\n')
fI.write('\end{document}\n')
if spec_file:
fsp.write('\hline\n')
fsp.write('\end{longtable}\n')
fsp.write('\end{document}\n')
if crit_file:
cr.write('\hline\n')
cr.write('\end{longtable}\n')
cr.write('\end{document}\n')
f.close()
sf.close()
fI.close()
print('data saved in: ', outfile, Ioutfile, Soutfile)
outfiles = [outfile, Ioutfile, Soutfile]
if spec_file:
fsp.close()
print('specimen data saved in: ', Specout)
outfiles.append(Specout)
if crit_file:
cr.close()
print('Selection criteria saved in: ', Critout)
outfiles.append(Critout)
return True, outfiles |
def discard(self, val):
"""
Remove the first occurrence of *val*.
If *val* is not a member, does nothing.
"""
_maxes = self._maxes
if not _maxes:
return
key = self._key(val)
pos = bisect_left(_maxes, key)
if pos == len(_maxes):
return
_keys = self._keys
_lists = self._lists
idx = bisect_left(_keys[pos], key)
len_keys = len(_keys)
len_sublist = len(_keys[pos])
while True:
if _keys[pos][idx] != key:
return
if _lists[pos][idx] == val:
self._delete(pos, idx)
return
idx += 1
if idx == len_sublist:
pos += 1
if pos == len_keys:
return
len_sublist = len(_keys[pos])
idx = 0 | Remove the first occurrence of *val*.
If *val* is not a member, does nothing. | Below is the the instruction that describes the task:
### Input:
Remove the first occurrence of *val*.
If *val* is not a member, does nothing.
### Response:
def discard(self, val):
"""
Remove the first occurrence of *val*.
If *val* is not a member, does nothing.
"""
_maxes = self._maxes
if not _maxes:
return
key = self._key(val)
pos = bisect_left(_maxes, key)
if pos == len(_maxes):
return
_keys = self._keys
_lists = self._lists
idx = bisect_left(_keys[pos], key)
len_keys = len(_keys)
len_sublist = len(_keys[pos])
while True:
if _keys[pos][idx] != key:
return
if _lists[pos][idx] == val:
self._delete(pos, idx)
return
idx += 1
if idx == len_sublist:
pos += 1
if pos == len_keys:
return
len_sublist = len(_keys[pos])
idx = 0 |
def deepcopy(self):
"""
Create a deep copy of the Heatmaps object.
Returns
-------
imgaug.HeatmapsOnImage
Deep copy.
"""
return HeatmapsOnImage(self.get_arr(), shape=self.shape, min_value=self.min_value, max_value=self.max_value) | Create a deep copy of the Heatmaps object.
Returns
-------
imgaug.HeatmapsOnImage
Deep copy. | Below is the the instruction that describes the task:
### Input:
Create a deep copy of the Heatmaps object.
Returns
-------
imgaug.HeatmapsOnImage
Deep copy.
### Response:
def deepcopy(self):
"""
Create a deep copy of the Heatmaps object.
Returns
-------
imgaug.HeatmapsOnImage
Deep copy.
"""
return HeatmapsOnImage(self.get_arr(), shape=self.shape, min_value=self.min_value, max_value=self.max_value) |
def _unassembled_reads2_out_file_name(self):
"""Checks if file name is set for reads2 output.
Returns absolute path."""
if self.Parameters['-2'].isOn():
unassembled_reads2 = self._absolute(
str(self.Parameters['-2'].Value))
else:
raise ValueError("No reads2 (flag -2) output path specified")
return unassembled_reads2 | Checks if file name is set for reads2 output.
Returns absolute path. | Below is the the instruction that describes the task:
### Input:
Checks if file name is set for reads2 output.
Returns absolute path.
### Response:
def _unassembled_reads2_out_file_name(self):
"""Checks if file name is set for reads2 output.
Returns absolute path."""
if self.Parameters['-2'].isOn():
unassembled_reads2 = self._absolute(
str(self.Parameters['-2'].Value))
else:
raise ValueError("No reads2 (flag -2) output path specified")
return unassembled_reads2 |
def matchlist_by_account(
self,
region,
encrypted_account_id,
queue=None,
begin_time=None,
end_time=None,
begin_index=None,
end_index=None,
season=None,
champion=None,
):
"""
Get matchlist for ranked games played on given account ID and platform ID
and filtered using given filter parameters, if any
A number of optional parameters are provided for filtering. It is up to the caller to
ensure that the combination of filter parameters provided is valid for the requested
account, otherwise, no matches may be returned.
Note that if either beginIndex or endIndex are specified, then both must be specified and
endIndex must be greater than beginIndex.
If endTime is specified, but not beginTime, then beginTime is effectively the start of the
account's match history.
If beginTime is specified, but not endTime, then endTime is effectively the current time.
Note that endTime should be greater than beginTime if both are specified, although there is
no maximum limit on their range.
:param string region: The region to execute this request on
:param string encrypted_account_id: The account ID.
:param Set[int] queue: Set of queue IDs for which to filtering matchlist.
:param long begin_time: The begin time to use for filtering matchlist specified as
epoch milliseconds.
:param long end_time: The end time to use for filtering matchlist specified as epoch
milliseconds.
:param int begin_index: The begin index to use for filtering matchlist.
:param int end_index: The end index to use for filtering matchlist.
:param Set[int] season: Set of season IDs for which to filtering matchlist.
:param Set[int] champion: Set of champion IDs for which to filtering matchlist.
:returns: MatchlistDto
"""
url, query = MatchApiV4Urls.matchlist_by_account(
region=region,
encrypted_account_id=encrypted_account_id,
queue=queue,
beginTime=begin_time,
endTime=end_time,
beginIndex=begin_index,
endIndex=end_index,
season=season,
champion=champion,
)
return self._raw_request(self.matchlist_by_account.__name__, region, url, query) | Get matchlist for ranked games played on given account ID and platform ID
and filtered using given filter parameters, if any
A number of optional parameters are provided for filtering. It is up to the caller to
ensure that the combination of filter parameters provided is valid for the requested
account, otherwise, no matches may be returned.
Note that if either beginIndex or endIndex are specified, then both must be specified and
endIndex must be greater than beginIndex.
If endTime is specified, but not beginTime, then beginTime is effectively the start of the
account's match history.
If beginTime is specified, but not endTime, then endTime is effectively the current time.
Note that endTime should be greater than beginTime if both are specified, although there is
no maximum limit on their range.
:param string region: The region to execute this request on
:param string encrypted_account_id: The account ID.
:param Set[int] queue: Set of queue IDs for which to filtering matchlist.
:param long begin_time: The begin time to use for filtering matchlist specified as
epoch milliseconds.
:param long end_time: The end time to use for filtering matchlist specified as epoch
milliseconds.
:param int begin_index: The begin index to use for filtering matchlist.
:param int end_index: The end index to use for filtering matchlist.
:param Set[int] season: Set of season IDs for which to filtering matchlist.
:param Set[int] champion: Set of champion IDs for which to filtering matchlist.
:returns: MatchlistDto | Below is the the instruction that describes the task:
### Input:
Get matchlist for ranked games played on given account ID and platform ID
and filtered using given filter parameters, if any
A number of optional parameters are provided for filtering. It is up to the caller to
ensure that the combination of filter parameters provided is valid for the requested
account, otherwise, no matches may be returned.
Note that if either beginIndex or endIndex are specified, then both must be specified and
endIndex must be greater than beginIndex.
If endTime is specified, but not beginTime, then beginTime is effectively the start of the
account's match history.
If beginTime is specified, but not endTime, then endTime is effectively the current time.
Note that endTime should be greater than beginTime if both are specified, although there is
no maximum limit on their range.
:param string region: The region to execute this request on
:param string encrypted_account_id: The account ID.
:param Set[int] queue: Set of queue IDs for which to filtering matchlist.
:param long begin_time: The begin time to use for filtering matchlist specified as
epoch milliseconds.
:param long end_time: The end time to use for filtering matchlist specified as epoch
milliseconds.
:param int begin_index: The begin index to use for filtering matchlist.
:param int end_index: The end index to use for filtering matchlist.
:param Set[int] season: Set of season IDs for which to filtering matchlist.
:param Set[int] champion: Set of champion IDs for which to filtering matchlist.
:returns: MatchlistDto
### Response:
def matchlist_by_account(
self,
region,
encrypted_account_id,
queue=None,
begin_time=None,
end_time=None,
begin_index=None,
end_index=None,
season=None,
champion=None,
):
"""
Get matchlist for ranked games played on given account ID and platform ID
and filtered using given filter parameters, if any
A number of optional parameters are provided for filtering. It is up to the caller to
ensure that the combination of filter parameters provided is valid for the requested
account, otherwise, no matches may be returned.
Note that if either beginIndex or endIndex are specified, then both must be specified and
endIndex must be greater than beginIndex.
If endTime is specified, but not beginTime, then beginTime is effectively the start of the
account's match history.
If beginTime is specified, but not endTime, then endTime is effectively the current time.
Note that endTime should be greater than beginTime if both are specified, although there is
no maximum limit on their range.
:param string region: The region to execute this request on
:param string encrypted_account_id: The account ID.
:param Set[int] queue: Set of queue IDs for which to filtering matchlist.
:param long begin_time: The begin time to use for filtering matchlist specified as
epoch milliseconds.
:param long end_time: The end time to use for filtering matchlist specified as epoch
milliseconds.
:param int begin_index: The begin index to use for filtering matchlist.
:param int end_index: The end index to use for filtering matchlist.
:param Set[int] season: Set of season IDs for which to filtering matchlist.
:param Set[int] champion: Set of champion IDs for which to filtering matchlist.
:returns: MatchlistDto
"""
url, query = MatchApiV4Urls.matchlist_by_account(
region=region,
encrypted_account_id=encrypted_account_id,
queue=queue,
beginTime=begin_time,
endTime=end_time,
beginIndex=begin_index,
endIndex=end_index,
season=season,
champion=champion,
)
return self._raw_request(self.matchlist_by_account.__name__, region, url, query) |
def iteritems(self):
"""
Iterate through the property names and values of this CIM instance.
Each iteration item is a tuple of the property name (in the original
lexical case) and the property value.
The order of properties is preserved.
"""
for key, val in self.properties.iteritems():
yield (key, val.value) | Iterate through the property names and values of this CIM instance.
Each iteration item is a tuple of the property name (in the original
lexical case) and the property value.
The order of properties is preserved. | Below is the the instruction that describes the task:
### Input:
Iterate through the property names and values of this CIM instance.
Each iteration item is a tuple of the property name (in the original
lexical case) and the property value.
The order of properties is preserved.
### Response:
def iteritems(self):
"""
Iterate through the property names and values of this CIM instance.
Each iteration item is a tuple of the property name (in the original
lexical case) and the property value.
The order of properties is preserved.
"""
for key, val in self.properties.iteritems():
yield (key, val.value) |
def get_jobs(self, project, **params):
"""
Gets jobs from project, filtered by parameters
:param project: project (repository name) to query data for
:param params: keyword arguments to filter results
"""
return self._get_json_list(self.JOBS_ENDPOINT, project, **params) | Gets jobs from project, filtered by parameters
:param project: project (repository name) to query data for
:param params: keyword arguments to filter results | Below is the the instruction that describes the task:
### Input:
Gets jobs from project, filtered by parameters
:param project: project (repository name) to query data for
:param params: keyword arguments to filter results
### Response:
def get_jobs(self, project, **params):
"""
Gets jobs from project, filtered by parameters
:param project: project (repository name) to query data for
:param params: keyword arguments to filter results
"""
return self._get_json_list(self.JOBS_ENDPOINT, project, **params) |
def fastknn(self, data: ['SASdata', str] = None,
display: str = None,
displayout: str = None,
id: str = None,
input: [str, list, dict] = None,
output: [str, bool, 'SASdata'] = None,
procopts: str = None,
stmtpassthrough: str = None,
**kwargs: dict) -> 'SASresults':
"""
Python method to call the FASTKNN procedure
Documentation link:
https://go.documentation.sas.com/?docsetId=casml&docsetTarget=casml_fastknn_toc.htm&docsetVersion=8.3&locale=en
:param data: SASdata object or string. This parameter is required.
:parm display: The display variable can only be a string type.
:parm displayout: The displayout variable can only be a string type.
:parm id: The id variable can only be a string type.
:parm input: The input variable can be a string, list or dict type. It refers to the dependent, y, or label variable.
:parm output: The output variable can be a string, boolean or SASdata type. The member name for a boolean is "_output".
:parm procopts: The procopts variable is a generic option available for advanced use. It can only be a string type.
:parm stmtpassthrough: The stmtpassthrough variable is a generic option available for advanced use. It can only be a string type.
:return: SAS Result Object
""" | Python method to call the FASTKNN procedure
Documentation link:
https://go.documentation.sas.com/?docsetId=casml&docsetTarget=casml_fastknn_toc.htm&docsetVersion=8.3&locale=en
:param data: SASdata object or string. This parameter is required.
:parm display: The display variable can only be a string type.
:parm displayout: The displayout variable can only be a string type.
:parm id: The id variable can only be a string type.
:parm input: The input variable can be a string, list or dict type. It refers to the dependent, y, or label variable.
:parm output: The output variable can be a string, boolean or SASdata type. The member name for a boolean is "_output".
:parm procopts: The procopts variable is a generic option available for advanced use. It can only be a string type.
:parm stmtpassthrough: The stmtpassthrough variable is a generic option available for advanced use. It can only be a string type.
:return: SAS Result Object | Below is the the instruction that describes the task:
### Input:
Python method to call the FASTKNN procedure
Documentation link:
https://go.documentation.sas.com/?docsetId=casml&docsetTarget=casml_fastknn_toc.htm&docsetVersion=8.3&locale=en
:param data: SASdata object or string. This parameter is required.
:parm display: The display variable can only be a string type.
:parm displayout: The displayout variable can only be a string type.
:parm id: The id variable can only be a string type.
:parm input: The input variable can be a string, list or dict type. It refers to the dependent, y, or label variable.
:parm output: The output variable can be a string, boolean or SASdata type. The member name for a boolean is "_output".
:parm procopts: The procopts variable is a generic option available for advanced use. It can only be a string type.
:parm stmtpassthrough: The stmtpassthrough variable is a generic option available for advanced use. It can only be a string type.
:return: SAS Result Object
### Response:
def fastknn(self, data: ['SASdata', str] = None,
display: str = None,
displayout: str = None,
id: str = None,
input: [str, list, dict] = None,
output: [str, bool, 'SASdata'] = None,
procopts: str = None,
stmtpassthrough: str = None,
**kwargs: dict) -> 'SASresults':
"""
Python method to call the FASTKNN procedure
Documentation link:
https://go.documentation.sas.com/?docsetId=casml&docsetTarget=casml_fastknn_toc.htm&docsetVersion=8.3&locale=en
:param data: SASdata object or string. This parameter is required.
:parm display: The display variable can only be a string type.
:parm displayout: The displayout variable can only be a string type.
:parm id: The id variable can only be a string type.
:parm input: The input variable can be a string, list or dict type. It refers to the dependent, y, or label variable.
:parm output: The output variable can be a string, boolean or SASdata type. The member name for a boolean is "_output".
:parm procopts: The procopts variable is a generic option available for advanced use. It can only be a string type.
:parm stmtpassthrough: The stmtpassthrough variable is a generic option available for advanced use. It can only be a string type.
:return: SAS Result Object
""" |
def cnst_AT(self, X):
r"""Compute :math:`A^T \mathbf{x}` where :math:`A \mathbf{x}` is
a component of ADMM problem constraint. In this case
:math:`A^T \mathbf{x} = (\Gamma_0^T \;\; \Gamma_1^T \;\; \ldots
\;\; I) \mathbf{x}`.
"""
return np.sum(self.cnst_A0T(X), axis=-1) + self.cnst_A1T(X) | r"""Compute :math:`A^T \mathbf{x}` where :math:`A \mathbf{x}` is
a component of ADMM problem constraint. In this case
:math:`A^T \mathbf{x} = (\Gamma_0^T \;\; \Gamma_1^T \;\; \ldots
\;\; I) \mathbf{x}`. | Below is the the instruction that describes the task:
### Input:
r"""Compute :math:`A^T \mathbf{x}` where :math:`A \mathbf{x}` is
a component of ADMM problem constraint. In this case
:math:`A^T \mathbf{x} = (\Gamma_0^T \;\; \Gamma_1^T \;\; \ldots
\;\; I) \mathbf{x}`.
### Response:
def cnst_AT(self, X):
r"""Compute :math:`A^T \mathbf{x}` where :math:`A \mathbf{x}` is
a component of ADMM problem constraint. In this case
:math:`A^T \mathbf{x} = (\Gamma_0^T \;\; \Gamma_1^T \;\; \ldots
\;\; I) \mathbf{x}`.
"""
return np.sum(self.cnst_A0T(X), axis=-1) + self.cnst_A1T(X) |
def companyDF(symbol, token='', version=''):
'''Company reference data
https://iexcloud.io/docs/api/#company
Updates at 4am and 5am UTC every day
Args:
symbol (string); Ticker to request
token (string); Access token
version (string); API version
Returns:
DataFrame: result
'''
c = company(symbol, token, version)
df = _companyToDF(c)
return df | Company reference data
https://iexcloud.io/docs/api/#company
Updates at 4am and 5am UTC every day
Args:
symbol (string); Ticker to request
token (string); Access token
version (string); API version
Returns:
DataFrame: result | Below is the the instruction that describes the task:
### Input:
Company reference data
https://iexcloud.io/docs/api/#company
Updates at 4am and 5am UTC every day
Args:
symbol (string); Ticker to request
token (string); Access token
version (string); API version
Returns:
DataFrame: result
### Response:
def companyDF(symbol, token='', version=''):
'''Company reference data
https://iexcloud.io/docs/api/#company
Updates at 4am and 5am UTC every day
Args:
symbol (string); Ticker to request
token (string); Access token
version (string); API version
Returns:
DataFrame: result
'''
c = company(symbol, token, version)
df = _companyToDF(c)
return df |
def list_contents(self):
"""List the contents of this directory
:return: A LsInfo object that contains directories and files
:rtype: :class:`~.LsInfo` or :class:`~.ErrorInfo`
Here is an example usage::
# let dirinfo be a DirectoryInfo object
ldata = dirinfo.list_contents()
if isinstance(ldata, ErrorInfo):
# Do some error handling
logger.warn("Error listing file info: (%s) %s", ldata.errno, ldata.message)
# It's of type LsInfo
else:
# Look at all the files
for finfo in ldata.files:
logger.info("Found file %s of size %s", finfo.path, finfo.size)
# Look at all the directories
for dinfo in ldata.directories:
logger.info("Found directory %s of last modified %s", dinfo.path, dinfo.last_modified)
"""
target = DeviceTarget(self.device_id)
return self._fssapi.list_files(target, self.path)[self.device_id] | List the contents of this directory
:return: A LsInfo object that contains directories and files
:rtype: :class:`~.LsInfo` or :class:`~.ErrorInfo`
Here is an example usage::
# let dirinfo be a DirectoryInfo object
ldata = dirinfo.list_contents()
if isinstance(ldata, ErrorInfo):
# Do some error handling
logger.warn("Error listing file info: (%s) %s", ldata.errno, ldata.message)
# It's of type LsInfo
else:
# Look at all the files
for finfo in ldata.files:
logger.info("Found file %s of size %s", finfo.path, finfo.size)
# Look at all the directories
for dinfo in ldata.directories:
logger.info("Found directory %s of last modified %s", dinfo.path, dinfo.last_modified) | Below is the the instruction that describes the task:
### Input:
List the contents of this directory
:return: A LsInfo object that contains directories and files
:rtype: :class:`~.LsInfo` or :class:`~.ErrorInfo`
Here is an example usage::
# let dirinfo be a DirectoryInfo object
ldata = dirinfo.list_contents()
if isinstance(ldata, ErrorInfo):
# Do some error handling
logger.warn("Error listing file info: (%s) %s", ldata.errno, ldata.message)
# It's of type LsInfo
else:
# Look at all the files
for finfo in ldata.files:
logger.info("Found file %s of size %s", finfo.path, finfo.size)
# Look at all the directories
for dinfo in ldata.directories:
logger.info("Found directory %s of last modified %s", dinfo.path, dinfo.last_modified)
### Response:
def list_contents(self):
"""List the contents of this directory
:return: A LsInfo object that contains directories and files
:rtype: :class:`~.LsInfo` or :class:`~.ErrorInfo`
Here is an example usage::
# let dirinfo be a DirectoryInfo object
ldata = dirinfo.list_contents()
if isinstance(ldata, ErrorInfo):
# Do some error handling
logger.warn("Error listing file info: (%s) %s", ldata.errno, ldata.message)
# It's of type LsInfo
else:
# Look at all the files
for finfo in ldata.files:
logger.info("Found file %s of size %s", finfo.path, finfo.size)
# Look at all the directories
for dinfo in ldata.directories:
logger.info("Found directory %s of last modified %s", dinfo.path, dinfo.last_modified)
"""
target = DeviceTarget(self.device_id)
return self._fssapi.list_files(target, self.path)[self.device_id] |
def _get_value_from_match(self, key, match):
"""
Gets the value of the property in the given MatchObject.
Args:
key (str): Key of the property looked-up.
match (MatchObject): The matched property.
Return:
The discovered value, as a string or boolean.
"""
value = match.groups(1)[0]
clean_value = str(value).lstrip().rstrip()
if clean_value == 'true':
self._log.info('Got value of "%s" as boolean true.', key)
return True
if clean_value == 'false':
self._log.info('Got value of "%s" as boolean false.', key)
return False
try:
float_value = float(clean_value)
self._log.info('Got value of "%s" as float "%f".',
key,
float_value)
return float_value
except ValueError:
self._log.info('Got value of "%s" as string "%s".',
key,
clean_value)
return clean_value | Gets the value of the property in the given MatchObject.
Args:
key (str): Key of the property looked-up.
match (MatchObject): The matched property.
Return:
The discovered value, as a string or boolean. | Below is the the instruction that describes the task:
### Input:
Gets the value of the property in the given MatchObject.
Args:
key (str): Key of the property looked-up.
match (MatchObject): The matched property.
Return:
The discovered value, as a string or boolean.
### Response:
def _get_value_from_match(self, key, match):
"""
Gets the value of the property in the given MatchObject.
Args:
key (str): Key of the property looked-up.
match (MatchObject): The matched property.
Return:
The discovered value, as a string or boolean.
"""
value = match.groups(1)[0]
clean_value = str(value).lstrip().rstrip()
if clean_value == 'true':
self._log.info('Got value of "%s" as boolean true.', key)
return True
if clean_value == 'false':
self._log.info('Got value of "%s" as boolean false.', key)
return False
try:
float_value = float(clean_value)
self._log.info('Got value of "%s" as float "%f".',
key,
float_value)
return float_value
except ValueError:
self._log.info('Got value of "%s" as string "%s".',
key,
clean_value)
return clean_value |
def _ions(self, f):
"""
This is a generator that returns the mzs being measured during
each time segment, one segment at a time.
"""
outside_pos = f.tell()
doff = find_offset(f, 4 * b'\xff' + 'HapsSearch'.encode('ascii'))
# actual end of prev section is 34 bytes before, but assume 1 rec
f.seek(doff - 62)
# seek backwards to find the FFFFFFFF header
while True:
f.seek(f.tell() - 8)
if f.read(4) == 4 * b'\xff':
break
f.seek(f.tell() + 64)
nsegments = struct.unpack('<I', f.read(4))[0]
for _ in range(nsegments):
# first 32 bytes are segment name, rest are something else?
f.seek(f.tell() + 96)
nions = struct.unpack('<I', f.read(4))[0]
ions = []
for _ in range(nions):
# TODO: check that itype is actually a SIM/full scan switch
i1, i2, _, _, _, _, itype, _ = struct.unpack('<' + 8 * 'I',
f.read(32))
if itype == 0: # SIM
ions.append(i1 / 100.)
else: # full scan
# TODO: this might be a little hacky?
# ideally we would need to know n for this, e.g.:
# ions += np.linspace(i1 / 100, i2 / 100, n).tolist()
ions += np.arange(i1 / 100., i2 / 100. + 1, 1).tolist()
# save the file position and load the position
# that we were at before we started this code
inside_pos = f.tell()
f.seek(outside_pos)
yield ions
outside_pos = f.tell()
f.seek(inside_pos)
f.seek(outside_pos) | This is a generator that returns the mzs being measured during
each time segment, one segment at a time. | Below is the the instruction that describes the task:
### Input:
This is a generator that returns the mzs being measured during
each time segment, one segment at a time.
### Response:
def _ions(self, f):
"""
This is a generator that returns the mzs being measured during
each time segment, one segment at a time.
"""
outside_pos = f.tell()
doff = find_offset(f, 4 * b'\xff' + 'HapsSearch'.encode('ascii'))
# actual end of prev section is 34 bytes before, but assume 1 rec
f.seek(doff - 62)
# seek backwards to find the FFFFFFFF header
while True:
f.seek(f.tell() - 8)
if f.read(4) == 4 * b'\xff':
break
f.seek(f.tell() + 64)
nsegments = struct.unpack('<I', f.read(4))[0]
for _ in range(nsegments):
# first 32 bytes are segment name, rest are something else?
f.seek(f.tell() + 96)
nions = struct.unpack('<I', f.read(4))[0]
ions = []
for _ in range(nions):
# TODO: check that itype is actually a SIM/full scan switch
i1, i2, _, _, _, _, itype, _ = struct.unpack('<' + 8 * 'I',
f.read(32))
if itype == 0: # SIM
ions.append(i1 / 100.)
else: # full scan
# TODO: this might be a little hacky?
# ideally we would need to know n for this, e.g.:
# ions += np.linspace(i1 / 100, i2 / 100, n).tolist()
ions += np.arange(i1 / 100., i2 / 100. + 1, 1).tolist()
# save the file position and load the position
# that we were at before we started this code
inside_pos = f.tell()
f.seek(outside_pos)
yield ions
outside_pos = f.tell()
f.seek(inside_pos)
f.seek(outside_pos) |
def create(self, count):
"""Create a pattern of the specified length."""
space, self.space = tee(self.space)
limit = reduce(mul, map(len, self.sets)) * self.position
logging.debug('limit: %s', limit)
if limit >= count:
return ''.join(islice(space, count))
else:
raise IndexError('{count} Overflows {sets}!'.format(
count=count, sets=self.sets)) | Create a pattern of the specified length. | Below is the the instruction that describes the task:
### Input:
Create a pattern of the specified length.
### Response:
def create(self, count):
"""Create a pattern of the specified length."""
space, self.space = tee(self.space)
limit = reduce(mul, map(len, self.sets)) * self.position
logging.debug('limit: %s', limit)
if limit >= count:
return ''.join(islice(space, count))
else:
raise IndexError('{count} Overflows {sets}!'.format(
count=count, sets=self.sets)) |
def generate(self, pattern=None):
"""
Generates and returns random name as a list of strings.
"""
lst = self._lists[pattern]
while True:
result = lst[self._randrange(lst.length)]
# 1. Check that there are no duplicates
# 2. Check that there are no duplicate prefixes
# 3. Check max slug length
n = len(result)
if (self._ensure_unique and len(set(result)) != n or
self._check_prefix and len(set(x[:self._check_prefix] for x in result)) != n or
self._max_slug_length and sum(len(x) for x in result) + n - 1 > self._max_slug_length):
continue
return result | Generates and returns random name as a list of strings. | Below is the the instruction that describes the task:
### Input:
Generates and returns random name as a list of strings.
### Response:
def generate(self, pattern=None):
"""
Generates and returns random name as a list of strings.
"""
lst = self._lists[pattern]
while True:
result = lst[self._randrange(lst.length)]
# 1. Check that there are no duplicates
# 2. Check that there are no duplicate prefixes
# 3. Check max slug length
n = len(result)
if (self._ensure_unique and len(set(result)) != n or
self._check_prefix and len(set(x[:self._check_prefix] for x in result)) != n or
self._max_slug_length and sum(len(x) for x in result) + n - 1 > self._max_slug_length):
continue
return result |
def _connect(self):
"Connects a socket to the server using options defined in `config`."
self.socket = socket.socket()
self.socket.connect((self.config['host'], self.config['port']))
self.cmd("NICK %s" % self.config['nick'])
self.cmd("USER %s %s bla :%s" %
(self.config['ident'], self.config['host'], self.config['realname'])) | Connects a socket to the server using options defined in `config`. | Below is the the instruction that describes the task:
### Input:
Connects a socket to the server using options defined in `config`.
### Response:
def _connect(self):
"Connects a socket to the server using options defined in `config`."
self.socket = socket.socket()
self.socket.connect((self.config['host'], self.config['port']))
self.cmd("NICK %s" % self.config['nick'])
self.cmd("USER %s %s bla :%s" %
(self.config['ident'], self.config['host'], self.config['realname'])) |
def dispatch_event(self,event_type,*args):
"""
Internal event handling method.
This method extends the behavior inherited from :py:meth:`pyglet.window.Window.dispatch_event()` by calling the various :py:meth:`handleEvent()` methods.
By default, :py:meth:`Peng.handleEvent()`\ , :py:meth:`handleEvent()` and :py:meth:`Menu.handleEvent()` are called in this order to handle events.
Note that some events may not be handled by all handlers during early startup.
"""
super(PengWindow,self).dispatch_event(event_type,*args)
try:
p = self.peng
m = self.menu
except AttributeError:
# To prevent early startup errors
if hasattr(self,"peng") and self.peng.cfg["debug.events.logerr"]:
print("Error:")
traceback.print_exc()
return
p.handleEvent(event_type,args,self)
self.handleEvent(event_type,args)
m.handleEvent(event_type,args) | Internal event handling method.
This method extends the behavior inherited from :py:meth:`pyglet.window.Window.dispatch_event()` by calling the various :py:meth:`handleEvent()` methods.
By default, :py:meth:`Peng.handleEvent()`\ , :py:meth:`handleEvent()` and :py:meth:`Menu.handleEvent()` are called in this order to handle events.
Note that some events may not be handled by all handlers during early startup. | Below is the the instruction that describes the task:
### Input:
Internal event handling method.
This method extends the behavior inherited from :py:meth:`pyglet.window.Window.dispatch_event()` by calling the various :py:meth:`handleEvent()` methods.
By default, :py:meth:`Peng.handleEvent()`\ , :py:meth:`handleEvent()` and :py:meth:`Menu.handleEvent()` are called in this order to handle events.
Note that some events may not be handled by all handlers during early startup.
### Response:
def dispatch_event(self,event_type,*args):
"""
Internal event handling method.
This method extends the behavior inherited from :py:meth:`pyglet.window.Window.dispatch_event()` by calling the various :py:meth:`handleEvent()` methods.
By default, :py:meth:`Peng.handleEvent()`\ , :py:meth:`handleEvent()` and :py:meth:`Menu.handleEvent()` are called in this order to handle events.
Note that some events may not be handled by all handlers during early startup.
"""
super(PengWindow,self).dispatch_event(event_type,*args)
try:
p = self.peng
m = self.menu
except AttributeError:
# To prevent early startup errors
if hasattr(self,"peng") and self.peng.cfg["debug.events.logerr"]:
print("Error:")
traceback.print_exc()
return
p.handleEvent(event_type,args,self)
self.handleEvent(event_type,args)
m.handleEvent(event_type,args) |
def resolveFilenameConflicts(self, dialog=True):
"""Goes through list of DPs to make sure that their destination names
do not clash. Applies new names. Returns True if some conflicts were resolved.
If dialog is True, shows confirrmation dialog."""
resolved = self.wdplv.resolveFilenameConflicts()
if resolved and dialog:
QMessageBox.warning(self, "Filename conflicts", """<P>
<NOBR>PURR has found duplicate destination filenames among your data products.</NOBR>
This is not allowed, so some filenames have been adjusted to avoid name clashes.
Please review the changes before saving this entry.
</P>""",
QMessageBox.Ok, 0)
return resolved | Goes through list of DPs to make sure that their destination names
do not clash. Applies new names. Returns True if some conflicts were resolved.
If dialog is True, shows confirrmation dialog. | Below is the the instruction that describes the task:
### Input:
Goes through list of DPs to make sure that their destination names
do not clash. Applies new names. Returns True if some conflicts were resolved.
If dialog is True, shows confirrmation dialog.
### Response:
def resolveFilenameConflicts(self, dialog=True):
"""Goes through list of DPs to make sure that their destination names
do not clash. Applies new names. Returns True if some conflicts were resolved.
If dialog is True, shows confirrmation dialog."""
resolved = self.wdplv.resolveFilenameConflicts()
if resolved and dialog:
QMessageBox.warning(self, "Filename conflicts", """<P>
<NOBR>PURR has found duplicate destination filenames among your data products.</NOBR>
This is not allowed, so some filenames have been adjusted to avoid name clashes.
Please review the changes before saving this entry.
</P>""",
QMessageBox.Ok, 0)
return resolved |
def profile(profile_name):
'''
Activate specified profile
CLI Example:
.. code-block:: bash
salt '*' tuned.profile virtual-guest
'''
# run tuned-adm with the profile specified
result = __salt__['cmd.retcode']('tuned-adm profile {0}'.format(profile_name))
if int(result) != 0:
return False
return '{0}'.format(profile_name) | Activate specified profile
CLI Example:
.. code-block:: bash
salt '*' tuned.profile virtual-guest | Below is the the instruction that describes the task:
### Input:
Activate specified profile
CLI Example:
.. code-block:: bash
salt '*' tuned.profile virtual-guest
### Response:
def profile(profile_name):
'''
Activate specified profile
CLI Example:
.. code-block:: bash
salt '*' tuned.profile virtual-guest
'''
# run tuned-adm with the profile specified
result = __salt__['cmd.retcode']('tuned-adm profile {0}'.format(profile_name))
if int(result) != 0:
return False
return '{0}'.format(profile_name) |
def fit_transform(self, Z):
"""Fit LSI model to X and perform dimensionality reduction on X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data.
Returns
-------
X_new : array, shape (n_samples, n_components)
Reduced version of X. This will always be a dense array.
"""
X = Z[:, 'X'] if isinstance(Z, DictRDD) else Z
check_rdd(X, (sp.spmatrix, np.ndarray))
if self.algorithm == "em":
X = X.persist() # boosting iterative svm
Sigma, V = svd_em(X, k=self.n_components, maxiter=self.n_iter,
tol=self.tol, compute_u=False,
seed=self.random_state)
self.components_ = V
X.unpersist()
return self.transform(Z)
else:
# TODO: raise warning non distributed
return super(SparkTruncatedSVD, self).fit_transform(X.tosparse()) | Fit LSI model to X and perform dimensionality reduction on X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data.
Returns
-------
X_new : array, shape (n_samples, n_components)
Reduced version of X. This will always be a dense array. | Below is the the instruction that describes the task:
### Input:
Fit LSI model to X and perform dimensionality reduction on X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data.
Returns
-------
X_new : array, shape (n_samples, n_components)
Reduced version of X. This will always be a dense array.
### Response:
def fit_transform(self, Z):
"""Fit LSI model to X and perform dimensionality reduction on X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data.
Returns
-------
X_new : array, shape (n_samples, n_components)
Reduced version of X. This will always be a dense array.
"""
X = Z[:, 'X'] if isinstance(Z, DictRDD) else Z
check_rdd(X, (sp.spmatrix, np.ndarray))
if self.algorithm == "em":
X = X.persist() # boosting iterative svm
Sigma, V = svd_em(X, k=self.n_components, maxiter=self.n_iter,
tol=self.tol, compute_u=False,
seed=self.random_state)
self.components_ = V
X.unpersist()
return self.transform(Z)
else:
# TODO: raise warning non distributed
return super(SparkTruncatedSVD, self).fit_transform(X.tosparse()) |
def get_generated_cols(X_original, X_transformed, to_transform):
"""
Returns a list of the generated/transformed columns.
Arguments:
X_original: df
the original (input) DataFrame.
X_transformed: df
the transformed (current) DataFrame.
to_transform: [str]
a list of columns that were transformed (as in the original DataFrame), commonly self.cols.
Output:
a list of columns that were transformed (as in the current DataFrame).
"""
original_cols = list(X_original.columns)
if len(to_transform) > 0:
[original_cols.remove(c) for c in to_transform]
current_cols = list(X_transformed.columns)
if len(original_cols) > 0:
[current_cols.remove(c) for c in original_cols]
return current_cols | Returns a list of the generated/transformed columns.
Arguments:
X_original: df
the original (input) DataFrame.
X_transformed: df
the transformed (current) DataFrame.
to_transform: [str]
a list of columns that were transformed (as in the original DataFrame), commonly self.cols.
Output:
a list of columns that were transformed (as in the current DataFrame). | Below is the the instruction that describes the task:
### Input:
Returns a list of the generated/transformed columns.
Arguments:
X_original: df
the original (input) DataFrame.
X_transformed: df
the transformed (current) DataFrame.
to_transform: [str]
a list of columns that were transformed (as in the original DataFrame), commonly self.cols.
Output:
a list of columns that were transformed (as in the current DataFrame).
### Response:
def get_generated_cols(X_original, X_transformed, to_transform):
"""
Returns a list of the generated/transformed columns.
Arguments:
X_original: df
the original (input) DataFrame.
X_transformed: df
the transformed (current) DataFrame.
to_transform: [str]
a list of columns that were transformed (as in the original DataFrame), commonly self.cols.
Output:
a list of columns that were transformed (as in the current DataFrame).
"""
original_cols = list(X_original.columns)
if len(to_transform) > 0:
[original_cols.remove(c) for c in to_transform]
current_cols = list(X_transformed.columns)
if len(original_cols) > 0:
[current_cols.remove(c) for c in original_cols]
return current_cols |
def write_groovy_script_and_configs(
filename, content, job_configs, view_configs=None):
"""Write out the groovy script and configs to file.
This writes the reconfigure script to the file location
and places the expanded configs in subdirectories 'view_configs' /
'job_configs' that the script can then access when run.
"""
with open(filename, 'w') as h:
h.write(content)
if view_configs:
view_config_dir = os.path.join(os.path.dirname(filename), 'view_configs')
if not os.path.isdir(view_config_dir):
os.makedirs(view_config_dir)
for config_name, config_body in view_configs.items():
config_filename = os.path.join(view_config_dir, config_name)
with open(config_filename, 'w') as config_fh:
config_fh.write(config_body)
job_config_dir = os.path.join(os.path.dirname(filename), 'job_configs')
if not os.path.isdir(job_config_dir):
os.makedirs(job_config_dir)
# prefix each config file with a serial number to maintain order
format_str = '%0' + str(len(str(len(job_configs)))) + 'd'
i = 0
for config_name, config_body in job_configs.items():
i += 1
config_filename = os.path.join(
job_config_dir,
format_str % i + ' ' + config_name)
with open(config_filename, 'w') as config_fh:
config_fh.write(config_body) | Write out the groovy script and configs to file.
This writes the reconfigure script to the file location
and places the expanded configs in subdirectories 'view_configs' /
'job_configs' that the script can then access when run. | Below is the the instruction that describes the task:
### Input:
Write out the groovy script and configs to file.
This writes the reconfigure script to the file location
and places the expanded configs in subdirectories 'view_configs' /
'job_configs' that the script can then access when run.
### Response:
def write_groovy_script_and_configs(
filename, content, job_configs, view_configs=None):
"""Write out the groovy script and configs to file.
This writes the reconfigure script to the file location
and places the expanded configs in subdirectories 'view_configs' /
'job_configs' that the script can then access when run.
"""
with open(filename, 'w') as h:
h.write(content)
if view_configs:
view_config_dir = os.path.join(os.path.dirname(filename), 'view_configs')
if not os.path.isdir(view_config_dir):
os.makedirs(view_config_dir)
for config_name, config_body in view_configs.items():
config_filename = os.path.join(view_config_dir, config_name)
with open(config_filename, 'w') as config_fh:
config_fh.write(config_body)
job_config_dir = os.path.join(os.path.dirname(filename), 'job_configs')
if not os.path.isdir(job_config_dir):
os.makedirs(job_config_dir)
# prefix each config file with a serial number to maintain order
format_str = '%0' + str(len(str(len(job_configs)))) + 'd'
i = 0
for config_name, config_body in job_configs.items():
i += 1
config_filename = os.path.join(
job_config_dir,
format_str % i + ' ' + config_name)
with open(config_filename, 'w') as config_fh:
config_fh.write(config_body) |
def et2utc(et, formatStr, prec, lenout=_default_len_out):
"""
Convert an input time from ephemeris seconds past J2000
to Calendar, Day-of-Year, or Julian Date format, UTC.
http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/et2utc_c.html
:param et: Input epoch, given in ephemeris seconds past J2000.
:type et: float
:param formatStr: Format of output epoch.
:type formatStr: str
:param prec: Digits of precision in fractional seconds or days.
:type prec: int
:param lenout: The length of the output string plus 1.
:type lenout: int
:return: Output time string in UTC
:rtype: str
"""
et = ctypes.c_double(et)
prec = ctypes.c_int(prec)
lenout = ctypes.c_int(lenout)
formatStr = stypes.stringToCharP(formatStr)
utcstr = stypes.stringToCharP(lenout)
libspice.et2utc_c(et, formatStr, prec, lenout, utcstr)
return stypes.toPythonString(utcstr) | Convert an input time from ephemeris seconds past J2000
to Calendar, Day-of-Year, or Julian Date format, UTC.
http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/et2utc_c.html
:param et: Input epoch, given in ephemeris seconds past J2000.
:type et: float
:param formatStr: Format of output epoch.
:type formatStr: str
:param prec: Digits of precision in fractional seconds or days.
:type prec: int
:param lenout: The length of the output string plus 1.
:type lenout: int
:return: Output time string in UTC
:rtype: str | Below is the the instruction that describes the task:
### Input:
Convert an input time from ephemeris seconds past J2000
to Calendar, Day-of-Year, or Julian Date format, UTC.
http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/et2utc_c.html
:param et: Input epoch, given in ephemeris seconds past J2000.
:type et: float
:param formatStr: Format of output epoch.
:type formatStr: str
:param prec: Digits of precision in fractional seconds or days.
:type prec: int
:param lenout: The length of the output string plus 1.
:type lenout: int
:return: Output time string in UTC
:rtype: str
### Response:
def et2utc(et, formatStr, prec, lenout=_default_len_out):
"""
Convert an input time from ephemeris seconds past J2000
to Calendar, Day-of-Year, or Julian Date format, UTC.
http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/et2utc_c.html
:param et: Input epoch, given in ephemeris seconds past J2000.
:type et: float
:param formatStr: Format of output epoch.
:type formatStr: str
:param prec: Digits of precision in fractional seconds or days.
:type prec: int
:param lenout: The length of the output string plus 1.
:type lenout: int
:return: Output time string in UTC
:rtype: str
"""
et = ctypes.c_double(et)
prec = ctypes.c_int(prec)
lenout = ctypes.c_int(lenout)
formatStr = stypes.stringToCharP(formatStr)
utcstr = stypes.stringToCharP(lenout)
libspice.et2utc_c(et, formatStr, prec, lenout, utcstr)
return stypes.toPythonString(utcstr) |
def _check_region_for_parsing(number, default_region):
"""Checks to see that the region code used is valid, or if it is not
valid, that the number to parse starts with a + symbol so that we can
attempt to infer the region from the number. Returns False if it cannot
use the region provided and the region cannot be inferred.
"""
if not _is_valid_region_code(default_region):
# If the number is None or empty, we can't infer the region.
if number is None or len(number) == 0:
return False
match = _PLUS_CHARS_PATTERN.match(number)
if match is None:
return False
return True | Checks to see that the region code used is valid, or if it is not
valid, that the number to parse starts with a + symbol so that we can
attempt to infer the region from the number. Returns False if it cannot
use the region provided and the region cannot be inferred. | Below is the the instruction that describes the task:
### Input:
Checks to see that the region code used is valid, or if it is not
valid, that the number to parse starts with a + symbol so that we can
attempt to infer the region from the number. Returns False if it cannot
use the region provided and the region cannot be inferred.
### Response:
def _check_region_for_parsing(number, default_region):
"""Checks to see that the region code used is valid, or if it is not
valid, that the number to parse starts with a + symbol so that we can
attempt to infer the region from the number. Returns False if it cannot
use the region provided and the region cannot be inferred.
"""
if not _is_valid_region_code(default_region):
# If the number is None or empty, we can't infer the region.
if number is None or len(number) == 0:
return False
match = _PLUS_CHARS_PATTERN.match(number)
if match is None:
return False
return True |
def start(self, request, application, extra_roles=None):
""" Continue the state machine at first state. """
# Get the authentication of the current user
roles = self._get_roles_for_request(request, application)
if extra_roles is not None:
roles.update(extra_roles)
# Ensure current user is authenticated. If user isn't applicant,
# leader, delegate or admin, they probably shouldn't be here.
if 'is_authorised' not in roles:
return HttpResponseForbidden('<h1>Access Denied</h1>')
# Go to first state.
return self._next(request, application, roles, self._first_state) | Continue the state machine at first state. | Below is the the instruction that describes the task:
### Input:
Continue the state machine at first state.
### Response:
def start(self, request, application, extra_roles=None):
""" Continue the state machine at first state. """
# Get the authentication of the current user
roles = self._get_roles_for_request(request, application)
if extra_roles is not None:
roles.update(extra_roles)
# Ensure current user is authenticated. If user isn't applicant,
# leader, delegate or admin, they probably shouldn't be here.
if 'is_authorised' not in roles:
return HttpResponseForbidden('<h1>Access Denied</h1>')
# Go to first state.
return self._next(request, application, roles, self._first_state) |
def migrator(state):
"""Tweaks will be lost for Cleverbot and its conversations."""
for tweak in ('tweak1', 'tweak2', 'tweak3'):
del state[0][tweak]
for convo in state[1]:
if tweak in convo:
del convo[tweak]
return state | Tweaks will be lost for Cleverbot and its conversations. | Below is the the instruction that describes the task:
### Input:
Tweaks will be lost for Cleverbot and its conversations.
### Response:
def migrator(state):
"""Tweaks will be lost for Cleverbot and its conversations."""
for tweak in ('tweak1', 'tweak2', 'tweak3'):
del state[0][tweak]
for convo in state[1]:
if tweak in convo:
del convo[tweak]
return state |
def formfield_for_foreignkey_helper(inline, *args, **kwargs):
"""
The implementation for ``RelatedContentInline.formfield_for_foreignkey``
This takes the takes all of the ``args`` and ``kwargs`` from the call to
``formfield_for_foreignkey`` and operates on this. It returns the updated
``args`` and ``kwargs`` to be passed on to ``super``.
This is solely an implementation detail as it's easier to test a function
than to provide all of the expectations that the ``GenericTabularInline``
has.
"""
db_field = args[0]
if db_field.name != "related_type":
return args, kwargs
initial_filter = getattr(settings, RELATED_TYPE_INITIAL_FILTER,
False)
if "initial" not in kwargs and initial_filter:
# TODO: handle gracefully if unable to load and in non-debug
initial = RelatedType.objects.get(**initial_filter).pk
kwargs["initial"] = initial
return args, kwargs | The implementation for ``RelatedContentInline.formfield_for_foreignkey``
This takes the takes all of the ``args`` and ``kwargs`` from the call to
``formfield_for_foreignkey`` and operates on this. It returns the updated
``args`` and ``kwargs`` to be passed on to ``super``.
This is solely an implementation detail as it's easier to test a function
than to provide all of the expectations that the ``GenericTabularInline``
has. | Below is the the instruction that describes the task:
### Input:
The implementation for ``RelatedContentInline.formfield_for_foreignkey``
This takes the takes all of the ``args`` and ``kwargs`` from the call to
``formfield_for_foreignkey`` and operates on this. It returns the updated
``args`` and ``kwargs`` to be passed on to ``super``.
This is solely an implementation detail as it's easier to test a function
than to provide all of the expectations that the ``GenericTabularInline``
has.
### Response:
def formfield_for_foreignkey_helper(inline, *args, **kwargs):
"""
The implementation for ``RelatedContentInline.formfield_for_foreignkey``
This takes the takes all of the ``args`` and ``kwargs`` from the call to
``formfield_for_foreignkey`` and operates on this. It returns the updated
``args`` and ``kwargs`` to be passed on to ``super``.
This is solely an implementation detail as it's easier to test a function
than to provide all of the expectations that the ``GenericTabularInline``
has.
"""
db_field = args[0]
if db_field.name != "related_type":
return args, kwargs
initial_filter = getattr(settings, RELATED_TYPE_INITIAL_FILTER,
False)
if "initial" not in kwargs and initial_filter:
# TODO: handle gracefully if unable to load and in non-debug
initial = RelatedType.objects.get(**initial_filter).pk
kwargs["initial"] = initial
return args, kwargs |
def _validate_sections(cls, sections):
"""Validates sections types and uniqueness."""
names = []
for section in sections:
if not hasattr(section, 'name'):
raise ConfigurationError('`sections` attribute requires a list of Section')
name = section.name
if name in names:
raise ConfigurationError('`%s` section name must be unique' % name)
names.append(name) | Validates sections types and uniqueness. | Below is the the instruction that describes the task:
### Input:
Validates sections types and uniqueness.
### Response:
def _validate_sections(cls, sections):
"""Validates sections types and uniqueness."""
names = []
for section in sections:
if not hasattr(section, 'name'):
raise ConfigurationError('`sections` attribute requires a list of Section')
name = section.name
if name in names:
raise ConfigurationError('`%s` section name must be unique' % name)
names.append(name) |
def _get_firmware_update_element(self):
"""Get the url for firmware update
:returns: firmware update url
:raises: Missing resource error on missing url
"""
fw_update_action = self._actions.update_firmware
if not fw_update_action:
raise (sushy.exceptions.
MissingActionError(action='#UpdateService.SimpleUpdate',
resource=self._path))
return fw_update_action | Get the url for firmware update
:returns: firmware update url
:raises: Missing resource error on missing url | Below is the the instruction that describes the task:
### Input:
Get the url for firmware update
:returns: firmware update url
:raises: Missing resource error on missing url
### Response:
def _get_firmware_update_element(self):
"""Get the url for firmware update
:returns: firmware update url
:raises: Missing resource error on missing url
"""
fw_update_action = self._actions.update_firmware
if not fw_update_action:
raise (sushy.exceptions.
MissingActionError(action='#UpdateService.SimpleUpdate',
resource=self._path))
return fw_update_action |
def hangup_all_calls(self):
"""REST Hangup All Live Calls Helper
"""
path = '/' + self.api_version + '/HangupAllCalls/'
method = 'POST'
return self.request(path, method) | REST Hangup All Live Calls Helper | Below is the the instruction that describes the task:
### Input:
REST Hangup All Live Calls Helper
### Response:
def hangup_all_calls(self):
"""REST Hangup All Live Calls Helper
"""
path = '/' + self.api_version + '/HangupAllCalls/'
method = 'POST'
return self.request(path, method) |
def HexEscape(self, string, match, **unused_kwargs):
"""Converts a hex escaped string."""
logging.debug('HexEscape matched {0:s}.'.format(string))
hex_string = match.group(1)
try:
hex_string = binascii.unhexlify(hex_string)
hex_string = codecs.decode(hex_string, 'utf-8')
self.string += hex_string
except (TypeError, binascii.Error):
raise errors.ParseError('Invalid hex escape {0!s}.'.format(hex_string)) | Converts a hex escaped string. | Below is the the instruction that describes the task:
### Input:
Converts a hex escaped string.
### Response:
def HexEscape(self, string, match, **unused_kwargs):
"""Converts a hex escaped string."""
logging.debug('HexEscape matched {0:s}.'.format(string))
hex_string = match.group(1)
try:
hex_string = binascii.unhexlify(hex_string)
hex_string = codecs.decode(hex_string, 'utf-8')
self.string += hex_string
except (TypeError, binascii.Error):
raise errors.ParseError('Invalid hex escape {0!s}.'.format(hex_string)) |
def warning(self, text):
"""
Posts a warning message adding a timestamp and logging level to it for both file and console handlers.
Logger uses a redraw rate because of console flickering. That means it will not draw new messages or progress
at the very time they are being logged but their timestamp will be captured at the right time. Logger will
redraw at a given time period AND when new messages or progress are logged. If you still want to force redraw
immediately (may produce flickering) then call 'flush' method.
:param text: The text to log into file and console.
"""
self.queue.put(dill.dumps(LogMessageCommand(text=text, level=logging.WARNING))) | Posts a warning message adding a timestamp and logging level to it for both file and console handlers.
Logger uses a redraw rate because of console flickering. That means it will not draw new messages or progress
at the very time they are being logged but their timestamp will be captured at the right time. Logger will
redraw at a given time period AND when new messages or progress are logged. If you still want to force redraw
immediately (may produce flickering) then call 'flush' method.
:param text: The text to log into file and console. | Below is the the instruction that describes the task:
### Input:
Posts a warning message adding a timestamp and logging level to it for both file and console handlers.
Logger uses a redraw rate because of console flickering. That means it will not draw new messages or progress
at the very time they are being logged but their timestamp will be captured at the right time. Logger will
redraw at a given time period AND when new messages or progress are logged. If you still want to force redraw
immediately (may produce flickering) then call 'flush' method.
:param text: The text to log into file and console.
### Response:
def warning(self, text):
"""
Posts a warning message adding a timestamp and logging level to it for both file and console handlers.
Logger uses a redraw rate because of console flickering. That means it will not draw new messages or progress
at the very time they are being logged but their timestamp will be captured at the right time. Logger will
redraw at a given time period AND when new messages or progress are logged. If you still want to force redraw
immediately (may produce flickering) then call 'flush' method.
:param text: The text to log into file and console.
"""
self.queue.put(dill.dumps(LogMessageCommand(text=text, level=logging.WARNING))) |
def update_rds_databases(self):
"""Update list of RDS Databases for the account / region
Returns:
`None`
"""
self.log.info('Updating RDS Databases for {} / {}'.format(
self.account, self.region
))
# All RDS resources are polled via a Lambda collector in a central account
rds_collector_account = AWSAccount.get(self.rds_collector_account)
rds_session = get_aws_session(rds_collector_account)
# Existing RDS resources come from database
existing_rds_dbs = RDSInstance.get_all(self.account, self.region)
try:
# Special session pinned to a single account for Lambda invocation so we
# don't have to manage lambdas in every account & region
lambda_client = rds_session.client('lambda', region_name=self.rds_collector_region)
# The AWS Config Lambda will collect all the non-compliant resources for all regions
# within the account
input_payload = json.dumps({"account_id": self.account.account_number,
"region": self.region,
"role": self.rds_role,
"config_rule_name": self.rds_config_rule_name
}).encode('utf-8')
response = lambda_client.invoke(FunctionName=self.rds_function_name, InvocationType='RequestResponse',
Payload=input_payload
)
response_payload = json.loads(response['Payload'].read().decode('utf-8'))
if response_payload['success']:
rds_dbs = response_payload['data']
if rds_dbs:
for db_instance in rds_dbs:
tags = {t['Key']: t['Value'] for t in db_instance['tags'] or {}}
properties = {
'tags': tags,
'metrics': None,
'engine': db_instance['engine'],
'creation_date': db_instance['creation_date']
}
if db_instance['resource_name'] in existing_rds_dbs:
rds = existing_rds_dbs[db_instance['resource_name']]
if rds.update(db_instance, properties):
self.log.debug('Change detected for RDS instance {}/{} '
.format(db_instance['resource_name'], properties))
else:
RDSInstance.create(
db_instance['resource_name'],
account_id=self.account.account_id,
location=db_instance['region'],
properties=properties,
tags=tags
)
# Removal of RDS instances
rk = set()
erk = set()
for database in rds_dbs:
rk.add(database['resource_name'])
for existing in existing_rds_dbs.keys():
erk.add(existing)
for resource_id in erk - rk:
db.session.delete(existing_rds_dbs[resource_id].resource)
self.log.debug('Removed RDS instances {}/{}'.format(
self.account.account_name,
resource_id
))
db.session.commit()
else:
self.log.error('RDS Lambda Execution Failed / {} / {} / {}'.
format(self.account.account_name, self.region, response_payload))
except Exception as e:
self.log.exception('There was a problem during RDS collection for {}/{}/{}'.format(
self.account.account_name, self.region, e
))
db.session.rollback() | Update list of RDS Databases for the account / region
Returns:
`None` | Below is the the instruction that describes the task:
### Input:
Update list of RDS Databases for the account / region
Returns:
`None`
### Response:
def update_rds_databases(self):
"""Update list of RDS Databases for the account / region
Returns:
`None`
"""
self.log.info('Updating RDS Databases for {} / {}'.format(
self.account, self.region
))
# All RDS resources are polled via a Lambda collector in a central account
rds_collector_account = AWSAccount.get(self.rds_collector_account)
rds_session = get_aws_session(rds_collector_account)
# Existing RDS resources come from database
existing_rds_dbs = RDSInstance.get_all(self.account, self.region)
try:
# Special session pinned to a single account for Lambda invocation so we
# don't have to manage lambdas in every account & region
lambda_client = rds_session.client('lambda', region_name=self.rds_collector_region)
# The AWS Config Lambda will collect all the non-compliant resources for all regions
# within the account
input_payload = json.dumps({"account_id": self.account.account_number,
"region": self.region,
"role": self.rds_role,
"config_rule_name": self.rds_config_rule_name
}).encode('utf-8')
response = lambda_client.invoke(FunctionName=self.rds_function_name, InvocationType='RequestResponse',
Payload=input_payload
)
response_payload = json.loads(response['Payload'].read().decode('utf-8'))
if response_payload['success']:
rds_dbs = response_payload['data']
if rds_dbs:
for db_instance in rds_dbs:
tags = {t['Key']: t['Value'] for t in db_instance['tags'] or {}}
properties = {
'tags': tags,
'metrics': None,
'engine': db_instance['engine'],
'creation_date': db_instance['creation_date']
}
if db_instance['resource_name'] in existing_rds_dbs:
rds = existing_rds_dbs[db_instance['resource_name']]
if rds.update(db_instance, properties):
self.log.debug('Change detected for RDS instance {}/{} '
.format(db_instance['resource_name'], properties))
else:
RDSInstance.create(
db_instance['resource_name'],
account_id=self.account.account_id,
location=db_instance['region'],
properties=properties,
tags=tags
)
# Removal of RDS instances
rk = set()
erk = set()
for database in rds_dbs:
rk.add(database['resource_name'])
for existing in existing_rds_dbs.keys():
erk.add(existing)
for resource_id in erk - rk:
db.session.delete(existing_rds_dbs[resource_id].resource)
self.log.debug('Removed RDS instances {}/{}'.format(
self.account.account_name,
resource_id
))
db.session.commit()
else:
self.log.error('RDS Lambda Execution Failed / {} / {} / {}'.
format(self.account.account_name, self.region, response_payload))
except Exception as e:
self.log.exception('There was a problem during RDS collection for {}/{}/{}'.format(
self.account.account_name, self.region, e
))
db.session.rollback() |
def func_str(func, args=[], kwargs={}, type_aliases=[], packed=False,
packkw=None, truncate=False):
"""
string representation of function definition
Returns:
str: a representation of func with args, kwargs, and type_aliases
Args:
func (function):
args (list): argument values (default = [])
kwargs (dict): kwargs values (default = {})
type_aliases (list): (default = [])
packed (bool): (default = False)
packkw (None): (default = None)
Returns:
str: func_str
CommandLine:
python -m utool.util_str --exec-func_str
Example:
>>> # ENABLE_DOCTEST
>>> from utool.util_str import * # NOQA
>>> func = byte_str
>>> args = [1024, 'MB']
>>> kwargs = dict(precision=2)
>>> type_aliases = []
>>> packed = False
>>> packkw = None
>>> _str = func_str(func, args, kwargs, type_aliases, packed, packkw)
>>> result = _str
>>> print(result)
byte_str(1024, 'MB', precision=2)
"""
import utool as ut
# if truncate:
# truncatekw = {'maxlen': 20}
# else:
truncatekw = {}
argrepr_list = ([] if args is None else
ut.get_itemstr_list(args, nl=False, truncate=truncate,
truncatekw=truncatekw))
kwrepr_list = ([] if kwargs is None else
ut.dict_itemstr_list(kwargs, explicit=True, nl=False,
truncate=truncate,
truncatekw=truncatekw))
repr_list = argrepr_list + kwrepr_list
argskwargs_str = ', '.join(repr_list)
_str = '%s(%s)' % (meta_util_six.get_funcname(func), argskwargs_str)
if packed:
packkw_ = dict(textwidth=80, nlprefix=' ', break_words=False)
if packkw is not None:
packkw_.update(packkw_)
_str = packstr(_str, **packkw_)
return _str | string representation of function definition
Returns:
str: a representation of func with args, kwargs, and type_aliases
Args:
func (function):
args (list): argument values (default = [])
kwargs (dict): kwargs values (default = {})
type_aliases (list): (default = [])
packed (bool): (default = False)
packkw (None): (default = None)
Returns:
str: func_str
CommandLine:
python -m utool.util_str --exec-func_str
Example:
>>> # ENABLE_DOCTEST
>>> from utool.util_str import * # NOQA
>>> func = byte_str
>>> args = [1024, 'MB']
>>> kwargs = dict(precision=2)
>>> type_aliases = []
>>> packed = False
>>> packkw = None
>>> _str = func_str(func, args, kwargs, type_aliases, packed, packkw)
>>> result = _str
>>> print(result)
byte_str(1024, 'MB', precision=2) | Below is the the instruction that describes the task:
### Input:
string representation of function definition
Returns:
str: a representation of func with args, kwargs, and type_aliases
Args:
func (function):
args (list): argument values (default = [])
kwargs (dict): kwargs values (default = {})
type_aliases (list): (default = [])
packed (bool): (default = False)
packkw (None): (default = None)
Returns:
str: func_str
CommandLine:
python -m utool.util_str --exec-func_str
Example:
>>> # ENABLE_DOCTEST
>>> from utool.util_str import * # NOQA
>>> func = byte_str
>>> args = [1024, 'MB']
>>> kwargs = dict(precision=2)
>>> type_aliases = []
>>> packed = False
>>> packkw = None
>>> _str = func_str(func, args, kwargs, type_aliases, packed, packkw)
>>> result = _str
>>> print(result)
byte_str(1024, 'MB', precision=2)
### Response:
def func_str(func, args=[], kwargs={}, type_aliases=[], packed=False,
packkw=None, truncate=False):
"""
string representation of function definition
Returns:
str: a representation of func with args, kwargs, and type_aliases
Args:
func (function):
args (list): argument values (default = [])
kwargs (dict): kwargs values (default = {})
type_aliases (list): (default = [])
packed (bool): (default = False)
packkw (None): (default = None)
Returns:
str: func_str
CommandLine:
python -m utool.util_str --exec-func_str
Example:
>>> # ENABLE_DOCTEST
>>> from utool.util_str import * # NOQA
>>> func = byte_str
>>> args = [1024, 'MB']
>>> kwargs = dict(precision=2)
>>> type_aliases = []
>>> packed = False
>>> packkw = None
>>> _str = func_str(func, args, kwargs, type_aliases, packed, packkw)
>>> result = _str
>>> print(result)
byte_str(1024, 'MB', precision=2)
"""
import utool as ut
# if truncate:
# truncatekw = {'maxlen': 20}
# else:
truncatekw = {}
argrepr_list = ([] if args is None else
ut.get_itemstr_list(args, nl=False, truncate=truncate,
truncatekw=truncatekw))
kwrepr_list = ([] if kwargs is None else
ut.dict_itemstr_list(kwargs, explicit=True, nl=False,
truncate=truncate,
truncatekw=truncatekw))
repr_list = argrepr_list + kwrepr_list
argskwargs_str = ', '.join(repr_list)
_str = '%s(%s)' % (meta_util_six.get_funcname(func), argskwargs_str)
if packed:
packkw_ = dict(textwidth=80, nlprefix=' ', break_words=False)
if packkw is not None:
packkw_.update(packkw_)
_str = packstr(_str, **packkw_)
return _str |
def generate_username(self, user_class):
""" Generate a new username for a user
"""
m = getattr(user_class, 'generate_username', None)
if m:
return m()
else:
max_length = user_class._meta.get_field(
self.username_field).max_length
return uuid.uuid4().hex[:max_length] | Generate a new username for a user | Below is the the instruction that describes the task:
### Input:
Generate a new username for a user
### Response:
def generate_username(self, user_class):
""" Generate a new username for a user
"""
m = getattr(user_class, 'generate_username', None)
if m:
return m()
else:
max_length = user_class._meta.get_field(
self.username_field).max_length
return uuid.uuid4().hex[:max_length] |
async def async_start(
cls,
middleware: typing.Union[typing.Iterable, Middleware] = None,
loop=None,
after_start=None,
before_stop=None,
**kwargs):
"""
Start an async spider
:param middleware: customize middleware or a list of middleware
:param loop:
:param after_start: hook
:param before_stop: hook
:return:
"""
loop = loop or asyncio.get_event_loop()
spider_ins = cls(middleware=middleware, loop=loop, is_async_start=True)
await spider_ins._start(
after_start=after_start, before_stop=before_stop) | Start an async spider
:param middleware: customize middleware or a list of middleware
:param loop:
:param after_start: hook
:param before_stop: hook
:return: | Below is the the instruction that describes the task:
### Input:
Start an async spider
:param middleware: customize middleware or a list of middleware
:param loop:
:param after_start: hook
:param before_stop: hook
:return:
### Response:
async def async_start(
cls,
middleware: typing.Union[typing.Iterable, Middleware] = None,
loop=None,
after_start=None,
before_stop=None,
**kwargs):
"""
Start an async spider
:param middleware: customize middleware or a list of middleware
:param loop:
:param after_start: hook
:param before_stop: hook
:return:
"""
loop = loop or asyncio.get_event_loop()
spider_ins = cls(middleware=middleware, loop=loop, is_async_start=True)
await spider_ins._start(
after_start=after_start, before_stop=before_stop) |
def _dict_from_lines(lines, key_nums, sep=None):
"""
Helper function to parse formatted text structured like:
value1 value2 ... sep key1, key2 ...
key_nums is a list giving the number of keys for each line. 0 if line should be skipped.
sep is a string denoting the character that separates the keys from the value (None if
no separator is present).
Returns:
dict{key1 : value1, key2 : value2, ...}
Raises:
ValueError if parsing fails.
"""
if is_string(lines):
lines = [lines]
if not isinstance(key_nums, collections.abc.Iterable):
key_nums = list(key_nums)
if len(lines) != len(key_nums):
err_msg = "lines = %s\n key_num = %s" % (str(lines), str(key_nums))
raise ValueError(err_msg)
kwargs = Namespace()
for (i, nk) in enumerate(key_nums):
if nk == 0: continue
line = lines[i]
tokens = [t.strip() for t in line.split()]
values, keys = tokens[:nk], "".join(tokens[nk:])
# Sanitize keys: In some case we might get strings in the form: foo[,bar]
keys.replace("[", "").replace("]", "")
keys = keys.split(",")
if sep is not None:
check = keys[0][0]
if check != sep:
raise ValueError("Expecting separator %s, got %s" % (sep, check))
keys[0] = keys[0][1:]
if len(values) != len(keys):
msg = "line: %s\n len(keys) != len(value)\nkeys: %s\n values: %s" % (line, keys, values)
raise ValueError(msg)
kwargs.update(zip(keys, values))
return kwargs | Helper function to parse formatted text structured like:
value1 value2 ... sep key1, key2 ...
key_nums is a list giving the number of keys for each line. 0 if line should be skipped.
sep is a string denoting the character that separates the keys from the value (None if
no separator is present).
Returns:
dict{key1 : value1, key2 : value2, ...}
Raises:
ValueError if parsing fails. | Below is the the instruction that describes the task:
### Input:
Helper function to parse formatted text structured like:
value1 value2 ... sep key1, key2 ...
key_nums is a list giving the number of keys for each line. 0 if line should be skipped.
sep is a string denoting the character that separates the keys from the value (None if
no separator is present).
Returns:
dict{key1 : value1, key2 : value2, ...}
Raises:
ValueError if parsing fails.
### Response:
def _dict_from_lines(lines, key_nums, sep=None):
"""
Helper function to parse formatted text structured like:
value1 value2 ... sep key1, key2 ...
key_nums is a list giving the number of keys for each line. 0 if line should be skipped.
sep is a string denoting the character that separates the keys from the value (None if
no separator is present).
Returns:
dict{key1 : value1, key2 : value2, ...}
Raises:
ValueError if parsing fails.
"""
if is_string(lines):
lines = [lines]
if not isinstance(key_nums, collections.abc.Iterable):
key_nums = list(key_nums)
if len(lines) != len(key_nums):
err_msg = "lines = %s\n key_num = %s" % (str(lines), str(key_nums))
raise ValueError(err_msg)
kwargs = Namespace()
for (i, nk) in enumerate(key_nums):
if nk == 0: continue
line = lines[i]
tokens = [t.strip() for t in line.split()]
values, keys = tokens[:nk], "".join(tokens[nk:])
# Sanitize keys: In some case we might get strings in the form: foo[,bar]
keys.replace("[", "").replace("]", "")
keys = keys.split(",")
if sep is not None:
check = keys[0][0]
if check != sep:
raise ValueError("Expecting separator %s, got %s" % (sep, check))
keys[0] = keys[0][1:]
if len(values) != len(keys):
msg = "line: %s\n len(keys) != len(value)\nkeys: %s\n values: %s" % (line, keys, values)
raise ValueError(msg)
kwargs.update(zip(keys, values))
return kwargs |
def new(self, bootstrap_with=None, use_timer=False, with_proof=False):
"""
Actual constructor of the solver.
"""
if not self.lingeling:
self.lingeling = pysolvers.lingeling_new()
if bootstrap_with:
for clause in bootstrap_with:
self.add_clause(clause)
self.use_timer = use_timer
self.call_time = 0.0 # time spent for the last call to oracle
self.accu_time = 0.0 # time accumulated for all calls to oracle
if with_proof:
self.prfile = tempfile.TemporaryFile()
pysolvers.lingeling_tracepr(self.lingeling, self.prfile) | Actual constructor of the solver. | Below is the the instruction that describes the task:
### Input:
Actual constructor of the solver.
### Response:
def new(self, bootstrap_with=None, use_timer=False, with_proof=False):
"""
Actual constructor of the solver.
"""
if not self.lingeling:
self.lingeling = pysolvers.lingeling_new()
if bootstrap_with:
for clause in bootstrap_with:
self.add_clause(clause)
self.use_timer = use_timer
self.call_time = 0.0 # time spent for the last call to oracle
self.accu_time = 0.0 # time accumulated for all calls to oracle
if with_proof:
self.prfile = tempfile.TemporaryFile()
pysolvers.lingeling_tracepr(self.lingeling, self.prfile) |
def page(self, status=values.unset, date_created_after=values.unset,
date_created_before=values.unset, room_sid=values.unset,
page_token=values.unset, page_number=values.unset,
page_size=values.unset):
"""
Retrieve a single page of CompositionInstance records from the API.
Request is executed immediately
:param CompositionInstance.Status status: Only show Compositions with the given status.
:param datetime date_created_after: Only show Compositions created on or after this ISO8601 date-time with timezone.
:param datetime date_created_before: Only show Compositions created before this ISO8601 date-time with timezone.
:param unicode room_sid: Only show Compositions with the given Room SID.
:param str page_token: PageToken provided by the API
:param int page_number: Page Number, this value is simply for client state
:param int page_size: Number of records to return, defaults to 50
:returns: Page of CompositionInstance
:rtype: twilio.rest.video.v1.composition.CompositionPage
"""
params = values.of({
'Status': status,
'DateCreatedAfter': serialize.iso8601_datetime(date_created_after),
'DateCreatedBefore': serialize.iso8601_datetime(date_created_before),
'RoomSid': room_sid,
'PageToken': page_token,
'Page': page_number,
'PageSize': page_size,
})
response = self._version.page(
'GET',
self._uri,
params=params,
)
return CompositionPage(self._version, response, self._solution) | Retrieve a single page of CompositionInstance records from the API.
Request is executed immediately
:param CompositionInstance.Status status: Only show Compositions with the given status.
:param datetime date_created_after: Only show Compositions created on or after this ISO8601 date-time with timezone.
:param datetime date_created_before: Only show Compositions created before this ISO8601 date-time with timezone.
:param unicode room_sid: Only show Compositions with the given Room SID.
:param str page_token: PageToken provided by the API
:param int page_number: Page Number, this value is simply for client state
:param int page_size: Number of records to return, defaults to 50
:returns: Page of CompositionInstance
:rtype: twilio.rest.video.v1.composition.CompositionPage | Below is the the instruction that describes the task:
### Input:
Retrieve a single page of CompositionInstance records from the API.
Request is executed immediately
:param CompositionInstance.Status status: Only show Compositions with the given status.
:param datetime date_created_after: Only show Compositions created on or after this ISO8601 date-time with timezone.
:param datetime date_created_before: Only show Compositions created before this ISO8601 date-time with timezone.
:param unicode room_sid: Only show Compositions with the given Room SID.
:param str page_token: PageToken provided by the API
:param int page_number: Page Number, this value is simply for client state
:param int page_size: Number of records to return, defaults to 50
:returns: Page of CompositionInstance
:rtype: twilio.rest.video.v1.composition.CompositionPage
### Response:
def page(self, status=values.unset, date_created_after=values.unset,
date_created_before=values.unset, room_sid=values.unset,
page_token=values.unset, page_number=values.unset,
page_size=values.unset):
"""
Retrieve a single page of CompositionInstance records from the API.
Request is executed immediately
:param CompositionInstance.Status status: Only show Compositions with the given status.
:param datetime date_created_after: Only show Compositions created on or after this ISO8601 date-time with timezone.
:param datetime date_created_before: Only show Compositions created before this ISO8601 date-time with timezone.
:param unicode room_sid: Only show Compositions with the given Room SID.
:param str page_token: PageToken provided by the API
:param int page_number: Page Number, this value is simply for client state
:param int page_size: Number of records to return, defaults to 50
:returns: Page of CompositionInstance
:rtype: twilio.rest.video.v1.composition.CompositionPage
"""
params = values.of({
'Status': status,
'DateCreatedAfter': serialize.iso8601_datetime(date_created_after),
'DateCreatedBefore': serialize.iso8601_datetime(date_created_before),
'RoomSid': room_sid,
'PageToken': page_token,
'Page': page_number,
'PageSize': page_size,
})
response = self._version.page(
'GET',
self._uri,
params=params,
)
return CompositionPage(self._version, response, self._solution) |
def round_value(val, unc=None, unc_down=None, method="publication"):
"""
Rounds a number *val* with a single symmetric uncertainty *unc* or asymmetric uncertainties
*unc* (interpreted as *up*) and *unc_down*, and calculates the orders of their magnitudes. They
both can be a float or a list of floats for simultaneous evaluation. When *val* is a
:py:class:`Number` instance, its combined uncertainty is used instead. Returns a 3-tuple
containing:
- The string representation of the central value.
- The string representations of the uncertainties in a list. For the symmetric case, this list
contains only one element.
- The decimal magnitude.
Examples:
.. code-block:: python
round_value(1.23, 0.456) # -> ("123", ["46"], -2)
round_value(1.23, 0.456, 0.987) # -> ("123", ["46", "99"], -2)
round_value(1.23, [0.456, 0.312]) # -> ("123", [["456", "312"]], -3)
vals = np.array([1.23, 4.56])
uncs = np.array([0.45678, 0.078])
round_value(vals, uncs) # -> (["1230", "4560"], [["457", "78"]], -3)
"""
if isinstance(val, Number):
unc, unc_down = val.get_uncertainty()
val = val.nominal
elif unc is None:
raise ValueError("unc must be set when val is not a Number instance")
# prepare unc values
asym = unc_down is not None
unc_up = unc
if not asym:
unc_down = unc_up
if not is_numpy(val):
# treat as lists for simultaneous rounding when not numpy arrays
passed_list = isinstance(unc_up, (list, tuple)) or isinstance(unc_down, (list, tuple))
unc_up = make_list(unc_up)
unc_down = make_list(unc_down)
# sanity checks
if len(unc_up) != len(unc_down):
raise ValueError("uncertainties should have same length when passed as lists")
elif any(unc < 0 for unc in unc_up):
raise ValueError("up uncertainties must be positive: {}".format(unc_up))
elif any(unc < 0 for unc in unc_down):
raise ValueError("down uncertainties must be positive: {}".format(unc_down))
# to determine the precision, use the uncertainty with the smallest magnitude
ref_mag = min(round_uncertainty(u, method=method)[1] for u in unc_up + unc_down)
# convert the uncertainty and central value to match the reference magnitude
scale = 1. / 10.**ref_mag
val_str = match_precision(scale * val, "1")
up_strs = [match_precision(scale * u, "1") for u in unc_up]
down_strs = [match_precision(scale * u, "1") for u in unc_down]
if passed_list:
return (val_str, [up_strs, down_strs] if asym else [up_strs], ref_mag)
else:
return (val_str, [up_strs[0], down_strs[0]] if asym else [up_strs[0]], ref_mag)
else:
# sanity checks
if (unc_up < 0).any():
raise ValueError("up uncertainties must be positive: {}".format(unc_up))
elif (unc_down < 0).any():
raise ValueError("down uncertainties must be positive: {}".format(unc_down))
# to determine the precision, use the uncertainty with the smallest magnitude
ref_mag_up = round_uncertainty(unc_up, method=method)[1]
ref_mag_down = round_uncertainty(unc_down, method=method)[1]
ref_mag = min(ref_mag_up.min(), ref_mag_down.min())
scale = 1. / 10.**ref_mag
val_str = match_precision(scale * val, "1")
up_str = match_precision(scale * unc_up, "1")
down_str = match_precision(scale * unc_down, "1")
return (val_str, [up_str, down_str] if asym else [up_str], ref_mag) | Rounds a number *val* with a single symmetric uncertainty *unc* or asymmetric uncertainties
*unc* (interpreted as *up*) and *unc_down*, and calculates the orders of their magnitudes. They
both can be a float or a list of floats for simultaneous evaluation. When *val* is a
:py:class:`Number` instance, its combined uncertainty is used instead. Returns a 3-tuple
containing:
- The string representation of the central value.
- The string representations of the uncertainties in a list. For the symmetric case, this list
contains only one element.
- The decimal magnitude.
Examples:
.. code-block:: python
round_value(1.23, 0.456) # -> ("123", ["46"], -2)
round_value(1.23, 0.456, 0.987) # -> ("123", ["46", "99"], -2)
round_value(1.23, [0.456, 0.312]) # -> ("123", [["456", "312"]], -3)
vals = np.array([1.23, 4.56])
uncs = np.array([0.45678, 0.078])
round_value(vals, uncs) # -> (["1230", "4560"], [["457", "78"]], -3) | Below is the the instruction that describes the task:
### Input:
Rounds a number *val* with a single symmetric uncertainty *unc* or asymmetric uncertainties
*unc* (interpreted as *up*) and *unc_down*, and calculates the orders of their magnitudes. They
both can be a float or a list of floats for simultaneous evaluation. When *val* is a
:py:class:`Number` instance, its combined uncertainty is used instead. Returns a 3-tuple
containing:
- The string representation of the central value.
- The string representations of the uncertainties in a list. For the symmetric case, this list
contains only one element.
- The decimal magnitude.
Examples:
.. code-block:: python
round_value(1.23, 0.456) # -> ("123", ["46"], -2)
round_value(1.23, 0.456, 0.987) # -> ("123", ["46", "99"], -2)
round_value(1.23, [0.456, 0.312]) # -> ("123", [["456", "312"]], -3)
vals = np.array([1.23, 4.56])
uncs = np.array([0.45678, 0.078])
round_value(vals, uncs) # -> (["1230", "4560"], [["457", "78"]], -3)
### Response:
def round_value(val, unc=None, unc_down=None, method="publication"):
"""
Rounds a number *val* with a single symmetric uncertainty *unc* or asymmetric uncertainties
*unc* (interpreted as *up*) and *unc_down*, and calculates the orders of their magnitudes. They
both can be a float or a list of floats for simultaneous evaluation. When *val* is a
:py:class:`Number` instance, its combined uncertainty is used instead. Returns a 3-tuple
containing:
- The string representation of the central value.
- The string representations of the uncertainties in a list. For the symmetric case, this list
contains only one element.
- The decimal magnitude.
Examples:
.. code-block:: python
round_value(1.23, 0.456) # -> ("123", ["46"], -2)
round_value(1.23, 0.456, 0.987) # -> ("123", ["46", "99"], -2)
round_value(1.23, [0.456, 0.312]) # -> ("123", [["456", "312"]], -3)
vals = np.array([1.23, 4.56])
uncs = np.array([0.45678, 0.078])
round_value(vals, uncs) # -> (["1230", "4560"], [["457", "78"]], -3)
"""
if isinstance(val, Number):
unc, unc_down = val.get_uncertainty()
val = val.nominal
elif unc is None:
raise ValueError("unc must be set when val is not a Number instance")
# prepare unc values
asym = unc_down is not None
unc_up = unc
if not asym:
unc_down = unc_up
if not is_numpy(val):
# treat as lists for simultaneous rounding when not numpy arrays
passed_list = isinstance(unc_up, (list, tuple)) or isinstance(unc_down, (list, tuple))
unc_up = make_list(unc_up)
unc_down = make_list(unc_down)
# sanity checks
if len(unc_up) != len(unc_down):
raise ValueError("uncertainties should have same length when passed as lists")
elif any(unc < 0 for unc in unc_up):
raise ValueError("up uncertainties must be positive: {}".format(unc_up))
elif any(unc < 0 for unc in unc_down):
raise ValueError("down uncertainties must be positive: {}".format(unc_down))
# to determine the precision, use the uncertainty with the smallest magnitude
ref_mag = min(round_uncertainty(u, method=method)[1] for u in unc_up + unc_down)
# convert the uncertainty and central value to match the reference magnitude
scale = 1. / 10.**ref_mag
val_str = match_precision(scale * val, "1")
up_strs = [match_precision(scale * u, "1") for u in unc_up]
down_strs = [match_precision(scale * u, "1") for u in unc_down]
if passed_list:
return (val_str, [up_strs, down_strs] if asym else [up_strs], ref_mag)
else:
return (val_str, [up_strs[0], down_strs[0]] if asym else [up_strs[0]], ref_mag)
else:
# sanity checks
if (unc_up < 0).any():
raise ValueError("up uncertainties must be positive: {}".format(unc_up))
elif (unc_down < 0).any():
raise ValueError("down uncertainties must be positive: {}".format(unc_down))
# to determine the precision, use the uncertainty with the smallest magnitude
ref_mag_up = round_uncertainty(unc_up, method=method)[1]
ref_mag_down = round_uncertainty(unc_down, method=method)[1]
ref_mag = min(ref_mag_up.min(), ref_mag_down.min())
scale = 1. / 10.**ref_mag
val_str = match_precision(scale * val, "1")
up_str = match_precision(scale * unc_up, "1")
down_str = match_precision(scale * unc_down, "1")
return (val_str, [up_str, down_str] if asym else [up_str], ref_mag) |
def items(self):
"Returns a list of (key, value) pairs as 2-tuples."
return (list(self._pb.IntMap.items()) + list(self._pb.StringMap.items()) +
list(self._pb.FloatMap.items()) + list(self._pb.BoolMap.items())) | Returns a list of (key, value) pairs as 2-tuples. | Below is the the instruction that describes the task:
### Input:
Returns a list of (key, value) pairs as 2-tuples.
### Response:
def items(self):
"Returns a list of (key, value) pairs as 2-tuples."
return (list(self._pb.IntMap.items()) + list(self._pb.StringMap.items()) +
list(self._pb.FloatMap.items()) + list(self._pb.BoolMap.items())) |
def str2midi(note_string):
"""
Given a note string name (e.g. "Bb4"), returns its MIDI pitch number.
"""
if note_string == "?":
return nan
data = note_string.strip().lower()
name2delta = {"c": -9, "d": -7, "e": -5, "f": -4, "g": -2, "a": 0, "b": 2}
accident2delta = {"b": -1, "#": 1, "x": 2}
accidents = list(it.takewhile(lambda el: el in accident2delta, data[1:]))
octave_delta = int(data[len(accidents) + 1:]) - 4
return (MIDI_A4 +
name2delta[data[0]] + # Name
sum(accident2delta[ac] for ac in accidents) + # Accident
12 * octave_delta # Octave
) | Given a note string name (e.g. "Bb4"), returns its MIDI pitch number. | Below is the the instruction that describes the task:
### Input:
Given a note string name (e.g. "Bb4"), returns its MIDI pitch number.
### Response:
def str2midi(note_string):
"""
Given a note string name (e.g. "Bb4"), returns its MIDI pitch number.
"""
if note_string == "?":
return nan
data = note_string.strip().lower()
name2delta = {"c": -9, "d": -7, "e": -5, "f": -4, "g": -2, "a": 0, "b": 2}
accident2delta = {"b": -1, "#": 1, "x": 2}
accidents = list(it.takewhile(lambda el: el in accident2delta, data[1:]))
octave_delta = int(data[len(accidents) + 1:]) - 4
return (MIDI_A4 +
name2delta[data[0]] + # Name
sum(accident2delta[ac] for ac in accidents) + # Accident
12 * octave_delta # Octave
) |
def list_storage_accounts_rg(access_token, subscription_id, rgname):
'''List the storage accounts in the specified resource group.
Args:
access_token (str): A valid Azure authentication token.
subscription_id (str): Azure subscription id.
rgname (str): Azure resource group name.
Returns:
HTTP response. JSON body list of storage accounts.
'''
endpoint = ''.join([get_rm_endpoint(),
'/subscriptions/', subscription_id,
'/resourcegroups/', rgname,
'/providers/Microsoft.Storage/storageAccounts',
'?api-version=', STORAGE_API])
return do_get(endpoint, access_token) | List the storage accounts in the specified resource group.
Args:
access_token (str): A valid Azure authentication token.
subscription_id (str): Azure subscription id.
rgname (str): Azure resource group name.
Returns:
HTTP response. JSON body list of storage accounts. | Below is the the instruction that describes the task:
### Input:
List the storage accounts in the specified resource group.
Args:
access_token (str): A valid Azure authentication token.
subscription_id (str): Azure subscription id.
rgname (str): Azure resource group name.
Returns:
HTTP response. JSON body list of storage accounts.
### Response:
def list_storage_accounts_rg(access_token, subscription_id, rgname):
'''List the storage accounts in the specified resource group.
Args:
access_token (str): A valid Azure authentication token.
subscription_id (str): Azure subscription id.
rgname (str): Azure resource group name.
Returns:
HTTP response. JSON body list of storage accounts.
'''
endpoint = ''.join([get_rm_endpoint(),
'/subscriptions/', subscription_id,
'/resourcegroups/', rgname,
'/providers/Microsoft.Storage/storageAccounts',
'?api-version=', STORAGE_API])
return do_get(endpoint, access_token) |
def add_bindings(self, g: Graph) -> "PrefixLibrary":
""" Add bindings in the library to the graph
:param g: graph to add prefixes to
:return: PrefixLibrary object
"""
for prefix, namespace in self:
g.bind(prefix.lower(), namespace)
return self | Add bindings in the library to the graph
:param g: graph to add prefixes to
:return: PrefixLibrary object | Below is the the instruction that describes the task:
### Input:
Add bindings in the library to the graph
:param g: graph to add prefixes to
:return: PrefixLibrary object
### Response:
def add_bindings(self, g: Graph) -> "PrefixLibrary":
""" Add bindings in the library to the graph
:param g: graph to add prefixes to
:return: PrefixLibrary object
"""
for prefix, namespace in self:
g.bind(prefix.lower(), namespace)
return self |
def id_exists(ids, mods, test=None, queue=False, **kwargs):
'''
Tests for the existence of a specific ID or list of IDs within the
specified SLS file(s). Similar to :py:func:`state.sls_exists
<salt.modules.state.sls_exists>`, returns True or False. The default
environment is base``, use ``saltenv`` to specify a different environment.
.. versionadded:: 2019.2.0
saltenv
Specify a salt fileserver environment from which to look for the SLS files
specified in the ``mods`` argument
CLI Example:
.. code-block:: bash
salt '*' state.id_exists create_myfile,update_template filestate saltenv=dev
'''
ids = salt.utils.args.split_input(ids)
ids = set(ids)
sls_ids = set(x['__id__'] for x in show_low_sls(mods, test=test, queue=queue, **kwargs))
return ids.issubset(sls_ids) | Tests for the existence of a specific ID or list of IDs within the
specified SLS file(s). Similar to :py:func:`state.sls_exists
<salt.modules.state.sls_exists>`, returns True or False. The default
environment is base``, use ``saltenv`` to specify a different environment.
.. versionadded:: 2019.2.0
saltenv
Specify a salt fileserver environment from which to look for the SLS files
specified in the ``mods`` argument
CLI Example:
.. code-block:: bash
salt '*' state.id_exists create_myfile,update_template filestate saltenv=dev | Below is the the instruction that describes the task:
### Input:
Tests for the existence of a specific ID or list of IDs within the
specified SLS file(s). Similar to :py:func:`state.sls_exists
<salt.modules.state.sls_exists>`, returns True or False. The default
environment is base``, use ``saltenv`` to specify a different environment.
.. versionadded:: 2019.2.0
saltenv
Specify a salt fileserver environment from which to look for the SLS files
specified in the ``mods`` argument
CLI Example:
.. code-block:: bash
salt '*' state.id_exists create_myfile,update_template filestate saltenv=dev
### Response:
def id_exists(ids, mods, test=None, queue=False, **kwargs):
'''
Tests for the existence of a specific ID or list of IDs within the
specified SLS file(s). Similar to :py:func:`state.sls_exists
<salt.modules.state.sls_exists>`, returns True or False. The default
environment is base``, use ``saltenv`` to specify a different environment.
.. versionadded:: 2019.2.0
saltenv
Specify a salt fileserver environment from which to look for the SLS files
specified in the ``mods`` argument
CLI Example:
.. code-block:: bash
salt '*' state.id_exists create_myfile,update_template filestate saltenv=dev
'''
ids = salt.utils.args.split_input(ids)
ids = set(ids)
sls_ids = set(x['__id__'] for x in show_low_sls(mods, test=test, queue=queue, **kwargs))
return ids.issubset(sls_ids) |
def _urlencode(items):
"""A Unicode-safe URLencoder."""
try:
return urllib.urlencode(items)
except UnicodeEncodeError:
return urllib.urlencode([(k, smart_str(v)) for k, v in items]) | A Unicode-safe URLencoder. | Below is the the instruction that describes the task:
### Input:
A Unicode-safe URLencoder.
### Response:
def _urlencode(items):
"""A Unicode-safe URLencoder."""
try:
return urllib.urlencode(items)
except UnicodeEncodeError:
return urllib.urlencode([(k, smart_str(v)) for k, v in items]) |
def parse_stream(response):
"""
take stream from docker-py lib and display it to the user.
this also builds a stream list and returns it.
"""
stream_data = []
stream = stdout
for data in response:
if data:
try:
data = data.decode('utf-8')
except AttributeError as e:
logger.exception("Unable to parse stream, Attribute Error Raised: {0}".format(e))
stream.write(data)
continue
try:
normalized_data = normalize_keys(json.loads(data))
except ValueError:
stream.write(data)
continue
except TypeError:
stream.write(data)
continue
if 'progress' in normalized_data:
stream_data.append(normalized_data)
_display_progress(normalized_data, stream)
elif 'error' in normalized_data:
_display_error(normalized_data, stream)
elif 'status' in normalized_data:
stream_data.append(normalized_data)
_display_status(normalized_data, stream)
elif 'stream' in normalized_data:
stream_data.append(normalized_data)
_display_stream(normalized_data, stream)
else:
stream.write(data)
stream.flush()
return stream_data | take stream from docker-py lib and display it to the user.
this also builds a stream list and returns it. | Below is the the instruction that describes the task:
### Input:
take stream from docker-py lib and display it to the user.
this also builds a stream list and returns it.
### Response:
def parse_stream(response):
"""
take stream from docker-py lib and display it to the user.
this also builds a stream list and returns it.
"""
stream_data = []
stream = stdout
for data in response:
if data:
try:
data = data.decode('utf-8')
except AttributeError as e:
logger.exception("Unable to parse stream, Attribute Error Raised: {0}".format(e))
stream.write(data)
continue
try:
normalized_data = normalize_keys(json.loads(data))
except ValueError:
stream.write(data)
continue
except TypeError:
stream.write(data)
continue
if 'progress' in normalized_data:
stream_data.append(normalized_data)
_display_progress(normalized_data, stream)
elif 'error' in normalized_data:
_display_error(normalized_data, stream)
elif 'status' in normalized_data:
stream_data.append(normalized_data)
_display_status(normalized_data, stream)
elif 'stream' in normalized_data:
stream_data.append(normalized_data)
_display_stream(normalized_data, stream)
else:
stream.write(data)
stream.flush()
return stream_data |
def _optimal_orientation_from_detector(detector_name, tc):
""" Low-level function to be called from _optimal_dec_from_detector
and _optimal_ra_from_detector"""
d = Detector(detector_name)
ra, dec = d.optimal_orientation(tc)
return ra, dec | Low-level function to be called from _optimal_dec_from_detector
and _optimal_ra_from_detector | Below is the the instruction that describes the task:
### Input:
Low-level function to be called from _optimal_dec_from_detector
and _optimal_ra_from_detector
### Response:
def _optimal_orientation_from_detector(detector_name, tc):
""" Low-level function to be called from _optimal_dec_from_detector
and _optimal_ra_from_detector"""
d = Detector(detector_name)
ra, dec = d.optimal_orientation(tc)
return ra, dec |
def validate_format(self, obj, pointer=None):
"""
================= ============
Expected draft04 Alias of
----------------- ------------
date-time rfc3339.datetime
email email
hostname hostname
ipv4 ipv4
ipv6 ipv6
uri uri
================= ============
"""
if 'format' in self.attrs:
substituted = {
'date-time': 'rfc3339.datetime',
'email': 'email',
'hostname': 'hostname',
'ipv4': 'ipv4',
'ipv6': 'ipv6',
'uri': 'uri',
}.get(self.attrs['format'], self.attrs['format'])
logger.debug('use %s', substituted)
try:
return self.formats[substituted](obj)
except ValidationError as error:
logger.error(error)
self.fail('Forbidden value', obj, pointer)
return obj | ================= ============
Expected draft04 Alias of
----------------- ------------
date-time rfc3339.datetime
email email
hostname hostname
ipv4 ipv4
ipv6 ipv6
uri uri
================= ============ | Below is the the instruction that describes the task:
### Input:
================= ============
Expected draft04 Alias of
----------------- ------------
date-time rfc3339.datetime
email email
hostname hostname
ipv4 ipv4
ipv6 ipv6
uri uri
================= ============
### Response:
def validate_format(self, obj, pointer=None):
"""
================= ============
Expected draft04 Alias of
----------------- ------------
date-time rfc3339.datetime
email email
hostname hostname
ipv4 ipv4
ipv6 ipv6
uri uri
================= ============
"""
if 'format' in self.attrs:
substituted = {
'date-time': 'rfc3339.datetime',
'email': 'email',
'hostname': 'hostname',
'ipv4': 'ipv4',
'ipv6': 'ipv6',
'uri': 'uri',
}.get(self.attrs['format'], self.attrs['format'])
logger.debug('use %s', substituted)
try:
return self.formats[substituted](obj)
except ValidationError as error:
logger.error(error)
self.fail('Forbidden value', obj, pointer)
return obj |
def on_key_down(self, event):
"""
If user does command v,
re-size window in case pasting has changed the content size.
"""
keycode = event.GetKeyCode()
meta_down = event.MetaDown() or event.GetCmdDown()
if keycode == 86 and meta_down:
# treat it as if it were a wx.EVT_TEXT_SIZE
self.do_fit(event) | If user does command v,
re-size window in case pasting has changed the content size. | Below is the the instruction that describes the task:
### Input:
If user does command v,
re-size window in case pasting has changed the content size.
### Response:
def on_key_down(self, event):
"""
If user does command v,
re-size window in case pasting has changed the content size.
"""
keycode = event.GetKeyCode()
meta_down = event.MetaDown() or event.GetCmdDown()
if keycode == 86 and meta_down:
# treat it as if it were a wx.EVT_TEXT_SIZE
self.do_fit(event) |
def fit_transform(self, Z, **fit_params):
"""Fit all the transforms one after the other and transform the
data, then use fit_transform on transformed data using the final
estimator."""
Zt, fit_params = self._pre_transform(Z, **fit_params)
if hasattr(self.steps[-1][-1], 'fit_transform'):
return self.steps[-1][-1].fit_transform(Zt, **fit_params)
else:
return self.steps[-1][-1].fit(Zt, **fit_params).transform(Zt) | Fit all the transforms one after the other and transform the
data, then use fit_transform on transformed data using the final
estimator. | Below is the the instruction that describes the task:
### Input:
Fit all the transforms one after the other and transform the
data, then use fit_transform on transformed data using the final
estimator.
### Response:
def fit_transform(self, Z, **fit_params):
"""Fit all the transforms one after the other and transform the
data, then use fit_transform on transformed data using the final
estimator."""
Zt, fit_params = self._pre_transform(Z, **fit_params)
if hasattr(self.steps[-1][-1], 'fit_transform'):
return self.steps[-1][-1].fit_transform(Zt, **fit_params)
else:
return self.steps[-1][-1].fit(Zt, **fit_params).transform(Zt) |
def get_manifest_list(image, registry, insecure=False, dockercfg_path=None):
"""Return manifest list for image.
:param image: ImageName, the remote image to inspect
:param registry: str, URI for registry, if URI schema is not provided,
https:// will be used
:param insecure: bool, when True registry's cert is not verified
:param dockercfg_path: str, dirname of .dockercfg location
:return: response, or None, with manifest list
"""
version = 'v2_list'
registry_session = RegistrySession(registry, insecure=insecure, dockercfg_path=dockercfg_path)
response, _ = get_manifest(image, registry_session, version)
return response | Return manifest list for image.
:param image: ImageName, the remote image to inspect
:param registry: str, URI for registry, if URI schema is not provided,
https:// will be used
:param insecure: bool, when True registry's cert is not verified
:param dockercfg_path: str, dirname of .dockercfg location
:return: response, or None, with manifest list | Below is the the instruction that describes the task:
### Input:
Return manifest list for image.
:param image: ImageName, the remote image to inspect
:param registry: str, URI for registry, if URI schema is not provided,
https:// will be used
:param insecure: bool, when True registry's cert is not verified
:param dockercfg_path: str, dirname of .dockercfg location
:return: response, or None, with manifest list
### Response:
def get_manifest_list(image, registry, insecure=False, dockercfg_path=None):
"""Return manifest list for image.
:param image: ImageName, the remote image to inspect
:param registry: str, URI for registry, if URI schema is not provided,
https:// will be used
:param insecure: bool, when True registry's cert is not verified
:param dockercfg_path: str, dirname of .dockercfg location
:return: response, or None, with manifest list
"""
version = 'v2_list'
registry_session = RegistrySession(registry, insecure=insecure, dockercfg_path=dockercfg_path)
response, _ = get_manifest(image, registry_session, version)
return response |
def _init_formats(self):
"""
Initialise default formats.
"""
theme = self._color_scheme
# normal message format
fmt = QtGui.QTextCharFormat()
fmt.setForeground(theme.foreground)
fmt.setBackground(theme.background)
self._formats[OutputFormat.NormalMessageFormat] = fmt
# error message
fmt = QtGui.QTextCharFormat()
fmt.setForeground(theme.error)
fmt.setBackground(theme.background)
self._formats[OutputFormat.ErrorMessageFormat] = fmt
# debug message
fmt = QtGui.QTextCharFormat()
fmt.setForeground(theme.custom)
fmt.setBackground(theme.background)
self._formats[OutputFormat.CustomFormat] = fmt | Initialise default formats. | Below is the the instruction that describes the task:
### Input:
Initialise default formats.
### Response:
def _init_formats(self):
"""
Initialise default formats.
"""
theme = self._color_scheme
# normal message format
fmt = QtGui.QTextCharFormat()
fmt.setForeground(theme.foreground)
fmt.setBackground(theme.background)
self._formats[OutputFormat.NormalMessageFormat] = fmt
# error message
fmt = QtGui.QTextCharFormat()
fmt.setForeground(theme.error)
fmt.setBackground(theme.background)
self._formats[OutputFormat.ErrorMessageFormat] = fmt
# debug message
fmt = QtGui.QTextCharFormat()
fmt.setForeground(theme.custom)
fmt.setBackground(theme.background)
self._formats[OutputFormat.CustomFormat] = fmt |
def _compute_handshake(self):
"""Compute the authentication handshake value.
:return: the computed hash value.
:returntype: `str`"""
return hashlib.sha1(to_utf8(self.stream_id)+to_utf8(self.secret)).hexdigest() | Compute the authentication handshake value.
:return: the computed hash value.
:returntype: `str` | Below is the the instruction that describes the task:
### Input:
Compute the authentication handshake value.
:return: the computed hash value.
:returntype: `str`
### Response:
def _compute_handshake(self):
"""Compute the authentication handshake value.
:return: the computed hash value.
:returntype: `str`"""
return hashlib.sha1(to_utf8(self.stream_id)+to_utf8(self.secret)).hexdigest() |
def get_env_short(env):
"""
Given an env, return <env_short> if env is valid
Args:
env: an environment, such as "prod", "staging", "proto<N>", "mgmt.<account_alias>"
Returns:
the shortname of the env, such as "prod", "staging", "proto", "mgmt"
Raises:
ValueError if env is misformatted or doesn't name a known environment
"""
env_valid(env)
if env.find(".") > -1:
env_short, ext = env.split(".")
else:
env_short = env.strip(".0123456789")
return env_short | Given an env, return <env_short> if env is valid
Args:
env: an environment, such as "prod", "staging", "proto<N>", "mgmt.<account_alias>"
Returns:
the shortname of the env, such as "prod", "staging", "proto", "mgmt"
Raises:
ValueError if env is misformatted or doesn't name a known environment | Below is the the instruction that describes the task:
### Input:
Given an env, return <env_short> if env is valid
Args:
env: an environment, such as "prod", "staging", "proto<N>", "mgmt.<account_alias>"
Returns:
the shortname of the env, such as "prod", "staging", "proto", "mgmt"
Raises:
ValueError if env is misformatted or doesn't name a known environment
### Response:
def get_env_short(env):
"""
Given an env, return <env_short> if env is valid
Args:
env: an environment, such as "prod", "staging", "proto<N>", "mgmt.<account_alias>"
Returns:
the shortname of the env, such as "prod", "staging", "proto", "mgmt"
Raises:
ValueError if env is misformatted or doesn't name a known environment
"""
env_valid(env)
if env.find(".") > -1:
env_short, ext = env.split(".")
else:
env_short = env.strip(".0123456789")
return env_short |
def get_most_recent_update_time(self):
"""
Indicated most recent update of the instance, assumption based on:
- if currentWorkflow exists, its startedAt time is most recent update.
- else max of workflowHistory startedAt is most recent update.
"""
def parse_time(t):
if t:
return time.gmtime(t/1000)
return None
try:
max_wf_started_at = max([i.get('startedAt') for i in self.workflowHistory])
return parse_time(max_wf_started_at)
except ValueError:
return None | Indicated most recent update of the instance, assumption based on:
- if currentWorkflow exists, its startedAt time is most recent update.
- else max of workflowHistory startedAt is most recent update. | Below is the the instruction that describes the task:
### Input:
Indicated most recent update of the instance, assumption based on:
- if currentWorkflow exists, its startedAt time is most recent update.
- else max of workflowHistory startedAt is most recent update.
### Response:
def get_most_recent_update_time(self):
"""
Indicated most recent update of the instance, assumption based on:
- if currentWorkflow exists, its startedAt time is most recent update.
- else max of workflowHistory startedAt is most recent update.
"""
def parse_time(t):
if t:
return time.gmtime(t/1000)
return None
try:
max_wf_started_at = max([i.get('startedAt') for i in self.workflowHistory])
return parse_time(max_wf_started_at)
except ValueError:
return None |
def localize(self, lng: str) -> str:
"""
Evaluate the given string with respect to the locale defined by ``lng``.
If no string is available in the currently active language, this will give you
the string in the system's default language. If this is unavailable as well, it
will give you the string in the first language available.
:param lng: A locale code, e.g. ``de``. If you specify a code including a country
or region like ``de-AT``, exact matches will be used preferably, but if only
a ``de`` or ``de-AT`` translation exists, this might be returned as well.
"""
if self.data is None:
return ""
if isinstance(self.data, dict):
firstpart = lng.split('-')[0]
similar = [l for l in self.data.keys() if (l.startswith(firstpart + "-") or firstpart == l) and l != lng]
if self.data.get(lng):
return self.data[lng]
elif self.data.get(firstpart):
return self.data[firstpart]
elif similar and any([self.data.get(s) for s in similar]):
for s in similar:
if self.data.get(s):
return self.data.get(s)
elif self.data.get(settings.LANGUAGE_CODE):
return self.data[settings.LANGUAGE_CODE]
elif len(self.data):
return list(self.data.items())[0][1]
else:
return ""
else:
return str(self.data) | Evaluate the given string with respect to the locale defined by ``lng``.
If no string is available in the currently active language, this will give you
the string in the system's default language. If this is unavailable as well, it
will give you the string in the first language available.
:param lng: A locale code, e.g. ``de``. If you specify a code including a country
or region like ``de-AT``, exact matches will be used preferably, but if only
a ``de`` or ``de-AT`` translation exists, this might be returned as well. | Below is the the instruction that describes the task:
### Input:
Evaluate the given string with respect to the locale defined by ``lng``.
If no string is available in the currently active language, this will give you
the string in the system's default language. If this is unavailable as well, it
will give you the string in the first language available.
:param lng: A locale code, e.g. ``de``. If you specify a code including a country
or region like ``de-AT``, exact matches will be used preferably, but if only
a ``de`` or ``de-AT`` translation exists, this might be returned as well.
### Response:
def localize(self, lng: str) -> str:
"""
Evaluate the given string with respect to the locale defined by ``lng``.
If no string is available in the currently active language, this will give you
the string in the system's default language. If this is unavailable as well, it
will give you the string in the first language available.
:param lng: A locale code, e.g. ``de``. If you specify a code including a country
or region like ``de-AT``, exact matches will be used preferably, but if only
a ``de`` or ``de-AT`` translation exists, this might be returned as well.
"""
if self.data is None:
return ""
if isinstance(self.data, dict):
firstpart = lng.split('-')[0]
similar = [l for l in self.data.keys() if (l.startswith(firstpart + "-") or firstpart == l) and l != lng]
if self.data.get(lng):
return self.data[lng]
elif self.data.get(firstpart):
return self.data[firstpart]
elif similar and any([self.data.get(s) for s in similar]):
for s in similar:
if self.data.get(s):
return self.data.get(s)
elif self.data.get(settings.LANGUAGE_CODE):
return self.data[settings.LANGUAGE_CODE]
elif len(self.data):
return list(self.data.items())[0][1]
else:
return ""
else:
return str(self.data) |
def get_changed_vars(section: SoS_Step):
'''changed vars are variables that are "shared" and therefore "provides"
to others '''
if 'shared' not in section.options:
return set()
changed_vars = set()
svars = section.options['shared']
if isinstance(svars, str):
changed_vars.add(svars)
svars = {svars: svars}
elif isinstance(svars, Sequence):
for item in svars:
if isinstance(item, str):
changed_vars.add(item)
elif isinstance(item, Mapping):
changed_vars |= set(item.keys())
else:
raise ValueError(
f'Option shared should be a string, a mapping of expression, or list of string or mappings. {svars} provided'
)
elif isinstance(svars, Mapping):
changed_vars |= set(svars.keys())
else:
raise ValueError(
f'Option shared should be a string, a mapping of expression, or list of string or mappings. {svars} provided'
)
return changed_vars | changed vars are variables that are "shared" and therefore "provides"
to others | Below is the the instruction that describes the task:
### Input:
changed vars are variables that are "shared" and therefore "provides"
to others
### Response:
def get_changed_vars(section: SoS_Step):
'''changed vars are variables that are "shared" and therefore "provides"
to others '''
if 'shared' not in section.options:
return set()
changed_vars = set()
svars = section.options['shared']
if isinstance(svars, str):
changed_vars.add(svars)
svars = {svars: svars}
elif isinstance(svars, Sequence):
for item in svars:
if isinstance(item, str):
changed_vars.add(item)
elif isinstance(item, Mapping):
changed_vars |= set(item.keys())
else:
raise ValueError(
f'Option shared should be a string, a mapping of expression, or list of string or mappings. {svars} provided'
)
elif isinstance(svars, Mapping):
changed_vars |= set(svars.keys())
else:
raise ValueError(
f'Option shared should be a string, a mapping of expression, or list of string or mappings. {svars} provided'
)
return changed_vars |
def selenium_retry(target=None, retry=True):
"""Decorator to turn on automatic retries of flaky selenium failures.
Decorate a robotframework library class to turn on retries for all
selenium calls from that library:
@selenium_retry
class MyLibrary(object):
# Decorate a method to turn it back off for that method
@selenium_retry(False)
def some_keyword(self):
self.selenium.click_button('foo')
Or turn it off by default but turn it on for some methods
(the class-level decorator is still required):
@selenium_retry(False)
class MyLibrary(object):
@selenium_retry(True)
def some_keyword(self):
self.selenium.click_button('foo')
"""
if isinstance(target, bool):
# Decorator was called with a single boolean argument
retry = target
target = None
def decorate(target):
if isinstance(target, type):
cls = target
# Metaclass time.
# We're going to generate a new subclass that:
# a) mixes in RetryingSeleniumLibraryMixin
# b) sets the initial value of `retry_selenium`
return type(
cls.__name__,
(cls, RetryingSeleniumLibraryMixin),
{"retry_selenium": retry, "__doc__": cls.__doc__},
)
func = target
@functools.wraps(func)
def run_with_retry(self, *args, **kwargs):
# Set the retry setting and run the original function.
old_retry = self.retry_selenium
self.retry = retry
try:
return func(self, *args, **kwargs)
finally:
# Restore the previous value
self.retry_selenium = old_retry
set_pdb_trace()
run_with_retry.is_selenium_retry_decorator = True
return run_with_retry
if target is None:
# Decorator is being used with arguments
return decorate
else:
# Decorator was used without arguments
return decorate(target) | Decorator to turn on automatic retries of flaky selenium failures.
Decorate a robotframework library class to turn on retries for all
selenium calls from that library:
@selenium_retry
class MyLibrary(object):
# Decorate a method to turn it back off for that method
@selenium_retry(False)
def some_keyword(self):
self.selenium.click_button('foo')
Or turn it off by default but turn it on for some methods
(the class-level decorator is still required):
@selenium_retry(False)
class MyLibrary(object):
@selenium_retry(True)
def some_keyword(self):
self.selenium.click_button('foo') | Below is the the instruction that describes the task:
### Input:
Decorator to turn on automatic retries of flaky selenium failures.
Decorate a robotframework library class to turn on retries for all
selenium calls from that library:
@selenium_retry
class MyLibrary(object):
# Decorate a method to turn it back off for that method
@selenium_retry(False)
def some_keyword(self):
self.selenium.click_button('foo')
Or turn it off by default but turn it on for some methods
(the class-level decorator is still required):
@selenium_retry(False)
class MyLibrary(object):
@selenium_retry(True)
def some_keyword(self):
self.selenium.click_button('foo')
### Response:
def selenium_retry(target=None, retry=True):
"""Decorator to turn on automatic retries of flaky selenium failures.
Decorate a robotframework library class to turn on retries for all
selenium calls from that library:
@selenium_retry
class MyLibrary(object):
# Decorate a method to turn it back off for that method
@selenium_retry(False)
def some_keyword(self):
self.selenium.click_button('foo')
Or turn it off by default but turn it on for some methods
(the class-level decorator is still required):
@selenium_retry(False)
class MyLibrary(object):
@selenium_retry(True)
def some_keyword(self):
self.selenium.click_button('foo')
"""
if isinstance(target, bool):
# Decorator was called with a single boolean argument
retry = target
target = None
def decorate(target):
if isinstance(target, type):
cls = target
# Metaclass time.
# We're going to generate a new subclass that:
# a) mixes in RetryingSeleniumLibraryMixin
# b) sets the initial value of `retry_selenium`
return type(
cls.__name__,
(cls, RetryingSeleniumLibraryMixin),
{"retry_selenium": retry, "__doc__": cls.__doc__},
)
func = target
@functools.wraps(func)
def run_with_retry(self, *args, **kwargs):
# Set the retry setting and run the original function.
old_retry = self.retry_selenium
self.retry = retry
try:
return func(self, *args, **kwargs)
finally:
# Restore the previous value
self.retry_selenium = old_retry
set_pdb_trace()
run_with_retry.is_selenium_retry_decorator = True
return run_with_retry
if target is None:
# Decorator is being used with arguments
return decorate
else:
# Decorator was used without arguments
return decorate(target) |
def branches(self):
"""Return a list of branches for given repository
:return: [str]
"""
# get all remote branches
refs = filter(lambda l: isinstance(l, git.RemoteReference), self.repo.references)
# filter out HEAD branch
refs = filter(lambda l: l.name != "origin/HEAD", refs)
# filter out all branches not starting with 'origin/'
refs = filter(lambda l: l.name.startswith("origin/"), refs)
for ref in refs:
self.refs[ref.name[7:]] = ref
# remove 'origin/' prefix
return map(lambda l: l.name[7:], refs) | Return a list of branches for given repository
:return: [str] | Below is the the instruction that describes the task:
### Input:
Return a list of branches for given repository
:return: [str]
### Response:
def branches(self):
"""Return a list of branches for given repository
:return: [str]
"""
# get all remote branches
refs = filter(lambda l: isinstance(l, git.RemoteReference), self.repo.references)
# filter out HEAD branch
refs = filter(lambda l: l.name != "origin/HEAD", refs)
# filter out all branches not starting with 'origin/'
refs = filter(lambda l: l.name.startswith("origin/"), refs)
for ref in refs:
self.refs[ref.name[7:]] = ref
# remove 'origin/' prefix
return map(lambda l: l.name[7:], refs) |
def resource_urls(request):
"""Global values to pass to templates"""
url_parsed = urlparse(settings.SEARCH_URL)
defaults = dict(
APP_NAME=__description__,
APP_VERSION=__version__,
SITE_URL=settings.SITE_URL.rstrip('/'),
SEARCH_TYPE=settings.SEARCH_TYPE,
SEARCH_URL=settings.SEARCH_URL,
SEARCH_IP='%s://%s:%s' % (url_parsed.scheme, url_parsed.hostname, url_parsed.port)
)
return defaults | Global values to pass to templates | Below is the the instruction that describes the task:
### Input:
Global values to pass to templates
### Response:
def resource_urls(request):
"""Global values to pass to templates"""
url_parsed = urlparse(settings.SEARCH_URL)
defaults = dict(
APP_NAME=__description__,
APP_VERSION=__version__,
SITE_URL=settings.SITE_URL.rstrip('/'),
SEARCH_TYPE=settings.SEARCH_TYPE,
SEARCH_URL=settings.SEARCH_URL,
SEARCH_IP='%s://%s:%s' % (url_parsed.scheme, url_parsed.hostname, url_parsed.port)
)
return defaults |
def list_users():
'''
Return a list of all users on Windows
Returns:
list: A list of all users on the system
CLI Example:
.. code-block:: bash
salt '*' user.list_users
'''
res = 0
user_list = []
dowhile = True
try:
while res or dowhile:
dowhile = False
(users, _, res) = win32net.NetUserEnum(
None,
0,
win32netcon.FILTER_NORMAL_ACCOUNT,
res,
win32netcon.MAX_PREFERRED_LENGTH
)
for user in users:
user_list.append(user['name'])
return user_list
except win32net.error:
pass | Return a list of all users on Windows
Returns:
list: A list of all users on the system
CLI Example:
.. code-block:: bash
salt '*' user.list_users | Below is the the instruction that describes the task:
### Input:
Return a list of all users on Windows
Returns:
list: A list of all users on the system
CLI Example:
.. code-block:: bash
salt '*' user.list_users
### Response:
def list_users():
'''
Return a list of all users on Windows
Returns:
list: A list of all users on the system
CLI Example:
.. code-block:: bash
salt '*' user.list_users
'''
res = 0
user_list = []
dowhile = True
try:
while res or dowhile:
dowhile = False
(users, _, res) = win32net.NetUserEnum(
None,
0,
win32netcon.FILTER_NORMAL_ACCOUNT,
res,
win32netcon.MAX_PREFERRED_LENGTH
)
for user in users:
user_list.append(user['name'])
return user_list
except win32net.error:
pass |
def record_set_properties(object_id, input_params={}, always_retry=True, **kwargs):
"""
Invokes the /record-xxxx/setProperties API method.
For more info, see: https://wiki.dnanexus.com/API-Specification-v1.0.0/Properties#API-method%3A-%2Fclass-xxxx%2FsetProperties
"""
return DXHTTPRequest('/%s/setProperties' % object_id, input_params, always_retry=always_retry, **kwargs) | Invokes the /record-xxxx/setProperties API method.
For more info, see: https://wiki.dnanexus.com/API-Specification-v1.0.0/Properties#API-method%3A-%2Fclass-xxxx%2FsetProperties | Below is the the instruction that describes the task:
### Input:
Invokes the /record-xxxx/setProperties API method.
For more info, see: https://wiki.dnanexus.com/API-Specification-v1.0.0/Properties#API-method%3A-%2Fclass-xxxx%2FsetProperties
### Response:
def record_set_properties(object_id, input_params={}, always_retry=True, **kwargs):
"""
Invokes the /record-xxxx/setProperties API method.
For more info, see: https://wiki.dnanexus.com/API-Specification-v1.0.0/Properties#API-method%3A-%2Fclass-xxxx%2FsetProperties
"""
return DXHTTPRequest('/%s/setProperties' % object_id, input_params, always_retry=always_retry, **kwargs) |
def _get_service_exec():
'''
Returns the path to the sysv service manager (either update-rc.d or
chkconfig)
'''
contextkey = 'systemd._get_service_exec'
if contextkey not in __context__:
executables = ('update-rc.d', 'chkconfig')
for executable in executables:
service_exec = salt.utils.path.which(executable)
if service_exec is not None:
break
else:
raise CommandExecutionError(
'Unable to find sysv service manager (tried {0})'.format(
', '.join(executables)
)
)
__context__[contextkey] = service_exec
return __context__[contextkey] | Returns the path to the sysv service manager (either update-rc.d or
chkconfig) | Below is the the instruction that describes the task:
### Input:
Returns the path to the sysv service manager (either update-rc.d or
chkconfig)
### Response:
def _get_service_exec():
'''
Returns the path to the sysv service manager (either update-rc.d or
chkconfig)
'''
contextkey = 'systemd._get_service_exec'
if contextkey not in __context__:
executables = ('update-rc.d', 'chkconfig')
for executable in executables:
service_exec = salt.utils.path.which(executable)
if service_exec is not None:
break
else:
raise CommandExecutionError(
'Unable to find sysv service manager (tried {0})'.format(
', '.join(executables)
)
)
__context__[contextkey] = service_exec
return __context__[contextkey] |
def get_all_policies(self, as_group=None, policy_names=None,
max_records=None, next_token=None):
"""
Returns descriptions of what each policy does. This action supports
pagination. If the response includes a token, there are more records
available. To get the additional records, repeat the request with the
response token as the NextToken parameter.
If no group name or list of policy names are provided, all
available policies are returned.
:type as_name: str
:param as_name: The name of the
:class:`boto.ec2.autoscale.group.AutoScalingGroup` to filter for.
:type names: list
:param names: List of policy names which should be searched for.
:type max_records: int
:param max_records: Maximum amount of groups to return.
"""
params = {}
if as_group:
params['AutoScalingGroupName'] = as_group
if policy_names:
self.build_list_params(params, policy_names, 'PolicyNames')
if max_records:
params['MaxRecords'] = max_records
if next_token:
params['NextToken'] = next_token
return self.get_list('DescribePolicies', params,
[('member', ScalingPolicy)]) | Returns descriptions of what each policy does. This action supports
pagination. If the response includes a token, there are more records
available. To get the additional records, repeat the request with the
response token as the NextToken parameter.
If no group name or list of policy names are provided, all
available policies are returned.
:type as_name: str
:param as_name: The name of the
:class:`boto.ec2.autoscale.group.AutoScalingGroup` to filter for.
:type names: list
:param names: List of policy names which should be searched for.
:type max_records: int
:param max_records: Maximum amount of groups to return. | Below is the the instruction that describes the task:
### Input:
Returns descriptions of what each policy does. This action supports
pagination. If the response includes a token, there are more records
available. To get the additional records, repeat the request with the
response token as the NextToken parameter.
If no group name or list of policy names are provided, all
available policies are returned.
:type as_name: str
:param as_name: The name of the
:class:`boto.ec2.autoscale.group.AutoScalingGroup` to filter for.
:type names: list
:param names: List of policy names which should be searched for.
:type max_records: int
:param max_records: Maximum amount of groups to return.
### Response:
def get_all_policies(self, as_group=None, policy_names=None,
max_records=None, next_token=None):
"""
Returns descriptions of what each policy does. This action supports
pagination. If the response includes a token, there are more records
available. To get the additional records, repeat the request with the
response token as the NextToken parameter.
If no group name or list of policy names are provided, all
available policies are returned.
:type as_name: str
:param as_name: The name of the
:class:`boto.ec2.autoscale.group.AutoScalingGroup` to filter for.
:type names: list
:param names: List of policy names which should be searched for.
:type max_records: int
:param max_records: Maximum amount of groups to return.
"""
params = {}
if as_group:
params['AutoScalingGroupName'] = as_group
if policy_names:
self.build_list_params(params, policy_names, 'PolicyNames')
if max_records:
params['MaxRecords'] = max_records
if next_token:
params['NextToken'] = next_token
return self.get_list('DescribePolicies', params,
[('member', ScalingPolicy)]) |
def _parse_byte_data(self, byte_data):
"""Extract the values from byte string."""
self.data_type = b''.join(unpack('cccc', byte_data[:4])).decode()
self.run = unpack('>i', byte_data[4:8])[0]
self.udp_sequence = unpack('>i', byte_data[8:12])[0]
self.timestamp, self.ns_ticks = unpack('>II', byte_data[12:20])
self.dom_id = unpack('>i', byte_data[20:24])[0]
dom_status_bits = unpack('>I', byte_data[24:28])[0]
self.dom_status = "{0:032b}".format(dom_status_bits)
self.human_readable_timestamp = datetime.datetime.fromtimestamp(
int(self.timestamp), UTC_TZ
).strftime('%Y-%m-%d %H:%M:%S') | Extract the values from byte string. | Below is the the instruction that describes the task:
### Input:
Extract the values from byte string.
### Response:
def _parse_byte_data(self, byte_data):
"""Extract the values from byte string."""
self.data_type = b''.join(unpack('cccc', byte_data[:4])).decode()
self.run = unpack('>i', byte_data[4:8])[0]
self.udp_sequence = unpack('>i', byte_data[8:12])[0]
self.timestamp, self.ns_ticks = unpack('>II', byte_data[12:20])
self.dom_id = unpack('>i', byte_data[20:24])[0]
dom_status_bits = unpack('>I', byte_data[24:28])[0]
self.dom_status = "{0:032b}".format(dom_status_bits)
self.human_readable_timestamp = datetime.datetime.fromtimestamp(
int(self.timestamp), UTC_TZ
).strftime('%Y-%m-%d %H:%M:%S') |
def super_mro(self):
"""Get the MRO which will be used to lookup attributes in this super."""
if not isinstance(self.mro_pointer, scoped_nodes.ClassDef):
raise exceptions.SuperError(
"The first argument to super must be a subtype of "
"type, not {mro_pointer}.",
super_=self,
)
if isinstance(self.type, scoped_nodes.ClassDef):
# `super(type, type)`, most likely in a class method.
self._class_based = True
mro_type = self.type
else:
mro_type = getattr(self.type, "_proxied", None)
if not isinstance(mro_type, (bases.Instance, scoped_nodes.ClassDef)):
raise exceptions.SuperError(
"The second argument to super must be an "
"instance or subtype of type, not {type}.",
super_=self,
)
if not mro_type.newstyle:
raise exceptions.SuperError(
"Unable to call super on old-style classes.", super_=self
)
mro = mro_type.mro()
if self.mro_pointer not in mro:
raise exceptions.SuperError(
"The second argument to super must be an "
"instance or subtype of type, not {type}.",
super_=self,
)
index = mro.index(self.mro_pointer)
return mro[index + 1 :] | Get the MRO which will be used to lookup attributes in this super. | Below is the the instruction that describes the task:
### Input:
Get the MRO which will be used to lookup attributes in this super.
### Response:
def super_mro(self):
"""Get the MRO which will be used to lookup attributes in this super."""
if not isinstance(self.mro_pointer, scoped_nodes.ClassDef):
raise exceptions.SuperError(
"The first argument to super must be a subtype of "
"type, not {mro_pointer}.",
super_=self,
)
if isinstance(self.type, scoped_nodes.ClassDef):
# `super(type, type)`, most likely in a class method.
self._class_based = True
mro_type = self.type
else:
mro_type = getattr(self.type, "_proxied", None)
if not isinstance(mro_type, (bases.Instance, scoped_nodes.ClassDef)):
raise exceptions.SuperError(
"The second argument to super must be an "
"instance or subtype of type, not {type}.",
super_=self,
)
if not mro_type.newstyle:
raise exceptions.SuperError(
"Unable to call super on old-style classes.", super_=self
)
mro = mro_type.mro()
if self.mro_pointer not in mro:
raise exceptions.SuperError(
"The second argument to super must be an "
"instance or subtype of type, not {type}.",
super_=self,
)
index = mro.index(self.mro_pointer)
return mro[index + 1 :] |
def export(self):
"""
Generate a NIDM-Results export.
"""
try:
if not os.path.isdir(self.export_dir):
os.mkdir(self.export_dir)
# Initialise main bundle
self._create_bundle(self.version)
self.add_object(self.software)
# Add model fitting steps
if not isinstance(self.model_fittings, list):
self.model_fittings = list(self.model_fittings.values())
for model_fitting in self.model_fittings:
# Design Matrix
# model_fitting.activity.used(model_fitting.design_matrix)
self.bundle.used(model_fitting.activity.id,
model_fitting.design_matrix.id)
self.add_object(model_fitting.design_matrix)
# *** Export visualisation of the design matrix
self.add_object(model_fitting.design_matrix.image)
if model_fitting.design_matrix.image.file is not None:
self.add_object(model_fitting.design_matrix.image.file)
if model_fitting.design_matrix.hrf_models is not None:
# drift model
self.add_object(model_fitting.design_matrix.drift_model)
if self.version['major'] > 1 or \
(self.version['major'] == 1 and
self.version['minor'] >= 3):
# Machine
# model_fitting.data.wasAttributedTo(model_fitting.machine)
self.bundle.wasAttributedTo(model_fitting.data.id,
model_fitting.machine.id)
self.add_object(model_fitting.machine)
# Imaged subject or group(s)
for sub in model_fitting.subjects:
self.add_object(sub)
# model_fitting.data.wasAttributedTo(sub)
self.bundle.wasAttributedTo(model_fitting.data.id,
sub.id)
# Data
# model_fitting.activity.used(model_fitting.data)
self.bundle.used(model_fitting.activity.id,
model_fitting.data.id)
self.add_object(model_fitting.data)
# Error Model
# model_fitting.activity.used(model_fitting.error_model)
self.bundle.used(model_fitting.activity.id,
model_fitting.error_model.id)
self.add_object(model_fitting.error_model)
# Parameter Estimate Maps
for param_estimate in model_fitting.param_estimates:
# param_estimate.wasGeneratedBy(model_fitting.activity)
self.bundle.wasGeneratedBy(param_estimate.id,
model_fitting.activity.id)
self.add_object(param_estimate)
self.add_object(param_estimate.coord_space)
self.add_object(param_estimate.file)
if param_estimate.derfrom is not None:
self.bundle.wasDerivedFrom(param_estimate.id,
param_estimate.derfrom.id)
self.add_object(param_estimate.derfrom)
self.add_object(param_estimate.derfrom.file,
export_file=False)
# Residual Mean Squares Map
# model_fitting.rms_map.wasGeneratedBy(model_fitting.activity)
self.add_object(model_fitting.rms_map)
self.bundle.wasGeneratedBy(model_fitting.rms_map.id,
model_fitting.activity.id)
self.add_object(model_fitting.rms_map.coord_space)
self.add_object(model_fitting.rms_map.file)
if model_fitting.rms_map.derfrom is not None:
self.bundle.wasDerivedFrom(
model_fitting.rms_map.id,
model_fitting.rms_map.derfrom.id)
self.add_object(model_fitting.rms_map.derfrom)
self.add_object(model_fitting.rms_map.derfrom.file,
export_file=False)
# Resels per Voxel Map
if model_fitting.rpv_map is not None:
self.add_object(model_fitting.rpv_map)
self.bundle.wasGeneratedBy(model_fitting.rpv_map.id,
model_fitting.activity.id)
self.add_object(model_fitting.rpv_map.coord_space)
self.add_object(model_fitting.rpv_map.file)
if model_fitting.rpv_map.inf_id is not None:
self.bundle.used(model_fitting.rpv_map.inf_id,
model_fitting.rpv_map.id)
if model_fitting.rpv_map.derfrom is not None:
self.bundle.wasDerivedFrom(
model_fitting.rpv_map.id,
model_fitting.rpv_map.derfrom.id)
self.add_object(model_fitting.rpv_map.derfrom)
self.add_object(model_fitting.rpv_map.derfrom.file,
export_file=False)
# Mask
# model_fitting.mask_map.wasGeneratedBy(model_fitting.activity)
self.bundle.wasGeneratedBy(model_fitting.mask_map.id,
model_fitting.activity.id)
self.add_object(model_fitting.mask_map)
if model_fitting.mask_map.derfrom is not None:
self.bundle.wasDerivedFrom(
model_fitting.mask_map.id,
model_fitting.mask_map.derfrom.id)
self.add_object(model_fitting.mask_map.derfrom)
self.add_object(model_fitting.mask_map.derfrom.file,
export_file=False)
# Create coordinate space export
self.add_object(model_fitting.mask_map.coord_space)
# Create "Mask map" entity
self.add_object(model_fitting.mask_map.file)
# Grand Mean map
# model_fitting.grand_mean_map.wasGeneratedBy(model_fitting.activity)
self.bundle.wasGeneratedBy(model_fitting.grand_mean_map.id,
model_fitting.activity.id)
self.add_object(model_fitting.grand_mean_map)
# Coordinate space entity
self.add_object(model_fitting.grand_mean_map.coord_space)
# Grand Mean Map entity
self.add_object(model_fitting.grand_mean_map.file)
# Model Parameters Estimation activity
self.add_object(model_fitting.activity)
self.bundle.wasAssociatedWith(model_fitting.activity.id,
self.software.id)
# model_fitting.activity.wasAssociatedWith(self.software)
# self.add_object(model_fitting)
# Add contrast estimation steps
analysis_masks = dict()
for (model_fitting_id, pe_ids), contrasts in list(
self.contrasts.items()):
for contrast in contrasts:
model_fitting = self._get_model_fitting(model_fitting_id)
# for contrast in contrasts:
# contrast.estimation.used(model_fitting.rms_map)
self.bundle.used(contrast.estimation.id,
model_fitting.rms_map.id)
# contrast.estimation.used(model_fitting.mask_map)
self.bundle.used(contrast.estimation.id,
model_fitting.mask_map.id)
analysis_masks[contrast.estimation.id] = \
model_fitting.mask_map.id
self.bundle.used(contrast.estimation.id,
contrast.weights.id)
self.bundle.used(contrast.estimation.id,
model_fitting.design_matrix.id)
# contrast.estimation.wasAssociatedWith(self.software)
self.bundle.wasAssociatedWith(contrast.estimation.id,
self.software.id)
for pe_id in pe_ids:
# contrast.estimation.used(pe_id)
self.bundle.used(contrast.estimation.id, pe_id)
# Create estimation activity
self.add_object(contrast.estimation)
# Create contrast weights
self.add_object(contrast.weights)
if contrast.contrast_map is not None:
# Create contrast Map
# contrast.contrast_map.wasGeneratedBy(contrast.estimation)
self.bundle.wasGeneratedBy(contrast.contrast_map.id,
contrast.estimation.id)
self.add_object(contrast.contrast_map)
self.add_object(contrast.contrast_map.coord_space)
# Copy contrast map in export directory
self.add_object(contrast.contrast_map.file)
if contrast.contrast_map.derfrom is not None:
self.bundle.wasDerivedFrom(
contrast.contrast_map.id,
contrast.contrast_map.derfrom.id)
self.add_object(contrast.contrast_map.derfrom)
self.add_object(contrast.contrast_map.derfrom.file,
export_file=False)
# Create Std Err. Map (T-tests) or Explained Mean Sq. Map
# (F-tests)
# contrast.stderr_or_expl_mean_sq_map.wasGeneratedBy
# (contrast.estimation)
stderr_explmeansq_map = (
contrast.stderr_or_expl_mean_sq_map)
self.bundle.wasGeneratedBy(
stderr_explmeansq_map.id,
contrast.estimation.id)
self.add_object(stderr_explmeansq_map)
self.add_object(
stderr_explmeansq_map.coord_space)
if isinstance(stderr_explmeansq_map,
ContrastStdErrMap) and \
stderr_explmeansq_map.contrast_var:
self.add_object(
stderr_explmeansq_map.contrast_var)
if stderr_explmeansq_map.var_coord_space:
self.add_object(
stderr_explmeansq_map.var_coord_space)
if stderr_explmeansq_map.contrast_var.coord_space:
self.add_object(
stderr_explmeansq_map.contrast_var.coord_space)
self.add_object(
stderr_explmeansq_map.contrast_var.file,
export_file=False)
self.bundle.wasDerivedFrom(
stderr_explmeansq_map.id,
stderr_explmeansq_map.contrast_var.id)
self.add_object(stderr_explmeansq_map.file)
# Create Statistic Map
# contrast.stat_map.wasGeneratedBy(contrast.estimation)
self.bundle.wasGeneratedBy(contrast.stat_map.id,
contrast.estimation.id)
self.add_object(contrast.stat_map)
self.add_object(contrast.stat_map.coord_space)
# Copy Statistical map in export directory
self.add_object(contrast.stat_map.file)
if contrast.stat_map.derfrom is not None:
self.bundle.wasDerivedFrom(
contrast.stat_map.id,
contrast.stat_map.derfrom.id)
self.add_object(contrast.stat_map.derfrom)
self.add_object(contrast.stat_map.derfrom.file,
export_file=False)
# Create Z Statistic Map
if contrast.z_stat_map:
# contrast.z_stat_map.wasGeneratedBy(contrast.estimation)
self.bundle.wasGeneratedBy(contrast.z_stat_map.id,
contrast.estimation.id)
self.add_object(contrast.z_stat_map)
self.add_object(contrast.z_stat_map.coord_space)
# Copy Statistical map in export directory
self.add_object(contrast.z_stat_map.file)
# self.add_object(contrast)
# Add inference steps
for contrast_id, inferences in list(self.inferences.items()):
contrast = self._get_contrast(contrast_id)
for inference in inferences:
if contrast.z_stat_map:
used_id = contrast.z_stat_map.id
else:
used_id = contrast.stat_map.id
# inference.inference_act.used(used_id)
self.bundle.used(inference.inference_act.id, used_id)
# inference.inference_act.wasAssociatedWith(self.software)
self.bundle.wasAssociatedWith(inference.inference_act.id,
self.software.id)
# self.add_object(inference)
# Excursion set
# inference.excursion_set.wasGeneratedBy(inference.inference_act)
self.bundle.wasGeneratedBy(inference.excursion_set.id,
inference.inference_act.id)
self.add_object(inference.excursion_set)
self.add_object(inference.excursion_set.coord_space)
if inference.excursion_set.visu is not None:
self.add_object(inference.excursion_set.visu)
if inference.excursion_set.visu.file is not None:
self.add_object(inference.excursion_set.visu.file)
# Copy "Excursion set map" file in export directory
self.add_object(inference.excursion_set.file)
if inference.excursion_set.clust_map is not None:
self.add_object(inference.excursion_set.clust_map)
self.add_object(inference.excursion_set.clust_map.file)
self.add_object(
inference.excursion_set.clust_map.coord_space)
if inference.excursion_set.mip is not None:
self.add_object(inference.excursion_set.mip)
self.add_object(inference.excursion_set.mip.file)
# Height threshold
if inference.height_thresh.equiv_thresh is not None:
for equiv in inference.height_thresh.equiv_thresh:
self.add_object(equiv)
self.add_object(inference.height_thresh)
# Extent threshold
if inference.extent_thresh.equiv_thresh is not None:
for equiv in inference.extent_thresh.equiv_thresh:
self.add_object(equiv)
self.add_object(inference.extent_thresh)
# Display Mask (potentially more than 1)
if inference.disp_mask:
for mask in inference.disp_mask:
# inference.inference_act.used(mask)
self.bundle.used(inference.inference_act.id,
mask.id)
self.add_object(mask)
# Create coordinate space entity
self.add_object(mask.coord_space)
# Create "Display Mask Map" entity
self.add_object(mask.file)
if mask.derfrom is not None:
self.bundle.wasDerivedFrom(mask.id,
mask.derfrom.id)
self.add_object(mask.derfrom)
self.add_object(mask.derfrom.file,
export_file=False)
# Search Space
self.bundle.wasGeneratedBy(inference.search_space.id,
inference.inference_act.id)
# inference.search_space.wasGeneratedBy(inference.inference_act)
self.add_object(inference.search_space)
self.add_object(inference.search_space.coord_space)
# Copy "Mask map" in export directory
self.add_object(inference.search_space.file)
# Peak Definition
if inference.peak_criteria:
# inference.inference_act.used(inference.peak_criteria)
self.bundle.used(inference.inference_act.id,
inference.peak_criteria.id)
self.add_object(inference.peak_criteria)
# Cluster Definition
if inference.cluster_criteria:
# inference.inference_act.used(inference.cluster_criteria)
self.bundle.used(inference.inference_act.id,
inference.cluster_criteria.id)
self.add_object(inference.cluster_criteria)
if inference.clusters:
# Clusters and peaks
for cluster in inference.clusters:
# cluster.wasDerivedFrom(inference.excursion_set)
self.bundle.wasDerivedFrom(
cluster.id, inference.excursion_set.id)
self.add_object(cluster)
for peak in cluster.peaks:
self.bundle.wasDerivedFrom(peak.id, cluster.id)
self.add_object(peak)
self.add_object(peak.coordinate)
if cluster.cog is not None:
self.bundle.wasDerivedFrom(cluster.cog.id,
cluster.id)
self.add_object(cluster.cog)
self.add_object(cluster.cog.coordinate)
# Inference activity
# inference.inference_act.wasAssociatedWith(inference.software_id)
# inference.inference_act.used(inference.height_thresh)
self.bundle.used(inference.inference_act.id,
inference.height_thresh.id)
# inference.inference_act.used(inference.extent_thresh)
self.bundle.used(inference.inference_act.id,
inference.extent_thresh.id)
self.bundle.used(inference.inference_act.id,
analysis_masks[contrast.estimation.id])
self.add_object(inference.inference_act)
# Write-out prov file
self.save_prov_to_files()
return self.out_dir
except Exception:
self.cleanup()
raise | Generate a NIDM-Results export. | Below is the the instruction that describes the task:
### Input:
Generate a NIDM-Results export.
### Response:
def export(self):
"""
Generate a NIDM-Results export.
"""
try:
if not os.path.isdir(self.export_dir):
os.mkdir(self.export_dir)
# Initialise main bundle
self._create_bundle(self.version)
self.add_object(self.software)
# Add model fitting steps
if not isinstance(self.model_fittings, list):
self.model_fittings = list(self.model_fittings.values())
for model_fitting in self.model_fittings:
# Design Matrix
# model_fitting.activity.used(model_fitting.design_matrix)
self.bundle.used(model_fitting.activity.id,
model_fitting.design_matrix.id)
self.add_object(model_fitting.design_matrix)
# *** Export visualisation of the design matrix
self.add_object(model_fitting.design_matrix.image)
if model_fitting.design_matrix.image.file is not None:
self.add_object(model_fitting.design_matrix.image.file)
if model_fitting.design_matrix.hrf_models is not None:
# drift model
self.add_object(model_fitting.design_matrix.drift_model)
if self.version['major'] > 1 or \
(self.version['major'] == 1 and
self.version['minor'] >= 3):
# Machine
# model_fitting.data.wasAttributedTo(model_fitting.machine)
self.bundle.wasAttributedTo(model_fitting.data.id,
model_fitting.machine.id)
self.add_object(model_fitting.machine)
# Imaged subject or group(s)
for sub in model_fitting.subjects:
self.add_object(sub)
# model_fitting.data.wasAttributedTo(sub)
self.bundle.wasAttributedTo(model_fitting.data.id,
sub.id)
# Data
# model_fitting.activity.used(model_fitting.data)
self.bundle.used(model_fitting.activity.id,
model_fitting.data.id)
self.add_object(model_fitting.data)
# Error Model
# model_fitting.activity.used(model_fitting.error_model)
self.bundle.used(model_fitting.activity.id,
model_fitting.error_model.id)
self.add_object(model_fitting.error_model)
# Parameter Estimate Maps
for param_estimate in model_fitting.param_estimates:
# param_estimate.wasGeneratedBy(model_fitting.activity)
self.bundle.wasGeneratedBy(param_estimate.id,
model_fitting.activity.id)
self.add_object(param_estimate)
self.add_object(param_estimate.coord_space)
self.add_object(param_estimate.file)
if param_estimate.derfrom is not None:
self.bundle.wasDerivedFrom(param_estimate.id,
param_estimate.derfrom.id)
self.add_object(param_estimate.derfrom)
self.add_object(param_estimate.derfrom.file,
export_file=False)
# Residual Mean Squares Map
# model_fitting.rms_map.wasGeneratedBy(model_fitting.activity)
self.add_object(model_fitting.rms_map)
self.bundle.wasGeneratedBy(model_fitting.rms_map.id,
model_fitting.activity.id)
self.add_object(model_fitting.rms_map.coord_space)
self.add_object(model_fitting.rms_map.file)
if model_fitting.rms_map.derfrom is not None:
self.bundle.wasDerivedFrom(
model_fitting.rms_map.id,
model_fitting.rms_map.derfrom.id)
self.add_object(model_fitting.rms_map.derfrom)
self.add_object(model_fitting.rms_map.derfrom.file,
export_file=False)
# Resels per Voxel Map
if model_fitting.rpv_map is not None:
self.add_object(model_fitting.rpv_map)
self.bundle.wasGeneratedBy(model_fitting.rpv_map.id,
model_fitting.activity.id)
self.add_object(model_fitting.rpv_map.coord_space)
self.add_object(model_fitting.rpv_map.file)
if model_fitting.rpv_map.inf_id is not None:
self.bundle.used(model_fitting.rpv_map.inf_id,
model_fitting.rpv_map.id)
if model_fitting.rpv_map.derfrom is not None:
self.bundle.wasDerivedFrom(
model_fitting.rpv_map.id,
model_fitting.rpv_map.derfrom.id)
self.add_object(model_fitting.rpv_map.derfrom)
self.add_object(model_fitting.rpv_map.derfrom.file,
export_file=False)
# Mask
# model_fitting.mask_map.wasGeneratedBy(model_fitting.activity)
self.bundle.wasGeneratedBy(model_fitting.mask_map.id,
model_fitting.activity.id)
self.add_object(model_fitting.mask_map)
if model_fitting.mask_map.derfrom is not None:
self.bundle.wasDerivedFrom(
model_fitting.mask_map.id,
model_fitting.mask_map.derfrom.id)
self.add_object(model_fitting.mask_map.derfrom)
self.add_object(model_fitting.mask_map.derfrom.file,
export_file=False)
# Create coordinate space export
self.add_object(model_fitting.mask_map.coord_space)
# Create "Mask map" entity
self.add_object(model_fitting.mask_map.file)
# Grand Mean map
# model_fitting.grand_mean_map.wasGeneratedBy(model_fitting.activity)
self.bundle.wasGeneratedBy(model_fitting.grand_mean_map.id,
model_fitting.activity.id)
self.add_object(model_fitting.grand_mean_map)
# Coordinate space entity
self.add_object(model_fitting.grand_mean_map.coord_space)
# Grand Mean Map entity
self.add_object(model_fitting.grand_mean_map.file)
# Model Parameters Estimation activity
self.add_object(model_fitting.activity)
self.bundle.wasAssociatedWith(model_fitting.activity.id,
self.software.id)
# model_fitting.activity.wasAssociatedWith(self.software)
# self.add_object(model_fitting)
# Add contrast estimation steps
analysis_masks = dict()
for (model_fitting_id, pe_ids), contrasts in list(
self.contrasts.items()):
for contrast in contrasts:
model_fitting = self._get_model_fitting(model_fitting_id)
# for contrast in contrasts:
# contrast.estimation.used(model_fitting.rms_map)
self.bundle.used(contrast.estimation.id,
model_fitting.rms_map.id)
# contrast.estimation.used(model_fitting.mask_map)
self.bundle.used(contrast.estimation.id,
model_fitting.mask_map.id)
analysis_masks[contrast.estimation.id] = \
model_fitting.mask_map.id
self.bundle.used(contrast.estimation.id,
contrast.weights.id)
self.bundle.used(contrast.estimation.id,
model_fitting.design_matrix.id)
# contrast.estimation.wasAssociatedWith(self.software)
self.bundle.wasAssociatedWith(contrast.estimation.id,
self.software.id)
for pe_id in pe_ids:
# contrast.estimation.used(pe_id)
self.bundle.used(contrast.estimation.id, pe_id)
# Create estimation activity
self.add_object(contrast.estimation)
# Create contrast weights
self.add_object(contrast.weights)
if contrast.contrast_map is not None:
# Create contrast Map
# contrast.contrast_map.wasGeneratedBy(contrast.estimation)
self.bundle.wasGeneratedBy(contrast.contrast_map.id,
contrast.estimation.id)
self.add_object(contrast.contrast_map)
self.add_object(contrast.contrast_map.coord_space)
# Copy contrast map in export directory
self.add_object(contrast.contrast_map.file)
if contrast.contrast_map.derfrom is not None:
self.bundle.wasDerivedFrom(
contrast.contrast_map.id,
contrast.contrast_map.derfrom.id)
self.add_object(contrast.contrast_map.derfrom)
self.add_object(contrast.contrast_map.derfrom.file,
export_file=False)
# Create Std Err. Map (T-tests) or Explained Mean Sq. Map
# (F-tests)
# contrast.stderr_or_expl_mean_sq_map.wasGeneratedBy
# (contrast.estimation)
stderr_explmeansq_map = (
contrast.stderr_or_expl_mean_sq_map)
self.bundle.wasGeneratedBy(
stderr_explmeansq_map.id,
contrast.estimation.id)
self.add_object(stderr_explmeansq_map)
self.add_object(
stderr_explmeansq_map.coord_space)
if isinstance(stderr_explmeansq_map,
ContrastStdErrMap) and \
stderr_explmeansq_map.contrast_var:
self.add_object(
stderr_explmeansq_map.contrast_var)
if stderr_explmeansq_map.var_coord_space:
self.add_object(
stderr_explmeansq_map.var_coord_space)
if stderr_explmeansq_map.contrast_var.coord_space:
self.add_object(
stderr_explmeansq_map.contrast_var.coord_space)
self.add_object(
stderr_explmeansq_map.contrast_var.file,
export_file=False)
self.bundle.wasDerivedFrom(
stderr_explmeansq_map.id,
stderr_explmeansq_map.contrast_var.id)
self.add_object(stderr_explmeansq_map.file)
# Create Statistic Map
# contrast.stat_map.wasGeneratedBy(contrast.estimation)
self.bundle.wasGeneratedBy(contrast.stat_map.id,
contrast.estimation.id)
self.add_object(contrast.stat_map)
self.add_object(contrast.stat_map.coord_space)
# Copy Statistical map in export directory
self.add_object(contrast.stat_map.file)
if contrast.stat_map.derfrom is not None:
self.bundle.wasDerivedFrom(
contrast.stat_map.id,
contrast.stat_map.derfrom.id)
self.add_object(contrast.stat_map.derfrom)
self.add_object(contrast.stat_map.derfrom.file,
export_file=False)
# Create Z Statistic Map
if contrast.z_stat_map:
# contrast.z_stat_map.wasGeneratedBy(contrast.estimation)
self.bundle.wasGeneratedBy(contrast.z_stat_map.id,
contrast.estimation.id)
self.add_object(contrast.z_stat_map)
self.add_object(contrast.z_stat_map.coord_space)
# Copy Statistical map in export directory
self.add_object(contrast.z_stat_map.file)
# self.add_object(contrast)
# Add inference steps
for contrast_id, inferences in list(self.inferences.items()):
contrast = self._get_contrast(contrast_id)
for inference in inferences:
if contrast.z_stat_map:
used_id = contrast.z_stat_map.id
else:
used_id = contrast.stat_map.id
# inference.inference_act.used(used_id)
self.bundle.used(inference.inference_act.id, used_id)
# inference.inference_act.wasAssociatedWith(self.software)
self.bundle.wasAssociatedWith(inference.inference_act.id,
self.software.id)
# self.add_object(inference)
# Excursion set
# inference.excursion_set.wasGeneratedBy(inference.inference_act)
self.bundle.wasGeneratedBy(inference.excursion_set.id,
inference.inference_act.id)
self.add_object(inference.excursion_set)
self.add_object(inference.excursion_set.coord_space)
if inference.excursion_set.visu is not None:
self.add_object(inference.excursion_set.visu)
if inference.excursion_set.visu.file is not None:
self.add_object(inference.excursion_set.visu.file)
# Copy "Excursion set map" file in export directory
self.add_object(inference.excursion_set.file)
if inference.excursion_set.clust_map is not None:
self.add_object(inference.excursion_set.clust_map)
self.add_object(inference.excursion_set.clust_map.file)
self.add_object(
inference.excursion_set.clust_map.coord_space)
if inference.excursion_set.mip is not None:
self.add_object(inference.excursion_set.mip)
self.add_object(inference.excursion_set.mip.file)
# Height threshold
if inference.height_thresh.equiv_thresh is not None:
for equiv in inference.height_thresh.equiv_thresh:
self.add_object(equiv)
self.add_object(inference.height_thresh)
# Extent threshold
if inference.extent_thresh.equiv_thresh is not None:
for equiv in inference.extent_thresh.equiv_thresh:
self.add_object(equiv)
self.add_object(inference.extent_thresh)
# Display Mask (potentially more than 1)
if inference.disp_mask:
for mask in inference.disp_mask:
# inference.inference_act.used(mask)
self.bundle.used(inference.inference_act.id,
mask.id)
self.add_object(mask)
# Create coordinate space entity
self.add_object(mask.coord_space)
# Create "Display Mask Map" entity
self.add_object(mask.file)
if mask.derfrom is not None:
self.bundle.wasDerivedFrom(mask.id,
mask.derfrom.id)
self.add_object(mask.derfrom)
self.add_object(mask.derfrom.file,
export_file=False)
# Search Space
self.bundle.wasGeneratedBy(inference.search_space.id,
inference.inference_act.id)
# inference.search_space.wasGeneratedBy(inference.inference_act)
self.add_object(inference.search_space)
self.add_object(inference.search_space.coord_space)
# Copy "Mask map" in export directory
self.add_object(inference.search_space.file)
# Peak Definition
if inference.peak_criteria:
# inference.inference_act.used(inference.peak_criteria)
self.bundle.used(inference.inference_act.id,
inference.peak_criteria.id)
self.add_object(inference.peak_criteria)
# Cluster Definition
if inference.cluster_criteria:
# inference.inference_act.used(inference.cluster_criteria)
self.bundle.used(inference.inference_act.id,
inference.cluster_criteria.id)
self.add_object(inference.cluster_criteria)
if inference.clusters:
# Clusters and peaks
for cluster in inference.clusters:
# cluster.wasDerivedFrom(inference.excursion_set)
self.bundle.wasDerivedFrom(
cluster.id, inference.excursion_set.id)
self.add_object(cluster)
for peak in cluster.peaks:
self.bundle.wasDerivedFrom(peak.id, cluster.id)
self.add_object(peak)
self.add_object(peak.coordinate)
if cluster.cog is not None:
self.bundle.wasDerivedFrom(cluster.cog.id,
cluster.id)
self.add_object(cluster.cog)
self.add_object(cluster.cog.coordinate)
# Inference activity
# inference.inference_act.wasAssociatedWith(inference.software_id)
# inference.inference_act.used(inference.height_thresh)
self.bundle.used(inference.inference_act.id,
inference.height_thresh.id)
# inference.inference_act.used(inference.extent_thresh)
self.bundle.used(inference.inference_act.id,
inference.extent_thresh.id)
self.bundle.used(inference.inference_act.id,
analysis_masks[contrast.estimation.id])
self.add_object(inference.inference_act)
# Write-out prov file
self.save_prov_to_files()
return self.out_dir
except Exception:
self.cleanup()
raise |
def add(self, uuid):
""" Adds a key to the HyperLogLog """
if uuid:
# Computing the hash
try:
x = hash64(uuid)
except UnicodeEncodeError:
x = hash64(uuid.encode('ascii', 'ignore'))
# Finding the register to update by using the first b bits as an index
j = x & ((1 << self.b) - 1)
# Remove those b bits
w = x >> self.b
# Find the first 0 in the remaining bit pattern
self.M[j] = max(self.M[j], self._get_rho(w, self.bitcount_arr)) | Adds a key to the HyperLogLog | Below is the the instruction that describes the task:
### Input:
Adds a key to the HyperLogLog
### Response:
def add(self, uuid):
""" Adds a key to the HyperLogLog """
if uuid:
# Computing the hash
try:
x = hash64(uuid)
except UnicodeEncodeError:
x = hash64(uuid.encode('ascii', 'ignore'))
# Finding the register to update by using the first b bits as an index
j = x & ((1 << self.b) - 1)
# Remove those b bits
w = x >> self.b
# Find the first 0 in the remaining bit pattern
self.M[j] = max(self.M[j], self._get_rho(w, self.bitcount_arr)) |
def raise_for_response(self, responses):
"""
Constructs appropriate exception from list of responses and raises it.
"""
exception_messages = [self.client.format_exception_message(response) for response in responses]
if len(exception_messages) == 1:
message = exception_messages[0]
else:
message = "[%s]" % ", ".join(exception_messages)
raise PostmarkerException(message) | Constructs appropriate exception from list of responses and raises it. | Below is the the instruction that describes the task:
### Input:
Constructs appropriate exception from list of responses and raises it.
### Response:
def raise_for_response(self, responses):
"""
Constructs appropriate exception from list of responses and raises it.
"""
exception_messages = [self.client.format_exception_message(response) for response in responses]
if len(exception_messages) == 1:
message = exception_messages[0]
else:
message = "[%s]" % ", ".join(exception_messages)
raise PostmarkerException(message) |
def com_google_fonts_check_name_license(ttFont, license):
"""Check copyright namerecords match license file."""
from fontbakery.constants import PLACEHOLDER_LICENSING_TEXT
failed = False
placeholder = PLACEHOLDER_LICENSING_TEXT[license]
entry_found = False
for i, nameRecord in enumerate(ttFont["name"].names):
if nameRecord.nameID == NameID.LICENSE_DESCRIPTION:
entry_found = True
value = nameRecord.toUnicode()
if value != placeholder:
failed = True
yield FAIL, Message("wrong", \
("License file {} exists but"
" NameID {} (LICENSE DESCRIPTION) value"
" on platform {} ({})"
" is not specified for that."
" Value was: \"{}\""
" Must be changed to \"{}\""
"").format(license,
NameID.LICENSE_DESCRIPTION,
nameRecord.platformID,
PlatformID(nameRecord.platformID).name,
value,
placeholder))
if not entry_found:
yield FAIL, Message("missing", \
("Font lacks NameID {} "
"(LICENSE DESCRIPTION). A proper licensing entry"
" must be set.").format(NameID.LICENSE_DESCRIPTION))
elif not failed:
yield PASS, "Licensing entry on name table is correctly set." | Check copyright namerecords match license file. | Below is the the instruction that describes the task:
### Input:
Check copyright namerecords match license file.
### Response:
def com_google_fonts_check_name_license(ttFont, license):
"""Check copyright namerecords match license file."""
from fontbakery.constants import PLACEHOLDER_LICENSING_TEXT
failed = False
placeholder = PLACEHOLDER_LICENSING_TEXT[license]
entry_found = False
for i, nameRecord in enumerate(ttFont["name"].names):
if nameRecord.nameID == NameID.LICENSE_DESCRIPTION:
entry_found = True
value = nameRecord.toUnicode()
if value != placeholder:
failed = True
yield FAIL, Message("wrong", \
("License file {} exists but"
" NameID {} (LICENSE DESCRIPTION) value"
" on platform {} ({})"
" is not specified for that."
" Value was: \"{}\""
" Must be changed to \"{}\""
"").format(license,
NameID.LICENSE_DESCRIPTION,
nameRecord.platformID,
PlatformID(nameRecord.platformID).name,
value,
placeholder))
if not entry_found:
yield FAIL, Message("missing", \
("Font lacks NameID {} "
"(LICENSE DESCRIPTION). A proper licensing entry"
" must be set.").format(NameID.LICENSE_DESCRIPTION))
elif not failed:
yield PASS, "Licensing entry on name table is correctly set." |
def render_dot(self, code, options, format, prefix='graphviz'):
# type: (nodes.NodeVisitor, unicode, Dict, unicode, unicode) -> Tuple[unicode, unicode]
"""Render graphviz code into a PNG or PDF output file."""
graphviz_dot = options.get('graphviz_dot', self.builder.config.graphviz_dot)
hashkey = (code + str(options) + str(graphviz_dot) +
str(self.builder.config.graphviz_dot_args)).encode('utf-8')
fname = '%s-%s.%s' % (prefix, sha1(hashkey).hexdigest(), format)
relfn = posixpath.join(self.builder.imgpath, fname)
outfn = path.join(self.builder.outdir, self.builder.imagedir, fname)
if path.isfile(outfn):
return relfn, outfn
if (hasattr(self.builder, '_graphviz_warned_dot') and
self.builder._graphviz_warned_dot.get(graphviz_dot)):
return None, None
ensuredir(path.dirname(outfn))
# graphviz expects UTF-8 by default
if isinstance(code, text_type):
code = code.encode('utf-8')
dot_args = [graphviz_dot]
dot_args.extend(self.builder.config.graphviz_dot_args)
dot_args.extend(['-T' + format, '-o' + outfn])
if format == 'png':
dot_args.extend(['-Tcmapx', '-o%s.map' % outfn])
try:
p = Popen(dot_args, stdout=PIPE, stdin=PIPE, stderr=PIPE)
except OSError as err:
if err.errno != ENOENT: # No such file or directory
raise
logger.warning(__('dot command %r cannot be run (needed for graphviz '
'output), check the graphviz_dot setting'), graphviz_dot)
if not hasattr(self.builder, '_graphviz_warned_dot'):
self.builder._graphviz_warned_dot = {}
self.builder._graphviz_warned_dot[graphviz_dot] = True
return None, None
try:
# Graphviz may close standard input when an error occurs,
# resulting in a broken pipe on communicate()
stdout, stderr = p.communicate(code)
except (OSError, IOError) as err:
if err.errno not in (EPIPE, EINVAL):
raise
# in this case, read the standard output and standard error streams
# directly, to get the error message(s)
stdout, stderr = p.stdout.read(), p.stderr.read()
p.wait()
if p.returncode != 0:
raise GraphvizError(__('dot exited with error:\n[stderr]\n%s\n'
'[stdout]\n%s') % (stderr, stdout))
if not path.isfile(outfn):
raise GraphvizError(__('dot did not produce an output file:\n[stderr]\n%s\n'
'[stdout]\n%s') % (stderr, stdout))
return relfn, outfn | Render graphviz code into a PNG or PDF output file. | Below is the the instruction that describes the task:
### Input:
Render graphviz code into a PNG or PDF output file.
### Response:
def render_dot(self, code, options, format, prefix='graphviz'):
# type: (nodes.NodeVisitor, unicode, Dict, unicode, unicode) -> Tuple[unicode, unicode]
"""Render graphviz code into a PNG or PDF output file."""
graphviz_dot = options.get('graphviz_dot', self.builder.config.graphviz_dot)
hashkey = (code + str(options) + str(graphviz_dot) +
str(self.builder.config.graphviz_dot_args)).encode('utf-8')
fname = '%s-%s.%s' % (prefix, sha1(hashkey).hexdigest(), format)
relfn = posixpath.join(self.builder.imgpath, fname)
outfn = path.join(self.builder.outdir, self.builder.imagedir, fname)
if path.isfile(outfn):
return relfn, outfn
if (hasattr(self.builder, '_graphviz_warned_dot') and
self.builder._graphviz_warned_dot.get(graphviz_dot)):
return None, None
ensuredir(path.dirname(outfn))
# graphviz expects UTF-8 by default
if isinstance(code, text_type):
code = code.encode('utf-8')
dot_args = [graphviz_dot]
dot_args.extend(self.builder.config.graphviz_dot_args)
dot_args.extend(['-T' + format, '-o' + outfn])
if format == 'png':
dot_args.extend(['-Tcmapx', '-o%s.map' % outfn])
try:
p = Popen(dot_args, stdout=PIPE, stdin=PIPE, stderr=PIPE)
except OSError as err:
if err.errno != ENOENT: # No such file or directory
raise
logger.warning(__('dot command %r cannot be run (needed for graphviz '
'output), check the graphviz_dot setting'), graphviz_dot)
if not hasattr(self.builder, '_graphviz_warned_dot'):
self.builder._graphviz_warned_dot = {}
self.builder._graphviz_warned_dot[graphviz_dot] = True
return None, None
try:
# Graphviz may close standard input when an error occurs,
# resulting in a broken pipe on communicate()
stdout, stderr = p.communicate(code)
except (OSError, IOError) as err:
if err.errno not in (EPIPE, EINVAL):
raise
# in this case, read the standard output and standard error streams
# directly, to get the error message(s)
stdout, stderr = p.stdout.read(), p.stderr.read()
p.wait()
if p.returncode != 0:
raise GraphvizError(__('dot exited with error:\n[stderr]\n%s\n'
'[stdout]\n%s') % (stderr, stdout))
if not path.isfile(outfn):
raise GraphvizError(__('dot did not produce an output file:\n[stderr]\n%s\n'
'[stdout]\n%s') % (stderr, stdout))
return relfn, outfn |
def regression_tikhonov(G, y, M, tau=0):
r"""Solve a regression problem on graph via Tikhonov minimization.
The function solves
.. math:: \operatorname*{arg min}_x \| M x - y \|_2^2 + \tau \ x^T L x
if :math:`\tau > 0`, and
.. math:: \operatorname*{arg min}_x x^T L x \ \text{ s. t. } \ y = M x
otherwise.
Parameters
----------
G : :class:`pygsp.graphs.Graph`
y : array, length G.n_vertices
Measurements.
M : array of boolean, length G.n_vertices
Masking vector.
tau : float
Regularization parameter.
Returns
-------
x : array, length G.n_vertices
Recovered values :math:`x`.
Examples
--------
>>> from pygsp import graphs, filters, learning
>>> import matplotlib.pyplot as plt
>>>
>>> G = graphs.Sensor(N=100, seed=42)
>>> G.estimate_lmax()
Create a smooth ground truth signal:
>>> filt = lambda x: 1 / (1 + 10*x)
>>> filt = filters.Filter(G, filt)
>>> rs = np.random.RandomState(42)
>>> signal = filt.analyze(rs.normal(size=G.n_vertices))
Construct a measurement signal from a binary mask:
>>> mask = rs.uniform(0, 1, G.n_vertices) > 0.5
>>> measures = signal.copy()
>>> measures[~mask] = np.nan
Solve the regression problem by reconstructing the signal:
>>> recovery = learning.regression_tikhonov(G, measures, mask, tau=0)
Plot the results:
>>> fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(10, 3))
>>> limits = [signal.min(), signal.max()]
>>> _ = G.plot_signal(signal, ax=ax1, limits=limits, title='Ground truth')
>>> _ = G.plot_signal(measures, ax=ax2, limits=limits, title='Measures')
>>> _ = G.plot_signal(recovery, ax=ax3, limits=limits, title='Recovery')
>>> _ = fig.tight_layout()
"""
if tau > 0:
y[M == False] = 0
if sparse.issparse(G.L):
def Op(x):
return (M * x.T).T + tau * (G.L.dot(x))
LinearOp = sparse.linalg.LinearOperator([G.N, G.N], Op)
if y.ndim > 1:
sol = np.empty(shape=y.shape)
res = np.empty(shape=y.shape[1])
for i in range(y.shape[1]):
sol[:, i], res[i] = sparse.linalg.cg(
LinearOp, y[:, i])
else:
sol, res = sparse.linalg.cg(LinearOp, y)
# TODO: do something with the residual...
return sol
else:
# Creating this matrix may be problematic in term of memory.
# Consider using an operator instead...
if type(G.L).__module__ == np.__name__:
LinearOp = np.diag(M*1) + tau * G.L
return np.linalg.solve(LinearOp, M * y)
else:
if np.prod(M.shape) != G.n_vertices:
raise ValueError("M should be of size [G.n_vertices,]")
indl = M
indu = (M == False)
Luu = G.L[indu, :][:, indu]
Wul = - G.L[indu, :][:, indl]
if sparse.issparse(G.L):
sol_part = sparse.linalg.spsolve(Luu, Wul.dot(y[indl]))
else:
sol_part = np.linalg.solve(Luu, np.matmul(Wul, y[indl]))
sol = y.copy()
sol[indu] = sol_part
return sol | r"""Solve a regression problem on graph via Tikhonov minimization.
The function solves
.. math:: \operatorname*{arg min}_x \| M x - y \|_2^2 + \tau \ x^T L x
if :math:`\tau > 0`, and
.. math:: \operatorname*{arg min}_x x^T L x \ \text{ s. t. } \ y = M x
otherwise.
Parameters
----------
G : :class:`pygsp.graphs.Graph`
y : array, length G.n_vertices
Measurements.
M : array of boolean, length G.n_vertices
Masking vector.
tau : float
Regularization parameter.
Returns
-------
x : array, length G.n_vertices
Recovered values :math:`x`.
Examples
--------
>>> from pygsp import graphs, filters, learning
>>> import matplotlib.pyplot as plt
>>>
>>> G = graphs.Sensor(N=100, seed=42)
>>> G.estimate_lmax()
Create a smooth ground truth signal:
>>> filt = lambda x: 1 / (1 + 10*x)
>>> filt = filters.Filter(G, filt)
>>> rs = np.random.RandomState(42)
>>> signal = filt.analyze(rs.normal(size=G.n_vertices))
Construct a measurement signal from a binary mask:
>>> mask = rs.uniform(0, 1, G.n_vertices) > 0.5
>>> measures = signal.copy()
>>> measures[~mask] = np.nan
Solve the regression problem by reconstructing the signal:
>>> recovery = learning.regression_tikhonov(G, measures, mask, tau=0)
Plot the results:
>>> fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(10, 3))
>>> limits = [signal.min(), signal.max()]
>>> _ = G.plot_signal(signal, ax=ax1, limits=limits, title='Ground truth')
>>> _ = G.plot_signal(measures, ax=ax2, limits=limits, title='Measures')
>>> _ = G.plot_signal(recovery, ax=ax3, limits=limits, title='Recovery')
>>> _ = fig.tight_layout() | Below is the the instruction that describes the task:
### Input:
r"""Solve a regression problem on graph via Tikhonov minimization.
The function solves
.. math:: \operatorname*{arg min}_x \| M x - y \|_2^2 + \tau \ x^T L x
if :math:`\tau > 0`, and
.. math:: \operatorname*{arg min}_x x^T L x \ \text{ s. t. } \ y = M x
otherwise.
Parameters
----------
G : :class:`pygsp.graphs.Graph`
y : array, length G.n_vertices
Measurements.
M : array of boolean, length G.n_vertices
Masking vector.
tau : float
Regularization parameter.
Returns
-------
x : array, length G.n_vertices
Recovered values :math:`x`.
Examples
--------
>>> from pygsp import graphs, filters, learning
>>> import matplotlib.pyplot as plt
>>>
>>> G = graphs.Sensor(N=100, seed=42)
>>> G.estimate_lmax()
Create a smooth ground truth signal:
>>> filt = lambda x: 1 / (1 + 10*x)
>>> filt = filters.Filter(G, filt)
>>> rs = np.random.RandomState(42)
>>> signal = filt.analyze(rs.normal(size=G.n_vertices))
Construct a measurement signal from a binary mask:
>>> mask = rs.uniform(0, 1, G.n_vertices) > 0.5
>>> measures = signal.copy()
>>> measures[~mask] = np.nan
Solve the regression problem by reconstructing the signal:
>>> recovery = learning.regression_tikhonov(G, measures, mask, tau=0)
Plot the results:
>>> fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(10, 3))
>>> limits = [signal.min(), signal.max()]
>>> _ = G.plot_signal(signal, ax=ax1, limits=limits, title='Ground truth')
>>> _ = G.plot_signal(measures, ax=ax2, limits=limits, title='Measures')
>>> _ = G.plot_signal(recovery, ax=ax3, limits=limits, title='Recovery')
>>> _ = fig.tight_layout()
### Response:
def regression_tikhonov(G, y, M, tau=0):
r"""Solve a regression problem on graph via Tikhonov minimization.
The function solves
.. math:: \operatorname*{arg min}_x \| M x - y \|_2^2 + \tau \ x^T L x
if :math:`\tau > 0`, and
.. math:: \operatorname*{arg min}_x x^T L x \ \text{ s. t. } \ y = M x
otherwise.
Parameters
----------
G : :class:`pygsp.graphs.Graph`
y : array, length G.n_vertices
Measurements.
M : array of boolean, length G.n_vertices
Masking vector.
tau : float
Regularization parameter.
Returns
-------
x : array, length G.n_vertices
Recovered values :math:`x`.
Examples
--------
>>> from pygsp import graphs, filters, learning
>>> import matplotlib.pyplot as plt
>>>
>>> G = graphs.Sensor(N=100, seed=42)
>>> G.estimate_lmax()
Create a smooth ground truth signal:
>>> filt = lambda x: 1 / (1 + 10*x)
>>> filt = filters.Filter(G, filt)
>>> rs = np.random.RandomState(42)
>>> signal = filt.analyze(rs.normal(size=G.n_vertices))
Construct a measurement signal from a binary mask:
>>> mask = rs.uniform(0, 1, G.n_vertices) > 0.5
>>> measures = signal.copy()
>>> measures[~mask] = np.nan
Solve the regression problem by reconstructing the signal:
>>> recovery = learning.regression_tikhonov(G, measures, mask, tau=0)
Plot the results:
>>> fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(10, 3))
>>> limits = [signal.min(), signal.max()]
>>> _ = G.plot_signal(signal, ax=ax1, limits=limits, title='Ground truth')
>>> _ = G.plot_signal(measures, ax=ax2, limits=limits, title='Measures')
>>> _ = G.plot_signal(recovery, ax=ax3, limits=limits, title='Recovery')
>>> _ = fig.tight_layout()
"""
if tau > 0:
y[M == False] = 0
if sparse.issparse(G.L):
def Op(x):
return (M * x.T).T + tau * (G.L.dot(x))
LinearOp = sparse.linalg.LinearOperator([G.N, G.N], Op)
if y.ndim > 1:
sol = np.empty(shape=y.shape)
res = np.empty(shape=y.shape[1])
for i in range(y.shape[1]):
sol[:, i], res[i] = sparse.linalg.cg(
LinearOp, y[:, i])
else:
sol, res = sparse.linalg.cg(LinearOp, y)
# TODO: do something with the residual...
return sol
else:
# Creating this matrix may be problematic in term of memory.
# Consider using an operator instead...
if type(G.L).__module__ == np.__name__:
LinearOp = np.diag(M*1) + tau * G.L
return np.linalg.solve(LinearOp, M * y)
else:
if np.prod(M.shape) != G.n_vertices:
raise ValueError("M should be of size [G.n_vertices,]")
indl = M
indu = (M == False)
Luu = G.L[indu, :][:, indu]
Wul = - G.L[indu, :][:, indl]
if sparse.issparse(G.L):
sol_part = sparse.linalg.spsolve(Luu, Wul.dot(y[indl]))
else:
sol_part = np.linalg.solve(Luu, np.matmul(Wul, y[indl]))
sol = y.copy()
sol[indu] = sol_part
return sol |
def ycoord(self):
"""The y coordinate :class:`xarray.Variable`"""
return self.decoder.get_y(self.data, coords=self.data.coords) | The y coordinate :class:`xarray.Variable` | Below is the the instruction that describes the task:
### Input:
The y coordinate :class:`xarray.Variable`
### Response:
def ycoord(self):
"""The y coordinate :class:`xarray.Variable`"""
return self.decoder.get_y(self.data, coords=self.data.coords) |
def update_job(JobId=None, RoleARN=None, Notification=None, Resources=None, AddressId=None, ShippingOption=None, Description=None, SnowballCapacityPreference=None, ForwardingAddressId=None):
"""
While a job's JobState value is New , you can update some of the information associated with a job. Once the job changes to a different job state, usually within 60 minutes of the job being created, this action is no longer available.
See also: AWS API Documentation
Examples
This action allows you to update certain parameters for a job. Once the job changes to a different job state, usually within 60 minutes of the job being created, this action is no longer available.
Expected Output:
:example: response = client.update_job(
JobId='string',
RoleARN='string',
Notification={
'SnsTopicARN': 'string',
'JobStatesToNotify': [
'New'|'PreparingAppliance'|'PreparingShipment'|'InTransitToCustomer'|'WithCustomer'|'InTransitToAWS'|'WithAWS'|'InProgress'|'Complete'|'Cancelled'|'Listing'|'Pending',
],
'NotifyAll': True|False
},
Resources={
'S3Resources': [
{
'BucketArn': 'string',
'KeyRange': {
'BeginMarker': 'string',
'EndMarker': 'string'
}
},
],
'LambdaResources': [
{
'LambdaArn': 'string',
'EventTriggers': [
{
'EventResourceARN': 'string'
},
]
},
]
},
AddressId='string',
ShippingOption='SECOND_DAY'|'NEXT_DAY'|'EXPRESS'|'STANDARD',
Description='string',
SnowballCapacityPreference='T50'|'T80'|'T100'|'NoPreference',
ForwardingAddressId='string'
)
:type JobId: string
:param JobId: [REQUIRED]
The job ID of the job that you want to update, for example JID123e4567-e89b-12d3-a456-426655440000 .
:type RoleARN: string
:param RoleARN: The new role Amazon Resource Name (ARN) that you want to associate with this job. To create a role ARN, use the CreateRole AWS Identity and Access Management (IAM) API action.
:type Notification: dict
:param Notification: The new or updated Notification object.
SnsTopicARN (string) --The new SNS TopicArn that you want to associate with this job. You can create Amazon Resource Names (ARNs) for topics by using the CreateTopic Amazon SNS API action.
You can subscribe email addresses to an Amazon SNS topic through the AWS Management Console, or by using the Subscribe AWS Simple Notification Service (SNS) API action.
JobStatesToNotify (list) --The list of job states that will trigger a notification for this job.
(string) --
NotifyAll (boolean) --Any change in job state will trigger a notification for this job.
:type Resources: dict
:param Resources: The updated S3Resource object (for a single Amazon S3 bucket or key range), or the updated JobResource object (for multiple buckets or key ranges).
S3Resources (list) --An array of S3Resource objects.
(dict) --Each S3Resource object represents an Amazon S3 bucket that your transferred data will be exported from or imported into. For export jobs, this object can have an optional KeyRange value. The length of the range is defined at job creation, and has either an inclusive BeginMarker , an inclusive EndMarker , or both. Ranges are UTF-8 binary sorted.
BucketArn (string) --The Amazon Resource Name (ARN) of an Amazon S3 bucket.
KeyRange (dict) --For export jobs, you can provide an optional KeyRange within a specific Amazon S3 bucket. The length of the range is defined at job creation, and has either an inclusive BeginMarker , an inclusive EndMarker , or both. Ranges are UTF-8 binary sorted.
BeginMarker (string) --The key that starts an optional key range for an export job. Ranges are inclusive and UTF-8 binary sorted.
EndMarker (string) --The key that ends an optional key range for an export job. Ranges are inclusive and UTF-8 binary sorted.
LambdaResources (list) --The Python-language Lambda functions for this job.
(dict) --Identifies
LambdaArn (string) --An Amazon Resource Name (ARN) that represents an AWS Lambda function to be triggered by PUT object actions on the associated local Amazon S3 resource.
EventTriggers (list) --The array of ARNs for S3Resource objects to trigger the LambdaResource objects associated with this job.
(dict) --The container for the EventTriggerDefinition$EventResourceARN .
EventResourceARN (string) --The Amazon Resource Name (ARN) for any local Amazon S3 resource that is an AWS Lambda function's event trigger associated with this job.
:type AddressId: string
:param AddressId: The ID of the updated Address object.
:type ShippingOption: string
:param ShippingOption: The updated shipping option value of this job's ShippingDetails object.
:type Description: string
:param Description: The updated description of this job's JobMetadata object.
:type SnowballCapacityPreference: string
:param SnowballCapacityPreference: The updated SnowballCapacityPreference of this job's JobMetadata object. The 50 TB Snowballs are only available in the US regions.
:type ForwardingAddressId: string
:param ForwardingAddressId: The updated ID for the forwarding address for a job. This field is not supported in most regions.
:rtype: dict
:return: {}
:returns:
(dict) --
"""
pass | While a job's JobState value is New , you can update some of the information associated with a job. Once the job changes to a different job state, usually within 60 minutes of the job being created, this action is no longer available.
See also: AWS API Documentation
Examples
This action allows you to update certain parameters for a job. Once the job changes to a different job state, usually within 60 minutes of the job being created, this action is no longer available.
Expected Output:
:example: response = client.update_job(
JobId='string',
RoleARN='string',
Notification={
'SnsTopicARN': 'string',
'JobStatesToNotify': [
'New'|'PreparingAppliance'|'PreparingShipment'|'InTransitToCustomer'|'WithCustomer'|'InTransitToAWS'|'WithAWS'|'InProgress'|'Complete'|'Cancelled'|'Listing'|'Pending',
],
'NotifyAll': True|False
},
Resources={
'S3Resources': [
{
'BucketArn': 'string',
'KeyRange': {
'BeginMarker': 'string',
'EndMarker': 'string'
}
},
],
'LambdaResources': [
{
'LambdaArn': 'string',
'EventTriggers': [
{
'EventResourceARN': 'string'
},
]
},
]
},
AddressId='string',
ShippingOption='SECOND_DAY'|'NEXT_DAY'|'EXPRESS'|'STANDARD',
Description='string',
SnowballCapacityPreference='T50'|'T80'|'T100'|'NoPreference',
ForwardingAddressId='string'
)
:type JobId: string
:param JobId: [REQUIRED]
The job ID of the job that you want to update, for example JID123e4567-e89b-12d3-a456-426655440000 .
:type RoleARN: string
:param RoleARN: The new role Amazon Resource Name (ARN) that you want to associate with this job. To create a role ARN, use the CreateRole AWS Identity and Access Management (IAM) API action.
:type Notification: dict
:param Notification: The new or updated Notification object.
SnsTopicARN (string) --The new SNS TopicArn that you want to associate with this job. You can create Amazon Resource Names (ARNs) for topics by using the CreateTopic Amazon SNS API action.
You can subscribe email addresses to an Amazon SNS topic through the AWS Management Console, or by using the Subscribe AWS Simple Notification Service (SNS) API action.
JobStatesToNotify (list) --The list of job states that will trigger a notification for this job.
(string) --
NotifyAll (boolean) --Any change in job state will trigger a notification for this job.
:type Resources: dict
:param Resources: The updated S3Resource object (for a single Amazon S3 bucket or key range), or the updated JobResource object (for multiple buckets or key ranges).
S3Resources (list) --An array of S3Resource objects.
(dict) --Each S3Resource object represents an Amazon S3 bucket that your transferred data will be exported from or imported into. For export jobs, this object can have an optional KeyRange value. The length of the range is defined at job creation, and has either an inclusive BeginMarker , an inclusive EndMarker , or both. Ranges are UTF-8 binary sorted.
BucketArn (string) --The Amazon Resource Name (ARN) of an Amazon S3 bucket.
KeyRange (dict) --For export jobs, you can provide an optional KeyRange within a specific Amazon S3 bucket. The length of the range is defined at job creation, and has either an inclusive BeginMarker , an inclusive EndMarker , or both. Ranges are UTF-8 binary sorted.
BeginMarker (string) --The key that starts an optional key range for an export job. Ranges are inclusive and UTF-8 binary sorted.
EndMarker (string) --The key that ends an optional key range for an export job. Ranges are inclusive and UTF-8 binary sorted.
LambdaResources (list) --The Python-language Lambda functions for this job.
(dict) --Identifies
LambdaArn (string) --An Amazon Resource Name (ARN) that represents an AWS Lambda function to be triggered by PUT object actions on the associated local Amazon S3 resource.
EventTriggers (list) --The array of ARNs for S3Resource objects to trigger the LambdaResource objects associated with this job.
(dict) --The container for the EventTriggerDefinition$EventResourceARN .
EventResourceARN (string) --The Amazon Resource Name (ARN) for any local Amazon S3 resource that is an AWS Lambda function's event trigger associated with this job.
:type AddressId: string
:param AddressId: The ID of the updated Address object.
:type ShippingOption: string
:param ShippingOption: The updated shipping option value of this job's ShippingDetails object.
:type Description: string
:param Description: The updated description of this job's JobMetadata object.
:type SnowballCapacityPreference: string
:param SnowballCapacityPreference: The updated SnowballCapacityPreference of this job's JobMetadata object. The 50 TB Snowballs are only available in the US regions.
:type ForwardingAddressId: string
:param ForwardingAddressId: The updated ID for the forwarding address for a job. This field is not supported in most regions.
:rtype: dict
:return: {}
:returns:
(dict) -- | Below is the the instruction that describes the task:
### Input:
While a job's JobState value is New , you can update some of the information associated with a job. Once the job changes to a different job state, usually within 60 minutes of the job being created, this action is no longer available.
See also: AWS API Documentation
Examples
This action allows you to update certain parameters for a job. Once the job changes to a different job state, usually within 60 minutes of the job being created, this action is no longer available.
Expected Output:
:example: response = client.update_job(
JobId='string',
RoleARN='string',
Notification={
'SnsTopicARN': 'string',
'JobStatesToNotify': [
'New'|'PreparingAppliance'|'PreparingShipment'|'InTransitToCustomer'|'WithCustomer'|'InTransitToAWS'|'WithAWS'|'InProgress'|'Complete'|'Cancelled'|'Listing'|'Pending',
],
'NotifyAll': True|False
},
Resources={
'S3Resources': [
{
'BucketArn': 'string',
'KeyRange': {
'BeginMarker': 'string',
'EndMarker': 'string'
}
},
],
'LambdaResources': [
{
'LambdaArn': 'string',
'EventTriggers': [
{
'EventResourceARN': 'string'
},
]
},
]
},
AddressId='string',
ShippingOption='SECOND_DAY'|'NEXT_DAY'|'EXPRESS'|'STANDARD',
Description='string',
SnowballCapacityPreference='T50'|'T80'|'T100'|'NoPreference',
ForwardingAddressId='string'
)
:type JobId: string
:param JobId: [REQUIRED]
The job ID of the job that you want to update, for example JID123e4567-e89b-12d3-a456-426655440000 .
:type RoleARN: string
:param RoleARN: The new role Amazon Resource Name (ARN) that you want to associate with this job. To create a role ARN, use the CreateRole AWS Identity and Access Management (IAM) API action.
:type Notification: dict
:param Notification: The new or updated Notification object.
SnsTopicARN (string) --The new SNS TopicArn that you want to associate with this job. You can create Amazon Resource Names (ARNs) for topics by using the CreateTopic Amazon SNS API action.
You can subscribe email addresses to an Amazon SNS topic through the AWS Management Console, or by using the Subscribe AWS Simple Notification Service (SNS) API action.
JobStatesToNotify (list) --The list of job states that will trigger a notification for this job.
(string) --
NotifyAll (boolean) --Any change in job state will trigger a notification for this job.
:type Resources: dict
:param Resources: The updated S3Resource object (for a single Amazon S3 bucket or key range), or the updated JobResource object (for multiple buckets or key ranges).
S3Resources (list) --An array of S3Resource objects.
(dict) --Each S3Resource object represents an Amazon S3 bucket that your transferred data will be exported from or imported into. For export jobs, this object can have an optional KeyRange value. The length of the range is defined at job creation, and has either an inclusive BeginMarker , an inclusive EndMarker , or both. Ranges are UTF-8 binary sorted.
BucketArn (string) --The Amazon Resource Name (ARN) of an Amazon S3 bucket.
KeyRange (dict) --For export jobs, you can provide an optional KeyRange within a specific Amazon S3 bucket. The length of the range is defined at job creation, and has either an inclusive BeginMarker , an inclusive EndMarker , or both. Ranges are UTF-8 binary sorted.
BeginMarker (string) --The key that starts an optional key range for an export job. Ranges are inclusive and UTF-8 binary sorted.
EndMarker (string) --The key that ends an optional key range for an export job. Ranges are inclusive and UTF-8 binary sorted.
LambdaResources (list) --The Python-language Lambda functions for this job.
(dict) --Identifies
LambdaArn (string) --An Amazon Resource Name (ARN) that represents an AWS Lambda function to be triggered by PUT object actions on the associated local Amazon S3 resource.
EventTriggers (list) --The array of ARNs for S3Resource objects to trigger the LambdaResource objects associated with this job.
(dict) --The container for the EventTriggerDefinition$EventResourceARN .
EventResourceARN (string) --The Amazon Resource Name (ARN) for any local Amazon S3 resource that is an AWS Lambda function's event trigger associated with this job.
:type AddressId: string
:param AddressId: The ID of the updated Address object.
:type ShippingOption: string
:param ShippingOption: The updated shipping option value of this job's ShippingDetails object.
:type Description: string
:param Description: The updated description of this job's JobMetadata object.
:type SnowballCapacityPreference: string
:param SnowballCapacityPreference: The updated SnowballCapacityPreference of this job's JobMetadata object. The 50 TB Snowballs are only available in the US regions.
:type ForwardingAddressId: string
:param ForwardingAddressId: The updated ID for the forwarding address for a job. This field is not supported in most regions.
:rtype: dict
:return: {}
:returns:
(dict) --
### Response:
def update_job(JobId=None, RoleARN=None, Notification=None, Resources=None, AddressId=None, ShippingOption=None, Description=None, SnowballCapacityPreference=None, ForwardingAddressId=None):
"""
While a job's JobState value is New , you can update some of the information associated with a job. Once the job changes to a different job state, usually within 60 minutes of the job being created, this action is no longer available.
See also: AWS API Documentation
Examples
This action allows you to update certain parameters for a job. Once the job changes to a different job state, usually within 60 minutes of the job being created, this action is no longer available.
Expected Output:
:example: response = client.update_job(
JobId='string',
RoleARN='string',
Notification={
'SnsTopicARN': 'string',
'JobStatesToNotify': [
'New'|'PreparingAppliance'|'PreparingShipment'|'InTransitToCustomer'|'WithCustomer'|'InTransitToAWS'|'WithAWS'|'InProgress'|'Complete'|'Cancelled'|'Listing'|'Pending',
],
'NotifyAll': True|False
},
Resources={
'S3Resources': [
{
'BucketArn': 'string',
'KeyRange': {
'BeginMarker': 'string',
'EndMarker': 'string'
}
},
],
'LambdaResources': [
{
'LambdaArn': 'string',
'EventTriggers': [
{
'EventResourceARN': 'string'
},
]
},
]
},
AddressId='string',
ShippingOption='SECOND_DAY'|'NEXT_DAY'|'EXPRESS'|'STANDARD',
Description='string',
SnowballCapacityPreference='T50'|'T80'|'T100'|'NoPreference',
ForwardingAddressId='string'
)
:type JobId: string
:param JobId: [REQUIRED]
The job ID of the job that you want to update, for example JID123e4567-e89b-12d3-a456-426655440000 .
:type RoleARN: string
:param RoleARN: The new role Amazon Resource Name (ARN) that you want to associate with this job. To create a role ARN, use the CreateRole AWS Identity and Access Management (IAM) API action.
:type Notification: dict
:param Notification: The new or updated Notification object.
SnsTopicARN (string) --The new SNS TopicArn that you want to associate with this job. You can create Amazon Resource Names (ARNs) for topics by using the CreateTopic Amazon SNS API action.
You can subscribe email addresses to an Amazon SNS topic through the AWS Management Console, or by using the Subscribe AWS Simple Notification Service (SNS) API action.
JobStatesToNotify (list) --The list of job states that will trigger a notification for this job.
(string) --
NotifyAll (boolean) --Any change in job state will trigger a notification for this job.
:type Resources: dict
:param Resources: The updated S3Resource object (for a single Amazon S3 bucket or key range), or the updated JobResource object (for multiple buckets or key ranges).
S3Resources (list) --An array of S3Resource objects.
(dict) --Each S3Resource object represents an Amazon S3 bucket that your transferred data will be exported from or imported into. For export jobs, this object can have an optional KeyRange value. The length of the range is defined at job creation, and has either an inclusive BeginMarker , an inclusive EndMarker , or both. Ranges are UTF-8 binary sorted.
BucketArn (string) --The Amazon Resource Name (ARN) of an Amazon S3 bucket.
KeyRange (dict) --For export jobs, you can provide an optional KeyRange within a specific Amazon S3 bucket. The length of the range is defined at job creation, and has either an inclusive BeginMarker , an inclusive EndMarker , or both. Ranges are UTF-8 binary sorted.
BeginMarker (string) --The key that starts an optional key range for an export job. Ranges are inclusive and UTF-8 binary sorted.
EndMarker (string) --The key that ends an optional key range for an export job. Ranges are inclusive and UTF-8 binary sorted.
LambdaResources (list) --The Python-language Lambda functions for this job.
(dict) --Identifies
LambdaArn (string) --An Amazon Resource Name (ARN) that represents an AWS Lambda function to be triggered by PUT object actions on the associated local Amazon S3 resource.
EventTriggers (list) --The array of ARNs for S3Resource objects to trigger the LambdaResource objects associated with this job.
(dict) --The container for the EventTriggerDefinition$EventResourceARN .
EventResourceARN (string) --The Amazon Resource Name (ARN) for any local Amazon S3 resource that is an AWS Lambda function's event trigger associated with this job.
:type AddressId: string
:param AddressId: The ID of the updated Address object.
:type ShippingOption: string
:param ShippingOption: The updated shipping option value of this job's ShippingDetails object.
:type Description: string
:param Description: The updated description of this job's JobMetadata object.
:type SnowballCapacityPreference: string
:param SnowballCapacityPreference: The updated SnowballCapacityPreference of this job's JobMetadata object. The 50 TB Snowballs are only available in the US regions.
:type ForwardingAddressId: string
:param ForwardingAddressId: The updated ID for the forwarding address for a job. This field is not supported in most regions.
:rtype: dict
:return: {}
:returns:
(dict) --
"""
pass |
def enforce_csrf(self, request):
"""
Enforce CSRF validation for session based authentication.
"""
reason = CSRFCheck().process_view(request, None, (), {})
if reason:
# CSRF failed, bail with explicit error message
raise exceptions.PermissionDenied('CSRF Failed: %s' % reason) | Enforce CSRF validation for session based authentication. | Below is the the instruction that describes the task:
### Input:
Enforce CSRF validation for session based authentication.
### Response:
def enforce_csrf(self, request):
"""
Enforce CSRF validation for session based authentication.
"""
reason = CSRFCheck().process_view(request, None, (), {})
if reason:
# CSRF failed, bail with explicit error message
raise exceptions.PermissionDenied('CSRF Failed: %s' % reason) |
def _condition_as_sql(self, qn, connection):
'''
Return sql for condition.
'''
def escape(value):
if isinstance(value, bool):
value = str(int(value))
if isinstance(value, six.string_types):
# Escape params used with LIKE
if '%' in value:
value = value.replace('%', '%%')
# Escape single quotes
if "'" in value:
value = value.replace("'", "''")
# Add single quote to text values
value = "'" + value + "'"
return value
sql, param = self.condition.query.where.as_sql(qn, connection)
param = map(escape, param)
return sql % tuple(param) | Return sql for condition. | Below is the the instruction that describes the task:
### Input:
Return sql for condition.
### Response:
def _condition_as_sql(self, qn, connection):
'''
Return sql for condition.
'''
def escape(value):
if isinstance(value, bool):
value = str(int(value))
if isinstance(value, six.string_types):
# Escape params used with LIKE
if '%' in value:
value = value.replace('%', '%%')
# Escape single quotes
if "'" in value:
value = value.replace("'", "''")
# Add single quote to text values
value = "'" + value + "'"
return value
sql, param = self.condition.query.where.as_sql(qn, connection)
param = map(escape, param)
return sql % tuple(param) |
def __parse_identities(self, json):
"""Parse identities using Eclipse format.
The Eclipse identities format is a JSON document under the "commiters"
key. The document should follow the next schema:
{
'committers' : {
'john': {
'affiliations': {
'1': {
'active': '2001-01-01',
'inactive': null,
'name': 'Organization 1'
}
},
'email': [
'john@example.com'
],
'first': 'John',
'id': 'john',
'last': 'Doe',
'primary': 'john.doe@example.com'
}
}
}
:parse json: JSON object to parse
:raise InvalidFormatError: raised when the format of the JSON is
not valid.
"""
try:
for committer in json['committers'].values():
name = self.__encode(committer['first'] + ' ' + committer['last'])
email = self.__encode(committer['primary'])
username = self.__encode(committer['id'])
uuid = username
uid = UniqueIdentity(uuid=uuid)
identity = Identity(name=name, email=email, username=username,
source=self.source, uuid=uuid)
uid.identities.append(identity)
if 'email' in committer:
for alt_email in committer['email']:
alt_email = self.__encode(alt_email)
if alt_email == email:
continue
identity = Identity(name=name, email=alt_email, username=username,
source=self.source, uuid=uuid)
uid.identities.append(identity)
if 'affiliations' in committer:
enrollments = self.__parse_affiliations_json(committer['affiliations'],
uuid)
for rol in enrollments:
uid.enrollments.append(rol)
self._identities[uuid] = uid
except KeyError as e:
msg = "invalid json format. Attribute %s not found" % e.args
raise InvalidFormatError(cause=msg) | Parse identities using Eclipse format.
The Eclipse identities format is a JSON document under the "commiters"
key. The document should follow the next schema:
{
'committers' : {
'john': {
'affiliations': {
'1': {
'active': '2001-01-01',
'inactive': null,
'name': 'Organization 1'
}
},
'email': [
'john@example.com'
],
'first': 'John',
'id': 'john',
'last': 'Doe',
'primary': 'john.doe@example.com'
}
}
}
:parse json: JSON object to parse
:raise InvalidFormatError: raised when the format of the JSON is
not valid. | Below is the the instruction that describes the task:
### Input:
Parse identities using Eclipse format.
The Eclipse identities format is a JSON document under the "commiters"
key. The document should follow the next schema:
{
'committers' : {
'john': {
'affiliations': {
'1': {
'active': '2001-01-01',
'inactive': null,
'name': 'Organization 1'
}
},
'email': [
'john@example.com'
],
'first': 'John',
'id': 'john',
'last': 'Doe',
'primary': 'john.doe@example.com'
}
}
}
:parse json: JSON object to parse
:raise InvalidFormatError: raised when the format of the JSON is
not valid.
### Response:
def __parse_identities(self, json):
"""Parse identities using Eclipse format.
The Eclipse identities format is a JSON document under the "commiters"
key. The document should follow the next schema:
{
'committers' : {
'john': {
'affiliations': {
'1': {
'active': '2001-01-01',
'inactive': null,
'name': 'Organization 1'
}
},
'email': [
'john@example.com'
],
'first': 'John',
'id': 'john',
'last': 'Doe',
'primary': 'john.doe@example.com'
}
}
}
:parse json: JSON object to parse
:raise InvalidFormatError: raised when the format of the JSON is
not valid.
"""
try:
for committer in json['committers'].values():
name = self.__encode(committer['first'] + ' ' + committer['last'])
email = self.__encode(committer['primary'])
username = self.__encode(committer['id'])
uuid = username
uid = UniqueIdentity(uuid=uuid)
identity = Identity(name=name, email=email, username=username,
source=self.source, uuid=uuid)
uid.identities.append(identity)
if 'email' in committer:
for alt_email in committer['email']:
alt_email = self.__encode(alt_email)
if alt_email == email:
continue
identity = Identity(name=name, email=alt_email, username=username,
source=self.source, uuid=uuid)
uid.identities.append(identity)
if 'affiliations' in committer:
enrollments = self.__parse_affiliations_json(committer['affiliations'],
uuid)
for rol in enrollments:
uid.enrollments.append(rol)
self._identities[uuid] = uid
except KeyError as e:
msg = "invalid json format. Attribute %s not found" % e.args
raise InvalidFormatError(cause=msg) |
def transcodeImage(self, media, height, width, opacity=100, saturation=100):
""" Returns the URL for a transcoded image from the specified media object.
Returns None if no media specified (needed if user tries to pass thumb
or art directly).
Parameters:
height (int): Height to transcode the image to.
width (int): Width to transcode the image to.
opacity (int): Opacity of the resulting image (possibly deprecated).
saturation (int): Saturating of the resulting image.
"""
if media:
transcode_url = '/photo/:/transcode?height=%s&width=%s&opacity=%s&saturation=%s&url=%s' % (
height, width, opacity, saturation, media)
return self.url(transcode_url, includeToken=True) | Returns the URL for a transcoded image from the specified media object.
Returns None if no media specified (needed if user tries to pass thumb
or art directly).
Parameters:
height (int): Height to transcode the image to.
width (int): Width to transcode the image to.
opacity (int): Opacity of the resulting image (possibly deprecated).
saturation (int): Saturating of the resulting image. | Below is the the instruction that describes the task:
### Input:
Returns the URL for a transcoded image from the specified media object.
Returns None if no media specified (needed if user tries to pass thumb
or art directly).
Parameters:
height (int): Height to transcode the image to.
width (int): Width to transcode the image to.
opacity (int): Opacity of the resulting image (possibly deprecated).
saturation (int): Saturating of the resulting image.
### Response:
def transcodeImage(self, media, height, width, opacity=100, saturation=100):
""" Returns the URL for a transcoded image from the specified media object.
Returns None if no media specified (needed if user tries to pass thumb
or art directly).
Parameters:
height (int): Height to transcode the image to.
width (int): Width to transcode the image to.
opacity (int): Opacity of the resulting image (possibly deprecated).
saturation (int): Saturating of the resulting image.
"""
if media:
transcode_url = '/photo/:/transcode?height=%s&width=%s&opacity=%s&saturation=%s&url=%s' % (
height, width, opacity, saturation, media)
return self.url(transcode_url, includeToken=True) |
def create_server(initialize=True):
"""Create a server"""
with provider() as p:
host_string = p.create_server()
if initialize:
env.host_string = host_string
initialize_server() | Create a server | Below is the the instruction that describes the task:
### Input:
Create a server
### Response:
def create_server(initialize=True):
"""Create a server"""
with provider() as p:
host_string = p.create_server()
if initialize:
env.host_string = host_string
initialize_server() |
def accordions(self, form):
"""
return the chlidren of the given form in a dict allowing to render them
in accordions with a grid layout
:param form: the form object
"""
fixed = []
accordions = OrderedDict()
for child in form.children:
section = getattr(child.schema, 'section', '')
if not section:
fixed.append(child)
else:
if section not in accordions.keys():
accordions[section] = {
'tag_id': random_tag_id(),
'children': [],
'name': section,
"error": False,
}
if child.error:
accordions[section]['error'] = True
accordions[section]['children'].append(child)
grids = getattr(self, "grids", {})
named_grids = getattr(self, 'named_grids', {})
if grids != {}:
method = self._childgroup
elif named_grids != {}:
method = self._childgroup_by_name
grids = named_grids
else:
warnings.warn(u"Missing both grids and named_grids argument")
for accordion in accordions.values():
name = accordion['name']
grid = grids.get(name)
if grid is not None:
children = accordion.pop('children')
accordion['rows'] = method(children, grid)
return fixed, accordions | return the chlidren of the given form in a dict allowing to render them
in accordions with a grid layout
:param form: the form object | Below is the the instruction that describes the task:
### Input:
return the chlidren of the given form in a dict allowing to render them
in accordions with a grid layout
:param form: the form object
### Response:
def accordions(self, form):
"""
return the chlidren of the given form in a dict allowing to render them
in accordions with a grid layout
:param form: the form object
"""
fixed = []
accordions = OrderedDict()
for child in form.children:
section = getattr(child.schema, 'section', '')
if not section:
fixed.append(child)
else:
if section not in accordions.keys():
accordions[section] = {
'tag_id': random_tag_id(),
'children': [],
'name': section,
"error": False,
}
if child.error:
accordions[section]['error'] = True
accordions[section]['children'].append(child)
grids = getattr(self, "grids", {})
named_grids = getattr(self, 'named_grids', {})
if grids != {}:
method = self._childgroup
elif named_grids != {}:
method = self._childgroup_by_name
grids = named_grids
else:
warnings.warn(u"Missing both grids and named_grids argument")
for accordion in accordions.values():
name = accordion['name']
grid = grids.get(name)
if grid is not None:
children = accordion.pop('children')
accordion['rows'] = method(children, grid)
return fixed, accordions |
def add(self, name, interface, inputs=None, outputs=None,
requirements=None, wall_time=None, annotations=None, **kwargs):
"""
Adds a processing Node to the pipeline
Parameters
----------
name : str
Name for the node
interface : nipype.Interface
The interface to use for the node
inputs : dict[str, (str, FileFormat) | (Node, str)]
Connections from inputs of the pipeline and outputs of other nodes
to inputs of node. The keys of the dictionary are the field names
and the values are 2-tuple containing either the name of the data
spec and the data format it is expected in for pipeline inputs or
the sending Node and the the name of an output of the sending Node.
Note that pipeline inputs can be specified outside this method
using the 'connect_input' method and connections between nodes with
the the 'connect' method.
outputs : dict[str, (str, FileFormat)]
Connections to outputs of the pipeline from fields of the
interface. The keys of the dictionary are the names of the data
specs that will be written to and the values are the interface
field name and the data format it is produced in. Note that output
connections can also be specified using the 'connect_output'
method.
requirements : list(Requirement)
List of required packages need for the node to run (default: [])
wall_time : float
Time required to execute the node in minutes (default: 1)
mem_gb : int
Required memory for the node in GB
n_procs : int
Preferred number of threads to run the node on (default: 1)
annotations : dict[str, *]
Additional annotations to add to the node, which may be used by
the Processor node to optimise execution (e.g. 'gpu': True)
iterfield : str
Name of field to be passed an iterable to iterator over.
If present, a MapNode will be created instead of a regular node
joinsource : str
Name of iterator field to join. Typically one of the implicit
iterators (i.e. Study.SUBJECT_ID or Study.VISIT_ID)
to join over the subjects and/or visits
joinfield : str
Name of field to pass the joined list when creating a JoinNode
Returns
-------
node : Node
The Node object that has been added to the pipeline
"""
if annotations is None:
annotations = {}
if requirements is None:
requirements = []
if wall_time is None:
wall_time = self.study.processor.default_wall_time
if 'mem_gb' not in kwargs or kwargs['mem_gb'] is None:
kwargs['mem_gb'] = self.study.processor.default_mem_gb
if 'iterfield' in kwargs:
if 'joinfield' in kwargs or 'joinsource' in kwargs:
raise ArcanaDesignError(
"Cannot provide both joinsource and iterfield to when "
"attempting to add '{}' node to {}"
.foramt(name, self._error_msg_loc))
node_cls = self.study.environment.node_types['map']
elif 'joinsource' in kwargs or 'joinfield' in kwargs:
if not ('joinfield' in kwargs and 'joinsource' in kwargs):
raise ArcanaDesignError(
"Both joinsource and joinfield kwargs are required to "
"create a JoinNode (see {})".format(name,
self._error_msg_loc))
joinsource = kwargs['joinsource']
if joinsource in self.study.ITERFIELDS:
self._iterator_joins.add(joinsource)
node_cls = self.study.environment.node_types['join']
# Prepend name of pipeline of joinsource to match name of nodes
kwargs['joinsource'] = '{}_{}'.format(self.name, joinsource)
else:
node_cls = self.study.environment.node_types['base']
# Create node
node = node_cls(self.study.environment,
interface,
name="{}_{}".format(self._name, name),
requirements=requirements,
wall_time=wall_time,
annotations=annotations,
**kwargs)
# Ensure node is added to workflow
self._workflow.add_nodes([node])
# Connect inputs, outputs and internal connections
if inputs is not None:
assert isinstance(inputs, dict)
for node_input, connect_from in inputs.items():
if isinstance(connect_from[0], basestring):
input_spec, input_format = connect_from
self.connect_input(input_spec, node,
node_input, input_format)
else:
conn_node, conn_field = connect_from
self.connect(conn_node, conn_field, node, node_input)
if outputs is not None:
assert isinstance(outputs, dict)
for output_spec, (node_output, output_format) in outputs.items():
self.connect_output(output_spec, node, node_output,
output_format)
return node | Adds a processing Node to the pipeline
Parameters
----------
name : str
Name for the node
interface : nipype.Interface
The interface to use for the node
inputs : dict[str, (str, FileFormat) | (Node, str)]
Connections from inputs of the pipeline and outputs of other nodes
to inputs of node. The keys of the dictionary are the field names
and the values are 2-tuple containing either the name of the data
spec and the data format it is expected in for pipeline inputs or
the sending Node and the the name of an output of the sending Node.
Note that pipeline inputs can be specified outside this method
using the 'connect_input' method and connections between nodes with
the the 'connect' method.
outputs : dict[str, (str, FileFormat)]
Connections to outputs of the pipeline from fields of the
interface. The keys of the dictionary are the names of the data
specs that will be written to and the values are the interface
field name and the data format it is produced in. Note that output
connections can also be specified using the 'connect_output'
method.
requirements : list(Requirement)
List of required packages need for the node to run (default: [])
wall_time : float
Time required to execute the node in minutes (default: 1)
mem_gb : int
Required memory for the node in GB
n_procs : int
Preferred number of threads to run the node on (default: 1)
annotations : dict[str, *]
Additional annotations to add to the node, which may be used by
the Processor node to optimise execution (e.g. 'gpu': True)
iterfield : str
Name of field to be passed an iterable to iterator over.
If present, a MapNode will be created instead of a regular node
joinsource : str
Name of iterator field to join. Typically one of the implicit
iterators (i.e. Study.SUBJECT_ID or Study.VISIT_ID)
to join over the subjects and/or visits
joinfield : str
Name of field to pass the joined list when creating a JoinNode
Returns
-------
node : Node
The Node object that has been added to the pipeline | Below is the the instruction that describes the task:
### Input:
Adds a processing Node to the pipeline
Parameters
----------
name : str
Name for the node
interface : nipype.Interface
The interface to use for the node
inputs : dict[str, (str, FileFormat) | (Node, str)]
Connections from inputs of the pipeline and outputs of other nodes
to inputs of node. The keys of the dictionary are the field names
and the values are 2-tuple containing either the name of the data
spec and the data format it is expected in for pipeline inputs or
the sending Node and the the name of an output of the sending Node.
Note that pipeline inputs can be specified outside this method
using the 'connect_input' method and connections between nodes with
the the 'connect' method.
outputs : dict[str, (str, FileFormat)]
Connections to outputs of the pipeline from fields of the
interface. The keys of the dictionary are the names of the data
specs that will be written to and the values are the interface
field name and the data format it is produced in. Note that output
connections can also be specified using the 'connect_output'
method.
requirements : list(Requirement)
List of required packages need for the node to run (default: [])
wall_time : float
Time required to execute the node in minutes (default: 1)
mem_gb : int
Required memory for the node in GB
n_procs : int
Preferred number of threads to run the node on (default: 1)
annotations : dict[str, *]
Additional annotations to add to the node, which may be used by
the Processor node to optimise execution (e.g. 'gpu': True)
iterfield : str
Name of field to be passed an iterable to iterator over.
If present, a MapNode will be created instead of a regular node
joinsource : str
Name of iterator field to join. Typically one of the implicit
iterators (i.e. Study.SUBJECT_ID or Study.VISIT_ID)
to join over the subjects and/or visits
joinfield : str
Name of field to pass the joined list when creating a JoinNode
Returns
-------
node : Node
The Node object that has been added to the pipeline
### Response:
def add(self, name, interface, inputs=None, outputs=None,
requirements=None, wall_time=None, annotations=None, **kwargs):
"""
Adds a processing Node to the pipeline
Parameters
----------
name : str
Name for the node
interface : nipype.Interface
The interface to use for the node
inputs : dict[str, (str, FileFormat) | (Node, str)]
Connections from inputs of the pipeline and outputs of other nodes
to inputs of node. The keys of the dictionary are the field names
and the values are 2-tuple containing either the name of the data
spec and the data format it is expected in for pipeline inputs or
the sending Node and the the name of an output of the sending Node.
Note that pipeline inputs can be specified outside this method
using the 'connect_input' method and connections between nodes with
the the 'connect' method.
outputs : dict[str, (str, FileFormat)]
Connections to outputs of the pipeline from fields of the
interface. The keys of the dictionary are the names of the data
specs that will be written to and the values are the interface
field name and the data format it is produced in. Note that output
connections can also be specified using the 'connect_output'
method.
requirements : list(Requirement)
List of required packages need for the node to run (default: [])
wall_time : float
Time required to execute the node in minutes (default: 1)
mem_gb : int
Required memory for the node in GB
n_procs : int
Preferred number of threads to run the node on (default: 1)
annotations : dict[str, *]
Additional annotations to add to the node, which may be used by
the Processor node to optimise execution (e.g. 'gpu': True)
iterfield : str
Name of field to be passed an iterable to iterator over.
If present, a MapNode will be created instead of a regular node
joinsource : str
Name of iterator field to join. Typically one of the implicit
iterators (i.e. Study.SUBJECT_ID or Study.VISIT_ID)
to join over the subjects and/or visits
joinfield : str
Name of field to pass the joined list when creating a JoinNode
Returns
-------
node : Node
The Node object that has been added to the pipeline
"""
if annotations is None:
annotations = {}
if requirements is None:
requirements = []
if wall_time is None:
wall_time = self.study.processor.default_wall_time
if 'mem_gb' not in kwargs or kwargs['mem_gb'] is None:
kwargs['mem_gb'] = self.study.processor.default_mem_gb
if 'iterfield' in kwargs:
if 'joinfield' in kwargs or 'joinsource' in kwargs:
raise ArcanaDesignError(
"Cannot provide both joinsource and iterfield to when "
"attempting to add '{}' node to {}"
.foramt(name, self._error_msg_loc))
node_cls = self.study.environment.node_types['map']
elif 'joinsource' in kwargs or 'joinfield' in kwargs:
if not ('joinfield' in kwargs and 'joinsource' in kwargs):
raise ArcanaDesignError(
"Both joinsource and joinfield kwargs are required to "
"create a JoinNode (see {})".format(name,
self._error_msg_loc))
joinsource = kwargs['joinsource']
if joinsource in self.study.ITERFIELDS:
self._iterator_joins.add(joinsource)
node_cls = self.study.environment.node_types['join']
# Prepend name of pipeline of joinsource to match name of nodes
kwargs['joinsource'] = '{}_{}'.format(self.name, joinsource)
else:
node_cls = self.study.environment.node_types['base']
# Create node
node = node_cls(self.study.environment,
interface,
name="{}_{}".format(self._name, name),
requirements=requirements,
wall_time=wall_time,
annotations=annotations,
**kwargs)
# Ensure node is added to workflow
self._workflow.add_nodes([node])
# Connect inputs, outputs and internal connections
if inputs is not None:
assert isinstance(inputs, dict)
for node_input, connect_from in inputs.items():
if isinstance(connect_from[0], basestring):
input_spec, input_format = connect_from
self.connect_input(input_spec, node,
node_input, input_format)
else:
conn_node, conn_field = connect_from
self.connect(conn_node, conn_field, node, node_input)
if outputs is not None:
assert isinstance(outputs, dict)
for output_spec, (node_output, output_format) in outputs.items():
self.connect_output(output_spec, node, node_output,
output_format)
return node |
def create_DOM_node_from_dict(d, name, parent_node):
"""
Dumps dict data to an ``xml.etree.ElementTree.SubElement`` DOM subtree
object and attaches it to the specified DOM parent node. The created
subtree object is named after the specified name. If the supplied dict is
``None`` no DOM node is created for it as well as no DOM subnodes are
generated for eventual ``None`` values found inside the dict
:param d: the input dictionary
:type d: dict
:param name: the name for the DOM subtree to be created
:type name: str
:param parent_node: the parent DOM node the newly created subtree must be
attached to
:type parent_node: ``xml.etree.ElementTree.Element`` or derivative objects
:returns: ``xml.etree.ElementTree.SubElementTree`` object
"""
if d is not None:
root_dict_node = ET.SubElement(parent_node, name)
for key, value in d.items():
if value is not None:
node = ET.SubElement(root_dict_node, key)
node.text = str(value)
return root_dict_node | Dumps dict data to an ``xml.etree.ElementTree.SubElement`` DOM subtree
object and attaches it to the specified DOM parent node. The created
subtree object is named after the specified name. If the supplied dict is
``None`` no DOM node is created for it as well as no DOM subnodes are
generated for eventual ``None`` values found inside the dict
:param d: the input dictionary
:type d: dict
:param name: the name for the DOM subtree to be created
:type name: str
:param parent_node: the parent DOM node the newly created subtree must be
attached to
:type parent_node: ``xml.etree.ElementTree.Element`` or derivative objects
:returns: ``xml.etree.ElementTree.SubElementTree`` object | Below is the the instruction that describes the task:
### Input:
Dumps dict data to an ``xml.etree.ElementTree.SubElement`` DOM subtree
object and attaches it to the specified DOM parent node. The created
subtree object is named after the specified name. If the supplied dict is
``None`` no DOM node is created for it as well as no DOM subnodes are
generated for eventual ``None`` values found inside the dict
:param d: the input dictionary
:type d: dict
:param name: the name for the DOM subtree to be created
:type name: str
:param parent_node: the parent DOM node the newly created subtree must be
attached to
:type parent_node: ``xml.etree.ElementTree.Element`` or derivative objects
:returns: ``xml.etree.ElementTree.SubElementTree`` object
### Response:
def create_DOM_node_from_dict(d, name, parent_node):
"""
Dumps dict data to an ``xml.etree.ElementTree.SubElement`` DOM subtree
object and attaches it to the specified DOM parent node. The created
subtree object is named after the specified name. If the supplied dict is
``None`` no DOM node is created for it as well as no DOM subnodes are
generated for eventual ``None`` values found inside the dict
:param d: the input dictionary
:type d: dict
:param name: the name for the DOM subtree to be created
:type name: str
:param parent_node: the parent DOM node the newly created subtree must be
attached to
:type parent_node: ``xml.etree.ElementTree.Element`` or derivative objects
:returns: ``xml.etree.ElementTree.SubElementTree`` object
"""
if d is not None:
root_dict_node = ET.SubElement(parent_node, name)
for key, value in d.items():
if value is not None:
node = ET.SubElement(root_dict_node, key)
node.text = str(value)
return root_dict_node |
def script_template(state, host, template_filename, chdir=None, **data):
'''
Generate, upload and execute a local script template on the remote host.
+ template_filename: local script template filename
+ chdir: directory to cd into before executing the script
'''
temp_file = state.get_temp_filename(template_filename)
yield files.template(state, host, template_filename, temp_file, **data)
yield chmod(temp_file, '+x')
if chdir:
yield 'cd {0} && {1}'.format(chdir, temp_file)
else:
yield temp_file | Generate, upload and execute a local script template on the remote host.
+ template_filename: local script template filename
+ chdir: directory to cd into before executing the script | Below is the the instruction that describes the task:
### Input:
Generate, upload and execute a local script template on the remote host.
+ template_filename: local script template filename
+ chdir: directory to cd into before executing the script
### Response:
def script_template(state, host, template_filename, chdir=None, **data):
'''
Generate, upload and execute a local script template on the remote host.
+ template_filename: local script template filename
+ chdir: directory to cd into before executing the script
'''
temp_file = state.get_temp_filename(template_filename)
yield files.template(state, host, template_filename, temp_file, **data)
yield chmod(temp_file, '+x')
if chdir:
yield 'cd {0} && {1}'.format(chdir, temp_file)
else:
yield temp_file |
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