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def get_subject(self, msg):
"""Extracts the subject line from an EmailMessage object."""
text, encoding = decode_header(msg['subject'])[-1]
try:
text = text.decode(encoding)
# If it's already decoded, ignore error
except AttributeError:
pass
return text | Extracts the subject line from an EmailMessage object. | Below is the the instruction that describes the task:
### Input:
Extracts the subject line from an EmailMessage object.
### Response:
def get_subject(self, msg):
"""Extracts the subject line from an EmailMessage object."""
text, encoding = decode_header(msg['subject'])[-1]
try:
text = text.decode(encoding)
# If it's already decoded, ignore error
except AttributeError:
pass
return text |
def tradingStatus(symbol=None, token='', version=''):
'''The Trading status message is used to indicate the current trading status of a security.
For IEX-listed securities, IEX acts as the primary market and has the authority to institute a trading halt or trading pause in a security due to news dissemination or regulatory reasons.
For non-IEX-listed securities, IEX abides by any regulatory trading halts and trading pauses instituted by the primary or listing market, as applicable.
IEX disseminates a full pre-market spin of Trading status messages indicating the trading status of all securities.
In the spin, IEX will send out a Trading status message with βTβ (Trading) for all securities that are eligible for trading at the start of the Pre-Market Session.
If a security is absent from the dissemination, firms should assume that the security is being treated as operationally halted in the IEX Trading System.
After the pre-market spin, IEX will use the Trading status message to relay changes in trading status for an individual security. Messages will be sent when a security is:
Halted
Paused*
Released into an Order Acceptance Period*
Released for trading
*The paused and released into an Order Acceptance Period status will be disseminated for IEX-listed securities only. Trading pauses on non-IEX-listed securities will be treated simply as a halt.
https://iexcloud.io/docs/api/#deep-trading-status
Args:
symbol (string); Ticker to request
token (string); Access token
version (string); API version
Returns:
dict: result
'''
_raiseIfNotStr(symbol)
if symbol:
return _getJson('deep/trading-status?symbols=' + symbol, token, version)
return _getJson('deep/trading-status', token, version) | The Trading status message is used to indicate the current trading status of a security.
For IEX-listed securities, IEX acts as the primary market and has the authority to institute a trading halt or trading pause in a security due to news dissemination or regulatory reasons.
For non-IEX-listed securities, IEX abides by any regulatory trading halts and trading pauses instituted by the primary or listing market, as applicable.
IEX disseminates a full pre-market spin of Trading status messages indicating the trading status of all securities.
In the spin, IEX will send out a Trading status message with βTβ (Trading) for all securities that are eligible for trading at the start of the Pre-Market Session.
If a security is absent from the dissemination, firms should assume that the security is being treated as operationally halted in the IEX Trading System.
After the pre-market spin, IEX will use the Trading status message to relay changes in trading status for an individual security. Messages will be sent when a security is:
Halted
Paused*
Released into an Order Acceptance Period*
Released for trading
*The paused and released into an Order Acceptance Period status will be disseminated for IEX-listed securities only. Trading pauses on non-IEX-listed securities will be treated simply as a halt.
https://iexcloud.io/docs/api/#deep-trading-status
Args:
symbol (string); Ticker to request
token (string); Access token
version (string); API version
Returns:
dict: result | Below is the the instruction that describes the task:
### Input:
The Trading status message is used to indicate the current trading status of a security.
For IEX-listed securities, IEX acts as the primary market and has the authority to institute a trading halt or trading pause in a security due to news dissemination or regulatory reasons.
For non-IEX-listed securities, IEX abides by any regulatory trading halts and trading pauses instituted by the primary or listing market, as applicable.
IEX disseminates a full pre-market spin of Trading status messages indicating the trading status of all securities.
In the spin, IEX will send out a Trading status message with βTβ (Trading) for all securities that are eligible for trading at the start of the Pre-Market Session.
If a security is absent from the dissemination, firms should assume that the security is being treated as operationally halted in the IEX Trading System.
After the pre-market spin, IEX will use the Trading status message to relay changes in trading status for an individual security. Messages will be sent when a security is:
Halted
Paused*
Released into an Order Acceptance Period*
Released for trading
*The paused and released into an Order Acceptance Period status will be disseminated for IEX-listed securities only. Trading pauses on non-IEX-listed securities will be treated simply as a halt.
https://iexcloud.io/docs/api/#deep-trading-status
Args:
symbol (string); Ticker to request
token (string); Access token
version (string); API version
Returns:
dict: result
### Response:
def tradingStatus(symbol=None, token='', version=''):
'''The Trading status message is used to indicate the current trading status of a security.
For IEX-listed securities, IEX acts as the primary market and has the authority to institute a trading halt or trading pause in a security due to news dissemination or regulatory reasons.
For non-IEX-listed securities, IEX abides by any regulatory trading halts and trading pauses instituted by the primary or listing market, as applicable.
IEX disseminates a full pre-market spin of Trading status messages indicating the trading status of all securities.
In the spin, IEX will send out a Trading status message with βTβ (Trading) for all securities that are eligible for trading at the start of the Pre-Market Session.
If a security is absent from the dissemination, firms should assume that the security is being treated as operationally halted in the IEX Trading System.
After the pre-market spin, IEX will use the Trading status message to relay changes in trading status for an individual security. Messages will be sent when a security is:
Halted
Paused*
Released into an Order Acceptance Period*
Released for trading
*The paused and released into an Order Acceptance Period status will be disseminated for IEX-listed securities only. Trading pauses on non-IEX-listed securities will be treated simply as a halt.
https://iexcloud.io/docs/api/#deep-trading-status
Args:
symbol (string); Ticker to request
token (string); Access token
version (string); API version
Returns:
dict: result
'''
_raiseIfNotStr(symbol)
if symbol:
return _getJson('deep/trading-status?symbols=' + symbol, token, version)
return _getJson('deep/trading-status', token, version) |
def _get_param_names(self):
"""
Get mappable parameters from YAML.
"""
template = Template(self.yaml_string)
names = ['yaml_string'] # always include the template
for match in re.finditer(template.pattern, template.template):
name = match.group('named') or match.group('braced')
assert name is not None
names.append(name)
return names | Get mappable parameters from YAML. | Below is the the instruction that describes the task:
### Input:
Get mappable parameters from YAML.
### Response:
def _get_param_names(self):
"""
Get mappable parameters from YAML.
"""
template = Template(self.yaml_string)
names = ['yaml_string'] # always include the template
for match in re.finditer(template.pattern, template.template):
name = match.group('named') or match.group('braced')
assert name is not None
names.append(name)
return names |
def acquire_hosting_device_slots(self, context, hosting_device, resource,
resource_type, resource_service, num,
exclusive=False):
"""Assign <num> slots in <hosting_device> to logical <resource>.
If exclusive is True the hosting device is bound to the resource's
tenant. Otherwise it is not bound to any tenant.
Returns True if allocation was granted, False otherwise.
"""
bound = hosting_device['tenant_bound']
if ((bound is not None and bound != resource['tenant_id']) or
(exclusive and not self._exclusively_used(context, hosting_device,
resource['tenant_id']))):
LOG.debug(
'Rejecting allocation of %(num)d slots in tenant %(bound)s '
'hosting device %(device)s to logical resource %(r_id)s due '
'to exclusive use conflict.',
{'num': num,
'bound': 'unbound' if bound is None else bound + ' bound',
'device': hosting_device['id'], 'r_id': resource['id']})
return False
with context.session.begin(subtransactions=True):
res_info = {'resource': resource, 'type': resource_type,
'service': resource_service}
slot_info, query = self._get_or_create_slot_allocation(
context, hosting_device, res_info)
if slot_info is None:
LOG.debug('Rejecting allocation of %(num)d slots in hosting '
'device %(device)s to logical resource %(r_id)s',
{'num': num, 'device': hosting_device['id'],
'r_id': resource['id']})
return False
new_allocation = num + slot_info.num_allocated
if hosting_device['template']['slot_capacity'] < new_allocation:
LOG.debug('Rejecting allocation of %(num)d slots in '
'hosting device %(device)s to logical resource '
'%(r_id)s due to insufficent slot availability.',
{'num': num, 'device': hosting_device['id'],
'r_id': resource['id']})
self._dispatch_pool_maintenance_job(hosting_device['template'])
return False
# handle any changes to exclusive usage by tenant
if exclusive and bound is None:
self._update_hosting_device_exclusivity(
context, hosting_device, resource['tenant_id'])
bound = resource['tenant_id']
elif not exclusive and bound is not None:
self._update_hosting_device_exclusivity(context,
hosting_device, None)
bound = None
slot_info.num_allocated = new_allocation
context.session.add(slot_info)
self._dispatch_pool_maintenance_job(hosting_device['template'])
# report success
LOG.info('Allocated %(num)d additional slots in tenant %(bound)s'
'bound hosting device %(hd_id)s. In total %(total)d '
'slots are now allocated in that hosting device for '
'logical resource %(r_id)s.',
{'num': num, 'bound': 'un-' if bound is None else bound + ' ',
'total': new_allocation, 'hd_id': hosting_device['id'],
'r_id': resource['id']})
return True | Assign <num> slots in <hosting_device> to logical <resource>.
If exclusive is True the hosting device is bound to the resource's
tenant. Otherwise it is not bound to any tenant.
Returns True if allocation was granted, False otherwise. | Below is the the instruction that describes the task:
### Input:
Assign <num> slots in <hosting_device> to logical <resource>.
If exclusive is True the hosting device is bound to the resource's
tenant. Otherwise it is not bound to any tenant.
Returns True if allocation was granted, False otherwise.
### Response:
def acquire_hosting_device_slots(self, context, hosting_device, resource,
resource_type, resource_service, num,
exclusive=False):
"""Assign <num> slots in <hosting_device> to logical <resource>.
If exclusive is True the hosting device is bound to the resource's
tenant. Otherwise it is not bound to any tenant.
Returns True if allocation was granted, False otherwise.
"""
bound = hosting_device['tenant_bound']
if ((bound is not None and bound != resource['tenant_id']) or
(exclusive and not self._exclusively_used(context, hosting_device,
resource['tenant_id']))):
LOG.debug(
'Rejecting allocation of %(num)d slots in tenant %(bound)s '
'hosting device %(device)s to logical resource %(r_id)s due '
'to exclusive use conflict.',
{'num': num,
'bound': 'unbound' if bound is None else bound + ' bound',
'device': hosting_device['id'], 'r_id': resource['id']})
return False
with context.session.begin(subtransactions=True):
res_info = {'resource': resource, 'type': resource_type,
'service': resource_service}
slot_info, query = self._get_or_create_slot_allocation(
context, hosting_device, res_info)
if slot_info is None:
LOG.debug('Rejecting allocation of %(num)d slots in hosting '
'device %(device)s to logical resource %(r_id)s',
{'num': num, 'device': hosting_device['id'],
'r_id': resource['id']})
return False
new_allocation = num + slot_info.num_allocated
if hosting_device['template']['slot_capacity'] < new_allocation:
LOG.debug('Rejecting allocation of %(num)d slots in '
'hosting device %(device)s to logical resource '
'%(r_id)s due to insufficent slot availability.',
{'num': num, 'device': hosting_device['id'],
'r_id': resource['id']})
self._dispatch_pool_maintenance_job(hosting_device['template'])
return False
# handle any changes to exclusive usage by tenant
if exclusive and bound is None:
self._update_hosting_device_exclusivity(
context, hosting_device, resource['tenant_id'])
bound = resource['tenant_id']
elif not exclusive and bound is not None:
self._update_hosting_device_exclusivity(context,
hosting_device, None)
bound = None
slot_info.num_allocated = new_allocation
context.session.add(slot_info)
self._dispatch_pool_maintenance_job(hosting_device['template'])
# report success
LOG.info('Allocated %(num)d additional slots in tenant %(bound)s'
'bound hosting device %(hd_id)s. In total %(total)d '
'slots are now allocated in that hosting device for '
'logical resource %(r_id)s.',
{'num': num, 'bound': 'un-' if bound is None else bound + ' ',
'total': new_allocation, 'hd_id': hosting_device['id'],
'r_id': resource['id']})
return True |
def compact(self, term_doc_matrix):
'''
Parameters
----------
term_doc_matrix : TermDocMatrix
Term document matrix object to compact
Returns
-------
TermDocMatrix
'''
rank_df = self.scorer.get_rank_df(term_doc_matrix)
return self._prune_higher_ranked_terms(term_doc_matrix, rank_df, self.rank) | Parameters
----------
term_doc_matrix : TermDocMatrix
Term document matrix object to compact
Returns
-------
TermDocMatrix | Below is the the instruction that describes the task:
### Input:
Parameters
----------
term_doc_matrix : TermDocMatrix
Term document matrix object to compact
Returns
-------
TermDocMatrix
### Response:
def compact(self, term_doc_matrix):
'''
Parameters
----------
term_doc_matrix : TermDocMatrix
Term document matrix object to compact
Returns
-------
TermDocMatrix
'''
rank_df = self.scorer.get_rank_df(term_doc_matrix)
return self._prune_higher_ranked_terms(term_doc_matrix, rank_df, self.rank) |
def batch_retrieve_overrides_in_course(self, course_id, assignment_overrides_id, assignment_overrides_assignment_id):
"""
Batch retrieve overrides in a course.
Returns a list of specified overrides in this course, providing
they target sections/groups/students visible to the current user.
Returns null elements in the list for requests that were not found.
"""
path = {}
data = {}
params = {}
# REQUIRED - PATH - course_id
"""ID"""
path["course_id"] = course_id
# REQUIRED - assignment_overrides[id]
"""Ids of overrides to retrieve"""
params["assignment_overrides[id]"] = assignment_overrides_id
# REQUIRED - assignment_overrides[assignment_id]
"""Ids of assignments for each override"""
params["assignment_overrides[assignment_id]"] = assignment_overrides_assignment_id
self.logger.debug("GET /api/v1/courses/{course_id}/assignments/overrides with query params: {params} and form data: {data}".format(params=params, data=data, **path))
return self.generic_request("GET", "/api/v1/courses/{course_id}/assignments/overrides".format(**path), data=data, params=params, all_pages=True) | Batch retrieve overrides in a course.
Returns a list of specified overrides in this course, providing
they target sections/groups/students visible to the current user.
Returns null elements in the list for requests that were not found. | Below is the the instruction that describes the task:
### Input:
Batch retrieve overrides in a course.
Returns a list of specified overrides in this course, providing
they target sections/groups/students visible to the current user.
Returns null elements in the list for requests that were not found.
### Response:
def batch_retrieve_overrides_in_course(self, course_id, assignment_overrides_id, assignment_overrides_assignment_id):
"""
Batch retrieve overrides in a course.
Returns a list of specified overrides in this course, providing
they target sections/groups/students visible to the current user.
Returns null elements in the list for requests that were not found.
"""
path = {}
data = {}
params = {}
# REQUIRED - PATH - course_id
"""ID"""
path["course_id"] = course_id
# REQUIRED - assignment_overrides[id]
"""Ids of overrides to retrieve"""
params["assignment_overrides[id]"] = assignment_overrides_id
# REQUIRED - assignment_overrides[assignment_id]
"""Ids of assignments for each override"""
params["assignment_overrides[assignment_id]"] = assignment_overrides_assignment_id
self.logger.debug("GET /api/v1/courses/{course_id}/assignments/overrides with query params: {params} and form data: {data}".format(params=params, data=data, **path))
return self.generic_request("GET", "/api/v1/courses/{course_id}/assignments/overrides".format(**path), data=data, params=params, all_pages=True) |
def _set_predictor(self, predictor):
"""Set predictor for continued training.
It is not recommended for user to call this function.
Please use init_model argument in engine.train() or engine.cv() instead.
"""
if predictor is self._predictor:
return self
if self.data is not None:
self._predictor = predictor
return self._free_handle()
else:
raise LightGBMError("Cannot set predictor after freed raw data, "
"set free_raw_data=False when construct Dataset to avoid this.") | Set predictor for continued training.
It is not recommended for user to call this function.
Please use init_model argument in engine.train() or engine.cv() instead. | Below is the the instruction that describes the task:
### Input:
Set predictor for continued training.
It is not recommended for user to call this function.
Please use init_model argument in engine.train() or engine.cv() instead.
### Response:
def _set_predictor(self, predictor):
"""Set predictor for continued training.
It is not recommended for user to call this function.
Please use init_model argument in engine.train() or engine.cv() instead.
"""
if predictor is self._predictor:
return self
if self.data is not None:
self._predictor = predictor
return self._free_handle()
else:
raise LightGBMError("Cannot set predictor after freed raw data, "
"set free_raw_data=False when construct Dataset to avoid this.") |
def split(self, bits_count):
""" Split array into smaller parts. Each small array is fixed-length WBinArray (length of that array is
bits_count).
:param bits_count: array length
:return: list of WBinArray
"""
result = []
array = WBinArray(self.__value, self.__size)
if (len(array) % bits_count) > 0:
array.resize(len(array) + (bits_count - (len(array) % bits_count)))
while len(array):
result.append(WBinArray(array[:bits_count], bits_count))
array = array[bits_count:]
return result | Split array into smaller parts. Each small array is fixed-length WBinArray (length of that array is
bits_count).
:param bits_count: array length
:return: list of WBinArray | Below is the the instruction that describes the task:
### Input:
Split array into smaller parts. Each small array is fixed-length WBinArray (length of that array is
bits_count).
:param bits_count: array length
:return: list of WBinArray
### Response:
def split(self, bits_count):
""" Split array into smaller parts. Each small array is fixed-length WBinArray (length of that array is
bits_count).
:param bits_count: array length
:return: list of WBinArray
"""
result = []
array = WBinArray(self.__value, self.__size)
if (len(array) % bits_count) > 0:
array.resize(len(array) + (bits_count - (len(array) % bits_count)))
while len(array):
result.append(WBinArray(array[:bits_count], bits_count))
array = array[bits_count:]
return result |
def check_differences(self):
""" In-depth check of mail differences.
Compare all mails of the duplicate set with each other, both in size
and content. Raise an error if we're not within the limits imposed by
the threshold setting.
"""
logger.info("Check that mail differences are within the limits.")
if self.conf.size_threshold < 0:
logger.info("Skip checking for size differences.")
if self.conf.content_threshold < 0:
logger.info("Skip checking for content differences.")
if self.conf.size_threshold < 0 and self.conf.content_threshold < 0:
return
# Compute differences of mail against one another.
for mail_a, mail_b in combinations(self.pool, 2):
# Compare mails on size.
if self.conf.size_threshold > -1:
size_difference = abs(mail_a.size - mail_b.size)
logger.debug("{} and {} differs by {} bytes in size.".format(
mail_a, mail_b, size_difference))
if size_difference > self.conf.size_threshold:
raise SizeDiffAboveThreshold
# Compare mails on content.
if self.conf.content_threshold > -1:
content_difference = self.diff(mail_a, mail_b)
logger.debug(
"{} and {} differs by {} bytes in content.".format(
mail_a, mail_b, content_difference))
if content_difference > self.conf.content_threshold:
if self.conf.show_diff:
logger.info(self.pretty_diff(mail_a, mail_b))
raise ContentDiffAboveThreshold | In-depth check of mail differences.
Compare all mails of the duplicate set with each other, both in size
and content. Raise an error if we're not within the limits imposed by
the threshold setting. | Below is the the instruction that describes the task:
### Input:
In-depth check of mail differences.
Compare all mails of the duplicate set with each other, both in size
and content. Raise an error if we're not within the limits imposed by
the threshold setting.
### Response:
def check_differences(self):
""" In-depth check of mail differences.
Compare all mails of the duplicate set with each other, both in size
and content. Raise an error if we're not within the limits imposed by
the threshold setting.
"""
logger.info("Check that mail differences are within the limits.")
if self.conf.size_threshold < 0:
logger.info("Skip checking for size differences.")
if self.conf.content_threshold < 0:
logger.info("Skip checking for content differences.")
if self.conf.size_threshold < 0 and self.conf.content_threshold < 0:
return
# Compute differences of mail against one another.
for mail_a, mail_b in combinations(self.pool, 2):
# Compare mails on size.
if self.conf.size_threshold > -1:
size_difference = abs(mail_a.size - mail_b.size)
logger.debug("{} and {} differs by {} bytes in size.".format(
mail_a, mail_b, size_difference))
if size_difference > self.conf.size_threshold:
raise SizeDiffAboveThreshold
# Compare mails on content.
if self.conf.content_threshold > -1:
content_difference = self.diff(mail_a, mail_b)
logger.debug(
"{} and {} differs by {} bytes in content.".format(
mail_a, mail_b, content_difference))
if content_difference > self.conf.content_threshold:
if self.conf.show_diff:
logger.info(self.pretty_diff(mail_a, mail_b))
raise ContentDiffAboveThreshold |
def avgwave(self, wavelengths=None):
"""Calculate the :ref:`average wavelength <synphot-formula-avgwv>`.
Parameters
----------
wavelengths : array-like, `~astropy.units.quantity.Quantity`, or `None`
Wavelength values for sampling.
If not a Quantity, assumed to be in Angstrom.
If `None`, `waveset` is used.
Returns
-------
avg_wave : `~astropy.units.quantity.Quantity`
Average wavelength.
"""
x = self._validate_wavelengths(wavelengths).value
y = self(x).value
num = np.trapz(y * x, x=x)
den = np.trapz(y, x=x)
if den == 0: # pragma: no cover
avg_wave = 0.0
else:
avg_wave = abs(num / den)
return avg_wave * self._internal_wave_unit | Calculate the :ref:`average wavelength <synphot-formula-avgwv>`.
Parameters
----------
wavelengths : array-like, `~astropy.units.quantity.Quantity`, or `None`
Wavelength values for sampling.
If not a Quantity, assumed to be in Angstrom.
If `None`, `waveset` is used.
Returns
-------
avg_wave : `~astropy.units.quantity.Quantity`
Average wavelength. | Below is the the instruction that describes the task:
### Input:
Calculate the :ref:`average wavelength <synphot-formula-avgwv>`.
Parameters
----------
wavelengths : array-like, `~astropy.units.quantity.Quantity`, or `None`
Wavelength values for sampling.
If not a Quantity, assumed to be in Angstrom.
If `None`, `waveset` is used.
Returns
-------
avg_wave : `~astropy.units.quantity.Quantity`
Average wavelength.
### Response:
def avgwave(self, wavelengths=None):
"""Calculate the :ref:`average wavelength <synphot-formula-avgwv>`.
Parameters
----------
wavelengths : array-like, `~astropy.units.quantity.Quantity`, or `None`
Wavelength values for sampling.
If not a Quantity, assumed to be in Angstrom.
If `None`, `waveset` is used.
Returns
-------
avg_wave : `~astropy.units.quantity.Quantity`
Average wavelength.
"""
x = self._validate_wavelengths(wavelengths).value
y = self(x).value
num = np.trapz(y * x, x=x)
den = np.trapz(y, x=x)
if den == 0: # pragma: no cover
avg_wave = 0.0
else:
avg_wave = abs(num / den)
return avg_wave * self._internal_wave_unit |
def show_correlation_matrix(self, correlation_matrix):
"""Shows the given correlation matrix as image
:param correlation_matrix: Correlation matrix of features
"""
cr_plot.create_correlation_matrix_plot(
correlation_matrix, self.title, self.headers_to_test
)
pyplot.show() | Shows the given correlation matrix as image
:param correlation_matrix: Correlation matrix of features | Below is the the instruction that describes the task:
### Input:
Shows the given correlation matrix as image
:param correlation_matrix: Correlation matrix of features
### Response:
def show_correlation_matrix(self, correlation_matrix):
"""Shows the given correlation matrix as image
:param correlation_matrix: Correlation matrix of features
"""
cr_plot.create_correlation_matrix_plot(
correlation_matrix, self.title, self.headers_to_test
)
pyplot.show() |
def get_parent_of_type(typ, obj):
"""
Finds first object up the parent chain of the given type.
If no parent of the given type exists None is returned.
Args:
typ(str or python class): The type of the model object we are
looking for.
obj (model object): Python model object which is the start of the
search process.
"""
if type(typ) is not text:
typ = typ.__name__
while hasattr(obj, 'parent'):
obj = obj.parent
if obj.__class__.__name__ == typ:
return obj | Finds first object up the parent chain of the given type.
If no parent of the given type exists None is returned.
Args:
typ(str or python class): The type of the model object we are
looking for.
obj (model object): Python model object which is the start of the
search process. | Below is the the instruction that describes the task:
### Input:
Finds first object up the parent chain of the given type.
If no parent of the given type exists None is returned.
Args:
typ(str or python class): The type of the model object we are
looking for.
obj (model object): Python model object which is the start of the
search process.
### Response:
def get_parent_of_type(typ, obj):
"""
Finds first object up the parent chain of the given type.
If no parent of the given type exists None is returned.
Args:
typ(str or python class): The type of the model object we are
looking for.
obj (model object): Python model object which is the start of the
search process.
"""
if type(typ) is not text:
typ = typ.__name__
while hasattr(obj, 'parent'):
obj = obj.parent
if obj.__class__.__name__ == typ:
return obj |
def launch_job(self, job_id):
"""
Convenience method for launching a job. We use POST for actions
outside of HTTP verbs (job launch in this case).
"""
assert self.api_version.lower() in ['0.01a', '0.1'], \
'This method is only supported in BETA (0.01) and ALPHA (0.01a) versions'
try:
self.create_job(job_id, {'submit_job_action': True})
except ValueError:
pass
return self.read_job(job_id) | Convenience method for launching a job. We use POST for actions
outside of HTTP verbs (job launch in this case). | Below is the the instruction that describes the task:
### Input:
Convenience method for launching a job. We use POST for actions
outside of HTTP verbs (job launch in this case).
### Response:
def launch_job(self, job_id):
"""
Convenience method for launching a job. We use POST for actions
outside of HTTP verbs (job launch in this case).
"""
assert self.api_version.lower() in ['0.01a', '0.1'], \
'This method is only supported in BETA (0.01) and ALPHA (0.01a) versions'
try:
self.create_job(job_id, {'submit_job_action': True})
except ValueError:
pass
return self.read_job(job_id) |
def printed_out(self, name):
"""
Create a string representation of the action
"""
opt = self.variables().optional_namestring()
req = self.variables().required_namestring()
out = ''
out += '| |\n'
out += '| |---{}({}{})\n'.format(name, req, opt)
if self.description:
out += '| | {}\n'.format(self.description)
return out | Create a string representation of the action | Below is the the instruction that describes the task:
### Input:
Create a string representation of the action
### Response:
def printed_out(self, name):
"""
Create a string representation of the action
"""
opt = self.variables().optional_namestring()
req = self.variables().required_namestring()
out = ''
out += '| |\n'
out += '| |---{}({}{})\n'.format(name, req, opt)
if self.description:
out += '| | {}\n'.format(self.description)
return out |
def send_short_lpp_packet(self, dest_id, data):
"""
Send ultra-wide-band LPP packet to dest_id
"""
pk = CRTPPacket()
pk.port = CRTPPort.LOCALIZATION
pk.channel = self.GENERIC_CH
pk.data = struct.pack('<BB', self.LPS_SHORT_LPP_PACKET, dest_id) + data
self._cf.send_packet(pk) | Send ultra-wide-band LPP packet to dest_id | Below is the the instruction that describes the task:
### Input:
Send ultra-wide-band LPP packet to dest_id
### Response:
def send_short_lpp_packet(self, dest_id, data):
"""
Send ultra-wide-band LPP packet to dest_id
"""
pk = CRTPPacket()
pk.port = CRTPPort.LOCALIZATION
pk.channel = self.GENERIC_CH
pk.data = struct.pack('<BB', self.LPS_SHORT_LPP_PACKET, dest_id) + data
self._cf.send_packet(pk) |
def network_delete_event(self, network_info):
"""Process network delete event."""
net_id = network_info['network_id']
if net_id not in self.network:
LOG.error('network_delete_event: net_id %s does not exist.',
net_id)
return
segid = self.network[net_id].get('segmentation_id')
tenant_id = self.network[net_id].get('tenant_id')
tenant_name = self.get_project_name(tenant_id)
net = utils.Dict2Obj(self.network[net_id])
if not tenant_name:
LOG.error('Project %(tenant_id)s does not exist.',
{'tenant_id': tenant_id})
self.update_network_db(net.id, constants.DELETE_FAIL)
return
try:
self.dcnm_client.delete_network(tenant_name, net)
# Put back the segmentation id into the pool.
self.seg_drvr.release_segmentation_id(segid)
# Remove entry from database and cache.
self.delete_network_db(net_id)
del self.network[net_id]
snets = [k for k in self.subnet if (
self.subnet[k].get('network_id') == net_id)]
[self.subnet.pop(s) for s in snets]
except dexc.DfaClientRequestFailed:
LOG.error('Failed to create network %(net)s.',
{'net': net.name})
self.update_network_db(net_id, constants.DELETE_FAIL)
# deleting all related VMs
instances = self.get_vms()
instances_related = [k for k in instances if k.network_id == net_id]
for vm in instances_related:
LOG.debug("deleting vm %s because network is deleted", vm.name)
self.delete_vm_function(vm.port_id, vm)
self.network_del_notif(tenant_id, tenant_name, net_id) | Process network delete event. | Below is the the instruction that describes the task:
### Input:
Process network delete event.
### Response:
def network_delete_event(self, network_info):
"""Process network delete event."""
net_id = network_info['network_id']
if net_id not in self.network:
LOG.error('network_delete_event: net_id %s does not exist.',
net_id)
return
segid = self.network[net_id].get('segmentation_id')
tenant_id = self.network[net_id].get('tenant_id')
tenant_name = self.get_project_name(tenant_id)
net = utils.Dict2Obj(self.network[net_id])
if not tenant_name:
LOG.error('Project %(tenant_id)s does not exist.',
{'tenant_id': tenant_id})
self.update_network_db(net.id, constants.DELETE_FAIL)
return
try:
self.dcnm_client.delete_network(tenant_name, net)
# Put back the segmentation id into the pool.
self.seg_drvr.release_segmentation_id(segid)
# Remove entry from database and cache.
self.delete_network_db(net_id)
del self.network[net_id]
snets = [k for k in self.subnet if (
self.subnet[k].get('network_id') == net_id)]
[self.subnet.pop(s) for s in snets]
except dexc.DfaClientRequestFailed:
LOG.error('Failed to create network %(net)s.',
{'net': net.name})
self.update_network_db(net_id, constants.DELETE_FAIL)
# deleting all related VMs
instances = self.get_vms()
instances_related = [k for k in instances if k.network_id == net_id]
for vm in instances_related:
LOG.debug("deleting vm %s because network is deleted", vm.name)
self.delete_vm_function(vm.port_id, vm)
self.network_del_notif(tenant_id, tenant_name, net_id) |
def create_backed_vol(self, name, backer, _format='qcow2'):
"""
TODO(rdelinger) think about changing _format
This is a pretty specialized function.
It takes an existing volume, and creates a new volume
that is backed by the existing volume
Sadly there is no easy way to do this in libvirt, the
best way I've found is to just create some xml and use the createXML
function
"""
vol_xml = ElementTree.Element('volume')
vol_name = ElementTree.SubElement(vol_xml, 'name')
name = '{0}.{1}'.format(name, _format)
vol_name.text = name
target = ElementTree.SubElement(vol_xml, 'target')
target_format = ElementTree.SubElement(target, 'format')
target_format.set('type', _format)
vol_cap = ElementTree.SubElement(vol_xml, 'capacity')
vol_cap.set('unit', 'bytes') # @TODO(rdelinger) this should be dynamic
vol_cap.text = backer.capacity
backing_store = ElementTree.SubElement(vol_xml, 'backingStore')
bs_path = ElementTree.SubElement(backing_store, 'path')
bs_path.text = backer.path
bs_format = ElementTree.SubElement(backing_store, 'format')
bs_format.set('type', backer.format)
XMLString = ElementTree.tostring(vol_xml)
self.virsp.createXML(XMLString, 0)
return self.find_volume(name) | TODO(rdelinger) think about changing _format
This is a pretty specialized function.
It takes an existing volume, and creates a new volume
that is backed by the existing volume
Sadly there is no easy way to do this in libvirt, the
best way I've found is to just create some xml and use the createXML
function | Below is the the instruction that describes the task:
### Input:
TODO(rdelinger) think about changing _format
This is a pretty specialized function.
It takes an existing volume, and creates a new volume
that is backed by the existing volume
Sadly there is no easy way to do this in libvirt, the
best way I've found is to just create some xml and use the createXML
function
### Response:
def create_backed_vol(self, name, backer, _format='qcow2'):
"""
TODO(rdelinger) think about changing _format
This is a pretty specialized function.
It takes an existing volume, and creates a new volume
that is backed by the existing volume
Sadly there is no easy way to do this in libvirt, the
best way I've found is to just create some xml and use the createXML
function
"""
vol_xml = ElementTree.Element('volume')
vol_name = ElementTree.SubElement(vol_xml, 'name')
name = '{0}.{1}'.format(name, _format)
vol_name.text = name
target = ElementTree.SubElement(vol_xml, 'target')
target_format = ElementTree.SubElement(target, 'format')
target_format.set('type', _format)
vol_cap = ElementTree.SubElement(vol_xml, 'capacity')
vol_cap.set('unit', 'bytes') # @TODO(rdelinger) this should be dynamic
vol_cap.text = backer.capacity
backing_store = ElementTree.SubElement(vol_xml, 'backingStore')
bs_path = ElementTree.SubElement(backing_store, 'path')
bs_path.text = backer.path
bs_format = ElementTree.SubElement(backing_store, 'format')
bs_format.set('type', backer.format)
XMLString = ElementTree.tostring(vol_xml)
self.virsp.createXML(XMLString, 0)
return self.find_volume(name) |
def get_context_data(self, **kwargs):
""" Returns the context data to provide to the template. """
context = super().get_context_data(**kwargs)
# Insert the considered topic and the associated forum into the context
topic = self.get_topic()
context['topic'] = topic
context['forum'] = topic.forum
# Handles the case when a poll is associated to the topic
try:
if hasattr(topic, 'poll') and topic.poll.options.exists():
context['poll'] = topic.poll
context['poll_form'] = self.poll_form_class(poll=topic.poll)
context['view_results_action'] = self.request.GET.get('view_results', None)
context['change_vote_action'] = self.request.GET.get('change_vote', None)
except ObjectDoesNotExist: # pragma: no cover
pass
return context | Returns the context data to provide to the template. | Below is the the instruction that describes the task:
### Input:
Returns the context data to provide to the template.
### Response:
def get_context_data(self, **kwargs):
""" Returns the context data to provide to the template. """
context = super().get_context_data(**kwargs)
# Insert the considered topic and the associated forum into the context
topic = self.get_topic()
context['topic'] = topic
context['forum'] = topic.forum
# Handles the case when a poll is associated to the topic
try:
if hasattr(topic, 'poll') and topic.poll.options.exists():
context['poll'] = topic.poll
context['poll_form'] = self.poll_form_class(poll=topic.poll)
context['view_results_action'] = self.request.GET.get('view_results', None)
context['change_vote_action'] = self.request.GET.get('change_vote', None)
except ObjectDoesNotExist: # pragma: no cover
pass
return context |
def serverdir():
""" Get the location of the server subpackage
"""
path = join(ROOT_DIR, 'server')
path = normpath(path)
if sys.platform == 'cygwin': path = realpath(path)
return path | Get the location of the server subpackage | Below is the the instruction that describes the task:
### Input:
Get the location of the server subpackage
### Response:
def serverdir():
""" Get the location of the server subpackage
"""
path = join(ROOT_DIR, 'server')
path = normpath(path)
if sys.platform == 'cygwin': path = realpath(path)
return path |
def binarize_signal(signal, treshold="auto", cut="higher"):
"""
Binarize a channel based on a continuous channel.
Parameters
----------
signal = array or list
The signal channel.
treshold = float
The treshold value by which to select the events. If "auto", takes the value between the max and the min.
cut = str
"higher" or "lower", define the events as above or under the treshold. For photosensors, a white screen corresponds usually to higher values. Therefore, if your events were signalled by a black colour, events values would be the lower ones, and you should set the cut to "lower".
Returns
----------
list
binary_signal
Example
----------
>>> import neurokit as nk
>>> binary_signal = nk.binarize_signal(signal, treshold=4)
Authors
----------
- `Dominique Makowski <https://dominiquemakowski.github.io/>`_
Dependencies
----------
None
"""
if treshold == "auto":
treshold = (np.max(np.array(signal)) - np.min(np.array(signal)))/2
signal = list(signal)
binary_signal = []
for i in range(len(signal)):
if cut == "higher":
if signal[i] > treshold:
binary_signal.append(1)
else:
binary_signal.append(0)
else:
if signal[i] < treshold:
binary_signal.append(1)
else:
binary_signal.append(0)
return(binary_signal) | Binarize a channel based on a continuous channel.
Parameters
----------
signal = array or list
The signal channel.
treshold = float
The treshold value by which to select the events. If "auto", takes the value between the max and the min.
cut = str
"higher" or "lower", define the events as above or under the treshold. For photosensors, a white screen corresponds usually to higher values. Therefore, if your events were signalled by a black colour, events values would be the lower ones, and you should set the cut to "lower".
Returns
----------
list
binary_signal
Example
----------
>>> import neurokit as nk
>>> binary_signal = nk.binarize_signal(signal, treshold=4)
Authors
----------
- `Dominique Makowski <https://dominiquemakowski.github.io/>`_
Dependencies
----------
None | Below is the the instruction that describes the task:
### Input:
Binarize a channel based on a continuous channel.
Parameters
----------
signal = array or list
The signal channel.
treshold = float
The treshold value by which to select the events. If "auto", takes the value between the max and the min.
cut = str
"higher" or "lower", define the events as above or under the treshold. For photosensors, a white screen corresponds usually to higher values. Therefore, if your events were signalled by a black colour, events values would be the lower ones, and you should set the cut to "lower".
Returns
----------
list
binary_signal
Example
----------
>>> import neurokit as nk
>>> binary_signal = nk.binarize_signal(signal, treshold=4)
Authors
----------
- `Dominique Makowski <https://dominiquemakowski.github.io/>`_
Dependencies
----------
None
### Response:
def binarize_signal(signal, treshold="auto", cut="higher"):
"""
Binarize a channel based on a continuous channel.
Parameters
----------
signal = array or list
The signal channel.
treshold = float
The treshold value by which to select the events. If "auto", takes the value between the max and the min.
cut = str
"higher" or "lower", define the events as above or under the treshold. For photosensors, a white screen corresponds usually to higher values. Therefore, if your events were signalled by a black colour, events values would be the lower ones, and you should set the cut to "lower".
Returns
----------
list
binary_signal
Example
----------
>>> import neurokit as nk
>>> binary_signal = nk.binarize_signal(signal, treshold=4)
Authors
----------
- `Dominique Makowski <https://dominiquemakowski.github.io/>`_
Dependencies
----------
None
"""
if treshold == "auto":
treshold = (np.max(np.array(signal)) - np.min(np.array(signal)))/2
signal = list(signal)
binary_signal = []
for i in range(len(signal)):
if cut == "higher":
if signal[i] > treshold:
binary_signal.append(1)
else:
binary_signal.append(0)
else:
if signal[i] < treshold:
binary_signal.append(1)
else:
binary_signal.append(0)
return(binary_signal) |
def permutation_entropy(time_series, order=3, delay=1, normalize=False):
"""Permutation Entropy.
Parameters
----------
time_series : list or np.array
Time series
order : int
Order of permutation entropy
delay : int
Time delay
normalize : bool
If True, divide by log2(factorial(m)) to normalize the entropy
between 0 and 1. Otherwise, return the permutation entropy in bit.
Returns
-------
pe : float
Permutation Entropy
References
----------
.. [1] Massimiliano Zanin et al. Permutation Entropy and Its Main
Biomedical and Econophysics Applications: A Review.
http://www.mdpi.com/1099-4300/14/8/1553/pdf
.. [2] Christoph Bandt and Bernd Pompe. Permutation entropy β a natural
complexity measure for time series.
http://stubber.math-inf.uni-greifswald.de/pub/full/prep/2001/11.pdf
Notes
-----
Last updated (Oct 2018) by Raphael Vallat ([email protected]):
- Major speed improvements
- Use of base 2 instead of base e
- Added normalization
Examples
--------
1. Permutation entropy with order 2
>>> x = [4, 7, 9, 10, 6, 11, 3]
>>> # Return a value between 0 and log2(factorial(order))
>>> print(permutation_entropy(x, order=2))
0.918
2. Normalized permutation entropy with order 3
>>> x = [4, 7, 9, 10, 6, 11, 3]
>>> # Return a value comprised between 0 and 1.
>>> print(permutation_entropy(x, order=3, normalize=True))
0.589
"""
x = np.array(time_series)
hashmult = np.power(order, np.arange(order))
# Embed x and sort the order of permutations
sorted_idx = _embed(x, order=order, delay=delay).argsort(kind='quicksort')
# Associate unique integer to each permutations
hashval = (np.multiply(sorted_idx, hashmult)).sum(1)
# Return the counts
_, c = np.unique(hashval, return_counts=True)
# Use np.true_divide for Python 2 compatibility
p = np.true_divide(c, c.sum())
pe = -np.multiply(p, np.log2(p)).sum()
if normalize:
pe /= np.log2(factorial(order))
return pe | Permutation Entropy.
Parameters
----------
time_series : list or np.array
Time series
order : int
Order of permutation entropy
delay : int
Time delay
normalize : bool
If True, divide by log2(factorial(m)) to normalize the entropy
between 0 and 1. Otherwise, return the permutation entropy in bit.
Returns
-------
pe : float
Permutation Entropy
References
----------
.. [1] Massimiliano Zanin et al. Permutation Entropy and Its Main
Biomedical and Econophysics Applications: A Review.
http://www.mdpi.com/1099-4300/14/8/1553/pdf
.. [2] Christoph Bandt and Bernd Pompe. Permutation entropy β a natural
complexity measure for time series.
http://stubber.math-inf.uni-greifswald.de/pub/full/prep/2001/11.pdf
Notes
-----
Last updated (Oct 2018) by Raphael Vallat ([email protected]):
- Major speed improvements
- Use of base 2 instead of base e
- Added normalization
Examples
--------
1. Permutation entropy with order 2
>>> x = [4, 7, 9, 10, 6, 11, 3]
>>> # Return a value between 0 and log2(factorial(order))
>>> print(permutation_entropy(x, order=2))
0.918
2. Normalized permutation entropy with order 3
>>> x = [4, 7, 9, 10, 6, 11, 3]
>>> # Return a value comprised between 0 and 1.
>>> print(permutation_entropy(x, order=3, normalize=True))
0.589 | Below is the the instruction that describes the task:
### Input:
Permutation Entropy.
Parameters
----------
time_series : list or np.array
Time series
order : int
Order of permutation entropy
delay : int
Time delay
normalize : bool
If True, divide by log2(factorial(m)) to normalize the entropy
between 0 and 1. Otherwise, return the permutation entropy in bit.
Returns
-------
pe : float
Permutation Entropy
References
----------
.. [1] Massimiliano Zanin et al. Permutation Entropy and Its Main
Biomedical and Econophysics Applications: A Review.
http://www.mdpi.com/1099-4300/14/8/1553/pdf
.. [2] Christoph Bandt and Bernd Pompe. Permutation entropy β a natural
complexity measure for time series.
http://stubber.math-inf.uni-greifswald.de/pub/full/prep/2001/11.pdf
Notes
-----
Last updated (Oct 2018) by Raphael Vallat ([email protected]):
- Major speed improvements
- Use of base 2 instead of base e
- Added normalization
Examples
--------
1. Permutation entropy with order 2
>>> x = [4, 7, 9, 10, 6, 11, 3]
>>> # Return a value between 0 and log2(factorial(order))
>>> print(permutation_entropy(x, order=2))
0.918
2. Normalized permutation entropy with order 3
>>> x = [4, 7, 9, 10, 6, 11, 3]
>>> # Return a value comprised between 0 and 1.
>>> print(permutation_entropy(x, order=3, normalize=True))
0.589
### Response:
def permutation_entropy(time_series, order=3, delay=1, normalize=False):
"""Permutation Entropy.
Parameters
----------
time_series : list or np.array
Time series
order : int
Order of permutation entropy
delay : int
Time delay
normalize : bool
If True, divide by log2(factorial(m)) to normalize the entropy
between 0 and 1. Otherwise, return the permutation entropy in bit.
Returns
-------
pe : float
Permutation Entropy
References
----------
.. [1] Massimiliano Zanin et al. Permutation Entropy and Its Main
Biomedical and Econophysics Applications: A Review.
http://www.mdpi.com/1099-4300/14/8/1553/pdf
.. [2] Christoph Bandt and Bernd Pompe. Permutation entropy β a natural
complexity measure for time series.
http://stubber.math-inf.uni-greifswald.de/pub/full/prep/2001/11.pdf
Notes
-----
Last updated (Oct 2018) by Raphael Vallat ([email protected]):
- Major speed improvements
- Use of base 2 instead of base e
- Added normalization
Examples
--------
1. Permutation entropy with order 2
>>> x = [4, 7, 9, 10, 6, 11, 3]
>>> # Return a value between 0 and log2(factorial(order))
>>> print(permutation_entropy(x, order=2))
0.918
2. Normalized permutation entropy with order 3
>>> x = [4, 7, 9, 10, 6, 11, 3]
>>> # Return a value comprised between 0 and 1.
>>> print(permutation_entropy(x, order=3, normalize=True))
0.589
"""
x = np.array(time_series)
hashmult = np.power(order, np.arange(order))
# Embed x and sort the order of permutations
sorted_idx = _embed(x, order=order, delay=delay).argsort(kind='quicksort')
# Associate unique integer to each permutations
hashval = (np.multiply(sorted_idx, hashmult)).sum(1)
# Return the counts
_, c = np.unique(hashval, return_counts=True)
# Use np.true_divide for Python 2 compatibility
p = np.true_divide(c, c.sum())
pe = -np.multiply(p, np.log2(p)).sum()
if normalize:
pe /= np.log2(factorial(order))
return pe |
def expand_as_args(args):
"""Returns `True` if `args` should be expanded as `*args`."""
return (isinstance(args, collections.Sequence) and
not _is_namedtuple(args) and not _force_leaf(args)) | Returns `True` if `args` should be expanded as `*args`. | Below is the the instruction that describes the task:
### Input:
Returns `True` if `args` should be expanded as `*args`.
### Response:
def expand_as_args(args):
"""Returns `True` if `args` should be expanded as `*args`."""
return (isinstance(args, collections.Sequence) and
not _is_namedtuple(args) and not _force_leaf(args)) |
def get_startup(self, id_):
""" Get startup based on id
"""
return _get_request(_STARTUP.format(c_api=_C_API_BEGINNING,
api=_API_VERSION,
id_=id_,
at=self.access_token)) | Get startup based on id | Below is the the instruction that describes the task:
### Input:
Get startup based on id
### Response:
def get_startup(self, id_):
""" Get startup based on id
"""
return _get_request(_STARTUP.format(c_api=_C_API_BEGINNING,
api=_API_VERSION,
id_=id_,
at=self.access_token)) |
def set_ifo(self,ifo):
"""
Set the ifo name to analyze. If the channel name for the job is defined,
then the name of the ifo is prepended to the channel name obtained
from the job configuration file and passed with a --channel-name option.
@param ifo: two letter ifo code (e.g. L1, H1 or H2).
"""
self.__ifo = ifo
if self.job().channel():
self.add_var_opt('channel-name', ifo + ':' + self.job().channel()) | Set the ifo name to analyze. If the channel name for the job is defined,
then the name of the ifo is prepended to the channel name obtained
from the job configuration file and passed with a --channel-name option.
@param ifo: two letter ifo code (e.g. L1, H1 or H2). | Below is the the instruction that describes the task:
### Input:
Set the ifo name to analyze. If the channel name for the job is defined,
then the name of the ifo is prepended to the channel name obtained
from the job configuration file and passed with a --channel-name option.
@param ifo: two letter ifo code (e.g. L1, H1 or H2).
### Response:
def set_ifo(self,ifo):
"""
Set the ifo name to analyze. If the channel name for the job is defined,
then the name of the ifo is prepended to the channel name obtained
from the job configuration file and passed with a --channel-name option.
@param ifo: two letter ifo code (e.g. L1, H1 or H2).
"""
self.__ifo = ifo
if self.job().channel():
self.add_var_opt('channel-name', ifo + ':' + self.job().channel()) |
def rename(self, old_name, new_name):
"""Rename key to a new name."""
try:
self.api.rename(mkey(old_name), mkey(new_name))
except ResponseError, exc:
if "no such key" in exc.args:
raise KeyError(old_name)
raise | Rename key to a new name. | Below is the the instruction that describes the task:
### Input:
Rename key to a new name.
### Response:
def rename(self, old_name, new_name):
"""Rename key to a new name."""
try:
self.api.rename(mkey(old_name), mkey(new_name))
except ResponseError, exc:
if "no such key" in exc.args:
raise KeyError(old_name)
raise |
def cli(self, prt=sys.stdout):
"""Command-line interface for go_draw script."""
kws = self.objdoc.get_docargs(prt=None)
godag = get_godag(kws['obo'], prt=None, loading_bar=False, optional_attrs=['relationship'])
usrgos = GetGOs(godag, max_gos=200).get_usrgos(kws.get('GO_FILE'), prt)
tcntobj = self._get_tcntobj(usrgos, godag, **kws) # Gets TermCounts or None
self.gosubdag = GoSubDag(usrgos, godag, relationships=True, tcntobj=tcntobj, prt=None)
grprdflt = GrouperDflts(self.gosubdag, kws['slims'])
ver_list = [godag.version, grprdflt.ver_goslims]
prt.write("{VER}\n".format(VER="\n".join(ver_list)))
sections = self._read_sections(kws['ifile'])
# print("SECSECSEC", sections)
hdrobj = HdrgosSections(self.gosubdag, grprdflt.hdrgos_dflt, sections)
grprobj = Grouper("init", usrgos, hdrobj, self.gosubdag)
# Write sections
objsecwr = WrSectionsTxt(grprobj, ver_list)
if not os.path.exists(kws['ifile']):
objsecwr.wr_txt_section_hdrgos(kws['ifile'])
objsecwr.wr_txt_section_hdrgos(kws['ofile'])
objsecpy = WrSectionsPy(grprobj, ver_list)
if 'py' in kws:
objsecpy.wr_py_sections(kws['py'], sections, doc=godag.version)
# Write user GO IDs in sections
sortobj = Sorter(grprobj)
objgowr = WrXlsxSortedGos("init", sortobj, ver_list)
objgowr.wr_txt_gos(kws['txt'], sortby=objsecpy.fncsortnt)
#objwr.wr_txt_section_hdrgos(kws['ofile'], sortby=objwr.fncsortnt)
self._prt_cnt_usrgos(usrgos, sys.stdout) | Command-line interface for go_draw script. | Below is the the instruction that describes the task:
### Input:
Command-line interface for go_draw script.
### Response:
def cli(self, prt=sys.stdout):
"""Command-line interface for go_draw script."""
kws = self.objdoc.get_docargs(prt=None)
godag = get_godag(kws['obo'], prt=None, loading_bar=False, optional_attrs=['relationship'])
usrgos = GetGOs(godag, max_gos=200).get_usrgos(kws.get('GO_FILE'), prt)
tcntobj = self._get_tcntobj(usrgos, godag, **kws) # Gets TermCounts or None
self.gosubdag = GoSubDag(usrgos, godag, relationships=True, tcntobj=tcntobj, prt=None)
grprdflt = GrouperDflts(self.gosubdag, kws['slims'])
ver_list = [godag.version, grprdflt.ver_goslims]
prt.write("{VER}\n".format(VER="\n".join(ver_list)))
sections = self._read_sections(kws['ifile'])
# print("SECSECSEC", sections)
hdrobj = HdrgosSections(self.gosubdag, grprdflt.hdrgos_dflt, sections)
grprobj = Grouper("init", usrgos, hdrobj, self.gosubdag)
# Write sections
objsecwr = WrSectionsTxt(grprobj, ver_list)
if not os.path.exists(kws['ifile']):
objsecwr.wr_txt_section_hdrgos(kws['ifile'])
objsecwr.wr_txt_section_hdrgos(kws['ofile'])
objsecpy = WrSectionsPy(grprobj, ver_list)
if 'py' in kws:
objsecpy.wr_py_sections(kws['py'], sections, doc=godag.version)
# Write user GO IDs in sections
sortobj = Sorter(grprobj)
objgowr = WrXlsxSortedGos("init", sortobj, ver_list)
objgowr.wr_txt_gos(kws['txt'], sortby=objsecpy.fncsortnt)
#objwr.wr_txt_section_hdrgos(kws['ofile'], sortby=objwr.fncsortnt)
self._prt_cnt_usrgos(usrgos, sys.stdout) |
def quick_str_input(prompt, default_value):
"""
Function to display a quick question for text input.
**Parameters:**
- **prompt:** Text / question to display
- **default_value:** Default value for no entry
**Returns:** text_type() or default_value.
"""
valid = False
str_val = default_value
while not valid:
input_val = raw_input(prompt + "[{0}]: ".format(default_value))
if input_val == "":
str_val = default_value
valid = True
else:
try:
str_val = text_type(input_val)
valid = True
except ValueError:
print("ERROR: must be text.")
valid = False
return str_val | Function to display a quick question for text input.
**Parameters:**
- **prompt:** Text / question to display
- **default_value:** Default value for no entry
**Returns:** text_type() or default_value. | Below is the the instruction that describes the task:
### Input:
Function to display a quick question for text input.
**Parameters:**
- **prompt:** Text / question to display
- **default_value:** Default value for no entry
**Returns:** text_type() or default_value.
### Response:
def quick_str_input(prompt, default_value):
"""
Function to display a quick question for text input.
**Parameters:**
- **prompt:** Text / question to display
- **default_value:** Default value for no entry
**Returns:** text_type() or default_value.
"""
valid = False
str_val = default_value
while not valid:
input_val = raw_input(prompt + "[{0}]: ".format(default_value))
if input_val == "":
str_val = default_value
valid = True
else:
try:
str_val = text_type(input_val)
valid = True
except ValueError:
print("ERROR: must be text.")
valid = False
return str_val |
def main():
"""
Main entry point for execution as a program (instead of as a module).
"""
args = parse_args()
logging.info('coursera_dl version %s', __version__)
completed_classes = []
classes_with_errors = []
mkdir_p(PATH_CACHE, 0o700)
if args.clear_cache:
shutil.rmtree(PATH_CACHE)
if args.list_courses:
logging.info('Listing enrolled courses')
list_courses(args)
return
session = get_session()
login(session, args.username, args.password)
if args.specialization:
args.class_names = expand_specializations(session, args.class_names)
for class_index, class_name in enumerate(args.class_names):
try:
logging.info('Downloading class: %s (%d / %d)',
class_name, class_index + 1, len(args.class_names))
error_occurred, completed = download_class(
session, args, class_name)
if completed:
completed_classes.append(class_name)
if error_occurred:
classes_with_errors.append(class_name)
except requests.exceptions.HTTPError as e:
logging.error('HTTPError %s', e)
if is_debug_run():
logging.exception('HTTPError %s', e)
except requests.exceptions.SSLError as e:
logging.error('SSLError %s', e)
print_ssl_error_message(e)
if is_debug_run():
raise
except ClassNotFound as e:
logging.error('Could not find class: %s', e)
except AuthenticationFailed as e:
logging.error('Could not authenticate: %s', e)
if class_index + 1 != len(args.class_names):
logging.info('Sleeping for %d seconds before downloading next course. '
'You can change this with --download-delay option.',
args.download_delay)
time.sleep(args.download_delay)
if completed_classes:
logging.info('-' * 80)
logging.info(
"Classes which appear completed: " + " ".join(completed_classes))
if classes_with_errors:
logging.info('-' * 80)
logging.info('The following classes had errors during the syllabus'
' parsing stage. You may want to review error messages and'
' courses (sometimes enrolling to the course or switching'
' session helps):')
for class_name in classes_with_errors:
logging.info('%s (https://www.coursera.org/learn/%s)',
class_name, class_name) | Main entry point for execution as a program (instead of as a module). | Below is the the instruction that describes the task:
### Input:
Main entry point for execution as a program (instead of as a module).
### Response:
def main():
"""
Main entry point for execution as a program (instead of as a module).
"""
args = parse_args()
logging.info('coursera_dl version %s', __version__)
completed_classes = []
classes_with_errors = []
mkdir_p(PATH_CACHE, 0o700)
if args.clear_cache:
shutil.rmtree(PATH_CACHE)
if args.list_courses:
logging.info('Listing enrolled courses')
list_courses(args)
return
session = get_session()
login(session, args.username, args.password)
if args.specialization:
args.class_names = expand_specializations(session, args.class_names)
for class_index, class_name in enumerate(args.class_names):
try:
logging.info('Downloading class: %s (%d / %d)',
class_name, class_index + 1, len(args.class_names))
error_occurred, completed = download_class(
session, args, class_name)
if completed:
completed_classes.append(class_name)
if error_occurred:
classes_with_errors.append(class_name)
except requests.exceptions.HTTPError as e:
logging.error('HTTPError %s', e)
if is_debug_run():
logging.exception('HTTPError %s', e)
except requests.exceptions.SSLError as e:
logging.error('SSLError %s', e)
print_ssl_error_message(e)
if is_debug_run():
raise
except ClassNotFound as e:
logging.error('Could not find class: %s', e)
except AuthenticationFailed as e:
logging.error('Could not authenticate: %s', e)
if class_index + 1 != len(args.class_names):
logging.info('Sleeping for %d seconds before downloading next course. '
'You can change this with --download-delay option.',
args.download_delay)
time.sleep(args.download_delay)
if completed_classes:
logging.info('-' * 80)
logging.info(
"Classes which appear completed: " + " ".join(completed_classes))
if classes_with_errors:
logging.info('-' * 80)
logging.info('The following classes had errors during the syllabus'
' parsing stage. You may want to review error messages and'
' courses (sometimes enrolling to the course or switching'
' session helps):')
for class_name in classes_with_errors:
logging.info('%s (https://www.coursera.org/learn/%s)',
class_name, class_name) |
def bsn(self) -> str:
"""Generate a random, but valid ``Burgerservicenummer``.
:returns: Random BSN.
:Example:
255159705
"""
def _is_valid_bsn(number: str) -> bool:
total = 0
multiplier = 9
for char in number:
multiplier = -multiplier if multiplier == 1 else multiplier
total += int(char) * multiplier
multiplier -= 1
result = total % 11 == 0
return result
a, b = (100000000, 999999999)
sample = str(self.random.randint(a, b))
while not _is_valid_bsn(sample):
sample = str(self.random.randint(a, b))
return sample | Generate a random, but valid ``Burgerservicenummer``.
:returns: Random BSN.
:Example:
255159705 | Below is the the instruction that describes the task:
### Input:
Generate a random, but valid ``Burgerservicenummer``.
:returns: Random BSN.
:Example:
255159705
### Response:
def bsn(self) -> str:
"""Generate a random, but valid ``Burgerservicenummer``.
:returns: Random BSN.
:Example:
255159705
"""
def _is_valid_bsn(number: str) -> bool:
total = 0
multiplier = 9
for char in number:
multiplier = -multiplier if multiplier == 1 else multiplier
total += int(char) * multiplier
multiplier -= 1
result = total % 11 == 0
return result
a, b = (100000000, 999999999)
sample = str(self.random.randint(a, b))
while not _is_valid_bsn(sample):
sample = str(self.random.randint(a, b))
return sample |
def eth_getBlockByNumber(self, number):
"""Get block body by block number.
:param number:
:return:
"""
block_hash = self.reader._get_block_hash(number)
block_number = _format_block_number(number)
body_key = body_prefix + block_number + block_hash
block_data = self.db.get(body_key)
body = rlp.decode(block_data, sedes=Block)
return body | Get block body by block number.
:param number:
:return: | Below is the the instruction that describes the task:
### Input:
Get block body by block number.
:param number:
:return:
### Response:
def eth_getBlockByNumber(self, number):
"""Get block body by block number.
:param number:
:return:
"""
block_hash = self.reader._get_block_hash(number)
block_number = _format_block_number(number)
body_key = body_prefix + block_number + block_hash
block_data = self.db.get(body_key)
body = rlp.decode(block_data, sedes=Block)
return body |
def execute(self, query, *args, **kwargs):
"""Asynchronously execute the specified CQL query.
The execute command also takes optional parameters and trace
keyword arguments. See cassandra-python documentation for
definition of those parameters.
"""
tornado_future = Future()
cassandra_future = self._session.execute_async(query, *args, **kwargs)
self._ioloop.add_callback(
self._callback, cassandra_future, tornado_future)
return tornado_future | Asynchronously execute the specified CQL query.
The execute command also takes optional parameters and trace
keyword arguments. See cassandra-python documentation for
definition of those parameters. | Below is the the instruction that describes the task:
### Input:
Asynchronously execute the specified CQL query.
The execute command also takes optional parameters and trace
keyword arguments. See cassandra-python documentation for
definition of those parameters.
### Response:
def execute(self, query, *args, **kwargs):
"""Asynchronously execute the specified CQL query.
The execute command also takes optional parameters and trace
keyword arguments. See cassandra-python documentation for
definition of those parameters.
"""
tornado_future = Future()
cassandra_future = self._session.execute_async(query, *args, **kwargs)
self._ioloop.add_callback(
self._callback, cassandra_future, tornado_future)
return tornado_future |
def thermal_expansion_coeff(self, structure, temperature, mode="debye"):
"""
Gets thermal expansion coefficient from third-order constants.
Args:
temperature (float): Temperature in kelvin, if not specified
will return non-cv-normalized value
structure (Structure): Structure to be used in directional heat
capacity determination, only necessary if temperature
is specified
mode (string): mode for finding average heat-capacity,
current supported modes are 'debye' and 'dulong-petit'
"""
soec = ElasticTensor(self[0])
v0 = (structure.volume * 1e-30 / structure.num_sites)
if mode == "debye":
td = soec.debye_temperature(structure)
t_ratio = temperature / td
integrand = lambda x: (x**4 * np.exp(x)) / (np.exp(x) - 1)**2
cv = 9 * 8.314 * t_ratio**3 * quad(integrand, 0, t_ratio**-1)[0]
elif mode == "dulong-petit":
cv = 3 * 8.314
else:
raise ValueError("Mode must be debye or dulong-petit")
tgt = self.get_tgt(temperature, structure)
alpha = np.einsum('ijkl,ij', soec.compliance_tensor, tgt)
alpha *= cv / (1e9 * v0 * 6.022e23)
return SquareTensor(alpha) | Gets thermal expansion coefficient from third-order constants.
Args:
temperature (float): Temperature in kelvin, if not specified
will return non-cv-normalized value
structure (Structure): Structure to be used in directional heat
capacity determination, only necessary if temperature
is specified
mode (string): mode for finding average heat-capacity,
current supported modes are 'debye' and 'dulong-petit' | Below is the the instruction that describes the task:
### Input:
Gets thermal expansion coefficient from third-order constants.
Args:
temperature (float): Temperature in kelvin, if not specified
will return non-cv-normalized value
structure (Structure): Structure to be used in directional heat
capacity determination, only necessary if temperature
is specified
mode (string): mode for finding average heat-capacity,
current supported modes are 'debye' and 'dulong-petit'
### Response:
def thermal_expansion_coeff(self, structure, temperature, mode="debye"):
"""
Gets thermal expansion coefficient from third-order constants.
Args:
temperature (float): Temperature in kelvin, if not specified
will return non-cv-normalized value
structure (Structure): Structure to be used in directional heat
capacity determination, only necessary if temperature
is specified
mode (string): mode for finding average heat-capacity,
current supported modes are 'debye' and 'dulong-petit'
"""
soec = ElasticTensor(self[0])
v0 = (structure.volume * 1e-30 / structure.num_sites)
if mode == "debye":
td = soec.debye_temperature(structure)
t_ratio = temperature / td
integrand = lambda x: (x**4 * np.exp(x)) / (np.exp(x) - 1)**2
cv = 9 * 8.314 * t_ratio**3 * quad(integrand, 0, t_ratio**-1)[0]
elif mode == "dulong-petit":
cv = 3 * 8.314
else:
raise ValueError("Mode must be debye or dulong-petit")
tgt = self.get_tgt(temperature, structure)
alpha = np.einsum('ijkl,ij', soec.compliance_tensor, tgt)
alpha *= cv / (1e9 * v0 * 6.022e23)
return SquareTensor(alpha) |
def api_version(self, verbose=False):
'''
Get information about the API
http://docs.opsview.com/doku.php?id=opsview4.6:restapi#api_version_information
'''
return self.__auth_req_get(self.rest_url, verbose=verbose) | Get information about the API
http://docs.opsview.com/doku.php?id=opsview4.6:restapi#api_version_information | Below is the the instruction that describes the task:
### Input:
Get information about the API
http://docs.opsview.com/doku.php?id=opsview4.6:restapi#api_version_information
### Response:
def api_version(self, verbose=False):
'''
Get information about the API
http://docs.opsview.com/doku.php?id=opsview4.6:restapi#api_version_information
'''
return self.__auth_req_get(self.rest_url, verbose=verbose) |
def accepts_contributor_roles(func):
"""
Decorator that accepts only contributor roles
:param func:
:return:
"""
if inspect.isclass(func):
apply_function_to_members(func, accepts_contributor_roles)
return func
else:
@functools.wraps(func)
def decorator(*args, **kwargs):
return accepts_roles(*ROLES_CONTRIBUTOR)(func)(*args, **kwargs)
return decorator | Decorator that accepts only contributor roles
:param func:
:return: | Below is the the instruction that describes the task:
### Input:
Decorator that accepts only contributor roles
:param func:
:return:
### Response:
def accepts_contributor_roles(func):
"""
Decorator that accepts only contributor roles
:param func:
:return:
"""
if inspect.isclass(func):
apply_function_to_members(func, accepts_contributor_roles)
return func
else:
@functools.wraps(func)
def decorator(*args, **kwargs):
return accepts_roles(*ROLES_CONTRIBUTOR)(func)(*args, **kwargs)
return decorator |
def add(self, data, overwrite=False):
"""Add given data string by guessing its format. The format must be
Motorola S-Records, Intel HEX or TI-TXT. Set `overwrite` to
``True`` to allow already added data to be overwritten.
"""
if is_srec(data):
self.add_srec(data, overwrite)
elif is_ihex(data):
self.add_ihex(data, overwrite)
elif is_ti_txt(data):
self.add_ti_txt(data, overwrite)
else:
raise UnsupportedFileFormatError() | Add given data string by guessing its format. The format must be
Motorola S-Records, Intel HEX or TI-TXT. Set `overwrite` to
``True`` to allow already added data to be overwritten. | Below is the the instruction that describes the task:
### Input:
Add given data string by guessing its format. The format must be
Motorola S-Records, Intel HEX or TI-TXT. Set `overwrite` to
``True`` to allow already added data to be overwritten.
### Response:
def add(self, data, overwrite=False):
"""Add given data string by guessing its format. The format must be
Motorola S-Records, Intel HEX or TI-TXT. Set `overwrite` to
``True`` to allow already added data to be overwritten.
"""
if is_srec(data):
self.add_srec(data, overwrite)
elif is_ihex(data):
self.add_ihex(data, overwrite)
elif is_ti_txt(data):
self.add_ti_txt(data, overwrite)
else:
raise UnsupportedFileFormatError() |
def calcFontScaling(self):
'''Calculates the current font size and left position for the current window.'''
self.ypx = self.figure.get_size_inches()[1]*self.figure.dpi
self.xpx = self.figure.get_size_inches()[0]*self.figure.dpi
self.fontSize = self.vertSize*(self.ypx/2.0)
self.leftPos = self.axes.get_xlim()[0]
self.rightPos = self.axes.get_xlim()[1] | Calculates the current font size and left position for the current window. | Below is the the instruction that describes the task:
### Input:
Calculates the current font size and left position for the current window.
### Response:
def calcFontScaling(self):
'''Calculates the current font size and left position for the current window.'''
self.ypx = self.figure.get_size_inches()[1]*self.figure.dpi
self.xpx = self.figure.get_size_inches()[0]*self.figure.dpi
self.fontSize = self.vertSize*(self.ypx/2.0)
self.leftPos = self.axes.get_xlim()[0]
self.rightPos = self.axes.get_xlim()[1] |
def z_angle_rotate(xy, theta):
"""
Rotated the input vector or set of vectors `xy` by the angle `theta`.
Parameters
----------
xy : array_like
The vector or array of vectors to transform. Must have shape
"""
xy = np.array(xy).T
theta = np.array(theta).T
out = np.zeros_like(xy)
out[...,0] = np.cos(theta)*xy[...,0] - np.sin(theta)*xy[...,1]
out[...,1] = np.sin(theta)*xy[...,0] + np.cos(theta)*xy[...,1]
return out.T | Rotated the input vector or set of vectors `xy` by the angle `theta`.
Parameters
----------
xy : array_like
The vector or array of vectors to transform. Must have shape | Below is the the instruction that describes the task:
### Input:
Rotated the input vector or set of vectors `xy` by the angle `theta`.
Parameters
----------
xy : array_like
The vector or array of vectors to transform. Must have shape
### Response:
def z_angle_rotate(xy, theta):
"""
Rotated the input vector or set of vectors `xy` by the angle `theta`.
Parameters
----------
xy : array_like
The vector or array of vectors to transform. Must have shape
"""
xy = np.array(xy).T
theta = np.array(theta).T
out = np.zeros_like(xy)
out[...,0] = np.cos(theta)*xy[...,0] - np.sin(theta)*xy[...,1]
out[...,1] = np.sin(theta)*xy[...,0] + np.cos(theta)*xy[...,1]
return out.T |
def get_portal_by_name(self, portal_name):
"""
Set active portal according to the name passed in 'portal_name'.
Returns dictionary of device 'serial_number: rid'
"""
portals = self.get_portals_list()
for p in portals:
# print("Checking {!r}".format(p))
if portal_name == p[1]:
# print("Found Portal!")
self.set_portal_name( p[1] )
self.set_portal_id( p[0] )
self.set_portal_cik( p[2][1]['info']['key'] )
# print("Active Portal Details:\nName: {0}\nId: {1}\nCIK: {2}".format(
# self.portal_name(),
# self.portal_id(),
# self.portal_cik()))
return p
return None | Set active portal according to the name passed in 'portal_name'.
Returns dictionary of device 'serial_number: rid' | Below is the the instruction that describes the task:
### Input:
Set active portal according to the name passed in 'portal_name'.
Returns dictionary of device 'serial_number: rid'
### Response:
def get_portal_by_name(self, portal_name):
"""
Set active portal according to the name passed in 'portal_name'.
Returns dictionary of device 'serial_number: rid'
"""
portals = self.get_portals_list()
for p in portals:
# print("Checking {!r}".format(p))
if portal_name == p[1]:
# print("Found Portal!")
self.set_portal_name( p[1] )
self.set_portal_id( p[0] )
self.set_portal_cik( p[2][1]['info']['key'] )
# print("Active Portal Details:\nName: {0}\nId: {1}\nCIK: {2}".format(
# self.portal_name(),
# self.portal_id(),
# self.portal_cik()))
return p
return None |
def pan_delta(self, dx_px, dy_px):
"""
This causes the scene to appear to translate right and up
(i.e., what really happens is the camera is translated left and down).
This is also called "panning" in some software packages.
Passing in negative delta values causes the opposite motion.
"""
direction = self.target - self.position
distance_from_target = direction.length()
direction = direction.normalized()
speed_per_radius = self.get_translation_speed(distance_from_target)
px_per_unit = self.vport_radius_px / speed_per_radius
right = direction ^ self.up
translation = (right * (-dx_px / px_per_unit) +
self.up * (-dy_px / px_per_unit))
self.position = self.position + translation
self.target = self.target + translation | This causes the scene to appear to translate right and up
(i.e., what really happens is the camera is translated left and down).
This is also called "panning" in some software packages.
Passing in negative delta values causes the opposite motion. | Below is the the instruction that describes the task:
### Input:
This causes the scene to appear to translate right and up
(i.e., what really happens is the camera is translated left and down).
This is also called "panning" in some software packages.
Passing in negative delta values causes the opposite motion.
### Response:
def pan_delta(self, dx_px, dy_px):
"""
This causes the scene to appear to translate right and up
(i.e., what really happens is the camera is translated left and down).
This is also called "panning" in some software packages.
Passing in negative delta values causes the opposite motion.
"""
direction = self.target - self.position
distance_from_target = direction.length()
direction = direction.normalized()
speed_per_radius = self.get_translation_speed(distance_from_target)
px_per_unit = self.vport_radius_px / speed_per_radius
right = direction ^ self.up
translation = (right * (-dx_px / px_per_unit) +
self.up * (-dy_px / px_per_unit))
self.position = self.position + translation
self.target = self.target + translation |
def hostcmd_push(base_path, project_name, engine_name, vars_files=None, config_file=None, **kwargs):
"""
Push images to a registry. Requires authenticating with the registry prior to starting
the push. If your engine's config file does not already contain an authorization for the
registry, pass username and/or password. If you exclude password, you will be prompted.
"""
assert_initialized(base_path, config_file)
config = get_config(base_path, vars_files=vars_files, engine_name=engine_name, project_name=project_name,
config_file=config_file)
engine_obj = load_engine(['LOGIN', 'PUSH'],
engine_name, config.project_name,
config['services'], **kwargs)
logger.debug('PROJECT NAME', project_name=config.project_name)
push_images(base_path,
config.image_namespace,
engine_obj,
config,
save_conductor=config.save_conductor,
**kwargs) | Push images to a registry. Requires authenticating with the registry prior to starting
the push. If your engine's config file does not already contain an authorization for the
registry, pass username and/or password. If you exclude password, you will be prompted. | Below is the the instruction that describes the task:
### Input:
Push images to a registry. Requires authenticating with the registry prior to starting
the push. If your engine's config file does not already contain an authorization for the
registry, pass username and/or password. If you exclude password, you will be prompted.
### Response:
def hostcmd_push(base_path, project_name, engine_name, vars_files=None, config_file=None, **kwargs):
"""
Push images to a registry. Requires authenticating with the registry prior to starting
the push. If your engine's config file does not already contain an authorization for the
registry, pass username and/or password. If you exclude password, you will be prompted.
"""
assert_initialized(base_path, config_file)
config = get_config(base_path, vars_files=vars_files, engine_name=engine_name, project_name=project_name,
config_file=config_file)
engine_obj = load_engine(['LOGIN', 'PUSH'],
engine_name, config.project_name,
config['services'], **kwargs)
logger.debug('PROJECT NAME', project_name=config.project_name)
push_images(base_path,
config.image_namespace,
engine_obj,
config,
save_conductor=config.save_conductor,
**kwargs) |
def mime(self):
"""Produce the final MIME message."""
author = self.author
sender = self.sender
if not author:
raise ValueError("You must specify an author.")
if not self.subject:
raise ValueError("You must specify a subject.")
if len(self.recipients) == 0:
raise ValueError("You must specify at least one recipient.")
if not self.plain:
raise ValueError("You must provide plain text content.")
# DISCUSS: Take the first author, or raise this error?
# if len(author) > 1 and len(sender) == 0:
# raise ValueError('If there are multiple authors of message, you must specify a sender!')
# if len(sender) > 1:
# raise ValueError('You must not specify more than one sender!')
if not self._dirty and self._processed:
return self._mime
self._processed = False
plain = MIMEText(self._callable(self.plain), 'plain', self.encoding)
rich = None
if self.rich:
rich = MIMEText(self._callable(self.rich), 'html', self.encoding)
message = self._mime_document(plain, rich)
headers = self._build_header_list(author, sender)
self._add_headers_to_message(message, headers)
self._mime = message
self._processed = True
self._dirty = False
return message | Produce the final MIME message. | Below is the the instruction that describes the task:
### Input:
Produce the final MIME message.
### Response:
def mime(self):
"""Produce the final MIME message."""
author = self.author
sender = self.sender
if not author:
raise ValueError("You must specify an author.")
if not self.subject:
raise ValueError("You must specify a subject.")
if len(self.recipients) == 0:
raise ValueError("You must specify at least one recipient.")
if not self.plain:
raise ValueError("You must provide plain text content.")
# DISCUSS: Take the first author, or raise this error?
# if len(author) > 1 and len(sender) == 0:
# raise ValueError('If there are multiple authors of message, you must specify a sender!')
# if len(sender) > 1:
# raise ValueError('You must not specify more than one sender!')
if not self._dirty and self._processed:
return self._mime
self._processed = False
plain = MIMEText(self._callable(self.plain), 'plain', self.encoding)
rich = None
if self.rich:
rich = MIMEText(self._callable(self.rich), 'html', self.encoding)
message = self._mime_document(plain, rich)
headers = self._build_header_list(author, sender)
self._add_headers_to_message(message, headers)
self._mime = message
self._processed = True
self._dirty = False
return message |
def two_lorentzian(freq, freq0_1, freq0_2, area1, area2, hwhm1, hwhm2, phase1,
phase2, offset, drift):
"""
A two-Lorentzian model.
This is simply the sum of two lorentzian functions in some part of the
spectrum. Each individual Lorentzian has its own peak frequency, area, hwhm
and phase, but they share common offset and drift parameters.
"""
return (lorentzian(freq, freq0_1, area1, hwhm1, phase1, offset, drift) +
lorentzian(freq, freq0_2, area2, hwhm2, phase2, offset, drift)) | A two-Lorentzian model.
This is simply the sum of two lorentzian functions in some part of the
spectrum. Each individual Lorentzian has its own peak frequency, area, hwhm
and phase, but they share common offset and drift parameters. | Below is the the instruction that describes the task:
### Input:
A two-Lorentzian model.
This is simply the sum of two lorentzian functions in some part of the
spectrum. Each individual Lorentzian has its own peak frequency, area, hwhm
and phase, but they share common offset and drift parameters.
### Response:
def two_lorentzian(freq, freq0_1, freq0_2, area1, area2, hwhm1, hwhm2, phase1,
phase2, offset, drift):
"""
A two-Lorentzian model.
This is simply the sum of two lorentzian functions in some part of the
spectrum. Each individual Lorentzian has its own peak frequency, area, hwhm
and phase, but they share common offset and drift parameters.
"""
return (lorentzian(freq, freq0_1, area1, hwhm1, phase1, offset, drift) +
lorentzian(freq, freq0_2, area2, hwhm2, phase2, offset, drift)) |
def blend(self, blend_function=stack):
"""Blend the datasets into one scene.
.. note::
Blending is not currently optimized for generator-based
MultiScene.
"""
new_scn = Scene()
common_datasets = self.shared_dataset_ids
for ds_id in common_datasets:
datasets = [scn[ds_id] for scn in self.scenes if ds_id in scn]
new_scn[ds_id] = blend_function(datasets)
return new_scn | Blend the datasets into one scene.
.. note::
Blending is not currently optimized for generator-based
MultiScene. | Below is the the instruction that describes the task:
### Input:
Blend the datasets into one scene.
.. note::
Blending is not currently optimized for generator-based
MultiScene.
### Response:
def blend(self, blend_function=stack):
"""Blend the datasets into one scene.
.. note::
Blending is not currently optimized for generator-based
MultiScene.
"""
new_scn = Scene()
common_datasets = self.shared_dataset_ids
for ds_id in common_datasets:
datasets = [scn[ds_id] for scn in self.scenes if ds_id in scn]
new_scn[ds_id] = blend_function(datasets)
return new_scn |
def normalize_modpath(modpath, hide_init=True, hide_main=False):
"""
Normalizes __init__ and __main__ paths.
Notes:
Adds __init__ if reasonable, but only removes __main__ by default
Args:
hide_init (bool): if True, always return package modules
as __init__.py files otherwise always return the dpath.
hide_init (bool): if True, always strip away main files otherwise
ignore __main__.py.
CommandLine:
xdoctest -m xdoctest.static_analysis normalize_modpath
Example:
>>> import xdoctest.static_analysis as static
>>> modpath = static.__file__
>>> assert static.normalize_modpath(modpath) == modpath.replace('.pyc', '.py')
>>> dpath = dirname(modpath)
>>> res0 = static.normalize_modpath(dpath, hide_init=0, hide_main=0)
>>> res1 = static.normalize_modpath(dpath, hide_init=0, hide_main=1)
>>> res2 = static.normalize_modpath(dpath, hide_init=1, hide_main=0)
>>> res3 = static.normalize_modpath(dpath, hide_init=1, hide_main=1)
>>> assert res0.endswith('__init__.py')
>>> assert res1.endswith('__init__.py')
>>> assert not res2.endswith('.py')
>>> assert not res3.endswith('.py')
"""
if six.PY2:
if modpath.endswith('.pyc'):
modpath = modpath[:-1]
if hide_init:
if basename(modpath) == '__init__.py':
modpath = dirname(modpath)
hide_main = True
else:
# add in init, if reasonable
modpath_with_init = join(modpath, '__init__.py')
if exists(modpath_with_init):
modpath = modpath_with_init
if hide_main:
# We can remove main, but dont add it
if basename(modpath) == '__main__.py':
# corner case where main might just be a module name not in a pkg
parallel_init = join(dirname(modpath), '__init__.py')
if exists(parallel_init):
modpath = dirname(modpath)
return modpath | Normalizes __init__ and __main__ paths.
Notes:
Adds __init__ if reasonable, but only removes __main__ by default
Args:
hide_init (bool): if True, always return package modules
as __init__.py files otherwise always return the dpath.
hide_init (bool): if True, always strip away main files otherwise
ignore __main__.py.
CommandLine:
xdoctest -m xdoctest.static_analysis normalize_modpath
Example:
>>> import xdoctest.static_analysis as static
>>> modpath = static.__file__
>>> assert static.normalize_modpath(modpath) == modpath.replace('.pyc', '.py')
>>> dpath = dirname(modpath)
>>> res0 = static.normalize_modpath(dpath, hide_init=0, hide_main=0)
>>> res1 = static.normalize_modpath(dpath, hide_init=0, hide_main=1)
>>> res2 = static.normalize_modpath(dpath, hide_init=1, hide_main=0)
>>> res3 = static.normalize_modpath(dpath, hide_init=1, hide_main=1)
>>> assert res0.endswith('__init__.py')
>>> assert res1.endswith('__init__.py')
>>> assert not res2.endswith('.py')
>>> assert not res3.endswith('.py') | Below is the the instruction that describes the task:
### Input:
Normalizes __init__ and __main__ paths.
Notes:
Adds __init__ if reasonable, but only removes __main__ by default
Args:
hide_init (bool): if True, always return package modules
as __init__.py files otherwise always return the dpath.
hide_init (bool): if True, always strip away main files otherwise
ignore __main__.py.
CommandLine:
xdoctest -m xdoctest.static_analysis normalize_modpath
Example:
>>> import xdoctest.static_analysis as static
>>> modpath = static.__file__
>>> assert static.normalize_modpath(modpath) == modpath.replace('.pyc', '.py')
>>> dpath = dirname(modpath)
>>> res0 = static.normalize_modpath(dpath, hide_init=0, hide_main=0)
>>> res1 = static.normalize_modpath(dpath, hide_init=0, hide_main=1)
>>> res2 = static.normalize_modpath(dpath, hide_init=1, hide_main=0)
>>> res3 = static.normalize_modpath(dpath, hide_init=1, hide_main=1)
>>> assert res0.endswith('__init__.py')
>>> assert res1.endswith('__init__.py')
>>> assert not res2.endswith('.py')
>>> assert not res3.endswith('.py')
### Response:
def normalize_modpath(modpath, hide_init=True, hide_main=False):
"""
Normalizes __init__ and __main__ paths.
Notes:
Adds __init__ if reasonable, but only removes __main__ by default
Args:
hide_init (bool): if True, always return package modules
as __init__.py files otherwise always return the dpath.
hide_init (bool): if True, always strip away main files otherwise
ignore __main__.py.
CommandLine:
xdoctest -m xdoctest.static_analysis normalize_modpath
Example:
>>> import xdoctest.static_analysis as static
>>> modpath = static.__file__
>>> assert static.normalize_modpath(modpath) == modpath.replace('.pyc', '.py')
>>> dpath = dirname(modpath)
>>> res0 = static.normalize_modpath(dpath, hide_init=0, hide_main=0)
>>> res1 = static.normalize_modpath(dpath, hide_init=0, hide_main=1)
>>> res2 = static.normalize_modpath(dpath, hide_init=1, hide_main=0)
>>> res3 = static.normalize_modpath(dpath, hide_init=1, hide_main=1)
>>> assert res0.endswith('__init__.py')
>>> assert res1.endswith('__init__.py')
>>> assert not res2.endswith('.py')
>>> assert not res3.endswith('.py')
"""
if six.PY2:
if modpath.endswith('.pyc'):
modpath = modpath[:-1]
if hide_init:
if basename(modpath) == '__init__.py':
modpath = dirname(modpath)
hide_main = True
else:
# add in init, if reasonable
modpath_with_init = join(modpath, '__init__.py')
if exists(modpath_with_init):
modpath = modpath_with_init
if hide_main:
# We can remove main, but dont add it
if basename(modpath) == '__main__.py':
# corner case where main might just be a module name not in a pkg
parallel_init = join(dirname(modpath), '__init__.py')
if exists(parallel_init):
modpath = dirname(modpath)
return modpath |
def _slice_replace(code, index, old, new):
"""Replace the string *old* with *new* across *index* in *code*."""
nodes = [str(node) for node in code.get(index)]
substring = "".join(nodes).replace(old, new)
code.nodes[index] = parse_anything(substring).nodes | Replace the string *old* with *new* across *index* in *code*. | Below is the the instruction that describes the task:
### Input:
Replace the string *old* with *new* across *index* in *code*.
### Response:
def _slice_replace(code, index, old, new):
"""Replace the string *old* with *new* across *index* in *code*."""
nodes = [str(node) for node in code.get(index)]
substring = "".join(nodes).replace(old, new)
code.nodes[index] = parse_anything(substring).nodes |
def get_languages_from_item(ct_item, item):
"""
Get the languages configured for the current item
:param ct_item:
:param item:
:return:
"""
try:
item_lan = TransItemLanguage.objects.filter(content_type__pk=ct_item.id, object_id=item.id).get()
languages = [lang.code for lang in item_lan.languages.all()]
return languages
except TransItemLanguage.DoesNotExist:
return [] | Get the languages configured for the current item
:param ct_item:
:param item:
:return: | Below is the the instruction that describes the task:
### Input:
Get the languages configured for the current item
:param ct_item:
:param item:
:return:
### Response:
def get_languages_from_item(ct_item, item):
"""
Get the languages configured for the current item
:param ct_item:
:param item:
:return:
"""
try:
item_lan = TransItemLanguage.objects.filter(content_type__pk=ct_item.id, object_id=item.id).get()
languages = [lang.code for lang in item_lan.languages.all()]
return languages
except TransItemLanguage.DoesNotExist:
return [] |
def plot_blob(
sampler, blobidx=0, label=None, last_step=False, figure=None, **kwargs
):
"""
Plot a metadata blob as a fit to spectral data or value distribution
Additional ``kwargs`` are passed to `plot_fit`.
Parameters
----------
sampler : `emcee.EnsembleSampler`
Sampler with a stored chain.
blobidx : int, optional
Metadata blob index to plot.
label : str, optional
Label for the value distribution. Labels for the fit plot can be passed
as ``xlabel`` and ``ylabel`` and will be passed to `plot_fit`.
Returns
-------
figure : `matplotlib.pyplot.Figure`
`matplotlib` figure instance containing the plot.
"""
modelx, model = _process_blob(sampler, blobidx, last_step)
if label is None:
label = "Model output {0}".format(blobidx)
if modelx is None:
# Blob is scalar, plot distribution
f = plot_distribution(model, label, figure=figure)
else:
f = plot_fit(
sampler,
modelidx=blobidx,
last_step=last_step,
label=label,
figure=figure,
**kwargs
)
return f | Plot a metadata blob as a fit to spectral data or value distribution
Additional ``kwargs`` are passed to `plot_fit`.
Parameters
----------
sampler : `emcee.EnsembleSampler`
Sampler with a stored chain.
blobidx : int, optional
Metadata blob index to plot.
label : str, optional
Label for the value distribution. Labels for the fit plot can be passed
as ``xlabel`` and ``ylabel`` and will be passed to `plot_fit`.
Returns
-------
figure : `matplotlib.pyplot.Figure`
`matplotlib` figure instance containing the plot. | Below is the the instruction that describes the task:
### Input:
Plot a metadata blob as a fit to spectral data or value distribution
Additional ``kwargs`` are passed to `plot_fit`.
Parameters
----------
sampler : `emcee.EnsembleSampler`
Sampler with a stored chain.
blobidx : int, optional
Metadata blob index to plot.
label : str, optional
Label for the value distribution. Labels for the fit plot can be passed
as ``xlabel`` and ``ylabel`` and will be passed to `plot_fit`.
Returns
-------
figure : `matplotlib.pyplot.Figure`
`matplotlib` figure instance containing the plot.
### Response:
def plot_blob(
sampler, blobidx=0, label=None, last_step=False, figure=None, **kwargs
):
"""
Plot a metadata blob as a fit to spectral data or value distribution
Additional ``kwargs`` are passed to `plot_fit`.
Parameters
----------
sampler : `emcee.EnsembleSampler`
Sampler with a stored chain.
blobidx : int, optional
Metadata blob index to plot.
label : str, optional
Label for the value distribution. Labels for the fit plot can be passed
as ``xlabel`` and ``ylabel`` and will be passed to `plot_fit`.
Returns
-------
figure : `matplotlib.pyplot.Figure`
`matplotlib` figure instance containing the plot.
"""
modelx, model = _process_blob(sampler, blobidx, last_step)
if label is None:
label = "Model output {0}".format(blobidx)
if modelx is None:
# Blob is scalar, plot distribution
f = plot_distribution(model, label, figure=figure)
else:
f = plot_fit(
sampler,
modelidx=blobidx,
last_step=last_step,
label=label,
figure=figure,
**kwargs
)
return f |
def style_data(self):
"""Applies self.style_function to each feature of self.data."""
def recursive_get(data, keys):
if len(keys):
return recursive_get(data.get(keys[0]), keys[1:])
else:
return data
geometries = recursive_get(self.data, self.object_path.split('.'))['geometries'] # noqa
for feature in geometries:
feature.setdefault('properties', {}).setdefault('style', {}).update(self.style_function(feature)) | Applies self.style_function to each feature of self.data. | Below is the the instruction that describes the task:
### Input:
Applies self.style_function to each feature of self.data.
### Response:
def style_data(self):
"""Applies self.style_function to each feature of self.data."""
def recursive_get(data, keys):
if len(keys):
return recursive_get(data.get(keys[0]), keys[1:])
else:
return data
geometries = recursive_get(self.data, self.object_path.split('.'))['geometries'] # noqa
for feature in geometries:
feature.setdefault('properties', {}).setdefault('style', {}).update(self.style_function(feature)) |
def build_query_string(self, data):
"""This method occurs after dumping the data into the class.
Args:
data (dict): dictionary of all the query values
Returns:
data (dict): ordered dict of all the values
"""
query = []
keys_to_be_removed = []
for key, value in data.items():
if key not in ['version', 'restApi', 'resourcePath']:
if not key == 'method':
if key == 'points':
value = ','.join(str(val) for val in value)
keys_to_be_removed.append(key)
query.append('{0}={1}'.format(key, value))
keys_to_be_removed.append(key)
keys_to_be_removed.append(key)
querystring = '&'.join(query)
data['query'] = '{0}?{1}'.format(data['method'], querystring)
for k in list(set(keys_to_be_removed)):
del data[k]
return data | This method occurs after dumping the data into the class.
Args:
data (dict): dictionary of all the query values
Returns:
data (dict): ordered dict of all the values | Below is the the instruction that describes the task:
### Input:
This method occurs after dumping the data into the class.
Args:
data (dict): dictionary of all the query values
Returns:
data (dict): ordered dict of all the values
### Response:
def build_query_string(self, data):
"""This method occurs after dumping the data into the class.
Args:
data (dict): dictionary of all the query values
Returns:
data (dict): ordered dict of all the values
"""
query = []
keys_to_be_removed = []
for key, value in data.items():
if key not in ['version', 'restApi', 'resourcePath']:
if not key == 'method':
if key == 'points':
value = ','.join(str(val) for val in value)
keys_to_be_removed.append(key)
query.append('{0}={1}'.format(key, value))
keys_to_be_removed.append(key)
keys_to_be_removed.append(key)
querystring = '&'.join(query)
data['query'] = '{0}?{1}'.format(data['method'], querystring)
for k in list(set(keys_to_be_removed)):
del data[k]
return data |
def from_bits(self, bits):
"""
Set this person from bits (ignores the id)
:param bits: Bits representing a person
:type bits: bytearray
:rtype: Person
:raises ValueError: Bits has an unexpected length
"""
# TODO include id
if len(bits) != Person.BITS_PER_PERSON:
raise ValueError(u"Person requires exactly {} bits".format(
Person.BITS_PER_PERSON
))
self.sitting = bool(bits[0])
return self | Set this person from bits (ignores the id)
:param bits: Bits representing a person
:type bits: bytearray
:rtype: Person
:raises ValueError: Bits has an unexpected length | Below is the the instruction that describes the task:
### Input:
Set this person from bits (ignores the id)
:param bits: Bits representing a person
:type bits: bytearray
:rtype: Person
:raises ValueError: Bits has an unexpected length
### Response:
def from_bits(self, bits):
"""
Set this person from bits (ignores the id)
:param bits: Bits representing a person
:type bits: bytearray
:rtype: Person
:raises ValueError: Bits has an unexpected length
"""
# TODO include id
if len(bits) != Person.BITS_PER_PERSON:
raise ValueError(u"Person requires exactly {} bits".format(
Person.BITS_PER_PERSON
))
self.sitting = bool(bits[0])
return self |
def create(style_dataset, content_dataset, style_feature=None,
content_feature=None, max_iterations=None, model='resnet-16',
verbose=True, batch_size = 6, **kwargs):
"""
Create a :class:`StyleTransfer` model.
Parameters
----------
style_dataset: SFrame
Input style images. The columns named by the ``style_feature`` parameters will
be extracted for training the model.
content_dataset : SFrame
Input content images. The columns named by the ``content_feature`` parameters will
be extracted for training the model.
style_feature: string
Name of the column containing the input images in style SFrame.
'None' (the default) indicates the only image column in the style SFrame
should be used as the feature.
content_feature: string
Name of the column containing the input images in content SFrame.
'None' (the default) indicates the only image column in the content
SFrame should be used as the feature.
max_iterations : int
The number of training iterations. If 'None' (the default), then it will
be automatically determined based on the amount of data you provide.
model : string optional
Style transfer model to use:
- "resnet-16" : Fast and small-sized residual network that uses
VGG-16 as reference network during training.
batch_size : int, optional
If you are getting memory errors, try decreasing this value. If you
have a powerful computer, increasing this value may improve training
throughput.
verbose : bool, optional
If True, print progress updates and model details.
Returns
-------
out : StyleTransfer
A trained :class:`StyleTransfer` model.
See Also
--------
StyleTransfer
Examples
--------
.. sourcecode:: python
# Create datasets
>>> content_dataset = turicreate.image_analysis.load_images('content_images/')
>>> style_dataset = turicreate.image_analysis.load_images('style_images/')
# Train a style transfer model
>>> model = turicreate.style_transfer.create(content_dataset, style_dataset)
# Stylize an image on all styles
>>> stylized_images = model.stylize(data)
# Visualize the stylized images
>>> stylized_images.explore()
"""
if len(style_dataset) == 0:
raise _ToolkitError("style_dataset SFrame cannot be empty")
if len(content_dataset) == 0:
raise _ToolkitError("content_dataset SFrame cannot be empty")
if(batch_size < 1):
raise _ToolkitError("'batch_size' must be greater than or equal to 1")
from ._sframe_loader import SFrameSTIter as _SFrameSTIter
import mxnet as _mx
from .._mxnet import _mxnet_utils
if style_feature is None:
style_feature = _tkutl._find_only_image_column(style_dataset)
if content_feature is None:
content_feature = _tkutl._find_only_image_column(content_dataset)
if verbose:
print("Using '{}' in style_dataset as feature column and using "
"'{}' in content_dataset as feature column".format(style_feature, content_feature))
_raise_error_if_not_training_sframe(style_dataset, style_feature)
_raise_error_if_not_training_sframe(content_dataset, content_feature)
params = {
'batch_size': batch_size,
'vgg16_content_loss_layer': 2, # conv3_3 layer
'lr': 0.001,
'content_loss_mult': 1.0,
'style_loss_mult': [1e-4, 1e-4, 1e-4, 1e-4], # conv 1-4 layers
'finetune_all_params': True,
'pretrained_weights': False,
'print_loss_breakdown': False,
'input_shape': (256, 256),
'training_content_loader_type': 'stretch',
'use_augmentation': False,
'sequential_image_processing': False,
# Only used if use_augmentaion is True
'aug_resize': 0,
'aug_min_object_covered': 0,
'aug_rand_crop': 0.9,
'aug_rand_pad': 0.9,
'aug_rand_gray': 0.0,
'aug_aspect_ratio': 1.25,
'aug_hue': 0.05,
'aug_brightness': 0.05,
'aug_saturation': 0.05,
'aug_contrast': 0.05,
'aug_horizontal_flip': True,
'aug_area_range': (.05, 1.5),
'aug_pca_noise': 0.0,
'aug_max_attempts': 20,
'aug_inter_method': 2,
}
if '_advanced_parameters' in kwargs:
# Make sure no additional parameters are provided
new_keys = set(kwargs['_advanced_parameters'].keys())
set_keys = set(params.keys())
unsupported = new_keys - set_keys
if unsupported:
raise _ToolkitError('Unknown advanced parameters: {}'.format(unsupported))
params.update(kwargs['_advanced_parameters'])
_content_loss_mult = params['content_loss_mult']
_style_loss_mult = params['style_loss_mult']
num_gpus = _mxnet_utils.get_num_gpus_in_use(max_devices=params['batch_size'])
batch_size_each = params['batch_size'] // max(num_gpus, 1)
batch_size = max(num_gpus, 1) * batch_size_each
input_shape = params['input_shape']
iterations = 0
if max_iterations is None:
max_iterations = len(style_dataset) * 10000
if verbose:
print('Setting max_iterations to be {}'.format(max_iterations))
# data loader
if params['use_augmentation']:
content_loader_type = '%s-with-augmentation' % params['training_content_loader_type']
else:
content_loader_type = params['training_content_loader_type']
content_images_loader = _SFrameSTIter(content_dataset, batch_size, shuffle=True,
feature_column=content_feature, input_shape=input_shape,
loader_type=content_loader_type, aug_params=params,
sequential=params['sequential_image_processing'])
ctx = _mxnet_utils.get_mxnet_context(max_devices=params['batch_size'])
num_styles = len(style_dataset)
# TRANSFORMER MODEL
from ._model import Transformer as _Transformer
transformer_model_path = _pre_trained_models.STYLE_TRANSFER_BASE_MODELS[model]().get_model_path()
transformer = _Transformer(num_styles, batch_size_each)
transformer.collect_params().initialize(ctx=ctx)
if params['pretrained_weights']:
transformer.load_params(transformer_model_path, ctx, allow_missing=True)
# For some reason, the transformer fails to hybridize for training, so we
# avoid this until resolved
# transformer.hybridize()
# VGG MODEL
from ._model import Vgg16 as _Vgg16
vgg_model_path = _pre_trained_models.STYLE_TRANSFER_BASE_MODELS['Vgg16']().get_model_path()
vgg_model = _Vgg16()
vgg_model.collect_params().initialize(ctx=ctx)
vgg_model.load_params(vgg_model_path, ctx=ctx, ignore_extra=True)
vgg_model.hybridize()
# TRAINER
from mxnet import gluon as _gluon
from ._model import gram_matrix as _gram_matrix
if params['finetune_all_params']:
trainable_params = transformer.collect_params()
else:
trainable_params = transformer.collect_params('.*gamma|.*beta')
trainer = _gluon.Trainer(trainable_params, 'adam', {'learning_rate': params['lr']})
mse_loss = _gluon.loss.L2Loss()
start_time = _time.time()
smoothed_loss = None
last_time = 0
cuda_gpus = _mxnet_utils.get_gpus_in_use(max_devices=params['batch_size'])
num_mxnet_gpus = len(cuda_gpus)
if verbose:
# Estimate memory usage (based on experiments)
cuda_mem_req = 260 + batch_size_each * 880 + num_styles * 1.4
_tkutl._print_neural_compute_device(cuda_gpus=cuda_gpus, use_mps=False,
cuda_mem_req=cuda_mem_req, has_mps_impl=False)
#
# Pre-compute gram matrices for style images
#
if verbose:
print('Analyzing visual features of the style images')
style_images_loader = _SFrameSTIter(style_dataset, batch_size, shuffle=False, num_epochs=1,
feature_column=style_feature, input_shape=input_shape,
loader_type='stretch',
sequential=params['sequential_image_processing'])
num_layers = len(params['style_loss_mult'])
gram_chunks = [[] for _ in range(num_layers)]
for s_batch in style_images_loader:
s_data = _gluon.utils.split_and_load(s_batch.data[0], ctx_list=ctx, batch_axis=0)
for s in s_data:
vgg16_s = _vgg16_data_prep(s)
ret = vgg_model(vgg16_s)
grams = [_gram_matrix(x) for x in ret]
for i, gram in enumerate(grams):
if gram.context != _mx.cpu(0):
gram = gram.as_in_context(_mx.cpu(0))
gram_chunks[i].append(gram)
del style_images_loader
grams = [
# The concatenated styles may be padded, so we slice overflow
_mx.nd.concat(*chunks, dim=0)[:num_styles]
for chunks in gram_chunks
]
# A context->grams look-up table, where all the gram matrices have been
# distributed
ctx_grams = {}
if ctx[0] == _mx.cpu(0):
ctx_grams[_mx.cpu(0)] = grams
else:
for ctx0 in ctx:
ctx_grams[ctx0] = [gram.as_in_context(ctx0) for gram in grams]
#
# Training loop
#
vgg_content_loss_layer = params['vgg16_content_loss_layer']
rs = _np.random.RandomState(1234)
while iterations < max_iterations:
content_images_loader.reset()
for c_batch in content_images_loader:
c_data = _gluon.utils.split_and_load(c_batch.data[0], ctx_list=ctx, batch_axis=0)
Ls = []
curr_content_loss = []
curr_style_loss = []
with _mx.autograd.record():
for c in c_data:
# Randomize styles to train
indices = _mx.nd.array(rs.randint(num_styles, size=batch_size_each),
dtype=_np.int64, ctx=c.context)
# Generate pastiche
p = transformer(c, indices)
# mean subtraction
vgg16_p = _vgg16_data_prep(p)
vgg16_c = _vgg16_data_prep(c)
# vgg forward
p_vgg_outputs = vgg_model(vgg16_p)
c_vgg_outputs = vgg_model(vgg16_c)
c_content_layer = c_vgg_outputs[vgg_content_loss_layer]
p_content_layer = p_vgg_outputs[vgg_content_loss_layer]
# Calculate Loss
# Style Loss between style image and stylized image
# Ls = sum of L2 norm of gram matrix of vgg16's conv layers
style_losses = []
for gram, p_vgg_output, style_loss_mult in zip(ctx_grams[c.context], p_vgg_outputs, _style_loss_mult):
gram_s_vgg = gram[indices]
gram_p_vgg = _gram_matrix(p_vgg_output)
style_losses.append(style_loss_mult * mse_loss(gram_s_vgg, gram_p_vgg))
style_loss = _mx.nd.add_n(*style_losses)
# Content Loss between content image and stylized image
# Lc = L2 norm at a single layer in vgg16
content_loss = _content_loss_mult * mse_loss(c_content_layer,
p_content_layer)
curr_content_loss.append(content_loss)
curr_style_loss.append(style_loss)
# Divide loss by large number to get into a more legible
# range
total_loss = (content_loss + style_loss) / 10000.0
Ls.append(total_loss)
for L in Ls:
L.backward()
cur_loss = _np.mean([L.asnumpy()[0] for L in Ls])
if smoothed_loss is None:
smoothed_loss = cur_loss
else:
smoothed_loss = 0.9 * smoothed_loss + 0.1 * cur_loss
iterations += 1
trainer.step(batch_size)
if verbose and iterations == 1:
# Print progress table header
column_names = ['Iteration', 'Loss', 'Elapsed Time']
num_columns = len(column_names)
column_width = max(map(lambda x: len(x), column_names)) + 2
hr = '+' + '+'.join(['-' * column_width] * num_columns) + '+'
print(hr)
print(('| {:<{width}}' * num_columns + '|').format(*column_names, width=column_width-1))
print(hr)
cur_time = _time.time()
if verbose and (cur_time > last_time + 10 or iterations == max_iterations):
# Print progress table row
elapsed_time = cur_time - start_time
print("| {cur_iter:<{width}}| {loss:<{width}.3f}| {time:<{width}.1f}|".format(
cur_iter = iterations, loss = smoothed_loss,
time = elapsed_time , width = column_width-1))
if params['print_loss_breakdown']:
print_content_loss = _np.mean([L.asnumpy()[0] for L in curr_content_loss])
print_style_loss = _np.mean([L.asnumpy()[0] for L in curr_style_loss])
print('Total Loss: {:6.3f} | Content Loss: {:6.3f} | Style Loss: {:6.3f}'.format(cur_loss, print_content_loss, print_style_loss))
last_time = cur_time
if iterations == max_iterations:
print(hr)
break
training_time = _time.time() - start_time
style_sa = style_dataset[style_feature]
idx_column = _tc.SArray(range(0, style_sa.shape[0]))
style_sframe = _tc.SFrame({"style": idx_column, style_feature: style_sa})
# Save the model state
state = {
'_model': transformer,
'_training_time_as_string': _seconds_as_string(training_time),
'batch_size': batch_size,
'num_styles': num_styles,
'model': model,
'input_image_shape': input_shape,
'styles': style_sframe,
'num_content_images': len(content_dataset),
'training_time': training_time,
'max_iterations': max_iterations,
'training_iterations': iterations,
'training_epochs': content_images_loader.cur_epoch,
'style_feature': style_feature,
'content_feature': content_feature,
"_index_column": "style",
'training_loss': smoothed_loss,
}
return StyleTransfer(state) | Create a :class:`StyleTransfer` model.
Parameters
----------
style_dataset: SFrame
Input style images. The columns named by the ``style_feature`` parameters will
be extracted for training the model.
content_dataset : SFrame
Input content images. The columns named by the ``content_feature`` parameters will
be extracted for training the model.
style_feature: string
Name of the column containing the input images in style SFrame.
'None' (the default) indicates the only image column in the style SFrame
should be used as the feature.
content_feature: string
Name of the column containing the input images in content SFrame.
'None' (the default) indicates the only image column in the content
SFrame should be used as the feature.
max_iterations : int
The number of training iterations. If 'None' (the default), then it will
be automatically determined based on the amount of data you provide.
model : string optional
Style transfer model to use:
- "resnet-16" : Fast and small-sized residual network that uses
VGG-16 as reference network during training.
batch_size : int, optional
If you are getting memory errors, try decreasing this value. If you
have a powerful computer, increasing this value may improve training
throughput.
verbose : bool, optional
If True, print progress updates and model details.
Returns
-------
out : StyleTransfer
A trained :class:`StyleTransfer` model.
See Also
--------
StyleTransfer
Examples
--------
.. sourcecode:: python
# Create datasets
>>> content_dataset = turicreate.image_analysis.load_images('content_images/')
>>> style_dataset = turicreate.image_analysis.load_images('style_images/')
# Train a style transfer model
>>> model = turicreate.style_transfer.create(content_dataset, style_dataset)
# Stylize an image on all styles
>>> stylized_images = model.stylize(data)
# Visualize the stylized images
>>> stylized_images.explore() | Below is the the instruction that describes the task:
### Input:
Create a :class:`StyleTransfer` model.
Parameters
----------
style_dataset: SFrame
Input style images. The columns named by the ``style_feature`` parameters will
be extracted for training the model.
content_dataset : SFrame
Input content images. The columns named by the ``content_feature`` parameters will
be extracted for training the model.
style_feature: string
Name of the column containing the input images in style SFrame.
'None' (the default) indicates the only image column in the style SFrame
should be used as the feature.
content_feature: string
Name of the column containing the input images in content SFrame.
'None' (the default) indicates the only image column in the content
SFrame should be used as the feature.
max_iterations : int
The number of training iterations. If 'None' (the default), then it will
be automatically determined based on the amount of data you provide.
model : string optional
Style transfer model to use:
- "resnet-16" : Fast and small-sized residual network that uses
VGG-16 as reference network during training.
batch_size : int, optional
If you are getting memory errors, try decreasing this value. If you
have a powerful computer, increasing this value may improve training
throughput.
verbose : bool, optional
If True, print progress updates and model details.
Returns
-------
out : StyleTransfer
A trained :class:`StyleTransfer` model.
See Also
--------
StyleTransfer
Examples
--------
.. sourcecode:: python
# Create datasets
>>> content_dataset = turicreate.image_analysis.load_images('content_images/')
>>> style_dataset = turicreate.image_analysis.load_images('style_images/')
# Train a style transfer model
>>> model = turicreate.style_transfer.create(content_dataset, style_dataset)
# Stylize an image on all styles
>>> stylized_images = model.stylize(data)
# Visualize the stylized images
>>> stylized_images.explore()
### Response:
def create(style_dataset, content_dataset, style_feature=None,
content_feature=None, max_iterations=None, model='resnet-16',
verbose=True, batch_size = 6, **kwargs):
"""
Create a :class:`StyleTransfer` model.
Parameters
----------
style_dataset: SFrame
Input style images. The columns named by the ``style_feature`` parameters will
be extracted for training the model.
content_dataset : SFrame
Input content images. The columns named by the ``content_feature`` parameters will
be extracted for training the model.
style_feature: string
Name of the column containing the input images in style SFrame.
'None' (the default) indicates the only image column in the style SFrame
should be used as the feature.
content_feature: string
Name of the column containing the input images in content SFrame.
'None' (the default) indicates the only image column in the content
SFrame should be used as the feature.
max_iterations : int
The number of training iterations. If 'None' (the default), then it will
be automatically determined based on the amount of data you provide.
model : string optional
Style transfer model to use:
- "resnet-16" : Fast and small-sized residual network that uses
VGG-16 as reference network during training.
batch_size : int, optional
If you are getting memory errors, try decreasing this value. If you
have a powerful computer, increasing this value may improve training
throughput.
verbose : bool, optional
If True, print progress updates and model details.
Returns
-------
out : StyleTransfer
A trained :class:`StyleTransfer` model.
See Also
--------
StyleTransfer
Examples
--------
.. sourcecode:: python
# Create datasets
>>> content_dataset = turicreate.image_analysis.load_images('content_images/')
>>> style_dataset = turicreate.image_analysis.load_images('style_images/')
# Train a style transfer model
>>> model = turicreate.style_transfer.create(content_dataset, style_dataset)
# Stylize an image on all styles
>>> stylized_images = model.stylize(data)
# Visualize the stylized images
>>> stylized_images.explore()
"""
if len(style_dataset) == 0:
raise _ToolkitError("style_dataset SFrame cannot be empty")
if len(content_dataset) == 0:
raise _ToolkitError("content_dataset SFrame cannot be empty")
if(batch_size < 1):
raise _ToolkitError("'batch_size' must be greater than or equal to 1")
from ._sframe_loader import SFrameSTIter as _SFrameSTIter
import mxnet as _mx
from .._mxnet import _mxnet_utils
if style_feature is None:
style_feature = _tkutl._find_only_image_column(style_dataset)
if content_feature is None:
content_feature = _tkutl._find_only_image_column(content_dataset)
if verbose:
print("Using '{}' in style_dataset as feature column and using "
"'{}' in content_dataset as feature column".format(style_feature, content_feature))
_raise_error_if_not_training_sframe(style_dataset, style_feature)
_raise_error_if_not_training_sframe(content_dataset, content_feature)
params = {
'batch_size': batch_size,
'vgg16_content_loss_layer': 2, # conv3_3 layer
'lr': 0.001,
'content_loss_mult': 1.0,
'style_loss_mult': [1e-4, 1e-4, 1e-4, 1e-4], # conv 1-4 layers
'finetune_all_params': True,
'pretrained_weights': False,
'print_loss_breakdown': False,
'input_shape': (256, 256),
'training_content_loader_type': 'stretch',
'use_augmentation': False,
'sequential_image_processing': False,
# Only used if use_augmentaion is True
'aug_resize': 0,
'aug_min_object_covered': 0,
'aug_rand_crop': 0.9,
'aug_rand_pad': 0.9,
'aug_rand_gray': 0.0,
'aug_aspect_ratio': 1.25,
'aug_hue': 0.05,
'aug_brightness': 0.05,
'aug_saturation': 0.05,
'aug_contrast': 0.05,
'aug_horizontal_flip': True,
'aug_area_range': (.05, 1.5),
'aug_pca_noise': 0.0,
'aug_max_attempts': 20,
'aug_inter_method': 2,
}
if '_advanced_parameters' in kwargs:
# Make sure no additional parameters are provided
new_keys = set(kwargs['_advanced_parameters'].keys())
set_keys = set(params.keys())
unsupported = new_keys - set_keys
if unsupported:
raise _ToolkitError('Unknown advanced parameters: {}'.format(unsupported))
params.update(kwargs['_advanced_parameters'])
_content_loss_mult = params['content_loss_mult']
_style_loss_mult = params['style_loss_mult']
num_gpus = _mxnet_utils.get_num_gpus_in_use(max_devices=params['batch_size'])
batch_size_each = params['batch_size'] // max(num_gpus, 1)
batch_size = max(num_gpus, 1) * batch_size_each
input_shape = params['input_shape']
iterations = 0
if max_iterations is None:
max_iterations = len(style_dataset) * 10000
if verbose:
print('Setting max_iterations to be {}'.format(max_iterations))
# data loader
if params['use_augmentation']:
content_loader_type = '%s-with-augmentation' % params['training_content_loader_type']
else:
content_loader_type = params['training_content_loader_type']
content_images_loader = _SFrameSTIter(content_dataset, batch_size, shuffle=True,
feature_column=content_feature, input_shape=input_shape,
loader_type=content_loader_type, aug_params=params,
sequential=params['sequential_image_processing'])
ctx = _mxnet_utils.get_mxnet_context(max_devices=params['batch_size'])
num_styles = len(style_dataset)
# TRANSFORMER MODEL
from ._model import Transformer as _Transformer
transformer_model_path = _pre_trained_models.STYLE_TRANSFER_BASE_MODELS[model]().get_model_path()
transformer = _Transformer(num_styles, batch_size_each)
transformer.collect_params().initialize(ctx=ctx)
if params['pretrained_weights']:
transformer.load_params(transformer_model_path, ctx, allow_missing=True)
# For some reason, the transformer fails to hybridize for training, so we
# avoid this until resolved
# transformer.hybridize()
# VGG MODEL
from ._model import Vgg16 as _Vgg16
vgg_model_path = _pre_trained_models.STYLE_TRANSFER_BASE_MODELS['Vgg16']().get_model_path()
vgg_model = _Vgg16()
vgg_model.collect_params().initialize(ctx=ctx)
vgg_model.load_params(vgg_model_path, ctx=ctx, ignore_extra=True)
vgg_model.hybridize()
# TRAINER
from mxnet import gluon as _gluon
from ._model import gram_matrix as _gram_matrix
if params['finetune_all_params']:
trainable_params = transformer.collect_params()
else:
trainable_params = transformer.collect_params('.*gamma|.*beta')
trainer = _gluon.Trainer(trainable_params, 'adam', {'learning_rate': params['lr']})
mse_loss = _gluon.loss.L2Loss()
start_time = _time.time()
smoothed_loss = None
last_time = 0
cuda_gpus = _mxnet_utils.get_gpus_in_use(max_devices=params['batch_size'])
num_mxnet_gpus = len(cuda_gpus)
if verbose:
# Estimate memory usage (based on experiments)
cuda_mem_req = 260 + batch_size_each * 880 + num_styles * 1.4
_tkutl._print_neural_compute_device(cuda_gpus=cuda_gpus, use_mps=False,
cuda_mem_req=cuda_mem_req, has_mps_impl=False)
#
# Pre-compute gram matrices for style images
#
if verbose:
print('Analyzing visual features of the style images')
style_images_loader = _SFrameSTIter(style_dataset, batch_size, shuffle=False, num_epochs=1,
feature_column=style_feature, input_shape=input_shape,
loader_type='stretch',
sequential=params['sequential_image_processing'])
num_layers = len(params['style_loss_mult'])
gram_chunks = [[] for _ in range(num_layers)]
for s_batch in style_images_loader:
s_data = _gluon.utils.split_and_load(s_batch.data[0], ctx_list=ctx, batch_axis=0)
for s in s_data:
vgg16_s = _vgg16_data_prep(s)
ret = vgg_model(vgg16_s)
grams = [_gram_matrix(x) for x in ret]
for i, gram in enumerate(grams):
if gram.context != _mx.cpu(0):
gram = gram.as_in_context(_mx.cpu(0))
gram_chunks[i].append(gram)
del style_images_loader
grams = [
# The concatenated styles may be padded, so we slice overflow
_mx.nd.concat(*chunks, dim=0)[:num_styles]
for chunks in gram_chunks
]
# A context->grams look-up table, where all the gram matrices have been
# distributed
ctx_grams = {}
if ctx[0] == _mx.cpu(0):
ctx_grams[_mx.cpu(0)] = grams
else:
for ctx0 in ctx:
ctx_grams[ctx0] = [gram.as_in_context(ctx0) for gram in grams]
#
# Training loop
#
vgg_content_loss_layer = params['vgg16_content_loss_layer']
rs = _np.random.RandomState(1234)
while iterations < max_iterations:
content_images_loader.reset()
for c_batch in content_images_loader:
c_data = _gluon.utils.split_and_load(c_batch.data[0], ctx_list=ctx, batch_axis=0)
Ls = []
curr_content_loss = []
curr_style_loss = []
with _mx.autograd.record():
for c in c_data:
# Randomize styles to train
indices = _mx.nd.array(rs.randint(num_styles, size=batch_size_each),
dtype=_np.int64, ctx=c.context)
# Generate pastiche
p = transformer(c, indices)
# mean subtraction
vgg16_p = _vgg16_data_prep(p)
vgg16_c = _vgg16_data_prep(c)
# vgg forward
p_vgg_outputs = vgg_model(vgg16_p)
c_vgg_outputs = vgg_model(vgg16_c)
c_content_layer = c_vgg_outputs[vgg_content_loss_layer]
p_content_layer = p_vgg_outputs[vgg_content_loss_layer]
# Calculate Loss
# Style Loss between style image and stylized image
# Ls = sum of L2 norm of gram matrix of vgg16's conv layers
style_losses = []
for gram, p_vgg_output, style_loss_mult in zip(ctx_grams[c.context], p_vgg_outputs, _style_loss_mult):
gram_s_vgg = gram[indices]
gram_p_vgg = _gram_matrix(p_vgg_output)
style_losses.append(style_loss_mult * mse_loss(gram_s_vgg, gram_p_vgg))
style_loss = _mx.nd.add_n(*style_losses)
# Content Loss between content image and stylized image
# Lc = L2 norm at a single layer in vgg16
content_loss = _content_loss_mult * mse_loss(c_content_layer,
p_content_layer)
curr_content_loss.append(content_loss)
curr_style_loss.append(style_loss)
# Divide loss by large number to get into a more legible
# range
total_loss = (content_loss + style_loss) / 10000.0
Ls.append(total_loss)
for L in Ls:
L.backward()
cur_loss = _np.mean([L.asnumpy()[0] for L in Ls])
if smoothed_loss is None:
smoothed_loss = cur_loss
else:
smoothed_loss = 0.9 * smoothed_loss + 0.1 * cur_loss
iterations += 1
trainer.step(batch_size)
if verbose and iterations == 1:
# Print progress table header
column_names = ['Iteration', 'Loss', 'Elapsed Time']
num_columns = len(column_names)
column_width = max(map(lambda x: len(x), column_names)) + 2
hr = '+' + '+'.join(['-' * column_width] * num_columns) + '+'
print(hr)
print(('| {:<{width}}' * num_columns + '|').format(*column_names, width=column_width-1))
print(hr)
cur_time = _time.time()
if verbose and (cur_time > last_time + 10 or iterations == max_iterations):
# Print progress table row
elapsed_time = cur_time - start_time
print("| {cur_iter:<{width}}| {loss:<{width}.3f}| {time:<{width}.1f}|".format(
cur_iter = iterations, loss = smoothed_loss,
time = elapsed_time , width = column_width-1))
if params['print_loss_breakdown']:
print_content_loss = _np.mean([L.asnumpy()[0] for L in curr_content_loss])
print_style_loss = _np.mean([L.asnumpy()[0] for L in curr_style_loss])
print('Total Loss: {:6.3f} | Content Loss: {:6.3f} | Style Loss: {:6.3f}'.format(cur_loss, print_content_loss, print_style_loss))
last_time = cur_time
if iterations == max_iterations:
print(hr)
break
training_time = _time.time() - start_time
style_sa = style_dataset[style_feature]
idx_column = _tc.SArray(range(0, style_sa.shape[0]))
style_sframe = _tc.SFrame({"style": idx_column, style_feature: style_sa})
# Save the model state
state = {
'_model': transformer,
'_training_time_as_string': _seconds_as_string(training_time),
'batch_size': batch_size,
'num_styles': num_styles,
'model': model,
'input_image_shape': input_shape,
'styles': style_sframe,
'num_content_images': len(content_dataset),
'training_time': training_time,
'max_iterations': max_iterations,
'training_iterations': iterations,
'training_epochs': content_images_loader.cur_epoch,
'style_feature': style_feature,
'content_feature': content_feature,
"_index_column": "style",
'training_loss': smoothed_loss,
}
return StyleTransfer(state) |
def use_certificate(self, cert):
"""
Load a certificate from a X509 object
:param cert: The X509 object
:return: None
"""
if not isinstance(cert, X509):
raise TypeError("cert must be an X509 instance")
use_result = _lib.SSL_CTX_use_certificate(self._context, cert._x509)
if not use_result:
_raise_current_error() | Load a certificate from a X509 object
:param cert: The X509 object
:return: None | Below is the the instruction that describes the task:
### Input:
Load a certificate from a X509 object
:param cert: The X509 object
:return: None
### Response:
def use_certificate(self, cert):
"""
Load a certificate from a X509 object
:param cert: The X509 object
:return: None
"""
if not isinstance(cert, X509):
raise TypeError("cert must be an X509 instance")
use_result = _lib.SSL_CTX_use_certificate(self._context, cert._x509)
if not use_result:
_raise_current_error() |
def circles(st, layer, axis, ax=None, talpha=1.0, cedge='white', cface='white'):
"""
Plots a set of circles corresponding to a slice through the platonic
structure. Copied from twoslice_overlay with comments, standaloneness.
Inputs
------
pos : array of particle positions; [N,3]
rad : array of particle radii; [N]
ax : plt.axis instance
layer : Which layer of the slice to use.
axis : The slice of the image, 0, 1, or 2.
cedge : edge color
cface : face color
talpha : Alpha of the thing
"""
pos = st.obj_get_positions()
rad = st.obj_get_radii()
shape = st.ishape.shape.tolist()
shape.pop(axis) #shape is now the shape of the image
if ax is None:
fig = plt.figure()
axisbg = 'white' if cface == 'black' else 'black'
sx, sy = ((1,shape[1]/float(shape[0])) if shape[0] > shape[1] else
(shape[0]/float(shape[1]), 1))
ax = fig.add_axes((0,0, sx, sy), axisbg=axisbg)
# get the index of the particles we want to include
particles = np.arange(len(pos))[np.abs(pos[:,axis] - layer) < rad]
# for each of these particles display the effective radius
# in the proper place
scale = 1.0 #np.max(shape).astype('float')
for i in particles:
p = pos[i].copy()
r = 2*np.sqrt(rad[i]**2 - (p[axis] - layer)**2)
#CIRCLE IS IN FIGURE COORDINATES!!!
if axis==0:
ix = 1; iy = 2
elif axis == 1:
ix = 0; iy = 2
elif axis==2:
ix = 0; iy = 1
c = Circle((p[ix]/scale, p[iy]/scale), radius=r/2/scale, fc=cface,
ec=cedge, alpha=talpha)
ax.add_patch(c)
# plt.axis([0,1,0,1])
plt.axis('equal') #circles not ellipses
return ax | Plots a set of circles corresponding to a slice through the platonic
structure. Copied from twoslice_overlay with comments, standaloneness.
Inputs
------
pos : array of particle positions; [N,3]
rad : array of particle radii; [N]
ax : plt.axis instance
layer : Which layer of the slice to use.
axis : The slice of the image, 0, 1, or 2.
cedge : edge color
cface : face color
talpha : Alpha of the thing | Below is the the instruction that describes the task:
### Input:
Plots a set of circles corresponding to a slice through the platonic
structure. Copied from twoslice_overlay with comments, standaloneness.
Inputs
------
pos : array of particle positions; [N,3]
rad : array of particle radii; [N]
ax : plt.axis instance
layer : Which layer of the slice to use.
axis : The slice of the image, 0, 1, or 2.
cedge : edge color
cface : face color
talpha : Alpha of the thing
### Response:
def circles(st, layer, axis, ax=None, talpha=1.0, cedge='white', cface='white'):
"""
Plots a set of circles corresponding to a slice through the platonic
structure. Copied from twoslice_overlay with comments, standaloneness.
Inputs
------
pos : array of particle positions; [N,3]
rad : array of particle radii; [N]
ax : plt.axis instance
layer : Which layer of the slice to use.
axis : The slice of the image, 0, 1, or 2.
cedge : edge color
cface : face color
talpha : Alpha of the thing
"""
pos = st.obj_get_positions()
rad = st.obj_get_radii()
shape = st.ishape.shape.tolist()
shape.pop(axis) #shape is now the shape of the image
if ax is None:
fig = plt.figure()
axisbg = 'white' if cface == 'black' else 'black'
sx, sy = ((1,shape[1]/float(shape[0])) if shape[0] > shape[1] else
(shape[0]/float(shape[1]), 1))
ax = fig.add_axes((0,0, sx, sy), axisbg=axisbg)
# get the index of the particles we want to include
particles = np.arange(len(pos))[np.abs(pos[:,axis] - layer) < rad]
# for each of these particles display the effective radius
# in the proper place
scale = 1.0 #np.max(shape).astype('float')
for i in particles:
p = pos[i].copy()
r = 2*np.sqrt(rad[i]**2 - (p[axis] - layer)**2)
#CIRCLE IS IN FIGURE COORDINATES!!!
if axis==0:
ix = 1; iy = 2
elif axis == 1:
ix = 0; iy = 2
elif axis==2:
ix = 0; iy = 1
c = Circle((p[ix]/scale, p[iy]/scale), radius=r/2/scale, fc=cface,
ec=cedge, alpha=talpha)
ax.add_patch(c)
# plt.axis([0,1,0,1])
plt.axis('equal') #circles not ellipses
return ax |
def visit_exact_match_value(self, node, fieldnames=None):
"""Generates a term query (exact search in ElasticSearch)."""
if not fieldnames:
fieldnames = ['_all']
else:
fieldnames = force_list(fieldnames)
if ElasticSearchVisitor.KEYWORD_TO_ES_FIELDNAME['exact-author'] == fieldnames[0]:
return self._generate_exact_author_query(node.value)
elif ElasticSearchVisitor.KEYWORD_TO_ES_FIELDNAME['type-code'] == fieldnames[0]:
return self._generate_type_code_query(node.value)
elif ElasticSearchVisitor.KEYWORD_TO_ES_FIELDNAME['journal'] == fieldnames:
return self._generate_journal_nested_queries(node.value)
bai_fieldnames = self._generate_fieldnames_if_bai_query(
node.value,
bai_field_variation=FieldVariations.raw,
query_bai_field_if_dots_in_name=False
)
if ElasticSearchVisitor.KEYWORD_TO_ES_FIELDNAME['date'] == fieldnames:
term_queries = []
for field in fieldnames:
term_query = \
{'term': {field: _truncate_date_value_according_on_date_field(field, node.value).dumps()}}
term_queries.append(
generate_nested_query(ElasticSearchVisitor.DATE_NESTED_QUERY_PATH, term_query)
if field in ElasticSearchVisitor.DATE_NESTED_FIELDS
else term_query
)
elif ElasticSearchVisitor.KEYWORD_TO_ES_FIELDNAME['author'] in fieldnames:
term_queries = [
generate_nested_query(ElasticSearchVisitor.AUTHORS_NESTED_QUERY_PATH, {'term': {field: node.value}})
for field in (bai_fieldnames or fieldnames)
]
else:
term_queries = [{'term': {field: node.value}} for field in (bai_fieldnames or fieldnames)]
return wrap_queries_in_bool_clauses_if_more_than_one(term_queries, use_must_clause=False) | Generates a term query (exact search in ElasticSearch). | Below is the the instruction that describes the task:
### Input:
Generates a term query (exact search in ElasticSearch).
### Response:
def visit_exact_match_value(self, node, fieldnames=None):
"""Generates a term query (exact search in ElasticSearch)."""
if not fieldnames:
fieldnames = ['_all']
else:
fieldnames = force_list(fieldnames)
if ElasticSearchVisitor.KEYWORD_TO_ES_FIELDNAME['exact-author'] == fieldnames[0]:
return self._generate_exact_author_query(node.value)
elif ElasticSearchVisitor.KEYWORD_TO_ES_FIELDNAME['type-code'] == fieldnames[0]:
return self._generate_type_code_query(node.value)
elif ElasticSearchVisitor.KEYWORD_TO_ES_FIELDNAME['journal'] == fieldnames:
return self._generate_journal_nested_queries(node.value)
bai_fieldnames = self._generate_fieldnames_if_bai_query(
node.value,
bai_field_variation=FieldVariations.raw,
query_bai_field_if_dots_in_name=False
)
if ElasticSearchVisitor.KEYWORD_TO_ES_FIELDNAME['date'] == fieldnames:
term_queries = []
for field in fieldnames:
term_query = \
{'term': {field: _truncate_date_value_according_on_date_field(field, node.value).dumps()}}
term_queries.append(
generate_nested_query(ElasticSearchVisitor.DATE_NESTED_QUERY_PATH, term_query)
if field in ElasticSearchVisitor.DATE_NESTED_FIELDS
else term_query
)
elif ElasticSearchVisitor.KEYWORD_TO_ES_FIELDNAME['author'] in fieldnames:
term_queries = [
generate_nested_query(ElasticSearchVisitor.AUTHORS_NESTED_QUERY_PATH, {'term': {field: node.value}})
for field in (bai_fieldnames or fieldnames)
]
else:
term_queries = [{'term': {field: node.value}} for field in (bai_fieldnames or fieldnames)]
return wrap_queries_in_bool_clauses_if_more_than_one(term_queries, use_must_clause=False) |
def _handleDelete(self):
""" Handles "delete" characters """
if self.cursorPos < len(self.inputBuffer):
self.inputBuffer = self.inputBuffer[0:self.cursorPos] + self.inputBuffer[self.cursorPos+1:]
self._refreshInputPrompt(len(self.inputBuffer)+1) | Handles "delete" characters | Below is the the instruction that describes the task:
### Input:
Handles "delete" characters
### Response:
def _handleDelete(self):
""" Handles "delete" characters """
if self.cursorPos < len(self.inputBuffer):
self.inputBuffer = self.inputBuffer[0:self.cursorPos] + self.inputBuffer[self.cursorPos+1:]
self._refreshInputPrompt(len(self.inputBuffer)+1) |
def verify_sc_url(url: str) -> bool:
"""Verify signature certificate URL against Amazon Alexa requirements.
Each call of Agent passes incoming utterances batch through skills filter,
agent skills, skills processor. Batch of dialog IDs can be provided, in
other case utterances indexes in incoming batch are used as dialog IDs.
Args:
url: Signature certificate URL from SignatureCertChainUrl HTTP header.
Returns:
result: True if verification was successful, False if not.
"""
parsed = urlsplit(url)
scheme: str = parsed.scheme
netloc: str = parsed.netloc
path: str = parsed.path
try:
port = parsed.port
except ValueError:
port = None
result = (scheme.lower() == 'https' and
netloc.lower().split(':')[0] == 's3.amazonaws.com' and
path.startswith('/echo.api/') and
(port == 443 or port is None))
return result | Verify signature certificate URL against Amazon Alexa requirements.
Each call of Agent passes incoming utterances batch through skills filter,
agent skills, skills processor. Batch of dialog IDs can be provided, in
other case utterances indexes in incoming batch are used as dialog IDs.
Args:
url: Signature certificate URL from SignatureCertChainUrl HTTP header.
Returns:
result: True if verification was successful, False if not. | Below is the the instruction that describes the task:
### Input:
Verify signature certificate URL against Amazon Alexa requirements.
Each call of Agent passes incoming utterances batch through skills filter,
agent skills, skills processor. Batch of dialog IDs can be provided, in
other case utterances indexes in incoming batch are used as dialog IDs.
Args:
url: Signature certificate URL from SignatureCertChainUrl HTTP header.
Returns:
result: True if verification was successful, False if not.
### Response:
def verify_sc_url(url: str) -> bool:
"""Verify signature certificate URL against Amazon Alexa requirements.
Each call of Agent passes incoming utterances batch through skills filter,
agent skills, skills processor. Batch of dialog IDs can be provided, in
other case utterances indexes in incoming batch are used as dialog IDs.
Args:
url: Signature certificate URL from SignatureCertChainUrl HTTP header.
Returns:
result: True if verification was successful, False if not.
"""
parsed = urlsplit(url)
scheme: str = parsed.scheme
netloc: str = parsed.netloc
path: str = parsed.path
try:
port = parsed.port
except ValueError:
port = None
result = (scheme.lower() == 'https' and
netloc.lower().split(':')[0] == 's3.amazonaws.com' and
path.startswith('/echo.api/') and
(port == 443 or port is None))
return result |
def write(self, handle):
'''Write metadata and point + analog frames to a file handle.
Parameters
----------
handle : file
Write metadata and C3D motion frames to the given file handle. The
writer does not close the handle.
'''
if not self._frames:
return
def add(name, desc, bpe, format, bytes, *dimensions):
group.add_param(name,
desc=desc,
bytes_per_element=bpe,
bytes=struct.pack(format, bytes),
dimensions=list(dimensions))
def add_str(name, desc, bytes, *dimensions):
group.add_param(name,
desc=desc,
bytes_per_element=-1,
bytes=bytes.encode('utf-8'),
dimensions=list(dimensions))
def add_empty_array(name, desc, bpe):
group.add_param(name, desc=desc, bytes_per_element=bpe, dimensions=[0])
points, analog = self._frames[0]
ppf = len(points)
# POINT group
group = self.add_group(1, 'POINT', 'POINT group')
add('USED', 'Number of 3d markers', 2, '<H', ppf)
add('FRAMES', 'frame count', 2, '<H', min(65535, len(self._frames)))
add('DATA_START', 'data block number', 2, '<H', 0)
add('SCALE', '3d scale factor', 4, '<f', self._point_scale)
add('RATE', '3d data capture rate', 4, '<f', self._point_rate)
add_str('X_SCREEN', 'X_SCREEN parameter', '+X', 2)
add_str('Y_SCREEN', 'Y_SCREEN parameter', '+Y', 2)
add_str('UNITS', '3d data units', self._point_units, len(self._point_units))
add_str('LABELS', 'labels', ''.join('M%03d ' % i for i in range(ppf)), 5, ppf)
add_str('DESCRIPTIONS', 'descriptions', ' ' * 16 * ppf, 16, ppf)
# ANALOG group
group = self.add_group(2, 'ANALOG', 'ANALOG group')
add('USED', 'analog channel count', 2, '<H', analog.shape[0])
add('RATE', 'analog samples per 3d frame', 4, '<f', analog.shape[1])
add('GEN_SCALE', 'analog general scale factor', 4, '<f', self._gen_scale)
add_empty_array('SCALE', 'analog channel scale factors', 4)
add_empty_array('OFFSET', 'analog channel offsets', 2)
# TRIAL group
group = self.add_group(3, 'TRIAL', 'TRIAL group')
add('ACTUAL_START_FIELD', 'actual start frame', 2, '<I', 1, 2)
add('ACTUAL_END_FIELD', 'actual end frame', 2, '<I', len(self._frames), 2)
# sync parameter information to header.
blocks = self.parameter_blocks()
self.get('POINT:DATA_START').bytes = struct.pack('<H', 2 + blocks)
self.header.data_block = 2 + blocks
self.header.frame_rate = self._point_rate
self.header.last_frame = min(len(self._frames), 65535)
self.header.point_count = ppf
self.header.analog_count = np.prod(analog.shape)
self.header.analog_per_frame = analog.shape[0]
self.header.scale_factor = self._point_scale
self._write_metadata(handle)
self._write_frames(handle) | Write metadata and point + analog frames to a file handle.
Parameters
----------
handle : file
Write metadata and C3D motion frames to the given file handle. The
writer does not close the handle. | Below is the the instruction that describes the task:
### Input:
Write metadata and point + analog frames to a file handle.
Parameters
----------
handle : file
Write metadata and C3D motion frames to the given file handle. The
writer does not close the handle.
### Response:
def write(self, handle):
'''Write metadata and point + analog frames to a file handle.
Parameters
----------
handle : file
Write metadata and C3D motion frames to the given file handle. The
writer does not close the handle.
'''
if not self._frames:
return
def add(name, desc, bpe, format, bytes, *dimensions):
group.add_param(name,
desc=desc,
bytes_per_element=bpe,
bytes=struct.pack(format, bytes),
dimensions=list(dimensions))
def add_str(name, desc, bytes, *dimensions):
group.add_param(name,
desc=desc,
bytes_per_element=-1,
bytes=bytes.encode('utf-8'),
dimensions=list(dimensions))
def add_empty_array(name, desc, bpe):
group.add_param(name, desc=desc, bytes_per_element=bpe, dimensions=[0])
points, analog = self._frames[0]
ppf = len(points)
# POINT group
group = self.add_group(1, 'POINT', 'POINT group')
add('USED', 'Number of 3d markers', 2, '<H', ppf)
add('FRAMES', 'frame count', 2, '<H', min(65535, len(self._frames)))
add('DATA_START', 'data block number', 2, '<H', 0)
add('SCALE', '3d scale factor', 4, '<f', self._point_scale)
add('RATE', '3d data capture rate', 4, '<f', self._point_rate)
add_str('X_SCREEN', 'X_SCREEN parameter', '+X', 2)
add_str('Y_SCREEN', 'Y_SCREEN parameter', '+Y', 2)
add_str('UNITS', '3d data units', self._point_units, len(self._point_units))
add_str('LABELS', 'labels', ''.join('M%03d ' % i for i in range(ppf)), 5, ppf)
add_str('DESCRIPTIONS', 'descriptions', ' ' * 16 * ppf, 16, ppf)
# ANALOG group
group = self.add_group(2, 'ANALOG', 'ANALOG group')
add('USED', 'analog channel count', 2, '<H', analog.shape[0])
add('RATE', 'analog samples per 3d frame', 4, '<f', analog.shape[1])
add('GEN_SCALE', 'analog general scale factor', 4, '<f', self._gen_scale)
add_empty_array('SCALE', 'analog channel scale factors', 4)
add_empty_array('OFFSET', 'analog channel offsets', 2)
# TRIAL group
group = self.add_group(3, 'TRIAL', 'TRIAL group')
add('ACTUAL_START_FIELD', 'actual start frame', 2, '<I', 1, 2)
add('ACTUAL_END_FIELD', 'actual end frame', 2, '<I', len(self._frames), 2)
# sync parameter information to header.
blocks = self.parameter_blocks()
self.get('POINT:DATA_START').bytes = struct.pack('<H', 2 + blocks)
self.header.data_block = 2 + blocks
self.header.frame_rate = self._point_rate
self.header.last_frame = min(len(self._frames), 65535)
self.header.point_count = ppf
self.header.analog_count = np.prod(analog.shape)
self.header.analog_per_frame = analog.shape[0]
self.header.scale_factor = self._point_scale
self._write_metadata(handle)
self._write_frames(handle) |
def sort_values(self, by=None, axis=0, ascending=True, inplace=False,
kind='quicksort', na_position='last'):
"""
Sort by the values along either axis.
Parameters
----------%(optional_by)s
axis : %(axes_single_arg)s, default 0
Axis to be sorted.
ascending : bool or list of bool, default True
Sort ascending vs. descending. Specify list for multiple sort
orders. If this is a list of bools, must match the length of
the by.
inplace : bool, default False
If True, perform operation in-place.
kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also ndarray.np.sort for more
information. `mergesort` is the only stable algorithm. For
DataFrames, this option is only applied when sorting on a single
column or label.
na_position : {'first', 'last'}, default 'last'
Puts NaNs at the beginning if `first`; `last` puts NaNs at the
end.
Returns
-------
sorted_obj : DataFrame or None
DataFrame with sorted values if inplace=False, None otherwise.
Examples
--------
>>> df = pd.DataFrame({
... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
... 'col2': [2, 1, 9, 8, 7, 4],
... 'col3': [0, 1, 9, 4, 2, 3],
... })
>>> df
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
3 NaN 8 4
4 D 7 2
5 C 4 3
Sort by col1
>>> df.sort_values(by=['col1'])
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort by multiple columns
>>> df.sort_values(by=['col1', 'col2'])
col1 col2 col3
1 A 1 1
0 A 2 0
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort Descending
>>> df.sort_values(by='col1', ascending=False)
col1 col2 col3
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
3 NaN 8 4
Putting NAs first
>>> df.sort_values(by='col1', ascending=False, na_position='first')
col1 col2 col3
3 NaN 8 4
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
"""
raise NotImplementedError("sort_values has not been implemented "
"on Panel or Panel4D objects.") | Sort by the values along either axis.
Parameters
----------%(optional_by)s
axis : %(axes_single_arg)s, default 0
Axis to be sorted.
ascending : bool or list of bool, default True
Sort ascending vs. descending. Specify list for multiple sort
orders. If this is a list of bools, must match the length of
the by.
inplace : bool, default False
If True, perform operation in-place.
kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also ndarray.np.sort for more
information. `mergesort` is the only stable algorithm. For
DataFrames, this option is only applied when sorting on a single
column or label.
na_position : {'first', 'last'}, default 'last'
Puts NaNs at the beginning if `first`; `last` puts NaNs at the
end.
Returns
-------
sorted_obj : DataFrame or None
DataFrame with sorted values if inplace=False, None otherwise.
Examples
--------
>>> df = pd.DataFrame({
... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
... 'col2': [2, 1, 9, 8, 7, 4],
... 'col3': [0, 1, 9, 4, 2, 3],
... })
>>> df
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
3 NaN 8 4
4 D 7 2
5 C 4 3
Sort by col1
>>> df.sort_values(by=['col1'])
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort by multiple columns
>>> df.sort_values(by=['col1', 'col2'])
col1 col2 col3
1 A 1 1
0 A 2 0
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort Descending
>>> df.sort_values(by='col1', ascending=False)
col1 col2 col3
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
3 NaN 8 4
Putting NAs first
>>> df.sort_values(by='col1', ascending=False, na_position='first')
col1 col2 col3
3 NaN 8 4
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1 | Below is the the instruction that describes the task:
### Input:
Sort by the values along either axis.
Parameters
----------%(optional_by)s
axis : %(axes_single_arg)s, default 0
Axis to be sorted.
ascending : bool or list of bool, default True
Sort ascending vs. descending. Specify list for multiple sort
orders. If this is a list of bools, must match the length of
the by.
inplace : bool, default False
If True, perform operation in-place.
kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also ndarray.np.sort for more
information. `mergesort` is the only stable algorithm. For
DataFrames, this option is only applied when sorting on a single
column or label.
na_position : {'first', 'last'}, default 'last'
Puts NaNs at the beginning if `first`; `last` puts NaNs at the
end.
Returns
-------
sorted_obj : DataFrame or None
DataFrame with sorted values if inplace=False, None otherwise.
Examples
--------
>>> df = pd.DataFrame({
... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
... 'col2': [2, 1, 9, 8, 7, 4],
... 'col3': [0, 1, 9, 4, 2, 3],
... })
>>> df
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
3 NaN 8 4
4 D 7 2
5 C 4 3
Sort by col1
>>> df.sort_values(by=['col1'])
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort by multiple columns
>>> df.sort_values(by=['col1', 'col2'])
col1 col2 col3
1 A 1 1
0 A 2 0
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort Descending
>>> df.sort_values(by='col1', ascending=False)
col1 col2 col3
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
3 NaN 8 4
Putting NAs first
>>> df.sort_values(by='col1', ascending=False, na_position='first')
col1 col2 col3
3 NaN 8 4
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
### Response:
def sort_values(self, by=None, axis=0, ascending=True, inplace=False,
kind='quicksort', na_position='last'):
"""
Sort by the values along either axis.
Parameters
----------%(optional_by)s
axis : %(axes_single_arg)s, default 0
Axis to be sorted.
ascending : bool or list of bool, default True
Sort ascending vs. descending. Specify list for multiple sort
orders. If this is a list of bools, must match the length of
the by.
inplace : bool, default False
If True, perform operation in-place.
kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also ndarray.np.sort for more
information. `mergesort` is the only stable algorithm. For
DataFrames, this option is only applied when sorting on a single
column or label.
na_position : {'first', 'last'}, default 'last'
Puts NaNs at the beginning if `first`; `last` puts NaNs at the
end.
Returns
-------
sorted_obj : DataFrame or None
DataFrame with sorted values if inplace=False, None otherwise.
Examples
--------
>>> df = pd.DataFrame({
... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
... 'col2': [2, 1, 9, 8, 7, 4],
... 'col3': [0, 1, 9, 4, 2, 3],
... })
>>> df
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
3 NaN 8 4
4 D 7 2
5 C 4 3
Sort by col1
>>> df.sort_values(by=['col1'])
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort by multiple columns
>>> df.sort_values(by=['col1', 'col2'])
col1 col2 col3
1 A 1 1
0 A 2 0
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort Descending
>>> df.sort_values(by='col1', ascending=False)
col1 col2 col3
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
3 NaN 8 4
Putting NAs first
>>> df.sort_values(by='col1', ascending=False, na_position='first')
col1 col2 col3
3 NaN 8 4
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
"""
raise NotImplementedError("sort_values has not been implemented "
"on Panel or Panel4D objects.") |
def send(self, request, **kwargs):
"""Send a given PreparedRequest."""
# Set defaults that the hooks can utilize to ensure they always have
# the correct parameters to reproduce the previous request.
kwargs.setdefault('stream', self.stream)
kwargs.setdefault('verify', self.verify)
kwargs.setdefault('cert', self.cert)
kwargs.setdefault('proxies', self.proxies)
# It's possible that users might accidentally send a Request object.
# Guard against that specific failure case.
if not isinstance(request, PreparedRequest):
raise ValueError('You can only send PreparedRequests.')
checked_urls = set()
while request.url in self.redirect_cache:
checked_urls.add(request.url)
new_url = self.redirect_cache.get(request.url)
if new_url in checked_urls:
break
request.url = new_url
# Set up variables needed for resolve_redirects and dispatching of hooks
allow_redirects = kwargs.pop('allow_redirects', True)
stream = kwargs.get('stream')
timeout = kwargs.get('timeout')
verify = kwargs.get('verify')
cert = kwargs.get('cert')
proxies = kwargs.get('proxies')
hooks = request.hooks
# Get the appropriate adapter to use
adapter = self.get_adapter(url=request.url)
# Start time (approximately) of the request
start = datetime.utcnow()
# Send the request
r = adapter.send(request, **kwargs)
# Total elapsed time of the request (approximately)
r.elapsed = datetime.utcnow() - start
# Response manipulation hooks
r = dispatch_hook('response', hooks, r, **kwargs)
# Persist cookies
if r.history:
# If the hooks create history then we want those cookies too
for resp in r.history:
extract_cookies_to_jar(self.cookies, resp.request, resp.raw)
extract_cookies_to_jar(self.cookies, request, r.raw)
# Redirect resolving generator.
gen = self.resolve_redirects(r, request,
stream=stream,
timeout=timeout,
verify=verify,
cert=cert,
proxies=proxies)
# Resolve redirects if allowed.
history = [resp for resp in gen] if allow_redirects else []
# Shuffle things around if there's history.
if history:
# Insert the first (original) request at the start
history.insert(0, r)
# Get the last request made
r = history.pop()
r.history = history
if not stream:
r.content
return r | Send a given PreparedRequest. | Below is the the instruction that describes the task:
### Input:
Send a given PreparedRequest.
### Response:
def send(self, request, **kwargs):
"""Send a given PreparedRequest."""
# Set defaults that the hooks can utilize to ensure they always have
# the correct parameters to reproduce the previous request.
kwargs.setdefault('stream', self.stream)
kwargs.setdefault('verify', self.verify)
kwargs.setdefault('cert', self.cert)
kwargs.setdefault('proxies', self.proxies)
# It's possible that users might accidentally send a Request object.
# Guard against that specific failure case.
if not isinstance(request, PreparedRequest):
raise ValueError('You can only send PreparedRequests.')
checked_urls = set()
while request.url in self.redirect_cache:
checked_urls.add(request.url)
new_url = self.redirect_cache.get(request.url)
if new_url in checked_urls:
break
request.url = new_url
# Set up variables needed for resolve_redirects and dispatching of hooks
allow_redirects = kwargs.pop('allow_redirects', True)
stream = kwargs.get('stream')
timeout = kwargs.get('timeout')
verify = kwargs.get('verify')
cert = kwargs.get('cert')
proxies = kwargs.get('proxies')
hooks = request.hooks
# Get the appropriate adapter to use
adapter = self.get_adapter(url=request.url)
# Start time (approximately) of the request
start = datetime.utcnow()
# Send the request
r = adapter.send(request, **kwargs)
# Total elapsed time of the request (approximately)
r.elapsed = datetime.utcnow() - start
# Response manipulation hooks
r = dispatch_hook('response', hooks, r, **kwargs)
# Persist cookies
if r.history:
# If the hooks create history then we want those cookies too
for resp in r.history:
extract_cookies_to_jar(self.cookies, resp.request, resp.raw)
extract_cookies_to_jar(self.cookies, request, r.raw)
# Redirect resolving generator.
gen = self.resolve_redirects(r, request,
stream=stream,
timeout=timeout,
verify=verify,
cert=cert,
proxies=proxies)
# Resolve redirects if allowed.
history = [resp for resp in gen] if allow_redirects else []
# Shuffle things around if there's history.
if history:
# Insert the first (original) request at the start
history.insert(0, r)
# Get the last request made
r = history.pop()
r.history = history
if not stream:
r.content
return r |
def grouper_nofill_str(n, iterable):
"""
Take a sequence and break it up into chunks of the specified size.
The last chunk may be smaller than size.
This works very similar to grouper_nofill, except
it works with strings as well.
>>> tuple(grouper_nofill_str(3, 'foobarbaz'))
('foo', 'bar', 'baz')
You can still use it on non-strings too if you like.
>>> tuple(grouper_nofill_str(42, []))
()
>>> tuple(grouper_nofill_str(3, list(range(10))))
([0, 1, 2], [3, 4, 5], [6, 7, 8], [9])
"""
res = more_itertools.chunked(iterable, n)
if isinstance(iterable, six.string_types):
res = (''.join(item) for item in res)
return res | Take a sequence and break it up into chunks of the specified size.
The last chunk may be smaller than size.
This works very similar to grouper_nofill, except
it works with strings as well.
>>> tuple(grouper_nofill_str(3, 'foobarbaz'))
('foo', 'bar', 'baz')
You can still use it on non-strings too if you like.
>>> tuple(grouper_nofill_str(42, []))
()
>>> tuple(grouper_nofill_str(3, list(range(10))))
([0, 1, 2], [3, 4, 5], [6, 7, 8], [9]) | Below is the the instruction that describes the task:
### Input:
Take a sequence and break it up into chunks of the specified size.
The last chunk may be smaller than size.
This works very similar to grouper_nofill, except
it works with strings as well.
>>> tuple(grouper_nofill_str(3, 'foobarbaz'))
('foo', 'bar', 'baz')
You can still use it on non-strings too if you like.
>>> tuple(grouper_nofill_str(42, []))
()
>>> tuple(grouper_nofill_str(3, list(range(10))))
([0, 1, 2], [3, 4, 5], [6, 7, 8], [9])
### Response:
def grouper_nofill_str(n, iterable):
"""
Take a sequence and break it up into chunks of the specified size.
The last chunk may be smaller than size.
This works very similar to grouper_nofill, except
it works with strings as well.
>>> tuple(grouper_nofill_str(3, 'foobarbaz'))
('foo', 'bar', 'baz')
You can still use it on non-strings too if you like.
>>> tuple(grouper_nofill_str(42, []))
()
>>> tuple(grouper_nofill_str(3, list(range(10))))
([0, 1, 2], [3, 4, 5], [6, 7, 8], [9])
"""
res = more_itertools.chunked(iterable, n)
if isinstance(iterable, six.string_types):
res = (''.join(item) for item in res)
return res |
def GetForwardedIps(self, interface, interface_ip=None):
"""Retrieve the list of configured forwarded IP addresses.
Args:
interface: string, the output device to query.
interface_ip: string, current interface ip address.
Returns:
list, the IP address strings.
"""
try:
ips = netifaces.ifaddresses(interface)
ips = ips[netifaces.AF_INET]
except (ValueError, IndexError):
return []
forwarded_ips = []
for ip in ips:
if ip['addr'] != interface_ip:
full_addr = '%s/%d' % (ip['addr'], netaddr.IPAddress(ip['netmask']).netmask_bits())
forwarded_ips.append(full_addr)
return self.ParseForwardedIps(forwarded_ips) | Retrieve the list of configured forwarded IP addresses.
Args:
interface: string, the output device to query.
interface_ip: string, current interface ip address.
Returns:
list, the IP address strings. | Below is the the instruction that describes the task:
### Input:
Retrieve the list of configured forwarded IP addresses.
Args:
interface: string, the output device to query.
interface_ip: string, current interface ip address.
Returns:
list, the IP address strings.
### Response:
def GetForwardedIps(self, interface, interface_ip=None):
"""Retrieve the list of configured forwarded IP addresses.
Args:
interface: string, the output device to query.
interface_ip: string, current interface ip address.
Returns:
list, the IP address strings.
"""
try:
ips = netifaces.ifaddresses(interface)
ips = ips[netifaces.AF_INET]
except (ValueError, IndexError):
return []
forwarded_ips = []
for ip in ips:
if ip['addr'] != interface_ip:
full_addr = '%s/%d' % (ip['addr'], netaddr.IPAddress(ip['netmask']).netmask_bits())
forwarded_ips.append(full_addr)
return self.ParseForwardedIps(forwarded_ips) |
def eval_genome(genome, config):
"""
This function will be run in parallel by ParallelEvaluator. It takes two
arguments (a single genome and the genome class configuration data) and
should return one float (that genome's fitness).
Note that this function needs to be in module scope for multiprocessing.Pool
(which is what ParallelEvaluator uses) to find it. Because of this, make
sure you check for __main__ before executing any code (as we do here in the
last few lines in the file), otherwise you'll have made a fork bomb
instead of a neuroevolution demo. :)
"""
net = neat.nn.FeedForwardNetwork.create(genome, config)
error = 4.0
for xi, xo in zip(xor_inputs, xor_outputs):
output = net.activate(xi)
error -= (output[0] - xo[0]) ** 2
return error | This function will be run in parallel by ParallelEvaluator. It takes two
arguments (a single genome and the genome class configuration data) and
should return one float (that genome's fitness).
Note that this function needs to be in module scope for multiprocessing.Pool
(which is what ParallelEvaluator uses) to find it. Because of this, make
sure you check for __main__ before executing any code (as we do here in the
last few lines in the file), otherwise you'll have made a fork bomb
instead of a neuroevolution demo. :) | Below is the the instruction that describes the task:
### Input:
This function will be run in parallel by ParallelEvaluator. It takes two
arguments (a single genome and the genome class configuration data) and
should return one float (that genome's fitness).
Note that this function needs to be in module scope for multiprocessing.Pool
(which is what ParallelEvaluator uses) to find it. Because of this, make
sure you check for __main__ before executing any code (as we do here in the
last few lines in the file), otherwise you'll have made a fork bomb
instead of a neuroevolution demo. :)
### Response:
def eval_genome(genome, config):
"""
This function will be run in parallel by ParallelEvaluator. It takes two
arguments (a single genome and the genome class configuration data) and
should return one float (that genome's fitness).
Note that this function needs to be in module scope for multiprocessing.Pool
(which is what ParallelEvaluator uses) to find it. Because of this, make
sure you check for __main__ before executing any code (as we do here in the
last few lines in the file), otherwise you'll have made a fork bomb
instead of a neuroevolution demo. :)
"""
net = neat.nn.FeedForwardNetwork.create(genome, config)
error = 4.0
for xi, xo in zip(xor_inputs, xor_outputs):
output = net.activate(xi)
error -= (output[0] - xo[0]) ** 2
return error |
def quantize_model(sym, arg_params, aux_params,
data_names=('data',), label_names=('softmax_label',),
ctx=cpu(), excluded_sym_names=None, calib_mode='entropy',
calib_data=None, num_calib_examples=None, calib_layer=None,
quantized_dtype='int8', logger=logging):
"""User-level API for generating a quantized model from a FP32 model w/ or w/o calibration.
The backend quantized operators are only enabled for Linux systems. Please do not run
inference using the quantized models on Windows for now.
The quantization implementation adopts the TensorFlow's approach:
https://www.tensorflow.org/performance/quantization.
The calibration implementation borrows the idea of Nvidia's 8-bit Inference with TensorRT:
http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf
and adapts the method to MXNet.
Parameters
----------
sym : str or Symbol
Defines the structure of a neural network for FP32 data types.
arg_params : dict
Dictionary of name to `NDArray`.
aux_params : dict
Dictionary of name to `NDArray`.
data_names : a list of strs
Data names required for creating a Module object to run forward propagation on the
calibration dataset.
label_names : a list of strs
Label names required for creating a Module object to run forward propagation on the
calibration dataset.
ctx : Context
Defines the device that users want to run forward propagation on the calibration
dataset for collecting layer output statistics. Currently, only supports single context.
excluded_sym_names : list of strings
A list of strings representing the names of the symbols that users want to excluding
from being quantized.
calib_mode : str
If calib_mode='none', no calibration will be used and the thresholds for
requantization after the corresponding layers will be calculated at runtime by
calling min and max operators. The quantized models generated in this
mode are normally 10-20% slower than those with calibrations during inference.
If calib_mode='naive', the min and max values of the layer outputs from a calibration
dataset will be directly taken as the thresholds for quantization.
If calib_mode='entropy' (default mode), the thresholds for quantization will be
derived such that the KL divergence between the distributions of FP32 layer outputs and
quantized layer outputs is minimized based upon the calibration dataset.
calib_data : DataIter
A data iterator initialized by the calibration dataset.
num_calib_examples : int or None
The maximum number of examples that user would like to use for calibration. If not provided,
the whole calibration dataset will be used.
calib_layer : function
Given a layer's output name in string, return True or False for deciding whether to
calibrate this layer. If yes, the statistics of the layer's output will be collected;
otherwise, no information of the layer's output will be collected. If not provided,
all the layers' outputs that need requantization will be collected.
quantized_dtype : str
The quantized destination type for input data. Currently support 'int8'
, 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
Default value is 'int8'.
logger : Object
A logging object for printing information during the process of quantization.
Returns
-------
tuple
A tuple of quantized symbol, quantized arg_params, and aux_params.
-------
"""
if excluded_sym_names is None:
excluded_sym_names = []
if not isinstance(excluded_sym_names, list):
raise ValueError('excluded_sym_names must be a list of strings representing'
' the names of the symbols that will not be quantized,'
' while received type %s' % str(type(excluded_sym_names)))
logger.info('Quantizing symbol')
if quantized_dtype not in ('int8', 'uint8', 'auto'):
raise ValueError('unknown quantized_dtype %s received,'
' expected `int8`, `uint8` or `auto`' % quantized_dtype)
qsym = _quantize_symbol(sym, excluded_symbols=excluded_sym_names,
offline_params=list(arg_params.keys()),
quantized_dtype=quantized_dtype)
th_dict = {}
if calib_mode is not None and calib_mode != 'none':
if not isinstance(ctx, Context):
raise ValueError('currently only supports single ctx, while received %s' % str(ctx))
if calib_data is None:
raise ValueError('calib_data must be provided when calib_mode=%s' % calib_mode)
if not isinstance(calib_data, DataIter):
raise ValueError('calib_data must be of DataIter type when calib_mode=%s,'
' while received type %s' % (calib_mode, str(type(calib_data))))
mod = Module(symbol=sym, data_names=data_names, label_names=label_names, context=ctx)
if len(calib_data.provide_label) > 0:
mod.bind(for_training=False, data_shapes=calib_data.provide_data,
label_shapes=calib_data.provide_label)
else:
mod.bind(for_training=False, data_shapes=calib_data.provide_data)
mod.set_params(arg_params, aux_params)
if calib_mode == 'entropy':
nd_dict, num_examples = _collect_layer_outputs(mod, calib_data,
include_layer=calib_layer,
max_num_examples=num_calib_examples,
logger=logger)
logger.info('Collected layer outputs from FP32 model using %d examples' % num_examples)
logger.info('Calculating optimal thresholds for quantization')
th_dict = _get_optimal_thresholds(nd_dict, quantized_dtype, logger=logger)
elif calib_mode == 'naive':
th_dict, num_examples = _collect_layer_output_min_max(
mod, calib_data, include_layer=calib_layer, max_num_examples=num_calib_examples,
logger=logger)
logger.info('Collected layer output min/max values from FP32 model using %d examples'
% num_examples)
else:
raise ValueError('unknown calibration mode %s received,'
' expected `none`, `naive`, or `entropy`' % calib_mode)
logger.info('Calibrating quantized symbol')
qsym = _calibrate_quantized_sym(qsym, th_dict)
logger.info('Quantizing parameters')
qarg_params = _quantize_params(qsym, arg_params, th_dict)
return qsym, qarg_params, aux_params | User-level API for generating a quantized model from a FP32 model w/ or w/o calibration.
The backend quantized operators are only enabled for Linux systems. Please do not run
inference using the quantized models on Windows for now.
The quantization implementation adopts the TensorFlow's approach:
https://www.tensorflow.org/performance/quantization.
The calibration implementation borrows the idea of Nvidia's 8-bit Inference with TensorRT:
http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf
and adapts the method to MXNet.
Parameters
----------
sym : str or Symbol
Defines the structure of a neural network for FP32 data types.
arg_params : dict
Dictionary of name to `NDArray`.
aux_params : dict
Dictionary of name to `NDArray`.
data_names : a list of strs
Data names required for creating a Module object to run forward propagation on the
calibration dataset.
label_names : a list of strs
Label names required for creating a Module object to run forward propagation on the
calibration dataset.
ctx : Context
Defines the device that users want to run forward propagation on the calibration
dataset for collecting layer output statistics. Currently, only supports single context.
excluded_sym_names : list of strings
A list of strings representing the names of the symbols that users want to excluding
from being quantized.
calib_mode : str
If calib_mode='none', no calibration will be used and the thresholds for
requantization after the corresponding layers will be calculated at runtime by
calling min and max operators. The quantized models generated in this
mode are normally 10-20% slower than those with calibrations during inference.
If calib_mode='naive', the min and max values of the layer outputs from a calibration
dataset will be directly taken as the thresholds for quantization.
If calib_mode='entropy' (default mode), the thresholds for quantization will be
derived such that the KL divergence between the distributions of FP32 layer outputs and
quantized layer outputs is minimized based upon the calibration dataset.
calib_data : DataIter
A data iterator initialized by the calibration dataset.
num_calib_examples : int or None
The maximum number of examples that user would like to use for calibration. If not provided,
the whole calibration dataset will be used.
calib_layer : function
Given a layer's output name in string, return True or False for deciding whether to
calibrate this layer. If yes, the statistics of the layer's output will be collected;
otherwise, no information of the layer's output will be collected. If not provided,
all the layers' outputs that need requantization will be collected.
quantized_dtype : str
The quantized destination type for input data. Currently support 'int8'
, 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
Default value is 'int8'.
logger : Object
A logging object for printing information during the process of quantization.
Returns
-------
tuple
A tuple of quantized symbol, quantized arg_params, and aux_params.
------- | Below is the the instruction that describes the task:
### Input:
User-level API for generating a quantized model from a FP32 model w/ or w/o calibration.
The backend quantized operators are only enabled for Linux systems. Please do not run
inference using the quantized models on Windows for now.
The quantization implementation adopts the TensorFlow's approach:
https://www.tensorflow.org/performance/quantization.
The calibration implementation borrows the idea of Nvidia's 8-bit Inference with TensorRT:
http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf
and adapts the method to MXNet.
Parameters
----------
sym : str or Symbol
Defines the structure of a neural network for FP32 data types.
arg_params : dict
Dictionary of name to `NDArray`.
aux_params : dict
Dictionary of name to `NDArray`.
data_names : a list of strs
Data names required for creating a Module object to run forward propagation on the
calibration dataset.
label_names : a list of strs
Label names required for creating a Module object to run forward propagation on the
calibration dataset.
ctx : Context
Defines the device that users want to run forward propagation on the calibration
dataset for collecting layer output statistics. Currently, only supports single context.
excluded_sym_names : list of strings
A list of strings representing the names of the symbols that users want to excluding
from being quantized.
calib_mode : str
If calib_mode='none', no calibration will be used and the thresholds for
requantization after the corresponding layers will be calculated at runtime by
calling min and max operators. The quantized models generated in this
mode are normally 10-20% slower than those with calibrations during inference.
If calib_mode='naive', the min and max values of the layer outputs from a calibration
dataset will be directly taken as the thresholds for quantization.
If calib_mode='entropy' (default mode), the thresholds for quantization will be
derived such that the KL divergence between the distributions of FP32 layer outputs and
quantized layer outputs is minimized based upon the calibration dataset.
calib_data : DataIter
A data iterator initialized by the calibration dataset.
num_calib_examples : int or None
The maximum number of examples that user would like to use for calibration. If not provided,
the whole calibration dataset will be used.
calib_layer : function
Given a layer's output name in string, return True or False for deciding whether to
calibrate this layer. If yes, the statistics of the layer's output will be collected;
otherwise, no information of the layer's output will be collected. If not provided,
all the layers' outputs that need requantization will be collected.
quantized_dtype : str
The quantized destination type for input data. Currently support 'int8'
, 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
Default value is 'int8'.
logger : Object
A logging object for printing information during the process of quantization.
Returns
-------
tuple
A tuple of quantized symbol, quantized arg_params, and aux_params.
-------
### Response:
def quantize_model(sym, arg_params, aux_params,
data_names=('data',), label_names=('softmax_label',),
ctx=cpu(), excluded_sym_names=None, calib_mode='entropy',
calib_data=None, num_calib_examples=None, calib_layer=None,
quantized_dtype='int8', logger=logging):
"""User-level API for generating a quantized model from a FP32 model w/ or w/o calibration.
The backend quantized operators are only enabled for Linux systems. Please do not run
inference using the quantized models on Windows for now.
The quantization implementation adopts the TensorFlow's approach:
https://www.tensorflow.org/performance/quantization.
The calibration implementation borrows the idea of Nvidia's 8-bit Inference with TensorRT:
http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf
and adapts the method to MXNet.
Parameters
----------
sym : str or Symbol
Defines the structure of a neural network for FP32 data types.
arg_params : dict
Dictionary of name to `NDArray`.
aux_params : dict
Dictionary of name to `NDArray`.
data_names : a list of strs
Data names required for creating a Module object to run forward propagation on the
calibration dataset.
label_names : a list of strs
Label names required for creating a Module object to run forward propagation on the
calibration dataset.
ctx : Context
Defines the device that users want to run forward propagation on the calibration
dataset for collecting layer output statistics. Currently, only supports single context.
excluded_sym_names : list of strings
A list of strings representing the names of the symbols that users want to excluding
from being quantized.
calib_mode : str
If calib_mode='none', no calibration will be used and the thresholds for
requantization after the corresponding layers will be calculated at runtime by
calling min and max operators. The quantized models generated in this
mode are normally 10-20% slower than those with calibrations during inference.
If calib_mode='naive', the min and max values of the layer outputs from a calibration
dataset will be directly taken as the thresholds for quantization.
If calib_mode='entropy' (default mode), the thresholds for quantization will be
derived such that the KL divergence between the distributions of FP32 layer outputs and
quantized layer outputs is minimized based upon the calibration dataset.
calib_data : DataIter
A data iterator initialized by the calibration dataset.
num_calib_examples : int or None
The maximum number of examples that user would like to use for calibration. If not provided,
the whole calibration dataset will be used.
calib_layer : function
Given a layer's output name in string, return True or False for deciding whether to
calibrate this layer. If yes, the statistics of the layer's output will be collected;
otherwise, no information of the layer's output will be collected. If not provided,
all the layers' outputs that need requantization will be collected.
quantized_dtype : str
The quantized destination type for input data. Currently support 'int8'
, 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
Default value is 'int8'.
logger : Object
A logging object for printing information during the process of quantization.
Returns
-------
tuple
A tuple of quantized symbol, quantized arg_params, and aux_params.
-------
"""
if excluded_sym_names is None:
excluded_sym_names = []
if not isinstance(excluded_sym_names, list):
raise ValueError('excluded_sym_names must be a list of strings representing'
' the names of the symbols that will not be quantized,'
' while received type %s' % str(type(excluded_sym_names)))
logger.info('Quantizing symbol')
if quantized_dtype not in ('int8', 'uint8', 'auto'):
raise ValueError('unknown quantized_dtype %s received,'
' expected `int8`, `uint8` or `auto`' % quantized_dtype)
qsym = _quantize_symbol(sym, excluded_symbols=excluded_sym_names,
offline_params=list(arg_params.keys()),
quantized_dtype=quantized_dtype)
th_dict = {}
if calib_mode is not None and calib_mode != 'none':
if not isinstance(ctx, Context):
raise ValueError('currently only supports single ctx, while received %s' % str(ctx))
if calib_data is None:
raise ValueError('calib_data must be provided when calib_mode=%s' % calib_mode)
if not isinstance(calib_data, DataIter):
raise ValueError('calib_data must be of DataIter type when calib_mode=%s,'
' while received type %s' % (calib_mode, str(type(calib_data))))
mod = Module(symbol=sym, data_names=data_names, label_names=label_names, context=ctx)
if len(calib_data.provide_label) > 0:
mod.bind(for_training=False, data_shapes=calib_data.provide_data,
label_shapes=calib_data.provide_label)
else:
mod.bind(for_training=False, data_shapes=calib_data.provide_data)
mod.set_params(arg_params, aux_params)
if calib_mode == 'entropy':
nd_dict, num_examples = _collect_layer_outputs(mod, calib_data,
include_layer=calib_layer,
max_num_examples=num_calib_examples,
logger=logger)
logger.info('Collected layer outputs from FP32 model using %d examples' % num_examples)
logger.info('Calculating optimal thresholds for quantization')
th_dict = _get_optimal_thresholds(nd_dict, quantized_dtype, logger=logger)
elif calib_mode == 'naive':
th_dict, num_examples = _collect_layer_output_min_max(
mod, calib_data, include_layer=calib_layer, max_num_examples=num_calib_examples,
logger=logger)
logger.info('Collected layer output min/max values from FP32 model using %d examples'
% num_examples)
else:
raise ValueError('unknown calibration mode %s received,'
' expected `none`, `naive`, or `entropy`' % calib_mode)
logger.info('Calibrating quantized symbol')
qsym = _calibrate_quantized_sym(qsym, th_dict)
logger.info('Quantizing parameters')
qarg_params = _quantize_params(qsym, arg_params, th_dict)
return qsym, qarg_params, aux_params |
def make_venv(self, dj_version):
"""Creates a virtual environment for a given Django version.
:param str dj_version:
:rtype: str
:return: path to created virtual env
"""
venv_path = self._get_venv_path(dj_version)
self.logger.info('Creating virtual environment for Django %s ...' % dj_version)
try:
create_venv(venv_path, **VENV_CREATE_KWARGS)
except ValueError:
self.logger.warning('Virtual environment directory already exists. Skipped.')
self.venv_install('django==%s' % dj_version, venv_path)
return venv_path | Creates a virtual environment for a given Django version.
:param str dj_version:
:rtype: str
:return: path to created virtual env | Below is the the instruction that describes the task:
### Input:
Creates a virtual environment for a given Django version.
:param str dj_version:
:rtype: str
:return: path to created virtual env
### Response:
def make_venv(self, dj_version):
"""Creates a virtual environment for a given Django version.
:param str dj_version:
:rtype: str
:return: path to created virtual env
"""
venv_path = self._get_venv_path(dj_version)
self.logger.info('Creating virtual environment for Django %s ...' % dj_version)
try:
create_venv(venv_path, **VENV_CREATE_KWARGS)
except ValueError:
self.logger.warning('Virtual environment directory already exists. Skipped.')
self.venv_install('django==%s' % dj_version, venv_path)
return venv_path |
def _resolve_name(name, package, level):
"""Return the absolute name of the module to be imported."""
if not hasattr(package, 'rindex'):
raise ValueError("'package' not set to a string")
dot = len(package)
for x in xrange(level, 1, -1):
try:
dot = package.rindex('.', 0, dot)
except ValueError:
raise ValueError("attempted relative import beyond top-level "
"package")
return "%s.%s" % (package[:dot], name) | Return the absolute name of the module to be imported. | Below is the the instruction that describes the task:
### Input:
Return the absolute name of the module to be imported.
### Response:
def _resolve_name(name, package, level):
"""Return the absolute name of the module to be imported."""
if not hasattr(package, 'rindex'):
raise ValueError("'package' not set to a string")
dot = len(package)
for x in xrange(level, 1, -1):
try:
dot = package.rindex('.', 0, dot)
except ValueError:
raise ValueError("attempted relative import beyond top-level "
"package")
return "%s.%s" % (package[:dot], name) |
def readPattern(self):
"""Read the entire color pattern
:return List of pattern line tuples
"""
if ( self.dev == None ): return ''
pattern=[]
for i in range(0,16): # FIXME: adjustable for diff blink(1) models
pattern.append( self.readPatternLine(i) )
return pattern | Read the entire color pattern
:return List of pattern line tuples | Below is the the instruction that describes the task:
### Input:
Read the entire color pattern
:return List of pattern line tuples
### Response:
def readPattern(self):
"""Read the entire color pattern
:return List of pattern line tuples
"""
if ( self.dev == None ): return ''
pattern=[]
for i in range(0,16): # FIXME: adjustable for diff blink(1) models
pattern.append( self.readPatternLine(i) )
return pattern |
def mark_flags_as_mutual_exclusive(flag_names, required=False,
flag_values=_flagvalues.FLAGS):
"""Ensures that only one flag among flag_names is not None.
Important note: This validator checks if flag values are None, and it does not
distinguish between default and explicit values. Therefore, this validator
does not make sense when applied to flags with default values other than None,
including other false values (e.g. False, 0, '', []). That includes multi
flags with a default value of [] instead of None.
Args:
flag_names: [str], names of the flags.
required: bool. If true, exactly one of the flags must have a value other
than None. Otherwise, at most one of the flags can have a value other
than None, and it is valid for all of the flags to be None.
flag_values: flags.FlagValues, optional FlagValues instance where the flags
are defined.
"""
for flag_name in flag_names:
if flag_values[flag_name].default is not None:
warnings.warn(
'Flag --{} has a non-None default value. That does not make sense '
'with mark_flags_as_mutual_exclusive, which checks whether the '
'listed flags have a value other than None.'.format(flag_name))
def validate_mutual_exclusion(flags_dict):
flag_count = sum(1 for val in flags_dict.values() if val is not None)
if flag_count == 1 or (not required and flag_count == 0):
return True
raise _exceptions.ValidationError(
'{} one of ({}) must have a value other than None.'.format(
'Exactly' if required else 'At most', ', '.join(flag_names)))
register_multi_flags_validator(
flag_names, validate_mutual_exclusion, flag_values=flag_values) | Ensures that only one flag among flag_names is not None.
Important note: This validator checks if flag values are None, and it does not
distinguish between default and explicit values. Therefore, this validator
does not make sense when applied to flags with default values other than None,
including other false values (e.g. False, 0, '', []). That includes multi
flags with a default value of [] instead of None.
Args:
flag_names: [str], names of the flags.
required: bool. If true, exactly one of the flags must have a value other
than None. Otherwise, at most one of the flags can have a value other
than None, and it is valid for all of the flags to be None.
flag_values: flags.FlagValues, optional FlagValues instance where the flags
are defined. | Below is the the instruction that describes the task:
### Input:
Ensures that only one flag among flag_names is not None.
Important note: This validator checks if flag values are None, and it does not
distinguish between default and explicit values. Therefore, this validator
does not make sense when applied to flags with default values other than None,
including other false values (e.g. False, 0, '', []). That includes multi
flags with a default value of [] instead of None.
Args:
flag_names: [str], names of the flags.
required: bool. If true, exactly one of the flags must have a value other
than None. Otherwise, at most one of the flags can have a value other
than None, and it is valid for all of the flags to be None.
flag_values: flags.FlagValues, optional FlagValues instance where the flags
are defined.
### Response:
def mark_flags_as_mutual_exclusive(flag_names, required=False,
flag_values=_flagvalues.FLAGS):
"""Ensures that only one flag among flag_names is not None.
Important note: This validator checks if flag values are None, and it does not
distinguish between default and explicit values. Therefore, this validator
does not make sense when applied to flags with default values other than None,
including other false values (e.g. False, 0, '', []). That includes multi
flags with a default value of [] instead of None.
Args:
flag_names: [str], names of the flags.
required: bool. If true, exactly one of the flags must have a value other
than None. Otherwise, at most one of the flags can have a value other
than None, and it is valid for all of the flags to be None.
flag_values: flags.FlagValues, optional FlagValues instance where the flags
are defined.
"""
for flag_name in flag_names:
if flag_values[flag_name].default is not None:
warnings.warn(
'Flag --{} has a non-None default value. That does not make sense '
'with mark_flags_as_mutual_exclusive, which checks whether the '
'listed flags have a value other than None.'.format(flag_name))
def validate_mutual_exclusion(flags_dict):
flag_count = sum(1 for val in flags_dict.values() if val is not None)
if flag_count == 1 or (not required and flag_count == 0):
return True
raise _exceptions.ValidationError(
'{} one of ({}) must have a value other than None.'.format(
'Exactly' if required else 'At most', ', '.join(flag_names)))
register_multi_flags_validator(
flag_names, validate_mutual_exclusion, flag_values=flag_values) |
def _set_allowed_services_and_actions(self, services):
"""Expect services to be a list of service dictionaries, each with `name` and `actions` keys."""
for service in services:
self.services[service['name']] = {}
for action in service['actions']:
name = action.pop('name')
self.services[service['name']][name] = action | Expect services to be a list of service dictionaries, each with `name` and `actions` keys. | Below is the the instruction that describes the task:
### Input:
Expect services to be a list of service dictionaries, each with `name` and `actions` keys.
### Response:
def _set_allowed_services_and_actions(self, services):
"""Expect services to be a list of service dictionaries, each with `name` and `actions` keys."""
for service in services:
self.services[service['name']] = {}
for action in service['actions']:
name = action.pop('name')
self.services[service['name']][name] = action |
def _read_parsed(self, lines):
"""
Read text fragments from a parsed format text file.
:param list lines: the lines of the parsed text file
:param dict parameters: additional parameters for parsing
(e.g., class/id regex strings)
"""
self.log(u"Parsing fragments from parsed text format")
pairs = []
for line in lines:
pieces = line.split(gc.PARSED_TEXT_SEPARATOR)
if len(pieces) == 2:
identifier = pieces[0].strip()
text = pieces[1].strip()
if len(identifier) > 0:
pairs.append((identifier, [text]))
self._create_text_fragments(pairs) | Read text fragments from a parsed format text file.
:param list lines: the lines of the parsed text file
:param dict parameters: additional parameters for parsing
(e.g., class/id regex strings) | Below is the the instruction that describes the task:
### Input:
Read text fragments from a parsed format text file.
:param list lines: the lines of the parsed text file
:param dict parameters: additional parameters for parsing
(e.g., class/id regex strings)
### Response:
def _read_parsed(self, lines):
"""
Read text fragments from a parsed format text file.
:param list lines: the lines of the parsed text file
:param dict parameters: additional parameters for parsing
(e.g., class/id regex strings)
"""
self.log(u"Parsing fragments from parsed text format")
pairs = []
for line in lines:
pieces = line.split(gc.PARSED_TEXT_SEPARATOR)
if len(pieces) == 2:
identifier = pieces[0].strip()
text = pieces[1].strip()
if len(identifier) > 0:
pairs.append((identifier, [text]))
self._create_text_fragments(pairs) |
def addmsg(self, msg_p):
"""
Push encoded message as a new frame. Message takes ownership of
submessage, so the original is destroyed in this call. Returns 0 on
success, -1 on error.
"""
return lib.zmsg_addmsg(self._as_parameter_, byref(zmsg_p.from_param(msg_p))) | Push encoded message as a new frame. Message takes ownership of
submessage, so the original is destroyed in this call. Returns 0 on
success, -1 on error. | Below is the the instruction that describes the task:
### Input:
Push encoded message as a new frame. Message takes ownership of
submessage, so the original is destroyed in this call. Returns 0 on
success, -1 on error.
### Response:
def addmsg(self, msg_p):
"""
Push encoded message as a new frame. Message takes ownership of
submessage, so the original is destroyed in this call. Returns 0 on
success, -1 on error.
"""
return lib.zmsg_addmsg(self._as_parameter_, byref(zmsg_p.from_param(msg_p))) |
def check_timers(self):
""" Awake process if timer has expired """
if self._current is None:
# Advance the clocks. Go to future!!
advance = min([self.clocks] + [x for x in self.timers if x is not None]) + 1
logger.debug(f"Advancing the clock from {self.clocks} to {advance}")
self.clocks = advance
for procid in range(len(self.timers)):
if self.timers[procid] is not None:
if self.clocks > self.timers[procid]:
self.procs[procid].PC += self.procs[procid].instruction.size
self.awake(procid) | Awake process if timer has expired | Below is the the instruction that describes the task:
### Input:
Awake process if timer has expired
### Response:
def check_timers(self):
""" Awake process if timer has expired """
if self._current is None:
# Advance the clocks. Go to future!!
advance = min([self.clocks] + [x for x in self.timers if x is not None]) + 1
logger.debug(f"Advancing the clock from {self.clocks} to {advance}")
self.clocks = advance
for procid in range(len(self.timers)):
if self.timers[procid] is not None:
if self.clocks > self.timers[procid]:
self.procs[procid].PC += self.procs[procid].instruction.size
self.awake(procid) |
def triangle_area(point1, point2, point3):
"""
Uses Heron's formula to find the area of a triangle
based on the coordinates of three points.
Args:
point1: list or tuple, the x y coordinate of point one.
point2: list or tuple, the x y coordinate of point two.
point3: list or tuple, the x y coordinate of point three.
Returns:
The area of a triangle as a floating point number.
Requires:
The math module, point_distance().
"""
"""Lengths of the three sides of the triangle"""
a = point_distance(point1, point2)
b = point_distance(point1, point3)
c = point_distance(point2, point3)
"""Where s is the semiperimeter"""
s = (a + b + c) / 2.0
"""Return the area of the triangle (using Heron's formula)"""
return math.sqrt(s * (s - a) * (s - b) * (s - c)) | Uses Heron's formula to find the area of a triangle
based on the coordinates of three points.
Args:
point1: list or tuple, the x y coordinate of point one.
point2: list or tuple, the x y coordinate of point two.
point3: list or tuple, the x y coordinate of point three.
Returns:
The area of a triangle as a floating point number.
Requires:
The math module, point_distance(). | Below is the the instruction that describes the task:
### Input:
Uses Heron's formula to find the area of a triangle
based on the coordinates of three points.
Args:
point1: list or tuple, the x y coordinate of point one.
point2: list or tuple, the x y coordinate of point two.
point3: list or tuple, the x y coordinate of point three.
Returns:
The area of a triangle as a floating point number.
Requires:
The math module, point_distance().
### Response:
def triangle_area(point1, point2, point3):
"""
Uses Heron's formula to find the area of a triangle
based on the coordinates of three points.
Args:
point1: list or tuple, the x y coordinate of point one.
point2: list or tuple, the x y coordinate of point two.
point3: list or tuple, the x y coordinate of point three.
Returns:
The area of a triangle as a floating point number.
Requires:
The math module, point_distance().
"""
"""Lengths of the three sides of the triangle"""
a = point_distance(point1, point2)
b = point_distance(point1, point3)
c = point_distance(point2, point3)
"""Where s is the semiperimeter"""
s = (a + b + c) / 2.0
"""Return the area of the triangle (using Heron's formula)"""
return math.sqrt(s * (s - a) * (s - b) * (s - c)) |
def _get_role_arn(name, **conn_params):
'''
Helper function to turn a name into an arn string,
returns None if not able to resolve
'''
if name.startswith('arn:aws:iam'):
return name
role = __salt__['boto_iam.describe_role'](name, **conn_params)
rolearn = role.get('arn') if role else None
return rolearn | Helper function to turn a name into an arn string,
returns None if not able to resolve | Below is the the instruction that describes the task:
### Input:
Helper function to turn a name into an arn string,
returns None if not able to resolve
### Response:
def _get_role_arn(name, **conn_params):
'''
Helper function to turn a name into an arn string,
returns None if not able to resolve
'''
if name.startswith('arn:aws:iam'):
return name
role = __salt__['boto_iam.describe_role'](name, **conn_params)
rolearn = role.get('arn') if role else None
return rolearn |
def value_str(sc):
"""
Returns the value part ("[*]", "<M>", "(foo)" etc.) of a menu entry.
sc: Symbol or Choice.
"""
if sc.type in (STRING, INT, HEX):
return "({})".format(sc.str_value)
# BOOL or TRISTATE
# The choice mode is an upper bound on the visibility of choice symbols, so
# we can check the choice symbols' own visibility to see if the choice is
# in y mode
if isinstance(sc, Symbol) and sc.choice and sc.visibility == 2:
# For choices in y mode, print '-->' next to the selected symbol
return "-->" if sc.choice.selection is sc else " "
tri_val_str = (" ", "M", "*")[sc.tri_value]
if len(sc.assignable) == 1:
# Pinned to a single value
return "-{}-".format(tri_val_str)
if sc.type == BOOL:
return "[{}]".format(tri_val_str)
if sc.type == TRISTATE:
if sc.assignable == (1, 2):
# m and y available
return "{" + tri_val_str + "}" # Gets a bit confusing with .format()
return "<{}>".format(tri_val_str) | Returns the value part ("[*]", "<M>", "(foo)" etc.) of a menu entry.
sc: Symbol or Choice. | Below is the the instruction that describes the task:
### Input:
Returns the value part ("[*]", "<M>", "(foo)" etc.) of a menu entry.
sc: Symbol or Choice.
### Response:
def value_str(sc):
"""
Returns the value part ("[*]", "<M>", "(foo)" etc.) of a menu entry.
sc: Symbol or Choice.
"""
if sc.type in (STRING, INT, HEX):
return "({})".format(sc.str_value)
# BOOL or TRISTATE
# The choice mode is an upper bound on the visibility of choice symbols, so
# we can check the choice symbols' own visibility to see if the choice is
# in y mode
if isinstance(sc, Symbol) and sc.choice and sc.visibility == 2:
# For choices in y mode, print '-->' next to the selected symbol
return "-->" if sc.choice.selection is sc else " "
tri_val_str = (" ", "M", "*")[sc.tri_value]
if len(sc.assignable) == 1:
# Pinned to a single value
return "-{}-".format(tri_val_str)
if sc.type == BOOL:
return "[{}]".format(tri_val_str)
if sc.type == TRISTATE:
if sc.assignable == (1, 2):
# m and y available
return "{" + tri_val_str + "}" # Gets a bit confusing with .format()
return "<{}>".format(tri_val_str) |
def load_mod_from_file(self, fpath):
"""Loads modules from a .py file into ShutIt if there are no modules from
this file already.
We expect to have a callable 'module/0' which returns one or more module
objects.
If this doesn't exist we assume that the .py file works in the old style
(automatically inserting the module into shutit_global) or it's not a shutit
module.
"""
shutit_global.shutit_global_object.yield_to_draw()
fpath = os.path.abspath(fpath)
file_ext = os.path.splitext(os.path.split(fpath)[-1])[-1]
if file_ext.lower() != '.py':
return
with open(fpath) as f:
content = f.read().splitlines()
ok = False
for line in content:
if line.strip() == 'from shutit_module import ShutItModule':
ok = True
break
if not ok:
self.log('Rejected file: ' + fpath,level=logging.DEBUG)
return
# Note that this attribute will only be set for 'new style' module loading, # this should be ok because 'old style' loading checks for duplicate # existing modules.
# TODO: this is quadratic complexity
existingmodules = [
m for m in self.shutit_modules
if getattr(m, '__module_file', None) == fpath
]
if existingmodules:
self.log('Module already seen: ' + fpath,level=logging.DEBUG)
return
# Looks like it's ok to load this file
self.log('Loading source for: ' + fpath,level=logging.DEBUG)
# Add this directory to the python path iff not already there.
directory = os.path.dirname(fpath)
if directory not in sys.path:
sys.path.append(os.path.dirname(fpath))
# TODO: use bytearray to encode?
mod_name = base64.b32encode(fpath.encode()).decode().replace('=', '')
pymod = imp.load_source(mod_name, fpath)
# Got the python module, now time to pull the shutit module(s) out of it.
targets = [
('module', self.shutit_modules), ('conn_module', self.conn_modules)
]
self.build['source'] = {}
for attr, target in targets:
modulefunc = getattr(pymod, attr, None)
# Old style or not a shutit module, nothing else to do
if not callable(modulefunc):
return
modules = modulefunc()
if not isinstance(modules, list):
modules = [modules]
for module in modules:
setattr(module, '__module_file', fpath)
ShutItModule.register(module.__class__)
target.add(module)
self.build['source'][fpath] = open(fpath).read() | Loads modules from a .py file into ShutIt if there are no modules from
this file already.
We expect to have a callable 'module/0' which returns one or more module
objects.
If this doesn't exist we assume that the .py file works in the old style
(automatically inserting the module into shutit_global) or it's not a shutit
module. | Below is the the instruction that describes the task:
### Input:
Loads modules from a .py file into ShutIt if there are no modules from
this file already.
We expect to have a callable 'module/0' which returns one or more module
objects.
If this doesn't exist we assume that the .py file works in the old style
(automatically inserting the module into shutit_global) or it's not a shutit
module.
### Response:
def load_mod_from_file(self, fpath):
"""Loads modules from a .py file into ShutIt if there are no modules from
this file already.
We expect to have a callable 'module/0' which returns one or more module
objects.
If this doesn't exist we assume that the .py file works in the old style
(automatically inserting the module into shutit_global) or it's not a shutit
module.
"""
shutit_global.shutit_global_object.yield_to_draw()
fpath = os.path.abspath(fpath)
file_ext = os.path.splitext(os.path.split(fpath)[-1])[-1]
if file_ext.lower() != '.py':
return
with open(fpath) as f:
content = f.read().splitlines()
ok = False
for line in content:
if line.strip() == 'from shutit_module import ShutItModule':
ok = True
break
if not ok:
self.log('Rejected file: ' + fpath,level=logging.DEBUG)
return
# Note that this attribute will only be set for 'new style' module loading, # this should be ok because 'old style' loading checks for duplicate # existing modules.
# TODO: this is quadratic complexity
existingmodules = [
m for m in self.shutit_modules
if getattr(m, '__module_file', None) == fpath
]
if existingmodules:
self.log('Module already seen: ' + fpath,level=logging.DEBUG)
return
# Looks like it's ok to load this file
self.log('Loading source for: ' + fpath,level=logging.DEBUG)
# Add this directory to the python path iff not already there.
directory = os.path.dirname(fpath)
if directory not in sys.path:
sys.path.append(os.path.dirname(fpath))
# TODO: use bytearray to encode?
mod_name = base64.b32encode(fpath.encode()).decode().replace('=', '')
pymod = imp.load_source(mod_name, fpath)
# Got the python module, now time to pull the shutit module(s) out of it.
targets = [
('module', self.shutit_modules), ('conn_module', self.conn_modules)
]
self.build['source'] = {}
for attr, target in targets:
modulefunc = getattr(pymod, attr, None)
# Old style or not a shutit module, nothing else to do
if not callable(modulefunc):
return
modules = modulefunc()
if not isinstance(modules, list):
modules = [modules]
for module in modules:
setattr(module, '__module_file', fpath)
ShutItModule.register(module.__class__)
target.add(module)
self.build['source'][fpath] = open(fpath).read() |
def NewFromJSON(data):
"""
Create a new User instance from a JSON dict.
Args:
data (dict): JSON dictionary representing a user.
Returns:
A User instance.
"""
if data.get('shakes', None):
shakes = [Shake.NewFromJSON(shk) for shk in data.get('shakes')]
else:
shakes = None
return User(
id=data.get('id', None),
name=data.get('name', None),
profile_image_url=data.get('profile_image_url', None),
about=data.get('about', None),
website=data.get('website', None),
shakes=shakes) | Create a new User instance from a JSON dict.
Args:
data (dict): JSON dictionary representing a user.
Returns:
A User instance. | Below is the the instruction that describes the task:
### Input:
Create a new User instance from a JSON dict.
Args:
data (dict): JSON dictionary representing a user.
Returns:
A User instance.
### Response:
def NewFromJSON(data):
"""
Create a new User instance from a JSON dict.
Args:
data (dict): JSON dictionary representing a user.
Returns:
A User instance.
"""
if data.get('shakes', None):
shakes = [Shake.NewFromJSON(shk) for shk in data.get('shakes')]
else:
shakes = None
return User(
id=data.get('id', None),
name=data.get('name', None),
profile_image_url=data.get('profile_image_url', None),
about=data.get('about', None),
website=data.get('website', None),
shakes=shakes) |
def _estimate_label_shape(self):
"""Helper function to estimate label shape"""
max_count = 0
self.reset()
try:
while True:
label, _ = self.next_sample()
label = self._parse_label(label)
max_count = max(max_count, label.shape[0])
except StopIteration:
pass
self.reset()
return (max_count, label.shape[1]) | Helper function to estimate label shape | Below is the the instruction that describes the task:
### Input:
Helper function to estimate label shape
### Response:
def _estimate_label_shape(self):
"""Helper function to estimate label shape"""
max_count = 0
self.reset()
try:
while True:
label, _ = self.next_sample()
label = self._parse_label(label)
max_count = max(max_count, label.shape[0])
except StopIteration:
pass
self.reset()
return (max_count, label.shape[1]) |
def stretch_weber_fechner(self, k, s0):
"""Stretch according to the Weber-Fechner law.
p = k.ln(S/S0)
p is perception, S is the stimulus, S0 is the stimulus threshold (the
highest unpercieved stimulus), and k is the factor.
"""
attrs = self.data.attrs
self.data = k * xu.log(self.data / s0)
self.data.attrs = attrs | Stretch according to the Weber-Fechner law.
p = k.ln(S/S0)
p is perception, S is the stimulus, S0 is the stimulus threshold (the
highest unpercieved stimulus), and k is the factor. | Below is the the instruction that describes the task:
### Input:
Stretch according to the Weber-Fechner law.
p = k.ln(S/S0)
p is perception, S is the stimulus, S0 is the stimulus threshold (the
highest unpercieved stimulus), and k is the factor.
### Response:
def stretch_weber_fechner(self, k, s0):
"""Stretch according to the Weber-Fechner law.
p = k.ln(S/S0)
p is perception, S is the stimulus, S0 is the stimulus threshold (the
highest unpercieved stimulus), and k is the factor.
"""
attrs = self.data.attrs
self.data = k * xu.log(self.data / s0)
self.data.attrs = attrs |
def read(path, encoding="utf-8"):
"""Read the content of the file.
Args:
path (str): Path to the file
encoding (str): File encoding. Default: utf-8
Returns:
str: File content or empty string if there was an error
"""
try:
with io.open(path, encoding=encoding) as f:
return f.read()
except Exception as e:
logger.error("read: %s failed. Error: %s", path, e)
return "" | Read the content of the file.
Args:
path (str): Path to the file
encoding (str): File encoding. Default: utf-8
Returns:
str: File content or empty string if there was an error | Below is the the instruction that describes the task:
### Input:
Read the content of the file.
Args:
path (str): Path to the file
encoding (str): File encoding. Default: utf-8
Returns:
str: File content or empty string if there was an error
### Response:
def read(path, encoding="utf-8"):
"""Read the content of the file.
Args:
path (str): Path to the file
encoding (str): File encoding. Default: utf-8
Returns:
str: File content or empty string if there was an error
"""
try:
with io.open(path, encoding=encoding) as f:
return f.read()
except Exception as e:
logger.error("read: %s failed. Error: %s", path, e)
return "" |
def send_zipfile(request, fileList):
"""
Create a ZIP file on disk and transmit it in chunks of 8KB,
without loading the whole file into memory. A similar approach can
be used for large dynamic PDF files.
"""
temp = tempfile.TemporaryFile()
archive = zipfile.ZipFile(temp, 'w', zipfile.ZIP_DEFLATED)
for artist,files in fileList.iteritems():
for f in files:
archive.write(f[0], '%s/%s' % (artist, f[1]))
archive.close()
wrapper = FixedFileWrapper(temp)
response = HttpResponse(wrapper, content_type='application/zip')
response['Content-Disposition'] = 'attachment; filename=FrogSources.zip'
response['Content-Length'] = temp.tell()
temp.seek(0)
return response | Create a ZIP file on disk and transmit it in chunks of 8KB,
without loading the whole file into memory. A similar approach can
be used for large dynamic PDF files. | Below is the the instruction that describes the task:
### Input:
Create a ZIP file on disk and transmit it in chunks of 8KB,
without loading the whole file into memory. A similar approach can
be used for large dynamic PDF files.
### Response:
def send_zipfile(request, fileList):
"""
Create a ZIP file on disk and transmit it in chunks of 8KB,
without loading the whole file into memory. A similar approach can
be used for large dynamic PDF files.
"""
temp = tempfile.TemporaryFile()
archive = zipfile.ZipFile(temp, 'w', zipfile.ZIP_DEFLATED)
for artist,files in fileList.iteritems():
for f in files:
archive.write(f[0], '%s/%s' % (artist, f[1]))
archive.close()
wrapper = FixedFileWrapper(temp)
response = HttpResponse(wrapper, content_type='application/zip')
response['Content-Disposition'] = 'attachment; filename=FrogSources.zip'
response['Content-Length'] = temp.tell()
temp.seek(0)
return response |
def cmd(send, msg, args):
"""Converts text to morse code.
Syntax: {command} [text]
"""
if not msg:
msg = gen_word()
morse = gen_morse(msg)
if len(morse) > 100:
send("Your morse is too long. Have you considered Western Union?")
else:
send(morse) | Converts text to morse code.
Syntax: {command} [text] | Below is the the instruction that describes the task:
### Input:
Converts text to morse code.
Syntax: {command} [text]
### Response:
def cmd(send, msg, args):
"""Converts text to morse code.
Syntax: {command} [text]
"""
if not msg:
msg = gen_word()
morse = gen_morse(msg)
if len(morse) > 100:
send("Your morse is too long. Have you considered Western Union?")
else:
send(morse) |
def is_multicast(text):
"""Is the textual-form network address a multicast address?
@param text: the textual address
@raises ValueError: the address family cannot be determined from the input.
@rtype: bool
"""
try:
first = ord(dns.ipv4.inet_aton(text)[0])
return (first >= 224 and first <= 239)
except Exception:
try:
first = ord(dns.ipv6.inet_aton(text)[0])
return (first == 255)
except Exception:
raise ValueError | Is the textual-form network address a multicast address?
@param text: the textual address
@raises ValueError: the address family cannot be determined from the input.
@rtype: bool | Below is the the instruction that describes the task:
### Input:
Is the textual-form network address a multicast address?
@param text: the textual address
@raises ValueError: the address family cannot be determined from the input.
@rtype: bool
### Response:
def is_multicast(text):
"""Is the textual-form network address a multicast address?
@param text: the textual address
@raises ValueError: the address family cannot be determined from the input.
@rtype: bool
"""
try:
first = ord(dns.ipv4.inet_aton(text)[0])
return (first >= 224 and first <= 239)
except Exception:
try:
first = ord(dns.ipv6.inet_aton(text)[0])
return (first == 255)
except Exception:
raise ValueError |
def toTypeURIs(namespace_map, alias_list_s):
"""Given a namespace mapping and a string containing a
comma-separated list of namespace aliases, return a list of type
URIs that correspond to those aliases.
@param namespace_map: The mapping from namespace URI to alias
@type namespace_map: openid.message.NamespaceMap
@param alias_list_s: The string containing the comma-separated
list of aliases. May also be None for convenience.
@type alias_list_s: str or NoneType
@returns: The list of namespace URIs that corresponds to the
supplied list of aliases. If the string was zero-length or
None, an empty list will be returned.
@raise KeyError: If an alias is present in the list of aliases but
is not present in the namespace map.
"""
uris = []
if alias_list_s:
for alias in alias_list_s.split(','):
type_uri = namespace_map.getNamespaceURI(alias)
if type_uri is None:
raise KeyError(
'No type is defined for attribute name %r' % (alias,))
else:
uris.append(type_uri)
return uris | Given a namespace mapping and a string containing a
comma-separated list of namespace aliases, return a list of type
URIs that correspond to those aliases.
@param namespace_map: The mapping from namespace URI to alias
@type namespace_map: openid.message.NamespaceMap
@param alias_list_s: The string containing the comma-separated
list of aliases. May also be None for convenience.
@type alias_list_s: str or NoneType
@returns: The list of namespace URIs that corresponds to the
supplied list of aliases. If the string was zero-length or
None, an empty list will be returned.
@raise KeyError: If an alias is present in the list of aliases but
is not present in the namespace map. | Below is the the instruction that describes the task:
### Input:
Given a namespace mapping and a string containing a
comma-separated list of namespace aliases, return a list of type
URIs that correspond to those aliases.
@param namespace_map: The mapping from namespace URI to alias
@type namespace_map: openid.message.NamespaceMap
@param alias_list_s: The string containing the comma-separated
list of aliases. May also be None for convenience.
@type alias_list_s: str or NoneType
@returns: The list of namespace URIs that corresponds to the
supplied list of aliases. If the string was zero-length or
None, an empty list will be returned.
@raise KeyError: If an alias is present in the list of aliases but
is not present in the namespace map.
### Response:
def toTypeURIs(namespace_map, alias_list_s):
"""Given a namespace mapping and a string containing a
comma-separated list of namespace aliases, return a list of type
URIs that correspond to those aliases.
@param namespace_map: The mapping from namespace URI to alias
@type namespace_map: openid.message.NamespaceMap
@param alias_list_s: The string containing the comma-separated
list of aliases. May also be None for convenience.
@type alias_list_s: str or NoneType
@returns: The list of namespace URIs that corresponds to the
supplied list of aliases. If the string was zero-length or
None, an empty list will be returned.
@raise KeyError: If an alias is present in the list of aliases but
is not present in the namespace map.
"""
uris = []
if alias_list_s:
for alias in alias_list_s.split(','):
type_uri = namespace_map.getNamespaceURI(alias)
if type_uri is None:
raise KeyError(
'No type is defined for attribute name %r' % (alias,))
else:
uris.append(type_uri)
return uris |
def __query_spec(self):
"""Get the spec to use for a query.
"""
operators = self.__modifiers.copy()
if self.__ordering:
operators["$orderby"] = self.__ordering
if self.__explain:
operators["$explain"] = True
if self.__hint:
operators["$hint"] = self.__hint
if self.__comment:
operators["$comment"] = self.__comment
if self.__max_scan:
operators["$maxScan"] = self.__max_scan
if self.__max_time_ms is not None:
operators["$maxTimeMS"] = self.__max_time_ms
if self.__max:
operators["$max"] = self.__max
if self.__min:
operators["$min"] = self.__min
if self.__return_key:
operators["$returnKey"] = self.__return_key
if self.__show_record_id:
# This is upgraded to showRecordId for MongoDB 3.2+ "find" command.
operators["$showDiskLoc"] = self.__show_record_id
if self.__snapshot:
operators["$snapshot"] = self.__snapshot
if operators:
# Make a shallow copy so we can cleanly rewind or clone.
spec = self.__spec.copy()
# White-listed commands must be wrapped in $query.
if "$query" not in spec:
# $query has to come first
spec = SON([("$query", spec)])
if not isinstance(spec, SON):
# Ensure the spec is SON. As order is important this will
# ensure its set before merging in any extra operators.
spec = SON(spec)
spec.update(operators)
return spec
# Have to wrap with $query if "query" is the first key.
# We can't just use $query anytime "query" is a key as
# that breaks commands like count and find_and_modify.
# Checking spec.keys()[0] covers the case that the spec
# was passed as an instance of SON or OrderedDict.
elif ("query" in self.__spec and
(len(self.__spec) == 1 or
next(iter(self.__spec)) == "query")):
return SON({"$query": self.__spec})
return self.__spec | Get the spec to use for a query. | Below is the the instruction that describes the task:
### Input:
Get the spec to use for a query.
### Response:
def __query_spec(self):
"""Get the spec to use for a query.
"""
operators = self.__modifiers.copy()
if self.__ordering:
operators["$orderby"] = self.__ordering
if self.__explain:
operators["$explain"] = True
if self.__hint:
operators["$hint"] = self.__hint
if self.__comment:
operators["$comment"] = self.__comment
if self.__max_scan:
operators["$maxScan"] = self.__max_scan
if self.__max_time_ms is not None:
operators["$maxTimeMS"] = self.__max_time_ms
if self.__max:
operators["$max"] = self.__max
if self.__min:
operators["$min"] = self.__min
if self.__return_key:
operators["$returnKey"] = self.__return_key
if self.__show_record_id:
# This is upgraded to showRecordId for MongoDB 3.2+ "find" command.
operators["$showDiskLoc"] = self.__show_record_id
if self.__snapshot:
operators["$snapshot"] = self.__snapshot
if operators:
# Make a shallow copy so we can cleanly rewind or clone.
spec = self.__spec.copy()
# White-listed commands must be wrapped in $query.
if "$query" not in spec:
# $query has to come first
spec = SON([("$query", spec)])
if not isinstance(spec, SON):
# Ensure the spec is SON. As order is important this will
# ensure its set before merging in any extra operators.
spec = SON(spec)
spec.update(operators)
return spec
# Have to wrap with $query if "query" is the first key.
# We can't just use $query anytime "query" is a key as
# that breaks commands like count and find_and_modify.
# Checking spec.keys()[0] covers the case that the spec
# was passed as an instance of SON or OrderedDict.
elif ("query" in self.__spec and
(len(self.__spec) == 1 or
next(iter(self.__spec)) == "query")):
return SON({"$query": self.__spec})
return self.__spec |
def followed_topic_num(self):
"""θ·εη¨ζ·ε
³ζ³¨ηθ―ι’ζ°
:return: ε
³ζ³¨ηθ―ι’ζ°
:rtype: int
"""
if self.url is not None:
tag = self.soup.find('div', class_='zm-profile-side-topics')
if tag is not None:
return int(re_get_number.match(
tag.parent.strong.text).group(1))
return 0 | θ·εη¨ζ·ε
³ζ³¨ηθ―ι’ζ°
:return: ε
³ζ³¨ηθ―ι’ζ°
:rtype: int | Below is the the instruction that describes the task:
### Input:
θ·εη¨ζ·ε
³ζ³¨ηθ―ι’ζ°
:return: ε
³ζ³¨ηθ―ι’ζ°
:rtype: int
### Response:
def followed_topic_num(self):
"""θ·εη¨ζ·ε
³ζ³¨ηθ―ι’ζ°
:return: ε
³ζ³¨ηθ―ι’ζ°
:rtype: int
"""
if self.url is not None:
tag = self.soup.find('div', class_='zm-profile-side-topics')
if tag is not None:
return int(re_get_number.match(
tag.parent.strong.text).group(1))
return 0 |
def response_to_json_dict(response, **kwargs):
"""
Standard place to convert responses to JSON.
:param response: requests response object
:param **kwargs: arguments accepted by json.loads
:returns: dict of JSON response
"""
if response.encoding is None:
response.encoding = 'utf-8'
return json.loads(response.text, **kwargs) | Standard place to convert responses to JSON.
:param response: requests response object
:param **kwargs: arguments accepted by json.loads
:returns: dict of JSON response | Below is the the instruction that describes the task:
### Input:
Standard place to convert responses to JSON.
:param response: requests response object
:param **kwargs: arguments accepted by json.loads
:returns: dict of JSON response
### Response:
def response_to_json_dict(response, **kwargs):
"""
Standard place to convert responses to JSON.
:param response: requests response object
:param **kwargs: arguments accepted by json.loads
:returns: dict of JSON response
"""
if response.encoding is None:
response.encoding = 'utf-8'
return json.loads(response.text, **kwargs) |
def get_dates(raw_table) -> "list of dates":
"""
Goes through the first column of input table and
returns the first sequence of dates it finds.
"""
dates = []
found_first = False
for i, dstr in enumerate([raw_table[i][0] for i in range(0, len(raw_table))]):
if dstr:
if len(dstr.split("/")) == 3:
d = datetime.datetime.strptime(dstr, '%m/%d/%Y')
elif len(dstr.split("-")) == 3:
d = datetime.datetime.strptime(dstr, '%Y-%m-%d')
else:
# Not necessarily an error, could just be a non-date cell
logging.debug("unknown date-format: {}".format(dstr))
continue
dates.append(d)
if not found_first:
found_first = True
logging.debug("Found first date: '{}' at i: {}".format(d.isoformat(), i))
elif found_first:
logging.debug("Last date: {}".format(d))
break
return dates | Goes through the first column of input table and
returns the first sequence of dates it finds. | Below is the the instruction that describes the task:
### Input:
Goes through the first column of input table and
returns the first sequence of dates it finds.
### Response:
def get_dates(raw_table) -> "list of dates":
"""
Goes through the first column of input table and
returns the first sequence of dates it finds.
"""
dates = []
found_first = False
for i, dstr in enumerate([raw_table[i][0] for i in range(0, len(raw_table))]):
if dstr:
if len(dstr.split("/")) == 3:
d = datetime.datetime.strptime(dstr, '%m/%d/%Y')
elif len(dstr.split("-")) == 3:
d = datetime.datetime.strptime(dstr, '%Y-%m-%d')
else:
# Not necessarily an error, could just be a non-date cell
logging.debug("unknown date-format: {}".format(dstr))
continue
dates.append(d)
if not found_first:
found_first = True
logging.debug("Found first date: '{}' at i: {}".format(d.isoformat(), i))
elif found_first:
logging.debug("Last date: {}".format(d))
break
return dates |
def write_url (self, url_data):
"""Write url_data.base_url."""
self.writeln(u"<tr>")
self.writeln(u'<td class="url">%s</td>' % self.part("url"))
self.write(u'<td class="url">')
self.write(u"`%s'" % cgi.escape(url_data.base_url))
self.writeln(u"</td></tr>") | Write url_data.base_url. | Below is the the instruction that describes the task:
### Input:
Write url_data.base_url.
### Response:
def write_url (self, url_data):
"""Write url_data.base_url."""
self.writeln(u"<tr>")
self.writeln(u'<td class="url">%s</td>' % self.part("url"))
self.write(u'<td class="url">')
self.write(u"`%s'" % cgi.escape(url_data.base_url))
self.writeln(u"</td></tr>") |
def _set_token(self):
"""Set the Cerberus token based on auth type"""
try:
self.token = os.environ['CERBERUS_TOKEN']
if self.verbose:
print("Overriding Cerberus token with environment variable.", file=sys.stderr)
logger.info("Overriding Cerberus token with environment variable.")
return
except:
pass
if self.username:
ua = UserAuth(self.cerberus_url, self.username, self.password)
self.token = ua.get_token()
else:
awsa = AWSAuth(self.cerberus_url, region=self.region, aws_session=self.aws_session, verbose=self.verbose)
self.token = awsa.get_token() | Set the Cerberus token based on auth type | Below is the the instruction that describes the task:
### Input:
Set the Cerberus token based on auth type
### Response:
def _set_token(self):
"""Set the Cerberus token based on auth type"""
try:
self.token = os.environ['CERBERUS_TOKEN']
if self.verbose:
print("Overriding Cerberus token with environment variable.", file=sys.stderr)
logger.info("Overriding Cerberus token with environment variable.")
return
except:
pass
if self.username:
ua = UserAuth(self.cerberus_url, self.username, self.password)
self.token = ua.get_token()
else:
awsa = AWSAuth(self.cerberus_url, region=self.region, aws_session=self.aws_session, verbose=self.verbose)
self.token = awsa.get_token() |
async def renew(self, session, *, dc=None):
"""Renews a TTL-based session
Parameters:
session (ObjectID): Session ID
dc (str): Specify datacenter that will be used.
Defaults to the agent's local datacenter.
Returns:
ObjectMeta: where value is session
Raises:
NotFound: session is absent
The response looks like this::
{
"LockDelay": datetime.timedelta(0, 15),
"Checks": [
"serfHealth"
],
"Node": "foobar",
"ID": "adf4238a-882b-9ddc-4a9d-5b6758e4159e",
"CreateIndex": 1086449
"Behavior": "release",
"TTL": datetime.timedelta(0, 15)
}
.. note:: Consul MAY return a TTL value higher than the one
specified during session creation. This indicates
the server is under high load and is requesting
clients renew less often.
"""
session_id = extract_attr(session, keys=["ID"])
response = await self._api.put("/v1/session/renew", session_id,
params={"dc": dc})
try:
result = response.body[0]
except IndexError:
meta = extract_meta(response.headers)
raise NotFound("No session for %r" % session_id, meta=meta)
return consul(result, meta=extract_meta(response.headers)) | Renews a TTL-based session
Parameters:
session (ObjectID): Session ID
dc (str): Specify datacenter that will be used.
Defaults to the agent's local datacenter.
Returns:
ObjectMeta: where value is session
Raises:
NotFound: session is absent
The response looks like this::
{
"LockDelay": datetime.timedelta(0, 15),
"Checks": [
"serfHealth"
],
"Node": "foobar",
"ID": "adf4238a-882b-9ddc-4a9d-5b6758e4159e",
"CreateIndex": 1086449
"Behavior": "release",
"TTL": datetime.timedelta(0, 15)
}
.. note:: Consul MAY return a TTL value higher than the one
specified during session creation. This indicates
the server is under high load and is requesting
clients renew less often. | Below is the the instruction that describes the task:
### Input:
Renews a TTL-based session
Parameters:
session (ObjectID): Session ID
dc (str): Specify datacenter that will be used.
Defaults to the agent's local datacenter.
Returns:
ObjectMeta: where value is session
Raises:
NotFound: session is absent
The response looks like this::
{
"LockDelay": datetime.timedelta(0, 15),
"Checks": [
"serfHealth"
],
"Node": "foobar",
"ID": "adf4238a-882b-9ddc-4a9d-5b6758e4159e",
"CreateIndex": 1086449
"Behavior": "release",
"TTL": datetime.timedelta(0, 15)
}
.. note:: Consul MAY return a TTL value higher than the one
specified during session creation. This indicates
the server is under high load and is requesting
clients renew less often.
### Response:
async def renew(self, session, *, dc=None):
"""Renews a TTL-based session
Parameters:
session (ObjectID): Session ID
dc (str): Specify datacenter that will be used.
Defaults to the agent's local datacenter.
Returns:
ObjectMeta: where value is session
Raises:
NotFound: session is absent
The response looks like this::
{
"LockDelay": datetime.timedelta(0, 15),
"Checks": [
"serfHealth"
],
"Node": "foobar",
"ID": "adf4238a-882b-9ddc-4a9d-5b6758e4159e",
"CreateIndex": 1086449
"Behavior": "release",
"TTL": datetime.timedelta(0, 15)
}
.. note:: Consul MAY return a TTL value higher than the one
specified during session creation. This indicates
the server is under high load and is requesting
clients renew less often.
"""
session_id = extract_attr(session, keys=["ID"])
response = await self._api.put("/v1/session/renew", session_id,
params={"dc": dc})
try:
result = response.body[0]
except IndexError:
meta = extract_meta(response.headers)
raise NotFound("No session for %r" % session_id, meta=meta)
return consul(result, meta=extract_meta(response.headers)) |
def reduce(source, func, initializer=None):
"""Apply a function of two arguments cumulatively to the items
of an asynchronous sequence, reducing the sequence to a single value.
If ``initializer`` is present, it is placed before the items
of the sequence in the calculation, and serves as a default when the
sequence is empty.
"""
acc = accumulate.raw(source, func, initializer)
return select.item.raw(acc, -1) | Apply a function of two arguments cumulatively to the items
of an asynchronous sequence, reducing the sequence to a single value.
If ``initializer`` is present, it is placed before the items
of the sequence in the calculation, and serves as a default when the
sequence is empty. | Below is the the instruction that describes the task:
### Input:
Apply a function of two arguments cumulatively to the items
of an asynchronous sequence, reducing the sequence to a single value.
If ``initializer`` is present, it is placed before the items
of the sequence in the calculation, and serves as a default when the
sequence is empty.
### Response:
def reduce(source, func, initializer=None):
"""Apply a function of two arguments cumulatively to the items
of an asynchronous sequence, reducing the sequence to a single value.
If ``initializer`` is present, it is placed before the items
of the sequence in the calculation, and serves as a default when the
sequence is empty.
"""
acc = accumulate.raw(source, func, initializer)
return select.item.raw(acc, -1) |
def create_init(self, path): # type: (str) -> None
"""
Create a minimal __init__ file with enough boiler plate to not add to lint messages
:param path:
:return:
"""
source = """# coding=utf-8
\"\"\"
Version
\"\"\"
__version__ = \"0.0.0\"
"""
with io.open(path, "w", encoding="utf-8") as outfile:
outfile.write(source) | Create a minimal __init__ file with enough boiler plate to not add to lint messages
:param path:
:return: | Below is the the instruction that describes the task:
### Input:
Create a minimal __init__ file with enough boiler plate to not add to lint messages
:param path:
:return:
### Response:
def create_init(self, path): # type: (str) -> None
"""
Create a minimal __init__ file with enough boiler plate to not add to lint messages
:param path:
:return:
"""
source = """# coding=utf-8
\"\"\"
Version
\"\"\"
__version__ = \"0.0.0\"
"""
with io.open(path, "w", encoding="utf-8") as outfile:
outfile.write(source) |
def gene_to_panels(self, case_obj):
"""Fetch all gene panels and group them by gene
Args:
case_obj(scout.models.Case)
Returns:
gene_dict(dict): A dictionary with gene as keys and a set of
panel names as value
"""
LOG.info("Building gene to panels")
gene_dict = {}
for panel_info in case_obj.get('panels', []):
panel_name = panel_info['panel_name']
panel_version = panel_info['version']
panel_obj = self.gene_panel(panel_name, version=panel_version)
if not panel_obj:
## Raise exception here???
LOG.warning("Panel: {0}, version {1} does not exist in database".format(panel_name, panel_version))
for gene in panel_obj['genes']:
hgnc_id = gene['hgnc_id']
if hgnc_id not in gene_dict:
gene_dict[hgnc_id] = set([panel_name])
continue
gene_dict[hgnc_id].add(panel_name)
LOG.info("Gene to panels done")
return gene_dict | Fetch all gene panels and group them by gene
Args:
case_obj(scout.models.Case)
Returns:
gene_dict(dict): A dictionary with gene as keys and a set of
panel names as value | Below is the the instruction that describes the task:
### Input:
Fetch all gene panels and group them by gene
Args:
case_obj(scout.models.Case)
Returns:
gene_dict(dict): A dictionary with gene as keys and a set of
panel names as value
### Response:
def gene_to_panels(self, case_obj):
"""Fetch all gene panels and group them by gene
Args:
case_obj(scout.models.Case)
Returns:
gene_dict(dict): A dictionary with gene as keys and a set of
panel names as value
"""
LOG.info("Building gene to panels")
gene_dict = {}
for panel_info in case_obj.get('panels', []):
panel_name = panel_info['panel_name']
panel_version = panel_info['version']
panel_obj = self.gene_panel(panel_name, version=panel_version)
if not panel_obj:
## Raise exception here???
LOG.warning("Panel: {0}, version {1} does not exist in database".format(panel_name, panel_version))
for gene in panel_obj['genes']:
hgnc_id = gene['hgnc_id']
if hgnc_id not in gene_dict:
gene_dict[hgnc_id] = set([panel_name])
continue
gene_dict[hgnc_id].add(panel_name)
LOG.info("Gene to panels done")
return gene_dict |
def get_count(self, prefix=''):
"""Return the total count of errors and warnings."""
return sum([self.counters[key]
for key in self.messages if key.startswith(prefix)]) | Return the total count of errors and warnings. | Below is the the instruction that describes the task:
### Input:
Return the total count of errors and warnings.
### Response:
def get_count(self, prefix=''):
"""Return the total count of errors and warnings."""
return sum([self.counters[key]
for key in self.messages if key.startswith(prefix)]) |
def load_watch():
'''
Loads some of the 6-axis inertial sensor data from my smartwatch project. The sensor data was
recorded as study subjects performed sets of 20 shoulder exercise repetitions while wearing a
smartwatch. It is a multivariate time series.
The study can be found here: https://arxiv.org/abs/1802.01489
Returns
-------
data : dict
data['X'] : list, length 140
| inertial sensor data, each element with shape [n_samples, 6]
| sampled at 50 Hz
data['y'] : array, length 140
target vector (exercise type)
data['side'] : array, length 140
the extremity side, 1 = right, 0 = left
data['subject'] : array, length 140
the subject (participant) number
data['X_labels'] : str list, length 6
ordered labels for the sensor data variables
data['y_labels'] :str list, length 7
ordered labels for the target (exercise type)
Examples
--------
>>> from seglearn.datasets import load_watch
>>> data = load_watch()
>>> print(data.keys())
'''
module_path = dirname(__file__)
data = np.load(module_path + "/data/watch_dataset.npy").item()
return data | Loads some of the 6-axis inertial sensor data from my smartwatch project. The sensor data was
recorded as study subjects performed sets of 20 shoulder exercise repetitions while wearing a
smartwatch. It is a multivariate time series.
The study can be found here: https://arxiv.org/abs/1802.01489
Returns
-------
data : dict
data['X'] : list, length 140
| inertial sensor data, each element with shape [n_samples, 6]
| sampled at 50 Hz
data['y'] : array, length 140
target vector (exercise type)
data['side'] : array, length 140
the extremity side, 1 = right, 0 = left
data['subject'] : array, length 140
the subject (participant) number
data['X_labels'] : str list, length 6
ordered labels for the sensor data variables
data['y_labels'] :str list, length 7
ordered labels for the target (exercise type)
Examples
--------
>>> from seglearn.datasets import load_watch
>>> data = load_watch()
>>> print(data.keys()) | Below is the the instruction that describes the task:
### Input:
Loads some of the 6-axis inertial sensor data from my smartwatch project. The sensor data was
recorded as study subjects performed sets of 20 shoulder exercise repetitions while wearing a
smartwatch. It is a multivariate time series.
The study can be found here: https://arxiv.org/abs/1802.01489
Returns
-------
data : dict
data['X'] : list, length 140
| inertial sensor data, each element with shape [n_samples, 6]
| sampled at 50 Hz
data['y'] : array, length 140
target vector (exercise type)
data['side'] : array, length 140
the extremity side, 1 = right, 0 = left
data['subject'] : array, length 140
the subject (participant) number
data['X_labels'] : str list, length 6
ordered labels for the sensor data variables
data['y_labels'] :str list, length 7
ordered labels for the target (exercise type)
Examples
--------
>>> from seglearn.datasets import load_watch
>>> data = load_watch()
>>> print(data.keys())
### Response:
def load_watch():
'''
Loads some of the 6-axis inertial sensor data from my smartwatch project. The sensor data was
recorded as study subjects performed sets of 20 shoulder exercise repetitions while wearing a
smartwatch. It is a multivariate time series.
The study can be found here: https://arxiv.org/abs/1802.01489
Returns
-------
data : dict
data['X'] : list, length 140
| inertial sensor data, each element with shape [n_samples, 6]
| sampled at 50 Hz
data['y'] : array, length 140
target vector (exercise type)
data['side'] : array, length 140
the extremity side, 1 = right, 0 = left
data['subject'] : array, length 140
the subject (participant) number
data['X_labels'] : str list, length 6
ordered labels for the sensor data variables
data['y_labels'] :str list, length 7
ordered labels for the target (exercise type)
Examples
--------
>>> from seglearn.datasets import load_watch
>>> data = load_watch()
>>> print(data.keys())
'''
module_path = dirname(__file__)
data = np.load(module_path + "/data/watch_dataset.npy").item()
return data |
def add_filter(self, ftype, func):
''' Register a new output filter. Whenever bottle hits a handler output
matching `ftype`, `func` is applyed to it. '''
if not isinstance(ftype, type):
raise TypeError("Expected type object, got %s" % type(ftype))
self.castfilter = [(t, f) for (t, f) in self.castfilter if t != ftype]
self.castfilter.append((ftype, func))
self.castfilter.sort() | Register a new output filter. Whenever bottle hits a handler output
matching `ftype`, `func` is applyed to it. | Below is the the instruction that describes the task:
### Input:
Register a new output filter. Whenever bottle hits a handler output
matching `ftype`, `func` is applyed to it.
### Response:
def add_filter(self, ftype, func):
''' Register a new output filter. Whenever bottle hits a handler output
matching `ftype`, `func` is applyed to it. '''
if not isinstance(ftype, type):
raise TypeError("Expected type object, got %s" % type(ftype))
self.castfilter = [(t, f) for (t, f) in self.castfilter if t != ftype]
self.castfilter.append((ftype, func))
self.castfilter.sort() |
def vgcreate(vgname, devices, **kwargs):
'''
Create an LVM volume group
CLI Examples:
.. code-block:: bash
salt mymachine lvm.vgcreate my_vg /dev/sdb1,/dev/sdb2
salt mymachine lvm.vgcreate my_vg /dev/sdb1 clustered=y
'''
if not vgname or not devices:
return 'Error: vgname and device(s) are both required'
if isinstance(devices, six.string_types):
devices = devices.split(',')
cmd = ['vgcreate', vgname]
for device in devices:
cmd.append(device)
valid = ('clustered', 'maxlogicalvolumes', 'maxphysicalvolumes',
'vgmetadatacopies', 'metadatacopies', 'physicalextentsize')
for var in kwargs:
if kwargs[var] and var in valid:
cmd.append('--{0}'.format(var))
cmd.append(kwargs[var])
out = __salt__['cmd.run'](cmd, python_shell=False).splitlines()
vgdata = vgdisplay(vgname)
vgdata['Output from vgcreate'] = out[0].strip()
return vgdata | Create an LVM volume group
CLI Examples:
.. code-block:: bash
salt mymachine lvm.vgcreate my_vg /dev/sdb1,/dev/sdb2
salt mymachine lvm.vgcreate my_vg /dev/sdb1 clustered=y | Below is the the instruction that describes the task:
### Input:
Create an LVM volume group
CLI Examples:
.. code-block:: bash
salt mymachine lvm.vgcreate my_vg /dev/sdb1,/dev/sdb2
salt mymachine lvm.vgcreate my_vg /dev/sdb1 clustered=y
### Response:
def vgcreate(vgname, devices, **kwargs):
'''
Create an LVM volume group
CLI Examples:
.. code-block:: bash
salt mymachine lvm.vgcreate my_vg /dev/sdb1,/dev/sdb2
salt mymachine lvm.vgcreate my_vg /dev/sdb1 clustered=y
'''
if not vgname or not devices:
return 'Error: vgname and device(s) are both required'
if isinstance(devices, six.string_types):
devices = devices.split(',')
cmd = ['vgcreate', vgname]
for device in devices:
cmd.append(device)
valid = ('clustered', 'maxlogicalvolumes', 'maxphysicalvolumes',
'vgmetadatacopies', 'metadatacopies', 'physicalextentsize')
for var in kwargs:
if kwargs[var] and var in valid:
cmd.append('--{0}'.format(var))
cmd.append(kwargs[var])
out = __salt__['cmd.run'](cmd, python_shell=False).splitlines()
vgdata = vgdisplay(vgname)
vgdata['Output from vgcreate'] = out[0].strip()
return vgdata |
def stop_instance(self, instance_id):
"""Stops the instance gracefully.
:param str instance_id: instance identifier
:raises: `InstanceError` if instance can not be stopped
"""
if not instance_id:
log.info("Instance to stop has no instance id")
return
gce = self._connect()
try:
request = gce.instances().delete(project=self._project_id,
instance=instance_id, zone=self._zone)
response = self._execute_request(request)
self._check_response(response)
except HttpError as e:
# If the instance does not exist, we get a 404
if e.resp.status == 404:
raise InstanceNotFoundError(
"Instance `{instance_id}` was not found"
.format(instance_id=instance_id))
else:
raise InstanceError(
"Could not stop instance `{instance_id}`: `{e}`"
.format(instance_id=instance_id, e=e))
except CloudProviderError as e:
raise InstanceError(
"Could not stop instance `{instance_id}`: `{e}`"
.format(instance_id=instance_id, e=e)) | Stops the instance gracefully.
:param str instance_id: instance identifier
:raises: `InstanceError` if instance can not be stopped | Below is the the instruction that describes the task:
### Input:
Stops the instance gracefully.
:param str instance_id: instance identifier
:raises: `InstanceError` if instance can not be stopped
### Response:
def stop_instance(self, instance_id):
"""Stops the instance gracefully.
:param str instance_id: instance identifier
:raises: `InstanceError` if instance can not be stopped
"""
if not instance_id:
log.info("Instance to stop has no instance id")
return
gce = self._connect()
try:
request = gce.instances().delete(project=self._project_id,
instance=instance_id, zone=self._zone)
response = self._execute_request(request)
self._check_response(response)
except HttpError as e:
# If the instance does not exist, we get a 404
if e.resp.status == 404:
raise InstanceNotFoundError(
"Instance `{instance_id}` was not found"
.format(instance_id=instance_id))
else:
raise InstanceError(
"Could not stop instance `{instance_id}`: `{e}`"
.format(instance_id=instance_id, e=e))
except CloudProviderError as e:
raise InstanceError(
"Could not stop instance `{instance_id}`: `{e}`"
.format(instance_id=instance_id, e=e)) |
def connect(self, hostkey=None, username='', password=None, pkey=None):
"""
Negotiate an SSH2 session, and optionally verify the server's host key
and authenticate using a password or private key. This is a shortcut
for L{start_client}, L{get_remote_server_key}, and
L{Transport.auth_password} or L{Transport.auth_publickey}. Use those
methods if you want more control.
You can use this method immediately after creating a Transport to
negotiate encryption with a server. If it fails, an exception will be
thrown. On success, the method will return cleanly, and an encrypted
session exists. You may immediately call L{open_channel} or
L{open_session} to get a L{Channel} object, which is used for data
transfer.
@note: If you fail to supply a password or private key, this method may
succeed, but a subsequent L{open_channel} or L{open_session} call may
fail because you haven't authenticated yet.
@param hostkey: the host key expected from the server, or C{None} if
you don't want to do host key verification.
@type hostkey: L{PKey<pkey.PKey>}
@param username: the username to authenticate as.
@type username: str
@param password: a password to use for authentication, if you want to
use password authentication; otherwise C{None}.
@type password: str
@param pkey: a private key to use for authentication, if you want to
use private key authentication; otherwise C{None}.
@type pkey: L{PKey<pkey.PKey>}
@raise SSHException: if the SSH2 negotiation fails, the host key
supplied by the server is incorrect, or authentication fails.
"""
if hostkey is not None:
self._preferred_keys = [ hostkey.get_name() ]
self.start_client()
# check host key if we were given one
if (hostkey is not None):
key = self.get_remote_server_key()
if (key.get_name() != hostkey.get_name()) or (str(key) != str(hostkey)):
self._log(DEBUG, 'Bad host key from server')
self._log(DEBUG, 'Expected: %s: %s' % (hostkey.get_name(), repr(str(hostkey))))
self._log(DEBUG, 'Got : %s: %s' % (key.get_name(), repr(str(key))))
raise SSHException('Bad host key from server')
self._log(DEBUG, 'Host key verified (%s)' % hostkey.get_name())
if (pkey is not None) or (password is not None):
if password is not None:
self._log(DEBUG, 'Attempting password auth...')
self.auth_password(username, password)
else:
self._log(DEBUG, 'Attempting public-key auth...')
self.auth_publickey(username, pkey)
return | Negotiate an SSH2 session, and optionally verify the server's host key
and authenticate using a password or private key. This is a shortcut
for L{start_client}, L{get_remote_server_key}, and
L{Transport.auth_password} or L{Transport.auth_publickey}. Use those
methods if you want more control.
You can use this method immediately after creating a Transport to
negotiate encryption with a server. If it fails, an exception will be
thrown. On success, the method will return cleanly, and an encrypted
session exists. You may immediately call L{open_channel} or
L{open_session} to get a L{Channel} object, which is used for data
transfer.
@note: If you fail to supply a password or private key, this method may
succeed, but a subsequent L{open_channel} or L{open_session} call may
fail because you haven't authenticated yet.
@param hostkey: the host key expected from the server, or C{None} if
you don't want to do host key verification.
@type hostkey: L{PKey<pkey.PKey>}
@param username: the username to authenticate as.
@type username: str
@param password: a password to use for authentication, if you want to
use password authentication; otherwise C{None}.
@type password: str
@param pkey: a private key to use for authentication, if you want to
use private key authentication; otherwise C{None}.
@type pkey: L{PKey<pkey.PKey>}
@raise SSHException: if the SSH2 negotiation fails, the host key
supplied by the server is incorrect, or authentication fails. | Below is the the instruction that describes the task:
### Input:
Negotiate an SSH2 session, and optionally verify the server's host key
and authenticate using a password or private key. This is a shortcut
for L{start_client}, L{get_remote_server_key}, and
L{Transport.auth_password} or L{Transport.auth_publickey}. Use those
methods if you want more control.
You can use this method immediately after creating a Transport to
negotiate encryption with a server. If it fails, an exception will be
thrown. On success, the method will return cleanly, and an encrypted
session exists. You may immediately call L{open_channel} or
L{open_session} to get a L{Channel} object, which is used for data
transfer.
@note: If you fail to supply a password or private key, this method may
succeed, but a subsequent L{open_channel} or L{open_session} call may
fail because you haven't authenticated yet.
@param hostkey: the host key expected from the server, or C{None} if
you don't want to do host key verification.
@type hostkey: L{PKey<pkey.PKey>}
@param username: the username to authenticate as.
@type username: str
@param password: a password to use for authentication, if you want to
use password authentication; otherwise C{None}.
@type password: str
@param pkey: a private key to use for authentication, if you want to
use private key authentication; otherwise C{None}.
@type pkey: L{PKey<pkey.PKey>}
@raise SSHException: if the SSH2 negotiation fails, the host key
supplied by the server is incorrect, or authentication fails.
### Response:
def connect(self, hostkey=None, username='', password=None, pkey=None):
"""
Negotiate an SSH2 session, and optionally verify the server's host key
and authenticate using a password or private key. This is a shortcut
for L{start_client}, L{get_remote_server_key}, and
L{Transport.auth_password} or L{Transport.auth_publickey}. Use those
methods if you want more control.
You can use this method immediately after creating a Transport to
negotiate encryption with a server. If it fails, an exception will be
thrown. On success, the method will return cleanly, and an encrypted
session exists. You may immediately call L{open_channel} or
L{open_session} to get a L{Channel} object, which is used for data
transfer.
@note: If you fail to supply a password or private key, this method may
succeed, but a subsequent L{open_channel} or L{open_session} call may
fail because you haven't authenticated yet.
@param hostkey: the host key expected from the server, or C{None} if
you don't want to do host key verification.
@type hostkey: L{PKey<pkey.PKey>}
@param username: the username to authenticate as.
@type username: str
@param password: a password to use for authentication, if you want to
use password authentication; otherwise C{None}.
@type password: str
@param pkey: a private key to use for authentication, if you want to
use private key authentication; otherwise C{None}.
@type pkey: L{PKey<pkey.PKey>}
@raise SSHException: if the SSH2 negotiation fails, the host key
supplied by the server is incorrect, or authentication fails.
"""
if hostkey is not None:
self._preferred_keys = [ hostkey.get_name() ]
self.start_client()
# check host key if we were given one
if (hostkey is not None):
key = self.get_remote_server_key()
if (key.get_name() != hostkey.get_name()) or (str(key) != str(hostkey)):
self._log(DEBUG, 'Bad host key from server')
self._log(DEBUG, 'Expected: %s: %s' % (hostkey.get_name(), repr(str(hostkey))))
self._log(DEBUG, 'Got : %s: %s' % (key.get_name(), repr(str(key))))
raise SSHException('Bad host key from server')
self._log(DEBUG, 'Host key verified (%s)' % hostkey.get_name())
if (pkey is not None) or (password is not None):
if password is not None:
self._log(DEBUG, 'Attempting password auth...')
self.auth_password(username, password)
else:
self._log(DEBUG, 'Attempting public-key auth...')
self.auth_publickey(username, pkey)
return |
def overview():
"""
Function to create an overview of the services.
Will print a list of ports found an the number of times the port was seen.
"""
search = Service.search()
search = search.filter("term", state='open')
search.aggs.bucket('port_count', 'terms', field='port', order={'_count': 'desc'}, size=100) \
.metric('unique_count', 'cardinality', field='address')
response = search.execute()
print_line("Port Count")
print_line("---------------")
for entry in response.aggregations.port_count.buckets:
print_line("{0:<7} {1}".format(entry.key, entry.unique_count.value)) | Function to create an overview of the services.
Will print a list of ports found an the number of times the port was seen. | Below is the the instruction that describes the task:
### Input:
Function to create an overview of the services.
Will print a list of ports found an the number of times the port was seen.
### Response:
def overview():
"""
Function to create an overview of the services.
Will print a list of ports found an the number of times the port was seen.
"""
search = Service.search()
search = search.filter("term", state='open')
search.aggs.bucket('port_count', 'terms', field='port', order={'_count': 'desc'}, size=100) \
.metric('unique_count', 'cardinality', field='address')
response = search.execute()
print_line("Port Count")
print_line("---------------")
for entry in response.aggregations.port_count.buckets:
print_line("{0:<7} {1}".format(entry.key, entry.unique_count.value)) |
Subsets and Splits