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zalando/patroni | patroni/postgresql.py | Postgresql.restore_configuration_files | def restore_configuration_files(self):
""" restore a previously saved postgresql.conf """
try:
for f in self._configuration_to_save:
config_file = os.path.join(self._config_dir, f)
backup_file = os.path.join(self._data_dir, f + '.backup')
if not os.path.isfile(config_file):
if os.path.isfile(backup_file):
shutil.copy(backup_file, config_file)
# Previously we didn't backup pg_ident.conf, if file is missing just create empty
elif f == 'pg_ident.conf':
open(config_file, 'w').close()
except IOError:
logger.exception('unable to restore configuration files from backup') | python | def restore_configuration_files(self):
""" restore a previously saved postgresql.conf """
try:
for f in self._configuration_to_save:
config_file = os.path.join(self._config_dir, f)
backup_file = os.path.join(self._data_dir, f + '.backup')
if not os.path.isfile(config_file):
if os.path.isfile(backup_file):
shutil.copy(backup_file, config_file)
# Previously we didn't backup pg_ident.conf, if file is missing just create empty
elif f == 'pg_ident.conf':
open(config_file, 'w').close()
except IOError:
logger.exception('unable to restore configuration files from backup') | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
restore a previously saved postgresql.conf
### Response:
def restore_configuration_files(self):
""" restore a previously saved postgresql.conf """
try:
for f in self._configuration_to_save:
config_file = os.path.join(self._config_dir, f)
backup_file = os.path.join(self._data_dir, f + '.backup')
if not os.path.isfile(config_file):
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shutil.copy(backup_file, config_file)
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fabioz/PyDev.Debugger | pydevd_attach_to_process/winappdbg/breakpoint.py | _BufferWatchCondition.remove | def remove(self, bw):
"""
Removes a buffer watch identifier.
@type bw: L{BufferWatch}
@param bw:
Buffer watch identifier.
@raise KeyError: The buffer watch identifier was already removed.
"""
try:
self.__ranges.remove(bw)
except KeyError:
if not bw.oneshot:
raise | python | def remove(self, bw):
"""
Removes a buffer watch identifier.
@type bw: L{BufferWatch}
@param bw:
Buffer watch identifier.
@raise KeyError: The buffer watch identifier was already removed.
"""
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Removes a buffer watch identifier.
@type bw: L{BufferWatch}
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SamLau95/nbinteract | nbinteract/cli.py | yes_or_no | def yes_or_no(question, default="yes"):
"""Ask a yes/no question via input() and return their answer.
"question" is a string that is presented to the user.
"default" is the presumed answer if the user just hits <Enter>.
It must be "yes" (the default), "no" or None (meaning
an answer is required of the user).
The "answer" return value is True for "yes" or False for "no".
"""
valid = {"yes": True, "y": True, "ye": True, "no": False, "n": False}
if default is None:
prompt = " [y/n] "
elif default == "yes":
prompt = " [Y/n] "
elif default == "no":
prompt = " [y/N] "
else:
raise ValueError("invalid default answer: '%s'" % default)
while True:
sys.stdout.write(
'{}[nbinteract]{} {}{}'.format(BLUE, NOCOLOR, question, prompt)
)
choice = input().lower()
if default is not None and choice == '':
return valid[default]
elif choice in valid:
return valid[choice]
else:
sys.stdout.write(
"Please respond with 'yes' or 'no' "
"(or 'y' or 'n').\n"
) | python | def yes_or_no(question, default="yes"):
"""Ask a yes/no question via input() and return their answer.
"question" is a string that is presented to the user.
"default" is the presumed answer if the user just hits <Enter>.
It must be "yes" (the default), "no" or None (meaning
an answer is required of the user).
The "answer" return value is True for "yes" or False for "no".
"""
valid = {"yes": True, "y": True, "ye": True, "no": False, "n": False}
if default is None:
prompt = " [y/n] "
elif default == "yes":
prompt = " [Y/n] "
elif default == "no":
prompt = " [y/N] "
else:
raise ValueError("invalid default answer: '%s'" % default)
while True:
sys.stdout.write(
'{}[nbinteract]{} {}{}'.format(BLUE, NOCOLOR, question, prompt)
)
choice = input().lower()
if default is not None and choice == '':
return valid[default]
elif choice in valid:
return valid[choice]
else:
sys.stdout.write(
"Please respond with 'yes' or 'no' "
"(or 'y' or 'n').\n"
) | [
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"question" is a string that is presented to the user.
"default" is the presumed answer if the user just hits <Enter>.
It must be "yes" (the default), "no" or None (meaning
an answer is required of the user).
The "answer" return value is True for "yes" or False for "no". | [
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] | train | https://github.com/SamLau95/nbinteract/blob/9f346452283831aad3f4416c04879f1d187ec3b7/nbinteract/cli.py#L126-L159 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Ask a yes/no question via input() and return their answer.
"question" is a string that is presented to the user.
"default" is the presumed answer if the user just hits <Enter>.
It must be "yes" (the default), "no" or None (meaning
an answer is required of the user).
The "answer" return value is True for "yes" or False for "no".
### Response:
def yes_or_no(question, default="yes"):
"""Ask a yes/no question via input() and return their answer.
"question" is a string that is presented to the user.
"default" is the presumed answer if the user just hits <Enter>.
It must be "yes" (the default), "no" or None (meaning
an answer is required of the user).
The "answer" return value is True for "yes" or False for "no".
"""
valid = {"yes": True, "y": True, "ye": True, "no": False, "n": False}
if default is None:
prompt = " [y/n] "
elif default == "yes":
prompt = " [Y/n] "
elif default == "no":
prompt = " [y/N] "
else:
raise ValueError("invalid default answer: '%s'" % default)
while True:
sys.stdout.write(
'{}[nbinteract]{} {}{}'.format(BLUE, NOCOLOR, question, prompt)
)
choice = input().lower()
if default is not None and choice == '':
return valid[default]
elif choice in valid:
return valid[choice]
else:
sys.stdout.write(
"Please respond with 'yes' or 'no' "
"(or 'y' or 'n').\n"
)
<|endoftext|> | [
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jbittel/django-mama-cas | mama_cas/callbacks.py | user_model_attributes | def user_model_attributes(user, service):
"""
Return all fields on the user object that are not in the list
of fields to ignore.
"""
ignore_fields = ['id', 'password']
attributes = {}
for field in user._meta.fields:
if field.name not in ignore_fields:
attributes[field.name] = getattr(user, field.name)
return attributes | python | def user_model_attributes(user, service):
"""
Return all fields on the user object that are not in the list
of fields to ignore.
"""
ignore_fields = ['id', 'password']
attributes = {}
for field in user._meta.fields:
if field.name not in ignore_fields:
attributes[field.name] = getattr(user, field.name)
return attributes | [
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sentinel-hub/sentinelhub-py | sentinelhub/download.py | _check_if_must_download | def _check_if_must_download(request_list, redownload):
"""
Updates request.will_download attribute of each request in request_list.
**Note:** the function mutates the elements of the list!
:param request_list: a list of ``DownloadRequest`` instances
:type: list[DownloadRequest]
:param redownload: tells whether to download the data again or not
:type: bool
"""
for request in request_list:
request.will_download = (request.save_response or request.return_data) \
and (not request.is_downloaded() or redownload) | python | def _check_if_must_download(request_list, redownload):
"""
Updates request.will_download attribute of each request in request_list.
**Note:** the function mutates the elements of the list!
:param request_list: a list of ``DownloadRequest`` instances
:type: list[DownloadRequest]
:param redownload: tells whether to download the data again or not
:type: bool
"""
for request in request_list:
request.will_download = (request.save_response or request.return_data) \
and (not request.is_downloaded() or redownload) | [
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You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Updates request.will_download attribute of each request in request_list.
**Note:** the function mutates the elements of the list!
:param request_list: a list of ``DownloadRequest`` instances
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:param redownload: tells whether to download the data again or not
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def _check_if_must_download(request_list, redownload):
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Updates request.will_download attribute of each request in request_list.
**Note:** the function mutates the elements of the list!
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Telefonica/toolium | toolium/pageelements/page_element.py | PageElement.web_element | def web_element(self):
"""Find WebElement using element locator
:returns: web element object
:rtype: selenium.webdriver.remote.webelement.WebElement or appium.webdriver.webelement.WebElement
"""
try:
self._find_web_element()
except NoSuchElementException as exception:
parent_msg = " and parent locator '{}'".format(self.parent) if self.parent else ''
msg = "Page element of type '%s' with locator %s%s not found"
self.logger.error(msg, type(self).__name__, self.locator, parent_msg)
exception.msg += "\n {}".format(msg % (type(self).__name__, self.locator, parent_msg))
raise exception
return self._web_element | python | def web_element(self):
"""Find WebElement using element locator
:returns: web element object
:rtype: selenium.webdriver.remote.webelement.WebElement or appium.webdriver.webelement.WebElement
"""
try:
self._find_web_element()
except NoSuchElementException as exception:
parent_msg = " and parent locator '{}'".format(self.parent) if self.parent else ''
msg = "Page element of type '%s' with locator %s%s not found"
self.logger.error(msg, type(self).__name__, self.locator, parent_msg)
exception.msg += "\n {}".format(msg % (type(self).__name__, self.locator, parent_msg))
raise exception
return self._web_element | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Find WebElement using element locator
:returns: web element object
:rtype: selenium.webdriver.remote.webelement.WebElement or appium.webdriver.webelement.WebElement
### Response:
def web_element(self):
"""Find WebElement using element locator
:returns: web element object
:rtype: selenium.webdriver.remote.webelement.WebElement or appium.webdriver.webelement.WebElement
"""
try:
self._find_web_element()
except NoSuchElementException as exception:
parent_msg = " and parent locator '{}'".format(self.parent) if self.parent else ''
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self.logger.error(msg, type(self).__name__, self.locator, parent_msg)
exception.msg += "\n {}".format(msg % (type(self).__name__, self.locator, parent_msg))
raise exception
return self._web_element
<|endoftext|> | [
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pyviz/imagen | imagen/image.py | GenericImage._process_channels | def _process_channels(self,p,**params_to_override):
"""
Add the channel information to the channel_data attribute.
"""
orig_image = self._image
for i in range(len(self._channel_data)):
self._image = self._original_channel_data[i]
self._channel_data[i] = self._reduced_call(**params_to_override)
self._image = orig_image
return self._channel_data | python | def _process_channels(self,p,**params_to_override):
"""
Add the channel information to the channel_data attribute.
"""
orig_image = self._image
for i in range(len(self._channel_data)):
self._image = self._original_channel_data[i]
self._channel_data[i] = self._reduced_call(**params_to_override)
self._image = orig_image
return self._channel_data | [
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You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Add the channel information to the channel_data attribute.
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def _process_channels(self,p,**params_to_override):
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Add the channel information to the channel_data attribute.
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] | train | https://github.com/GeorgeArgyros/symautomata/blob/f5d66533573b27e155bec3f36b8c00b8e3937cb3/symautomata/pythondfa.py#L160-L165 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
If DFA is empty, create a sink state
### Response:
def define(self):
"""If DFA is empty, create a sink state"""
if len(self.states) == 0:
for char in self.alphabet:
self.add_arc(0, 0, char)
self[0].final = False
<|endoftext|> | [
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Robin8Put/pmes | pdms/views.py | PriceHandler.put | async def put(self, cid):
"""Update price of current content
Accepts:
Query string args:
- "cid" - int
Request body params:
- "access_type" - str
- "price" - int
- "coinid" - str
Returns:
dict with following fields:
- "confirmed": None
- "txid" - str
- "description" - str
- "content" - str
- "read_access" - int
- "write_access" - int
- "cid" - int
- "txid" - str
- "seller_pubkey" - str
- "seller_access_string": None or str
Verified: True
"""
if settings.SIGNATURE_VERIFICATION:
super().verify()
try:
body = json.loads(self.request.body)
except:
self.set_status(400)
self.write({"error":400, "reason":"Unexpected data format. JSON required"})
raise tornado.web.Finish
# Get data from signed message
public_key = body.get("public_key", None)
if isinstance(body["message"], str):
message = json.loads(body["message"])
elif isinstance(body["message"], dict):
message = body["message"]
price = message.get("price")
access_type = message.get("access_type")
coinid = message.get("coinid")
# Check if required fields exists
if not any([price, access_type, coinid]):
self.set_status(400)
self.write({"error":400, "reason":"Missed price and access type for content"})
# Set bridges url
if coinid in settings.bridges.keys():
self.account.blockchain.setendpoint(settings.bridges[coinid])
else:
self.set_status(400)
self.write({"error":400, "reason":"Invalid coin ID"})
raise tornado.web.Finish
# Get public key hex or checksum format
check = self.account.validator[coinid](public_key)
# Get content owner address
owneraddr = await self.account.blockchain.ownerbycid(cid=cid)
if isinstance(owneraddr, dict):
if "error" in owneraddr.keys():
self.set_status(404)
self.write({"error":404, "reason":"Owner not found."})
raise tornado.web.Finish
# Check if current content belongs to current user
if owneraddr != check:
self.set_status(403)
self.write({"error":403, "reason":"Owner does not match."})
raise tornado.web.Finish
response = {"cid":cid, "coinid":coinid}
# Make setprice request to the bridge
if access_type == "write_price":
result = await self.account.blockchain.setwriteprice(cid=cid, write_price=price)
response["write_access"] = result["price"]
elif access_type == "read_price":
result = await self.account.blockchain.setreadprice(cid=cid, read_price=price)
response["read_access"] = result["price"]
# Fee
fee = await billing.set_price_fee(cid=cid, price=price, owneraddr=owneraddr)
if "error" in fee.keys():
self.set_status(fee["error"])
self.write(fee)
raise tornado.web.Finish
self.write(response) | python | async def put(self, cid):
"""Update price of current content
Accepts:
Query string args:
- "cid" - int
Request body params:
- "access_type" - str
- "price" - int
- "coinid" - str
Returns:
dict with following fields:
- "confirmed": None
- "txid" - str
- "description" - str
- "content" - str
- "read_access" - int
- "write_access" - int
- "cid" - int
- "txid" - str
- "seller_pubkey" - str
- "seller_access_string": None or str
Verified: True
"""
if settings.SIGNATURE_VERIFICATION:
super().verify()
try:
body = json.loads(self.request.body)
except:
self.set_status(400)
self.write({"error":400, "reason":"Unexpected data format. JSON required"})
raise tornado.web.Finish
# Get data from signed message
public_key = body.get("public_key", None)
if isinstance(body["message"], str):
message = json.loads(body["message"])
elif isinstance(body["message"], dict):
message = body["message"]
price = message.get("price")
access_type = message.get("access_type")
coinid = message.get("coinid")
# Check if required fields exists
if not any([price, access_type, coinid]):
self.set_status(400)
self.write({"error":400, "reason":"Missed price and access type for content"})
# Set bridges url
if coinid in settings.bridges.keys():
self.account.blockchain.setendpoint(settings.bridges[coinid])
else:
self.set_status(400)
self.write({"error":400, "reason":"Invalid coin ID"})
raise tornado.web.Finish
# Get public key hex or checksum format
check = self.account.validator[coinid](public_key)
# Get content owner address
owneraddr = await self.account.blockchain.ownerbycid(cid=cid)
if isinstance(owneraddr, dict):
if "error" in owneraddr.keys():
self.set_status(404)
self.write({"error":404, "reason":"Owner not found."})
raise tornado.web.Finish
# Check if current content belongs to current user
if owneraddr != check:
self.set_status(403)
self.write({"error":403, "reason":"Owner does not match."})
raise tornado.web.Finish
response = {"cid":cid, "coinid":coinid}
# Make setprice request to the bridge
if access_type == "write_price":
result = await self.account.blockchain.setwriteprice(cid=cid, write_price=price)
response["write_access"] = result["price"]
elif access_type == "read_price":
result = await self.account.blockchain.setreadprice(cid=cid, read_price=price)
response["read_access"] = result["price"]
# Fee
fee = await billing.set_price_fee(cid=cid, price=price, owneraddr=owneraddr)
if "error" in fee.keys():
self.set_status(fee["error"])
self.write(fee)
raise tornado.web.Finish
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] | Update price of current content
Accepts:
Query string args:
- "cid" - int
Request body params:
- "access_type" - str
- "price" - int
- "coinid" - str
Returns:
dict with following fields:
- "confirmed": None
- "txid" - str
- "description" - str
- "content" - str
- "read_access" - int
- "write_access" - int
- "cid" - int
- "txid" - str
- "seller_pubkey" - str
- "seller_access_string": None or str
Verified: True | [
"Update",
"price",
"of",
"current",
"content"
] | train | https://github.com/Robin8Put/pmes/blob/338bec94162098f05b75bad035417317e1252fd2/pdms/views.py#L449-L545 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Update price of current content
Accepts:
Query string args:
- "cid" - int
Request body params:
- "access_type" - str
- "price" - int
- "coinid" - str
Returns:
dict with following fields:
- "confirmed": None
- "txid" - str
- "description" - str
- "content" - str
- "read_access" - int
- "write_access" - int
- "cid" - int
- "txid" - str
- "seller_pubkey" - str
- "seller_access_string": None or str
Verified: True
### Response:
async def put(self, cid):
"""Update price of current content
Accepts:
Query string args:
- "cid" - int
Request body params:
- "access_type" - str
- "price" - int
- "coinid" - str
Returns:
dict with following fields:
- "confirmed": None
- "txid" - str
- "description" - str
- "content" - str
- "read_access" - int
- "write_access" - int
- "cid" - int
- "txid" - str
- "seller_pubkey" - str
- "seller_access_string": None or str
Verified: True
"""
if settings.SIGNATURE_VERIFICATION:
super().verify()
try:
body = json.loads(self.request.body)
except:
self.set_status(400)
self.write({"error":400, "reason":"Unexpected data format. JSON required"})
raise tornado.web.Finish
# Get data from signed message
public_key = body.get("public_key", None)
if isinstance(body["message"], str):
message = json.loads(body["message"])
elif isinstance(body["message"], dict):
message = body["message"]
price = message.get("price")
access_type = message.get("access_type")
coinid = message.get("coinid")
# Check if required fields exists
if not any([price, access_type, coinid]):
self.set_status(400)
self.write({"error":400, "reason":"Missed price and access type for content"})
# Set bridges url
if coinid in settings.bridges.keys():
self.account.blockchain.setendpoint(settings.bridges[coinid])
else:
self.set_status(400)
self.write({"error":400, "reason":"Invalid coin ID"})
raise tornado.web.Finish
# Get public key hex or checksum format
check = self.account.validator[coinid](public_key)
# Get content owner address
owneraddr = await self.account.blockchain.ownerbycid(cid=cid)
if isinstance(owneraddr, dict):
if "error" in owneraddr.keys():
self.set_status(404)
self.write({"error":404, "reason":"Owner not found."})
raise tornado.web.Finish
# Check if current content belongs to current user
if owneraddr != check:
self.set_status(403)
self.write({"error":403, "reason":"Owner does not match."})
raise tornado.web.Finish
response = {"cid":cid, "coinid":coinid}
# Make setprice request to the bridge
if access_type == "write_price":
result = await self.account.blockchain.setwriteprice(cid=cid, write_price=price)
response["write_access"] = result["price"]
elif access_type == "read_price":
result = await self.account.blockchain.setreadprice(cid=cid, read_price=price)
response["read_access"] = result["price"]
# Fee
fee = await billing.set_price_fee(cid=cid, price=price, owneraddr=owneraddr)
if "error" in fee.keys():
self.set_status(fee["error"])
self.write(fee)
raise tornado.web.Finish
self.write(response)
<|endoftext|> | [
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dcos/shakedown | shakedown/dcos/agent.py | partition_agent | def partition_agent(host):
""" Partition a node from all network traffic except for SSH and loopback
:param hostname: host or IP of the machine to partition from the cluster
"""
network.save_iptables(host)
network.flush_all_rules(host)
network.allow_all_traffic(host)
network.run_iptables(host, ALLOW_SSH)
network.run_iptables(host, ALLOW_PING)
network.run_iptables(host, DISALLOW_MESOS)
network.run_iptables(host, DISALLOW_INPUT) | python | def partition_agent(host):
""" Partition a node from all network traffic except for SSH and loopback
:param hostname: host or IP of the machine to partition from the cluster
"""
network.save_iptables(host)
network.flush_all_rules(host)
network.allow_all_traffic(host)
network.run_iptables(host, ALLOW_SSH)
network.run_iptables(host, ALLOW_PING)
network.run_iptables(host, DISALLOW_MESOS)
network.run_iptables(host, DISALLOW_INPUT) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Partition a node from all network traffic except for SSH and loopback
:param hostname: host or IP of the machine to partition from the cluster
### Response:
def partition_agent(host):
""" Partition a node from all network traffic except for SSH and loopback
:param hostname: host or IP of the machine to partition from the cluster
"""
network.save_iptables(host)
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network.run_iptables(host, DISALLOW_INPUT)
<|endoftext|> | [
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cjdrake/pyeda | pyeda/parsing/lex.py | action | def action(toktype):
"""Return a parser action property."""
def outer(func):
"""Return a function that pushes a token onto the token queue."""
def inner(lexer, text):
"""Push a token onto the token queue."""
value = func(lexer, text)
lexer.tokens.append(toktype(value, lexer.lineno, lexer.offset))
return inner
return outer | python | def action(toktype):
"""Return a parser action property."""
def outer(func):
"""Return a function that pushes a token onto the token queue."""
def inner(lexer, text):
"""Push a token onto the token queue."""
value = func(lexer, text)
lexer.tokens.append(toktype(value, lexer.lineno, lexer.offset))
return inner
return outer | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Return a parser action property.
### Response:
def action(toktype):
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def outer(func):
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def inner(lexer, text):
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value = func(lexer, text)
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return outer
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pandas-dev/pandas | pandas/core/algorithms.py | take_2d_multi | def take_2d_multi(arr, indexer, out=None, fill_value=np.nan, mask_info=None,
allow_fill=True):
"""
Specialized Cython take which sets NaN values in one pass
"""
if indexer is None or (indexer[0] is None and indexer[1] is None):
row_idx = np.arange(arr.shape[0], dtype=np.int64)
col_idx = np.arange(arr.shape[1], dtype=np.int64)
indexer = row_idx, col_idx
dtype, fill_value = arr.dtype, arr.dtype.type()
else:
row_idx, col_idx = indexer
if row_idx is None:
row_idx = np.arange(arr.shape[0], dtype=np.int64)
else:
row_idx = ensure_int64(row_idx)
if col_idx is None:
col_idx = np.arange(arr.shape[1], dtype=np.int64)
else:
col_idx = ensure_int64(col_idx)
indexer = row_idx, col_idx
if not allow_fill:
dtype, fill_value = arr.dtype, arr.dtype.type()
mask_info = None, False
else:
# check for promotion based on types only (do this first because
# it's faster than computing a mask)
dtype, fill_value = maybe_promote(arr.dtype, fill_value)
if dtype != arr.dtype and (out is None or out.dtype != dtype):
# check if promotion is actually required based on indexer
if mask_info is not None:
(row_mask, col_mask), (row_needs, col_needs) = mask_info
else:
row_mask = row_idx == -1
col_mask = col_idx == -1
row_needs = row_mask.any()
col_needs = col_mask.any()
mask_info = (row_mask, col_mask), (row_needs, col_needs)
if row_needs or col_needs:
if out is not None and out.dtype != dtype:
raise TypeError('Incompatible type for fill_value')
else:
# if not, then depromote, set fill_value to dummy
# (it won't be used but we don't want the cython code
# to crash when trying to cast it to dtype)
dtype, fill_value = arr.dtype, arr.dtype.type()
# at this point, it's guaranteed that dtype can hold both the arr values
# and the fill_value
if out is None:
out_shape = len(row_idx), len(col_idx)
out = np.empty(out_shape, dtype=dtype)
func = _take_2d_multi_dict.get((arr.dtype.name, out.dtype.name), None)
if func is None and arr.dtype != out.dtype:
func = _take_2d_multi_dict.get((out.dtype.name, out.dtype.name), None)
if func is not None:
func = _convert_wrapper(func, out.dtype)
if func is None:
def func(arr, indexer, out, fill_value=np.nan):
_take_2d_multi_object(arr, indexer, out, fill_value=fill_value,
mask_info=mask_info)
func(arr, indexer, out=out, fill_value=fill_value)
return out | python | def take_2d_multi(arr, indexer, out=None, fill_value=np.nan, mask_info=None,
allow_fill=True):
"""
Specialized Cython take which sets NaN values in one pass
"""
if indexer is None or (indexer[0] is None and indexer[1] is None):
row_idx = np.arange(arr.shape[0], dtype=np.int64)
col_idx = np.arange(arr.shape[1], dtype=np.int64)
indexer = row_idx, col_idx
dtype, fill_value = arr.dtype, arr.dtype.type()
else:
row_idx, col_idx = indexer
if row_idx is None:
row_idx = np.arange(arr.shape[0], dtype=np.int64)
else:
row_idx = ensure_int64(row_idx)
if col_idx is None:
col_idx = np.arange(arr.shape[1], dtype=np.int64)
else:
col_idx = ensure_int64(col_idx)
indexer = row_idx, col_idx
if not allow_fill:
dtype, fill_value = arr.dtype, arr.dtype.type()
mask_info = None, False
else:
# check for promotion based on types only (do this first because
# it's faster than computing a mask)
dtype, fill_value = maybe_promote(arr.dtype, fill_value)
if dtype != arr.dtype and (out is None or out.dtype != dtype):
# check if promotion is actually required based on indexer
if mask_info is not None:
(row_mask, col_mask), (row_needs, col_needs) = mask_info
else:
row_mask = row_idx == -1
col_mask = col_idx == -1
row_needs = row_mask.any()
col_needs = col_mask.any()
mask_info = (row_mask, col_mask), (row_needs, col_needs)
if row_needs or col_needs:
if out is not None and out.dtype != dtype:
raise TypeError('Incompatible type for fill_value')
else:
# if not, then depromote, set fill_value to dummy
# (it won't be used but we don't want the cython code
# to crash when trying to cast it to dtype)
dtype, fill_value = arr.dtype, arr.dtype.type()
# at this point, it's guaranteed that dtype can hold both the arr values
# and the fill_value
if out is None:
out_shape = len(row_idx), len(col_idx)
out = np.empty(out_shape, dtype=dtype)
func = _take_2d_multi_dict.get((arr.dtype.name, out.dtype.name), None)
if func is None and arr.dtype != out.dtype:
func = _take_2d_multi_dict.get((out.dtype.name, out.dtype.name), None)
if func is not None:
func = _convert_wrapper(func, out.dtype)
if func is None:
def func(arr, indexer, out, fill_value=np.nan):
_take_2d_multi_object(arr, indexer, out, fill_value=fill_value,
mask_info=mask_info)
func(arr, indexer, out=out, fill_value=fill_value)
return out | [
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"Specialized",
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] | train | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/algorithms.py#L1672-L1737 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Specialized Cython take which sets NaN values in one pass
### Response:
def take_2d_multi(arr, indexer, out=None, fill_value=np.nan, mask_info=None,
allow_fill=True):
"""
Specialized Cython take which sets NaN values in one pass
"""
if indexer is None or (indexer[0] is None and indexer[1] is None):
row_idx = np.arange(arr.shape[0], dtype=np.int64)
col_idx = np.arange(arr.shape[1], dtype=np.int64)
indexer = row_idx, col_idx
dtype, fill_value = arr.dtype, arr.dtype.type()
else:
row_idx, col_idx = indexer
if row_idx is None:
row_idx = np.arange(arr.shape[0], dtype=np.int64)
else:
row_idx = ensure_int64(row_idx)
if col_idx is None:
col_idx = np.arange(arr.shape[1], dtype=np.int64)
else:
col_idx = ensure_int64(col_idx)
indexer = row_idx, col_idx
if not allow_fill:
dtype, fill_value = arr.dtype, arr.dtype.type()
mask_info = None, False
else:
# check for promotion based on types only (do this first because
# it's faster than computing a mask)
dtype, fill_value = maybe_promote(arr.dtype, fill_value)
if dtype != arr.dtype and (out is None or out.dtype != dtype):
# check if promotion is actually required based on indexer
if mask_info is not None:
(row_mask, col_mask), (row_needs, col_needs) = mask_info
else:
row_mask = row_idx == -1
col_mask = col_idx == -1
row_needs = row_mask.any()
col_needs = col_mask.any()
mask_info = (row_mask, col_mask), (row_needs, col_needs)
if row_needs or col_needs:
if out is not None and out.dtype != dtype:
raise TypeError('Incompatible type for fill_value')
else:
# if not, then depromote, set fill_value to dummy
# (it won't be used but we don't want the cython code
# to crash when trying to cast it to dtype)
dtype, fill_value = arr.dtype, arr.dtype.type()
# at this point, it's guaranteed that dtype can hold both the arr values
# and the fill_value
if out is None:
out_shape = len(row_idx), len(col_idx)
out = np.empty(out_shape, dtype=dtype)
func = _take_2d_multi_dict.get((arr.dtype.name, out.dtype.name), None)
if func is None and arr.dtype != out.dtype:
func = _take_2d_multi_dict.get((out.dtype.name, out.dtype.name), None)
if func is not None:
func = _convert_wrapper(func, out.dtype)
if func is None:
def func(arr, indexer, out, fill_value=np.nan):
_take_2d_multi_object(arr, indexer, out, fill_value=fill_value,
mask_info=mask_info)
func(arr, indexer, out=out, fill_value=fill_value)
return out
<|endoftext|> | [
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ulule/django-linguist | linguist/mixins.py | ModelMixin.override_language | def override_language(self, language):
"""
Context manager to override the instance language.
"""
previous_language = self._linguist.language
self._linguist.language = language
yield
self._linguist.language = previous_language | python | def override_language(self, language):
"""
Context manager to override the instance language.
"""
previous_language = self._linguist.language
self._linguist.language = language
yield
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Context manager to override the instance language.
### Response:
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DreamLab/VmShepherd | src/vmshepherd/iaas/openstack_driver.py | OpenStackDriver.initialize | async def initialize(self):
'''
Initialize static data like images and flavores and set it as object property
'''
flavors = await self._list_flavors()
images = await self._list_images()
self.flavors_map = bidict()
self.images_map = bidict()
self.images_details = {}
for flavor in flavors:
self.flavors_map.put(flavor['id'], flavor['name'], on_dup_key='OVERWRITE', on_dup_val='OVERWRITE')
for image in images:
# @TODO filetes :
# @TODO filtering by owner
# if hasattr(image, 'owner_id') and image.owner_id in self.config['image_owner_ids']:
# @TODO enable filtering by tag
# if 'lastest' in image.tags:
self.images_details[image['id']] = {
'name': image['name'],
'created_at': image['created_at'],
'latest': 'latest' in image['tags']
}
self.images_map.put(image['id'], image['name'], on_dup_key='OVERWRITE', on_dup_val='OVERWRITE') | python | async def initialize(self):
'''
Initialize static data like images and flavores and set it as object property
'''
flavors = await self._list_flavors()
images = await self._list_images()
self.flavors_map = bidict()
self.images_map = bidict()
self.images_details = {}
for flavor in flavors:
self.flavors_map.put(flavor['id'], flavor['name'], on_dup_key='OVERWRITE', on_dup_val='OVERWRITE')
for image in images:
# @TODO filetes :
# @TODO filtering by owner
# if hasattr(image, 'owner_id') and image.owner_id in self.config['image_owner_ids']:
# @TODO enable filtering by tag
# if 'lastest' in image.tags:
self.images_details[image['id']] = {
'name': image['name'],
'created_at': image['created_at'],
'latest': 'latest' in image['tags']
}
self.images_map.put(image['id'], image['name'], on_dup_key='OVERWRITE', on_dup_val='OVERWRITE') | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Initialize static data like images and flavores and set it as object property
### Response:
async def initialize(self):
'''
Initialize static data like images and flavores and set it as object property
'''
flavors = await self._list_flavors()
images = await self._list_images()
self.flavors_map = bidict()
self.images_map = bidict()
self.images_details = {}
for flavor in flavors:
self.flavors_map.put(flavor['id'], flavor['name'], on_dup_key='OVERWRITE', on_dup_val='OVERWRITE')
for image in images:
# @TODO filetes :
# @TODO filtering by owner
# if hasattr(image, 'owner_id') and image.owner_id in self.config['image_owner_ids']:
# @TODO enable filtering by tag
# if 'lastest' in image.tags:
self.images_details[image['id']] = {
'name': image['name'],
'created_at': image['created_at'],
'latest': 'latest' in image['tags']
}
self.images_map.put(image['id'], image['name'], on_dup_key='OVERWRITE', on_dup_val='OVERWRITE')
<|endoftext|> | [
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MartinThoma/hwrt | hwrt/datasets/inkml.py | read | def read(filepath):
"""
Read a single InkML file
Parameters
----------
filepath : string
path to the (readable) InkML file
Returns
-------
HandwrittenData :
The parsed InkML file as a HandwrittenData object
"""
import xml.etree.ElementTree
root = xml.etree.ElementTree.parse(filepath).getroot()
# Get the raw data
recording = []
strokes = sorted(root.findall('{http://www.w3.org/2003/InkML}trace'),
key=lambda child: int(child.attrib['id']))
time = 0
for stroke in strokes:
stroke = stroke.text.strip().split(',')
stroke = [point.strip().split(' ') for point in stroke]
if len(stroke[0]) == 3:
stroke = [{'x': float(x), 'y': float(y), 'time': float(t)}
for x, y, t in stroke]
else:
stroke = [{'x': float(x), 'y': float(y)} for x, y in stroke]
new_stroke = []
for p in stroke:
new_stroke.append({'x': p['x'], 'y': p['y'], 'time': time})
time += 20
stroke = new_stroke
time += 200
recording.append(stroke)
# Get LaTeX
formula_in_latex = None
annotations = root.findall('{http://www.w3.org/2003/InkML}annotation')
for annotation in annotations:
if annotation.attrib['type'] == 'truth':
formula_in_latex = annotation.text
hw = handwritten_data.HandwrittenData(json.dumps(recording),
formula_in_latex=formula_in_latex)
for annotation in annotations:
if annotation.attrib['type'] == 'writer':
hw.writer = annotation.text
elif annotation.attrib['type'] == 'category':
hw.category = annotation.text
elif annotation.attrib['type'] == 'expression':
hw.expression = annotation.text
# Get segmentation
segmentation = []
trace_groups = root.findall('{http://www.w3.org/2003/InkML}traceGroup')
if len(trace_groups) != 1:
raise Exception('Malformed InkML',
('Exactly 1 top level traceGroup expected, found %i. '
'(%s) - probably no ground truth?') %
(len(trace_groups), filepath))
trace_group = trace_groups[0]
symbol_stream = [] # has to be consistent with segmentation
for tg in trace_group.findall('{http://www.w3.org/2003/InkML}traceGroup'):
annotations = tg.findall('{http://www.w3.org/2003/InkML}annotation')
if len(annotations) != 1:
raise ValueError("%i annotations found for '%s'." %
(len(annotations), filepath))
db_id = formula_to_dbid(normalize_symbol_name(annotations[0].text))
symbol_stream.append(db_id)
trace_views = tg.findall('{http://www.w3.org/2003/InkML}traceView')
symbol = []
for traceView in trace_views:
symbol.append(int(traceView.attrib['traceDataRef']))
segmentation.append(symbol)
hw.symbol_stream = symbol_stream
hw.segmentation = segmentation
_flat_seg = [stroke2 for symbol2 in segmentation for stroke2 in symbol2]
assert len(_flat_seg) == len(recording), \
("Segmentation had length %i, but recording has %i strokes (%s)" %
(len(_flat_seg), len(recording), filepath))
assert set(_flat_seg) == set(range(len(_flat_seg)))
hw.inkml = beautify_xml(filepath)
hw.filepath = filepath
return hw | python | def read(filepath):
"""
Read a single InkML file
Parameters
----------
filepath : string
path to the (readable) InkML file
Returns
-------
HandwrittenData :
The parsed InkML file as a HandwrittenData object
"""
import xml.etree.ElementTree
root = xml.etree.ElementTree.parse(filepath).getroot()
# Get the raw data
recording = []
strokes = sorted(root.findall('{http://www.w3.org/2003/InkML}trace'),
key=lambda child: int(child.attrib['id']))
time = 0
for stroke in strokes:
stroke = stroke.text.strip().split(',')
stroke = [point.strip().split(' ') for point in stroke]
if len(stroke[0]) == 3:
stroke = [{'x': float(x), 'y': float(y), 'time': float(t)}
for x, y, t in stroke]
else:
stroke = [{'x': float(x), 'y': float(y)} for x, y in stroke]
new_stroke = []
for p in stroke:
new_stroke.append({'x': p['x'], 'y': p['y'], 'time': time})
time += 20
stroke = new_stroke
time += 200
recording.append(stroke)
# Get LaTeX
formula_in_latex = None
annotations = root.findall('{http://www.w3.org/2003/InkML}annotation')
for annotation in annotations:
if annotation.attrib['type'] == 'truth':
formula_in_latex = annotation.text
hw = handwritten_data.HandwrittenData(json.dumps(recording),
formula_in_latex=formula_in_latex)
for annotation in annotations:
if annotation.attrib['type'] == 'writer':
hw.writer = annotation.text
elif annotation.attrib['type'] == 'category':
hw.category = annotation.text
elif annotation.attrib['type'] == 'expression':
hw.expression = annotation.text
# Get segmentation
segmentation = []
trace_groups = root.findall('{http://www.w3.org/2003/InkML}traceGroup')
if len(trace_groups) != 1:
raise Exception('Malformed InkML',
('Exactly 1 top level traceGroup expected, found %i. '
'(%s) - probably no ground truth?') %
(len(trace_groups), filepath))
trace_group = trace_groups[0]
symbol_stream = [] # has to be consistent with segmentation
for tg in trace_group.findall('{http://www.w3.org/2003/InkML}traceGroup'):
annotations = tg.findall('{http://www.w3.org/2003/InkML}annotation')
if len(annotations) != 1:
raise ValueError("%i annotations found for '%s'." %
(len(annotations), filepath))
db_id = formula_to_dbid(normalize_symbol_name(annotations[0].text))
symbol_stream.append(db_id)
trace_views = tg.findall('{http://www.w3.org/2003/InkML}traceView')
symbol = []
for traceView in trace_views:
symbol.append(int(traceView.attrib['traceDataRef']))
segmentation.append(symbol)
hw.symbol_stream = symbol_stream
hw.segmentation = segmentation
_flat_seg = [stroke2 for symbol2 in segmentation for stroke2 in symbol2]
assert len(_flat_seg) == len(recording), \
("Segmentation had length %i, but recording has %i strokes (%s)" %
(len(_flat_seg), len(recording), filepath))
assert set(_flat_seg) == set(range(len(_flat_seg)))
hw.inkml = beautify_xml(filepath)
hw.filepath = filepath
return hw | [
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] | Read a single InkML file
Parameters
----------
filepath : string
path to the (readable) InkML file
Returns
-------
HandwrittenData :
The parsed InkML file as a HandwrittenData object | [
"Read",
"a",
"single",
"InkML",
"file"
] | train | https://github.com/MartinThoma/hwrt/blob/725c21a3d0f5a30b8492cbc184b3688ceb364e1c/hwrt/datasets/inkml.py#L70-L155 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Read a single InkML file
Parameters
----------
filepath : string
path to the (readable) InkML file
Returns
-------
HandwrittenData :
The parsed InkML file as a HandwrittenData object
### Response:
def read(filepath):
"""
Read a single InkML file
Parameters
----------
filepath : string
path to the (readable) InkML file
Returns
-------
HandwrittenData :
The parsed InkML file as a HandwrittenData object
"""
import xml.etree.ElementTree
root = xml.etree.ElementTree.parse(filepath).getroot()
# Get the raw data
recording = []
strokes = sorted(root.findall('{http://www.w3.org/2003/InkML}trace'),
key=lambda child: int(child.attrib['id']))
time = 0
for stroke in strokes:
stroke = stroke.text.strip().split(',')
stroke = [point.strip().split(' ') for point in stroke]
if len(stroke[0]) == 3:
stroke = [{'x': float(x), 'y': float(y), 'time': float(t)}
for x, y, t in stroke]
else:
stroke = [{'x': float(x), 'y': float(y)} for x, y in stroke]
new_stroke = []
for p in stroke:
new_stroke.append({'x': p['x'], 'y': p['y'], 'time': time})
time += 20
stroke = new_stroke
time += 200
recording.append(stroke)
# Get LaTeX
formula_in_latex = None
annotations = root.findall('{http://www.w3.org/2003/InkML}annotation')
for annotation in annotations:
if annotation.attrib['type'] == 'truth':
formula_in_latex = annotation.text
hw = handwritten_data.HandwrittenData(json.dumps(recording),
formula_in_latex=formula_in_latex)
for annotation in annotations:
if annotation.attrib['type'] == 'writer':
hw.writer = annotation.text
elif annotation.attrib['type'] == 'category':
hw.category = annotation.text
elif annotation.attrib['type'] == 'expression':
hw.expression = annotation.text
# Get segmentation
segmentation = []
trace_groups = root.findall('{http://www.w3.org/2003/InkML}traceGroup')
if len(trace_groups) != 1:
raise Exception('Malformed InkML',
('Exactly 1 top level traceGroup expected, found %i. '
'(%s) - probably no ground truth?') %
(len(trace_groups), filepath))
trace_group = trace_groups[0]
symbol_stream = [] # has to be consistent with segmentation
for tg in trace_group.findall('{http://www.w3.org/2003/InkML}traceGroup'):
annotations = tg.findall('{http://www.w3.org/2003/InkML}annotation')
if len(annotations) != 1:
raise ValueError("%i annotations found for '%s'." %
(len(annotations), filepath))
db_id = formula_to_dbid(normalize_symbol_name(annotations[0].text))
symbol_stream.append(db_id)
trace_views = tg.findall('{http://www.w3.org/2003/InkML}traceView')
symbol = []
for traceView in trace_views:
symbol.append(int(traceView.attrib['traceDataRef']))
segmentation.append(symbol)
hw.symbol_stream = symbol_stream
hw.segmentation = segmentation
_flat_seg = [stroke2 for symbol2 in segmentation for stroke2 in symbol2]
assert len(_flat_seg) == len(recording), \
("Segmentation had length %i, but recording has %i strokes (%s)" %
(len(_flat_seg), len(recording), filepath))
assert set(_flat_seg) == set(range(len(_flat_seg)))
hw.inkml = beautify_xml(filepath)
hw.filepath = filepath
return hw
<|endoftext|> | [
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gwastro/pycbc | pycbc/events/coinc.py | LiveCoincTimeslideBackgroundEstimator.add_singles | def add_singles(self, results):
"""Add singles to the bacckground estimate and find candidates
Parameters
----------
results: dict of arrays
Dictionary of dictionaries indexed by ifo and keys such as 'snr',
'chisq', etc. The specific format it determined by the
LiveBatchMatchedFilter class.
Returns
-------
coinc_results: dict of arrays
A dictionary of arrays containing the coincident results.
"""
# Let's see how large everything is
logging.info('BKG Coincs %s stored %s bytes',
len(self.coincs), self.coincs.nbytes)
# If there are no results just return
valid_ifos = [k for k in results.keys() if results[k] and k in self.ifos]
if len(valid_ifos) == 0: return {}
# Add single triggers to the internal buffer
self._add_singles_to_buffer(results, ifos=valid_ifos)
# Calculate zerolag and background coincidences
_, coinc_results = self._find_coincs(results, ifos=valid_ifos)
# record if a coinc is possible in this chunk
if len(valid_ifos) == 2:
coinc_results['coinc_possible'] = True
return coinc_results | python | def add_singles(self, results):
"""Add singles to the bacckground estimate and find candidates
Parameters
----------
results: dict of arrays
Dictionary of dictionaries indexed by ifo and keys such as 'snr',
'chisq', etc. The specific format it determined by the
LiveBatchMatchedFilter class.
Returns
-------
coinc_results: dict of arrays
A dictionary of arrays containing the coincident results.
"""
# Let's see how large everything is
logging.info('BKG Coincs %s stored %s bytes',
len(self.coincs), self.coincs.nbytes)
# If there are no results just return
valid_ifos = [k for k in results.keys() if results[k] and k in self.ifos]
if len(valid_ifos) == 0: return {}
# Add single triggers to the internal buffer
self._add_singles_to_buffer(results, ifos=valid_ifos)
# Calculate zerolag and background coincidences
_, coinc_results = self._find_coincs(results, ifos=valid_ifos)
# record if a coinc is possible in this chunk
if len(valid_ifos) == 2:
coinc_results['coinc_possible'] = True
return coinc_results | [
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Parameters
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results: dict of arrays
Dictionary of dictionaries indexed by ifo and keys such as 'snr',
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LiveBatchMatchedFilter class.
Returns
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coinc_results: dict of arrays
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Add singles to the bacckground estimate and find candidates
Parameters
----------
results: dict of arrays
Dictionary of dictionaries indexed by ifo and keys such as 'snr',
'chisq', etc. The specific format it determined by the
LiveBatchMatchedFilter class.
Returns
-------
coinc_results: dict of arrays
A dictionary of arrays containing the coincident results.
### Response:
def add_singles(self, results):
"""Add singles to the bacckground estimate and find candidates
Parameters
----------
results: dict of arrays
Dictionary of dictionaries indexed by ifo and keys such as 'snr',
'chisq', etc. The specific format it determined by the
LiveBatchMatchedFilter class.
Returns
-------
coinc_results: dict of arrays
A dictionary of arrays containing the coincident results.
"""
# Let's see how large everything is
logging.info('BKG Coincs %s stored %s bytes',
len(self.coincs), self.coincs.nbytes)
# If there are no results just return
valid_ifos = [k for k in results.keys() if results[k] and k in self.ifos]
if len(valid_ifos) == 0: return {}
# Add single triggers to the internal buffer
self._add_singles_to_buffer(results, ifos=valid_ifos)
# Calculate zerolag and background coincidences
_, coinc_results = self._find_coincs(results, ifos=valid_ifos)
# record if a coinc is possible in this chunk
if len(valid_ifos) == 2:
coinc_results['coinc_possible'] = True
return coinc_results
<|endoftext|> | [
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mojaie/chorus | chorus/v2000writer.py | mols_to_file | def mols_to_file(mols, path):
"""Save molecules to the SDFile format file
Args:
mols: list of molecule objects
path: file path to save
"""
with open(path, 'w') as f:
f.write(mols_to_text(mols)) | python | def mols_to_file(mols, path):
"""Save molecules to the SDFile format file
Args:
mols: list of molecule objects
path: file path to save
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Save molecules to the SDFile format file
Args:
mols: list of molecule objects
path: file path to save
### Response:
def mols_to_file(mols, path):
"""Save molecules to the SDFile format file
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mols: list of molecule objects
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blockstack/blockstack-core | blockstack/lib/atlas.py | atlasdb_init | def atlasdb_init( path, zonefile_dir, db, peer_seeds, peer_blacklist, recover=False, validate=False):
"""
Set up the atlas node:
* create the db if it doesn't exist
* go through all the names and verify that we have the *current* zonefiles
* if we don't, queue them for fetching.
* set up the peer db
@db should be an instance of BlockstackDB
@initial_peers should be a list of URLs
Return the newly-initialized peer table
"""
global ATLASDB_SQL
peer_table = {}
if os.path.exists( path ):
log.debug("Atlas DB exists at %s" % path)
con = atlasdb_open( path )
atlasdb_last_block = atlasdb_get_lastblock( con=con, path=path )
if atlasdb_last_block is None:
atlasdb_last_block = FIRST_BLOCK_MAINNET
log.debug("Synchronize zonefiles from %s to %s" % (atlasdb_last_block, db.lastblock) )
atlasdb_queue_zonefiles( con, db, atlasdb_last_block, zonefile_dir, recover=recover, validate=validate)
log.debug("Refreshing seed peers")
for peer in peer_seeds:
# forcibly add seed peers
atlasdb_add_peer( peer, con=con, peer_table=peer_table, ping_on_evict=False )
# re-try fetching zonefiles from storage if we don't have them yet
atlasdb_reset_zonefile_tried_storage( con=con, path=path )
# load up peer table from the db
log.debug("Loading peer table")
peer_table = atlasdb_load_peer_table( con=con, path=path )
# cache zonefile inventory and count
atlasdb_cache_zonefile_info( con=con )
con.close()
else:
log.debug("Initializing Atlas DB at %s" % path)
lines = [l + ";" for l in ATLASDB_SQL.split(";")]
con = sqlite3.connect( path, isolation_level=None )
for line in lines:
db_query_execute(con, line, ())
con.row_factory = atlasdb_row_factory
# populate from db
log.debug("Queuing all zonefiles")
atlasdb_queue_zonefiles( con, db, FIRST_BLOCK_MAINNET, zonefile_dir, recover=recover, validate=validate)
log.debug("Adding seed peers")
for peer in peer_seeds:
atlasdb_add_peer( peer, con=con, peer_table=peer_table )
atlasdb_cache_zonefile_info( con=con )
con.close()
log.debug("peer_table: {}".format(peer_table.keys()))
# whitelist and blacklist
for peer_url in peer_seeds:
host, port = url_to_host_port( peer_url )
peer_hostport = "%s:%s" % (host, port)
if peer_hostport not in peer_table.keys():
atlasdb_add_peer( peer_hostport, path=path, peer_table=peer_table )
log.debug("peer_table: {}".format(peer_table.keys()))
peer_table[peer_hostport]['whitelisted'] = True
for peer_url in peer_blacklist:
host, port = url_to_host_port( peer_url )
peer_hostport = "%s:%s" % (host, port)
if peer_hostport not in peer_table.keys():
atlasdb_add_peer( peer_hostport, path=path, peer_table=peer_table )
log.debug("peer_table: {}".format(peer_table.keys()))
peer_table[peer_hostport]['blacklisted'] = True
return peer_table | python | def atlasdb_init( path, zonefile_dir, db, peer_seeds, peer_blacklist, recover=False, validate=False):
"""
Set up the atlas node:
* create the db if it doesn't exist
* go through all the names and verify that we have the *current* zonefiles
* if we don't, queue them for fetching.
* set up the peer db
@db should be an instance of BlockstackDB
@initial_peers should be a list of URLs
Return the newly-initialized peer table
"""
global ATLASDB_SQL
peer_table = {}
if os.path.exists( path ):
log.debug("Atlas DB exists at %s" % path)
con = atlasdb_open( path )
atlasdb_last_block = atlasdb_get_lastblock( con=con, path=path )
if atlasdb_last_block is None:
atlasdb_last_block = FIRST_BLOCK_MAINNET
log.debug("Synchronize zonefiles from %s to %s" % (atlasdb_last_block, db.lastblock) )
atlasdb_queue_zonefiles( con, db, atlasdb_last_block, zonefile_dir, recover=recover, validate=validate)
log.debug("Refreshing seed peers")
for peer in peer_seeds:
# forcibly add seed peers
atlasdb_add_peer( peer, con=con, peer_table=peer_table, ping_on_evict=False )
# re-try fetching zonefiles from storage if we don't have them yet
atlasdb_reset_zonefile_tried_storage( con=con, path=path )
# load up peer table from the db
log.debug("Loading peer table")
peer_table = atlasdb_load_peer_table( con=con, path=path )
# cache zonefile inventory and count
atlasdb_cache_zonefile_info( con=con )
con.close()
else:
log.debug("Initializing Atlas DB at %s" % path)
lines = [l + ";" for l in ATLASDB_SQL.split(";")]
con = sqlite3.connect( path, isolation_level=None )
for line in lines:
db_query_execute(con, line, ())
con.row_factory = atlasdb_row_factory
# populate from db
log.debug("Queuing all zonefiles")
atlasdb_queue_zonefiles( con, db, FIRST_BLOCK_MAINNET, zonefile_dir, recover=recover, validate=validate)
log.debug("Adding seed peers")
for peer in peer_seeds:
atlasdb_add_peer( peer, con=con, peer_table=peer_table )
atlasdb_cache_zonefile_info( con=con )
con.close()
log.debug("peer_table: {}".format(peer_table.keys()))
# whitelist and blacklist
for peer_url in peer_seeds:
host, port = url_to_host_port( peer_url )
peer_hostport = "%s:%s" % (host, port)
if peer_hostport not in peer_table.keys():
atlasdb_add_peer( peer_hostport, path=path, peer_table=peer_table )
log.debug("peer_table: {}".format(peer_table.keys()))
peer_table[peer_hostport]['whitelisted'] = True
for peer_url in peer_blacklist:
host, port = url_to_host_port( peer_url )
peer_hostport = "%s:%s" % (host, port)
if peer_hostport not in peer_table.keys():
atlasdb_add_peer( peer_hostport, path=path, peer_table=peer_table )
log.debug("peer_table: {}".format(peer_table.keys()))
peer_table[peer_hostport]['blacklisted'] = True
return peer_table | [
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] | train | https://github.com/blockstack/blockstack-core/blob/1dcfdd39b152d29ce13e736a6a1a0981401a0505/blockstack/lib/atlas.py#L1260-L1352 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Set up the atlas node:
* create the db if it doesn't exist
* go through all the names and verify that we have the *current* zonefiles
* if we don't, queue them for fetching.
* set up the peer db
@db should be an instance of BlockstackDB
@initial_peers should be a list of URLs
Return the newly-initialized peer table
### Response:
def atlasdb_init( path, zonefile_dir, db, peer_seeds, peer_blacklist, recover=False, validate=False):
"""
Set up the atlas node:
* create the db if it doesn't exist
* go through all the names and verify that we have the *current* zonefiles
* if we don't, queue them for fetching.
* set up the peer db
@db should be an instance of BlockstackDB
@initial_peers should be a list of URLs
Return the newly-initialized peer table
"""
global ATLASDB_SQL
peer_table = {}
if os.path.exists( path ):
log.debug("Atlas DB exists at %s" % path)
con = atlasdb_open( path )
atlasdb_last_block = atlasdb_get_lastblock( con=con, path=path )
if atlasdb_last_block is None:
atlasdb_last_block = FIRST_BLOCK_MAINNET
log.debug("Synchronize zonefiles from %s to %s" % (atlasdb_last_block, db.lastblock) )
atlasdb_queue_zonefiles( con, db, atlasdb_last_block, zonefile_dir, recover=recover, validate=validate)
log.debug("Refreshing seed peers")
for peer in peer_seeds:
# forcibly add seed peers
atlasdb_add_peer( peer, con=con, peer_table=peer_table, ping_on_evict=False )
# re-try fetching zonefiles from storage if we don't have them yet
atlasdb_reset_zonefile_tried_storage( con=con, path=path )
# load up peer table from the db
log.debug("Loading peer table")
peer_table = atlasdb_load_peer_table( con=con, path=path )
# cache zonefile inventory and count
atlasdb_cache_zonefile_info( con=con )
con.close()
else:
log.debug("Initializing Atlas DB at %s" % path)
lines = [l + ";" for l in ATLASDB_SQL.split(";")]
con = sqlite3.connect( path, isolation_level=None )
for line in lines:
db_query_execute(con, line, ())
con.row_factory = atlasdb_row_factory
# populate from db
log.debug("Queuing all zonefiles")
atlasdb_queue_zonefiles( con, db, FIRST_BLOCK_MAINNET, zonefile_dir, recover=recover, validate=validate)
log.debug("Adding seed peers")
for peer in peer_seeds:
atlasdb_add_peer( peer, con=con, peer_table=peer_table )
atlasdb_cache_zonefile_info( con=con )
con.close()
log.debug("peer_table: {}".format(peer_table.keys()))
# whitelist and blacklist
for peer_url in peer_seeds:
host, port = url_to_host_port( peer_url )
peer_hostport = "%s:%s" % (host, port)
if peer_hostport not in peer_table.keys():
atlasdb_add_peer( peer_hostport, path=path, peer_table=peer_table )
log.debug("peer_table: {}".format(peer_table.keys()))
peer_table[peer_hostport]['whitelisted'] = True
for peer_url in peer_blacklist:
host, port = url_to_host_port( peer_url )
peer_hostport = "%s:%s" % (host, port)
if peer_hostport not in peer_table.keys():
atlasdb_add_peer( peer_hostport, path=path, peer_table=peer_table )
log.debug("peer_table: {}".format(peer_table.keys()))
peer_table[peer_hostport]['blacklisted'] = True
return peer_table
<|endoftext|> | [
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matousc89/padasip | padasip/filters/base_filter.py | AdaptiveFilter.init_weights | def init_weights(self, w, n=-1):
"""
This function initialises the adaptive weights of the filter.
**Args:**
* `w` : initial weights of filter. Possible values are:
* array with initial weights (1 dimensional array) of filter size
* "random" : create random weights
* "zeros" : create zero value weights
**Kwargs:**
* `n` : size of filter (int) - number of filter coefficients.
**Returns:**
* `y` : output value (float) calculated from input array.
"""
if n == -1:
n = self.n
if type(w) == str:
if w == "random":
w = np.random.normal(0, 0.5, n)
elif w == "zeros":
w = np.zeros(n)
else:
raise ValueError('Impossible to understand the w')
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try:
w = np.array(w, dtype="float64")
except:
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self.w = w | python | def init_weights(self, w, n=-1):
"""
This function initialises the adaptive weights of the filter.
**Args:**
* `w` : initial weights of filter. Possible values are:
* array with initial weights (1 dimensional array) of filter size
* "random" : create random weights
* "zeros" : create zero value weights
**Kwargs:**
* `n` : size of filter (int) - number of filter coefficients.
**Returns:**
* `y` : output value (float) calculated from input array.
"""
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try:
w = np.array(w, dtype="float64")
except:
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**Args:**
* `w` : initial weights of filter. Possible values are:
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* "random" : create random weights
* "zeros" : create zero value weights
**Kwargs:**
* `n` : size of filter (int) - number of filter coefficients.
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
This function initialises the adaptive weights of the filter.
**Args:**
* `w` : initial weights of filter. Possible values are:
* array with initial weights (1 dimensional array) of filter size
* "random" : create random weights
* "zeros" : create zero value weights
**Kwargs:**
* `n` : size of filter (int) - number of filter coefficients.
**Returns:**
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### Response:
def init_weights(self, w, n=-1):
"""
This function initialises the adaptive weights of the filter.
**Args:**
* `w` : initial weights of filter. Possible values are:
* array with initial weights (1 dimensional array) of filter size
* "random" : create random weights
* "zeros" : create zero value weights
**Kwargs:**
* `n` : size of filter (int) - number of filter coefficients.
**Returns:**
* `y` : output value (float) calculated from input array.
"""
if n == -1:
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if w == "random":
w = np.random.normal(0, 0.5, n)
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w = np.zeros(n)
else:
raise ValueError('Impossible to understand the w')
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try:
w = np.array(w, dtype="float64")
except:
raise ValueError('Impossible to understand the w')
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self.w = w
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google/grr | grr/server/grr_response_server/flow_utils.py | WaitForFlow | def WaitForFlow(flow_urn,
token=None,
timeout=DEFAULT_TIMEOUT,
max_sleep_time=1,
min_sleep_time=0.2,
dampening_multiplier=0.9):
"""Waits for a flow to finish, polling while we wait.
Args:
flow_urn: The urn of the flow to wait for.
token: The datastore access token.
timeout: How long to wait before giving up, usually because the client has
gone away.
max_sleep_time: The initial and longest time to wait in between polls.
min_sleep_time: The final and shortest time to wait in between polls.
dampening_multiplier: The current sleep time is multiplied by this number on
each iteration. Controls how fast the polling reaches its minimum sleep
time. You probably want this to be less than 1, unless you want to wait an
increasing amount of time in between flows.
Raises:
IOError: If we time out while waiting for the client.
"""
start_time = time.time()
sleep_time = max_sleep_time
while True:
# Reopen the AFF4Object to check if its status has changed, and also make
# sure it's a flow.
with aff4.FACTORY.Open(
flow_urn, token=token, aff4_type=flow.GRRFlow) as flow_obj:
# Stop if the flow is done or has timed out.
if time.time() - start_time > timeout:
logging.warning("Timed out after waiting %ss for %s!", timeout,
flow_obj)
raise IOError("Timed out trying to access client! Is it connected?")
if not flow_obj.GetRunner().IsRunning():
break
# Decrease the time we sleep each iteration.
sleep_time = max(sleep_time * dampening_multiplier, min_sleep_time)
time.sleep(sleep_time)
logging.debug("Waiting for %s, sleeping for %.3fs", flow_obj, sleep_time) | python | def WaitForFlow(flow_urn,
token=None,
timeout=DEFAULT_TIMEOUT,
max_sleep_time=1,
min_sleep_time=0.2,
dampening_multiplier=0.9):
"""Waits for a flow to finish, polling while we wait.
Args:
flow_urn: The urn of the flow to wait for.
token: The datastore access token.
timeout: How long to wait before giving up, usually because the client has
gone away.
max_sleep_time: The initial and longest time to wait in between polls.
min_sleep_time: The final and shortest time to wait in between polls.
dampening_multiplier: The current sleep time is multiplied by this number on
each iteration. Controls how fast the polling reaches its minimum sleep
time. You probably want this to be less than 1, unless you want to wait an
increasing amount of time in between flows.
Raises:
IOError: If we time out while waiting for the client.
"""
start_time = time.time()
sleep_time = max_sleep_time
while True:
# Reopen the AFF4Object to check if its status has changed, and also make
# sure it's a flow.
with aff4.FACTORY.Open(
flow_urn, token=token, aff4_type=flow.GRRFlow) as flow_obj:
# Stop if the flow is done or has timed out.
if time.time() - start_time > timeout:
logging.warning("Timed out after waiting %ss for %s!", timeout,
flow_obj)
raise IOError("Timed out trying to access client! Is it connected?")
if not flow_obj.GetRunner().IsRunning():
break
# Decrease the time we sleep each iteration.
sleep_time = max(sleep_time * dampening_multiplier, min_sleep_time)
time.sleep(sleep_time)
logging.debug("Waiting for %s, sleeping for %.3fs", flow_obj, sleep_time) | [
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Args:
flow_urn: The urn of the flow to wait for.
token: The datastore access token.
timeout: How long to wait before giving up, usually because the client has
gone away.
max_sleep_time: The initial and longest time to wait in between polls.
min_sleep_time: The final and shortest time to wait in between polls.
dampening_multiplier: The current sleep time is multiplied by this number on
each iteration. Controls how fast the polling reaches its minimum sleep
time. You probably want this to be less than 1, unless you want to wait an
increasing amount of time in between flows.
Raises:
IOError: If we time out while waiting for the client. | [
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] | train | https://github.com/google/grr/blob/5cef4e8e2f0d5df43ea4877e9c798e0bf60bfe74/grr/server/grr_response_server/flow_utils.py#L88-L130 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Waits for a flow to finish, polling while we wait.
Args:
flow_urn: The urn of the flow to wait for.
token: The datastore access token.
timeout: How long to wait before giving up, usually because the client has
gone away.
max_sleep_time: The initial and longest time to wait in between polls.
min_sleep_time: The final and shortest time to wait in between polls.
dampening_multiplier: The current sleep time is multiplied by this number on
each iteration. Controls how fast the polling reaches its minimum sleep
time. You probably want this to be less than 1, unless you want to wait an
increasing amount of time in between flows.
Raises:
IOError: If we time out while waiting for the client.
### Response:
def WaitForFlow(flow_urn,
token=None,
timeout=DEFAULT_TIMEOUT,
max_sleep_time=1,
min_sleep_time=0.2,
dampening_multiplier=0.9):
"""Waits for a flow to finish, polling while we wait.
Args:
flow_urn: The urn of the flow to wait for.
token: The datastore access token.
timeout: How long to wait before giving up, usually because the client has
gone away.
max_sleep_time: The initial and longest time to wait in between polls.
min_sleep_time: The final and shortest time to wait in between polls.
dampening_multiplier: The current sleep time is multiplied by this number on
each iteration. Controls how fast the polling reaches its minimum sleep
time. You probably want this to be less than 1, unless you want to wait an
increasing amount of time in between flows.
Raises:
IOError: If we time out while waiting for the client.
"""
start_time = time.time()
sleep_time = max_sleep_time
while True:
# Reopen the AFF4Object to check if its status has changed, and also make
# sure it's a flow.
with aff4.FACTORY.Open(
flow_urn, token=token, aff4_type=flow.GRRFlow) as flow_obj:
# Stop if the flow is done or has timed out.
if time.time() - start_time > timeout:
logging.warning("Timed out after waiting %ss for %s!", timeout,
flow_obj)
raise IOError("Timed out trying to access client! Is it connected?")
if not flow_obj.GetRunner().IsRunning():
break
# Decrease the time we sleep each iteration.
sleep_time = max(sleep_time * dampening_multiplier, min_sleep_time)
time.sleep(sleep_time)
logging.debug("Waiting for %s, sleeping for %.3fs", flow_obj, sleep_time)
<|endoftext|> | [
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alefnula/tea | tea/utils/compress.py | unzip | def unzip(archive, destination, filenames=None):
"""Unzip a zip archive into destination directory.
It unzips either the whole archive or specific file(s) from the archive.
Usage:
>>> output = os.path.join(os.getcwd(), 'output')
>>> # Archive can be an instance of a ZipFile class
>>> archive = zipfile.ZipFile('test.zip', 'r')
>>> # Or just a filename
>>> archive = 'test.zip'
>>> # Extracts all files
>>> unzip(archive, output)
>>> # Extract only one file
>>> unzip(archive, output, 'my_file.txt')
>>> # Extract a list of files
>>> unzip(archive, output, ['my_file1.txt', 'my_file2.txt'])
>>> unzip_file('test.zip', 'my_file.txt', output)
Args:
archive (zipfile.ZipFile or str): Zipfile object to extract from or
path to the zip archive.
destination (str): Path to the output directory.
filenames (str or list of str or None): Path(s) to the filename(s)
inside the zip archive that you want to extract.
"""
close = False
try:
if not isinstance(archive, zipfile.ZipFile):
archive = zipfile.ZipFile(archive, "r", allowZip64=True)
close = True
logger.info("Extracting: %s -> %s" % (archive.filename, destination))
if isinstance(filenames, str):
filenames = [filenames]
if filenames is None: # extract all
filenames = archive.namelist()
for filename in filenames:
if filename.endswith("/"): # it's a directory
shell.mkdir(os.path.join(destination, filename))
else:
if not _extract_file(archive, destination, filename):
raise Exception()
logger.info('Extracting zip archive "%s" succeeded' % archive.filename)
return True
except Exception:
logger.exception("Error while unzipping archive %s" % archive.filename)
return False
finally:
if close:
archive.close() | python | def unzip(archive, destination, filenames=None):
"""Unzip a zip archive into destination directory.
It unzips either the whole archive or specific file(s) from the archive.
Usage:
>>> output = os.path.join(os.getcwd(), 'output')
>>> # Archive can be an instance of a ZipFile class
>>> archive = zipfile.ZipFile('test.zip', 'r')
>>> # Or just a filename
>>> archive = 'test.zip'
>>> # Extracts all files
>>> unzip(archive, output)
>>> # Extract only one file
>>> unzip(archive, output, 'my_file.txt')
>>> # Extract a list of files
>>> unzip(archive, output, ['my_file1.txt', 'my_file2.txt'])
>>> unzip_file('test.zip', 'my_file.txt', output)
Args:
archive (zipfile.ZipFile or str): Zipfile object to extract from or
path to the zip archive.
destination (str): Path to the output directory.
filenames (str or list of str or None): Path(s) to the filename(s)
inside the zip archive that you want to extract.
"""
close = False
try:
if not isinstance(archive, zipfile.ZipFile):
archive = zipfile.ZipFile(archive, "r", allowZip64=True)
close = True
logger.info("Extracting: %s -> %s" % (archive.filename, destination))
if isinstance(filenames, str):
filenames = [filenames]
if filenames is None: # extract all
filenames = archive.namelist()
for filename in filenames:
if filename.endswith("/"): # it's a directory
shell.mkdir(os.path.join(destination, filename))
else:
if not _extract_file(archive, destination, filename):
raise Exception()
logger.info('Extracting zip archive "%s" succeeded' % archive.filename)
return True
except Exception:
logger.exception("Error while unzipping archive %s" % archive.filename)
return False
finally:
if close:
archive.close() | [
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It unzips either the whole archive or specific file(s) from the archive.
Usage:
>>> output = os.path.join(os.getcwd(), 'output')
>>> # Archive can be an instance of a ZipFile class
>>> archive = zipfile.ZipFile('test.zip', 'r')
>>> # Or just a filename
>>> archive = 'test.zip'
>>> # Extracts all files
>>> unzip(archive, output)
>>> # Extract only one file
>>> unzip(archive, output, 'my_file.txt')
>>> # Extract a list of files
>>> unzip(archive, output, ['my_file1.txt', 'my_file2.txt'])
>>> unzip_file('test.zip', 'my_file.txt', output)
Args:
archive (zipfile.ZipFile or str): Zipfile object to extract from or
path to the zip archive.
destination (str): Path to the output directory.
filenames (str or list of str or None): Path(s) to the filename(s)
inside the zip archive that you want to extract. | [
"Unzip",
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] | train | https://github.com/alefnula/tea/blob/f5a0a724a425ec4f9dd2c7fe966ef06faf3a15a3/tea/utils/compress.py#L40-L89 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Unzip a zip archive into destination directory.
It unzips either the whole archive or specific file(s) from the archive.
Usage:
>>> output = os.path.join(os.getcwd(), 'output')
>>> # Archive can be an instance of a ZipFile class
>>> archive = zipfile.ZipFile('test.zip', 'r')
>>> # Or just a filename
>>> archive = 'test.zip'
>>> # Extracts all files
>>> unzip(archive, output)
>>> # Extract only one file
>>> unzip(archive, output, 'my_file.txt')
>>> # Extract a list of files
>>> unzip(archive, output, ['my_file1.txt', 'my_file2.txt'])
>>> unzip_file('test.zip', 'my_file.txt', output)
Args:
archive (zipfile.ZipFile or str): Zipfile object to extract from or
path to the zip archive.
destination (str): Path to the output directory.
filenames (str or list of str or None): Path(s) to the filename(s)
inside the zip archive that you want to extract.
### Response:
def unzip(archive, destination, filenames=None):
"""Unzip a zip archive into destination directory.
It unzips either the whole archive or specific file(s) from the archive.
Usage:
>>> output = os.path.join(os.getcwd(), 'output')
>>> # Archive can be an instance of a ZipFile class
>>> archive = zipfile.ZipFile('test.zip', 'r')
>>> # Or just a filename
>>> archive = 'test.zip'
>>> # Extracts all files
>>> unzip(archive, output)
>>> # Extract only one file
>>> unzip(archive, output, 'my_file.txt')
>>> # Extract a list of files
>>> unzip(archive, output, ['my_file1.txt', 'my_file2.txt'])
>>> unzip_file('test.zip', 'my_file.txt', output)
Args:
archive (zipfile.ZipFile or str): Zipfile object to extract from or
path to the zip archive.
destination (str): Path to the output directory.
filenames (str or list of str or None): Path(s) to the filename(s)
inside the zip archive that you want to extract.
"""
close = False
try:
if not isinstance(archive, zipfile.ZipFile):
archive = zipfile.ZipFile(archive, "r", allowZip64=True)
close = True
logger.info("Extracting: %s -> %s" % (archive.filename, destination))
if isinstance(filenames, str):
filenames = [filenames]
if filenames is None: # extract all
filenames = archive.namelist()
for filename in filenames:
if filename.endswith("/"): # it's a directory
shell.mkdir(os.path.join(destination, filename))
else:
if not _extract_file(archive, destination, filename):
raise Exception()
logger.info('Extracting zip archive "%s" succeeded' % archive.filename)
return True
except Exception:
logger.exception("Error while unzipping archive %s" % archive.filename)
return False
finally:
if close:
archive.close()
<|endoftext|> | [
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astropy/photutils | photutils/isophote/geometry.py | EllipseGeometry.to_polar | def to_polar(self, x, y):
"""
Return the radius and polar angle in the ellipse coordinate
system given (x, y) pixel image coordinates.
This function takes care of the different definitions for
position angle (PA) and polar angle (phi):
.. math::
-\\pi < PA < \\pi
0 < phi < 2 \\pi
Note that radius can be anything. The solution is not tied to
the semimajor axis length, but to the center position and tilt
angle.
Parameters
----------
x, y : float
The (x, y) image coordinates.
Returns
-------
radius, angle : float
The ellipse radius and polar angle.
"""
# We split in between a scalar version and a
# vectorized version. This is necessary for
# now so we don't pay a heavy speed penalty
# that is incurred when using vectorized code.
# The split in two separate functions helps in
# the profiling analysis: most of the time is
# spent in the scalar function.
if isinstance(x, int) or isinstance(x, float):
return self._to_polar_scalar(x, y)
else:
return self._to_polar_vectorized(x, y) | python | def to_polar(self, x, y):
"""
Return the radius and polar angle in the ellipse coordinate
system given (x, y) pixel image coordinates.
This function takes care of the different definitions for
position angle (PA) and polar angle (phi):
.. math::
-\\pi < PA < \\pi
0 < phi < 2 \\pi
Note that radius can be anything. The solution is not tied to
the semimajor axis length, but to the center position and tilt
angle.
Parameters
----------
x, y : float
The (x, y) image coordinates.
Returns
-------
radius, angle : float
The ellipse radius and polar angle.
"""
# We split in between a scalar version and a
# vectorized version. This is necessary for
# now so we don't pay a heavy speed penalty
# that is incurred when using vectorized code.
# The split in two separate functions helps in
# the profiling analysis: most of the time is
# spent in the scalar function.
if isinstance(x, int) or isinstance(x, float):
return self._to_polar_scalar(x, y)
else:
return self._to_polar_vectorized(x, y) | [
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.. math::
-\\pi < PA < \\pi
0 < phi < 2 \\pi
Note that radius can be anything. The solution is not tied to
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Parameters
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x, y : float
The (x, y) image coordinates.
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Return the radius and polar angle in the ellipse coordinate
system given (x, y) pixel image coordinates.
This function takes care of the different definitions for
position angle (PA) and polar angle (phi):
.. math::
-\\pi < PA < \\pi
0 < phi < 2 \\pi
Note that radius can be anything. The solution is not tied to
the semimajor axis length, but to the center position and tilt
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Parameters
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x, y : float
The (x, y) image coordinates.
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radius, angle : float
The ellipse radius and polar angle.
### Response:
def to_polar(self, x, y):
"""
Return the radius and polar angle in the ellipse coordinate
system given (x, y) pixel image coordinates.
This function takes care of the different definitions for
position angle (PA) and polar angle (phi):
.. math::
-\\pi < PA < \\pi
0 < phi < 2 \\pi
Note that radius can be anything. The solution is not tied to
the semimajor axis length, but to the center position and tilt
angle.
Parameters
----------
x, y : float
The (x, y) image coordinates.
Returns
-------
radius, angle : float
The ellipse radius and polar angle.
"""
# We split in between a scalar version and a
# vectorized version. This is necessary for
# now so we don't pay a heavy speed penalty
# that is incurred when using vectorized code.
# The split in two separate functions helps in
# the profiling analysis: most of the time is
# spent in the scalar function.
if isinstance(x, int) or isinstance(x, float):
return self._to_polar_scalar(x, y)
else:
return self._to_polar_vectorized(x, y)
<|endoftext|> | [
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stbraun/fuzzing | fuzzing/fuzzer.py | FuzzExecutor._execute | def _execute(self, app_, file_):
"""Run app with file as input.
:param app_: application to run.
:param file_: file to run app with.
:return: success True, else False
:rtype: bool
"""
app_name = os.path.basename(app_)
args = [app_]
args.extend(self.args[app_])
args.append(file_)
process = subprocess.Popen(args)
time.sleep(1)
status = {True: Status.SUCCESS, False: Status.FAILED}
crashed = process.poll()
result = status[crashed is None]
self.stats_.add(app_name, result)
if result is Status.SUCCESS:
# process did not crash, so just terminate it
process.terminate() | python | def _execute(self, app_, file_):
"""Run app with file as input.
:param app_: application to run.
:param file_: file to run app with.
:return: success True, else False
:rtype: bool
"""
app_name = os.path.basename(app_)
args = [app_]
args.extend(self.args[app_])
args.append(file_)
process = subprocess.Popen(args)
time.sleep(1)
status = {True: Status.SUCCESS, False: Status.FAILED}
crashed = process.poll()
result = status[crashed is None]
self.stats_.add(app_name, result)
if result is Status.SUCCESS:
# process did not crash, so just terminate it
process.terminate() | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Run app with file as input.
:param app_: application to run.
:param file_: file to run app with.
:return: success True, else False
:rtype: bool
### Response:
def _execute(self, app_, file_):
"""Run app with file as input.
:param app_: application to run.
:param file_: file to run app with.
:return: success True, else False
:rtype: bool
"""
app_name = os.path.basename(app_)
args = [app_]
args.extend(self.args[app_])
args.append(file_)
process = subprocess.Popen(args)
time.sleep(1)
status = {True: Status.SUCCESS, False: Status.FAILED}
crashed = process.poll()
result = status[crashed is None]
self.stats_.add(app_name, result)
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# process did not crash, so just terminate it
process.terminate()
<|endoftext|> | [
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rosenbrockc/fortpy | fortpy/scripts/analyze.py | FortpyShell.do_rmdep | def do_rmdep(self, args):
"""Removes dependent variables currently set for plotting/tabulating etc."""
for arg in args.split():
if arg in self.curargs["dependents"]:
self.curargs["dependents"].remove(arg)
if arg in self.curargs["plottypes"]:
del self.curargs["plottypes"][arg]
if arg in self.curargs["twinplots"]:
del self.curargs["twinplots"][arg]
if arg in self.curargs["colors"]:
del self.curargs["colors"][arg]
if arg in self.curargs["labels"]:
del self.curargs["labels"][arg]
if arg in self.curargs["markers"]:
del self.curargs["markers"][arg]
if arg in self.curargs["lines"]:
del self.curargs["lines"][arg] | python | def do_rmdep(self, args):
"""Removes dependent variables currently set for plotting/tabulating etc."""
for arg in args.split():
if arg in self.curargs["dependents"]:
self.curargs["dependents"].remove(arg)
if arg in self.curargs["plottypes"]:
del self.curargs["plottypes"][arg]
if arg in self.curargs["twinplots"]:
del self.curargs["twinplots"][arg]
if arg in self.curargs["colors"]:
del self.curargs["colors"][arg]
if arg in self.curargs["labels"]:
del self.curargs["labels"][arg]
if arg in self.curargs["markers"]:
del self.curargs["markers"][arg]
if arg in self.curargs["lines"]:
del self.curargs["lines"][arg] | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Removes dependent variables currently set for plotting/tabulating etc.
### Response:
def do_rmdep(self, args):
"""Removes dependent variables currently set for plotting/tabulating etc."""
for arg in args.split():
if arg in self.curargs["dependents"]:
self.curargs["dependents"].remove(arg)
if arg in self.curargs["plottypes"]:
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del self.curargs["labels"][arg]
if arg in self.curargs["markers"]:
del self.curargs["markers"][arg]
if arg in self.curargs["lines"]:
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kubernetes-client/python | kubernetes/client/models/runtime_raw_extension.py | RuntimeRawExtension.raw | def raw(self, raw):
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Raw is the underlying serialization of this object.
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self._raw = raw | python | def raw(self, raw):
"""
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juju/charm-helpers | charmhelpers/contrib/database/mysql.py | MySQLHelper.get_allowed_units | def get_allowed_units(self, database, username, relation_id=None):
"""Get list of units with access grants for database with username.
This is typically used to provide shared-db relations with a list of
which units have been granted access to the given database.
"""
self.connect(password=self.get_mysql_root_password())
allowed_units = set()
for unit in related_units(relation_id):
settings = relation_get(rid=relation_id, unit=unit)
# First check for setting with prefix, then without
for attr in ["%s_hostname" % (database), 'hostname']:
hosts = settings.get(attr, None)
if hosts:
break
if hosts:
# hostname can be json-encoded list of hostnames
try:
hosts = json.loads(hosts)
except ValueError:
hosts = [hosts]
else:
hosts = [settings['private-address']]
if hosts:
for host in hosts:
host = self.normalize_address(host)
if self.grant_exists(database, username, host):
log("Grant exists for host '%s' on db '%s'" %
(host, database), level=DEBUG)
if unit not in allowed_units:
allowed_units.add(unit)
else:
log("Grant does NOT exist for host '%s' on db '%s'" %
(host, database), level=DEBUG)
else:
log("No hosts found for grant check", level=INFO)
return allowed_units | python | def get_allowed_units(self, database, username, relation_id=None):
"""Get list of units with access grants for database with username.
This is typically used to provide shared-db relations with a list of
which units have been granted access to the given database.
"""
self.connect(password=self.get_mysql_root_password())
allowed_units = set()
for unit in related_units(relation_id):
settings = relation_get(rid=relation_id, unit=unit)
# First check for setting with prefix, then without
for attr in ["%s_hostname" % (database), 'hostname']:
hosts = settings.get(attr, None)
if hosts:
break
if hosts:
# hostname can be json-encoded list of hostnames
try:
hosts = json.loads(hosts)
except ValueError:
hosts = [hosts]
else:
hosts = [settings['private-address']]
if hosts:
for host in hosts:
host = self.normalize_address(host)
if self.grant_exists(database, username, host):
log("Grant exists for host '%s' on db '%s'" %
(host, database), level=DEBUG)
if unit not in allowed_units:
allowed_units.add(unit)
else:
log("Grant does NOT exist for host '%s' on db '%s'" %
(host, database), level=DEBUG)
else:
log("No hosts found for grant check", level=INFO)
return allowed_units | [
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This is typically used to provide shared-db relations with a list of
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Get list of units with access grants for database with username.
This is typically used to provide shared-db relations with a list of
which units have been granted access to the given database.
### Response:
def get_allowed_units(self, database, username, relation_id=None):
"""Get list of units with access grants for database with username.
This is typically used to provide shared-db relations with a list of
which units have been granted access to the given database.
"""
self.connect(password=self.get_mysql_root_password())
allowed_units = set()
for unit in related_units(relation_id):
settings = relation_get(rid=relation_id, unit=unit)
# First check for setting with prefix, then without
for attr in ["%s_hostname" % (database), 'hostname']:
hosts = settings.get(attr, None)
if hosts:
break
if hosts:
# hostname can be json-encoded list of hostnames
try:
hosts = json.loads(hosts)
except ValueError:
hosts = [hosts]
else:
hosts = [settings['private-address']]
if hosts:
for host in hosts:
host = self.normalize_address(host)
if self.grant_exists(database, username, host):
log("Grant exists for host '%s' on db '%s'" %
(host, database), level=DEBUG)
if unit not in allowed_units:
allowed_units.add(unit)
else:
log("Grant does NOT exist for host '%s' on db '%s'" %
(host, database), level=DEBUG)
else:
log("No hosts found for grant check", level=INFO)
return allowed_units
<|endoftext|> | [
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for orig, new in self._converted_names.items():
if orig in self._convert_strl:
idx = self._convert_strl.index(orig)
self._convert_strl[idx] = new | python | def _update_strl_names(self):
"""Update column names for conversion to strl if they might have been
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You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
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Update column names for conversion to strl if they might have been
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ming060/robotframework-uiautomatorlibrary | uiautomatorlibrary/Mobile.py | Mobile.scroll_to_beginning_vertically | def scroll_to_beginning_vertically(self, steps=10, *args,**selectors):
"""
Scroll the object which has *selectors* attributes to *beginning* vertically.
See `Scroll Forward Vertically` for more details.
"""
return self.device(**selectors).scroll.vert.toBeginning(steps=steps) | python | def scroll_to_beginning_vertically(self, steps=10, *args,**selectors):
"""
Scroll the object which has *selectors* attributes to *beginning* vertically.
See `Scroll Forward Vertically` for more details.
"""
return self.device(**selectors).scroll.vert.toBeginning(steps=steps) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Scroll the object which has *selectors* attributes to *beginning* vertically.
See `Scroll Forward Vertically` for more details.
### Response:
def scroll_to_beginning_vertically(self, steps=10, *args,**selectors):
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Scroll the object which has *selectors* attributes to *beginning* vertically.
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You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
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Output all recorded metrics
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ray-project/ray | python/ray/experimental/array/distributed/linalg.py | tsqr | def tsqr(a):
"""Perform a QR decomposition of a tall-skinny matrix.
Args:
a: A distributed matrix with shape MxN (suppose K = min(M, N)).
Returns:
A tuple of q (a DistArray) and r (a numpy array) satisfying the
following.
- If q_full = ray.get(DistArray, q).assemble(), then
q_full.shape == (M, K).
- np.allclose(np.dot(q_full.T, q_full), np.eye(K)) == True.
- If r_val = ray.get(np.ndarray, r), then r_val.shape == (K, N).
- np.allclose(r, np.triu(r)) == True.
"""
if len(a.shape) != 2:
raise Exception("tsqr requires len(a.shape) == 2, but a.shape is "
"{}".format(a.shape))
if a.num_blocks[1] != 1:
raise Exception("tsqr requires a.num_blocks[1] == 1, but a.num_blocks "
"is {}".format(a.num_blocks))
num_blocks = a.num_blocks[0]
K = int(np.ceil(np.log2(num_blocks))) + 1
q_tree = np.empty((num_blocks, K), dtype=object)
current_rs = []
for i in range(num_blocks):
block = a.objectids[i, 0]
q, r = ra.linalg.qr.remote(block)
q_tree[i, 0] = q
current_rs.append(r)
for j in range(1, K):
new_rs = []
for i in range(int(np.ceil(1.0 * len(current_rs) / 2))):
stacked_rs = ra.vstack.remote(*current_rs[(2 * i):(2 * i + 2)])
q, r = ra.linalg.qr.remote(stacked_rs)
q_tree[i, j] = q
new_rs.append(r)
current_rs = new_rs
assert len(current_rs) == 1, "len(current_rs) = " + str(len(current_rs))
# handle the special case in which the whole DistArray "a" fits in one
# block and has fewer rows than columns, this is a bit ugly so think about
# how to remove it
if a.shape[0] >= a.shape[1]:
q_shape = a.shape
else:
q_shape = [a.shape[0], a.shape[0]]
q_num_blocks = core.DistArray.compute_num_blocks(q_shape)
q_objectids = np.empty(q_num_blocks, dtype=object)
q_result = core.DistArray(q_shape, q_objectids)
# reconstruct output
for i in range(num_blocks):
q_block_current = q_tree[i, 0]
ith_index = i
for j in range(1, K):
if np.mod(ith_index, 2) == 0:
lower = [0, 0]
upper = [a.shape[1], core.BLOCK_SIZE]
else:
lower = [a.shape[1], 0]
upper = [2 * a.shape[1], core.BLOCK_SIZE]
ith_index //= 2
q_block_current = ra.dot.remote(
q_block_current,
ra.subarray.remote(q_tree[ith_index, j], lower, upper))
q_result.objectids[i] = q_block_current
r = current_rs[0]
return q_result, ray.get(r) | python | def tsqr(a):
"""Perform a QR decomposition of a tall-skinny matrix.
Args:
a: A distributed matrix with shape MxN (suppose K = min(M, N)).
Returns:
A tuple of q (a DistArray) and r (a numpy array) satisfying the
following.
- If q_full = ray.get(DistArray, q).assemble(), then
q_full.shape == (M, K).
- np.allclose(np.dot(q_full.T, q_full), np.eye(K)) == True.
- If r_val = ray.get(np.ndarray, r), then r_val.shape == (K, N).
- np.allclose(r, np.triu(r)) == True.
"""
if len(a.shape) != 2:
raise Exception("tsqr requires len(a.shape) == 2, but a.shape is "
"{}".format(a.shape))
if a.num_blocks[1] != 1:
raise Exception("tsqr requires a.num_blocks[1] == 1, but a.num_blocks "
"is {}".format(a.num_blocks))
num_blocks = a.num_blocks[0]
K = int(np.ceil(np.log2(num_blocks))) + 1
q_tree = np.empty((num_blocks, K), dtype=object)
current_rs = []
for i in range(num_blocks):
block = a.objectids[i, 0]
q, r = ra.linalg.qr.remote(block)
q_tree[i, 0] = q
current_rs.append(r)
for j in range(1, K):
new_rs = []
for i in range(int(np.ceil(1.0 * len(current_rs) / 2))):
stacked_rs = ra.vstack.remote(*current_rs[(2 * i):(2 * i + 2)])
q, r = ra.linalg.qr.remote(stacked_rs)
q_tree[i, j] = q
new_rs.append(r)
current_rs = new_rs
assert len(current_rs) == 1, "len(current_rs) = " + str(len(current_rs))
# handle the special case in which the whole DistArray "a" fits in one
# block and has fewer rows than columns, this is a bit ugly so think about
# how to remove it
if a.shape[0] >= a.shape[1]:
q_shape = a.shape
else:
q_shape = [a.shape[0], a.shape[0]]
q_num_blocks = core.DistArray.compute_num_blocks(q_shape)
q_objectids = np.empty(q_num_blocks, dtype=object)
q_result = core.DistArray(q_shape, q_objectids)
# reconstruct output
for i in range(num_blocks):
q_block_current = q_tree[i, 0]
ith_index = i
for j in range(1, K):
if np.mod(ith_index, 2) == 0:
lower = [0, 0]
upper = [a.shape[1], core.BLOCK_SIZE]
else:
lower = [a.shape[1], 0]
upper = [2 * a.shape[1], core.BLOCK_SIZE]
ith_index //= 2
q_block_current = ra.dot.remote(
q_block_current,
ra.subarray.remote(q_tree[ith_index, j], lower, upper))
q_result.objectids[i] = q_block_current
r = current_rs[0]
return q_result, ray.get(r) | [
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] | Perform a QR decomposition of a tall-skinny matrix.
Args:
a: A distributed matrix with shape MxN (suppose K = min(M, N)).
Returns:
A tuple of q (a DistArray) and r (a numpy array) satisfying the
following.
- If q_full = ray.get(DistArray, q).assemble(), then
q_full.shape == (M, K).
- np.allclose(np.dot(q_full.T, q_full), np.eye(K)) == True.
- If r_val = ray.get(np.ndarray, r), then r_val.shape == (K, N).
- np.allclose(r, np.triu(r)) == True. | [
"Perform",
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] | train | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/array/distributed/linalg.py#L15-L84 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Perform a QR decomposition of a tall-skinny matrix.
Args:
a: A distributed matrix with shape MxN (suppose K = min(M, N)).
Returns:
A tuple of q (a DistArray) and r (a numpy array) satisfying the
following.
- If q_full = ray.get(DistArray, q).assemble(), then
q_full.shape == (M, K).
- np.allclose(np.dot(q_full.T, q_full), np.eye(K)) == True.
- If r_val = ray.get(np.ndarray, r), then r_val.shape == (K, N).
- np.allclose(r, np.triu(r)) == True.
### Response:
def tsqr(a):
"""Perform a QR decomposition of a tall-skinny matrix.
Args:
a: A distributed matrix with shape MxN (suppose K = min(M, N)).
Returns:
A tuple of q (a DistArray) and r (a numpy array) satisfying the
following.
- If q_full = ray.get(DistArray, q).assemble(), then
q_full.shape == (M, K).
- np.allclose(np.dot(q_full.T, q_full), np.eye(K)) == True.
- If r_val = ray.get(np.ndarray, r), then r_val.shape == (K, N).
- np.allclose(r, np.triu(r)) == True.
"""
if len(a.shape) != 2:
raise Exception("tsqr requires len(a.shape) == 2, but a.shape is "
"{}".format(a.shape))
if a.num_blocks[1] != 1:
raise Exception("tsqr requires a.num_blocks[1] == 1, but a.num_blocks "
"is {}".format(a.num_blocks))
num_blocks = a.num_blocks[0]
K = int(np.ceil(np.log2(num_blocks))) + 1
q_tree = np.empty((num_blocks, K), dtype=object)
current_rs = []
for i in range(num_blocks):
block = a.objectids[i, 0]
q, r = ra.linalg.qr.remote(block)
q_tree[i, 0] = q
current_rs.append(r)
for j in range(1, K):
new_rs = []
for i in range(int(np.ceil(1.0 * len(current_rs) / 2))):
stacked_rs = ra.vstack.remote(*current_rs[(2 * i):(2 * i + 2)])
q, r = ra.linalg.qr.remote(stacked_rs)
q_tree[i, j] = q
new_rs.append(r)
current_rs = new_rs
assert len(current_rs) == 1, "len(current_rs) = " + str(len(current_rs))
# handle the special case in which the whole DistArray "a" fits in one
# block and has fewer rows than columns, this is a bit ugly so think about
# how to remove it
if a.shape[0] >= a.shape[1]:
q_shape = a.shape
else:
q_shape = [a.shape[0], a.shape[0]]
q_num_blocks = core.DistArray.compute_num_blocks(q_shape)
q_objectids = np.empty(q_num_blocks, dtype=object)
q_result = core.DistArray(q_shape, q_objectids)
# reconstruct output
for i in range(num_blocks):
q_block_current = q_tree[i, 0]
ith_index = i
for j in range(1, K):
if np.mod(ith_index, 2) == 0:
lower = [0, 0]
upper = [a.shape[1], core.BLOCK_SIZE]
else:
lower = [a.shape[1], 0]
upper = [2 * a.shape[1], core.BLOCK_SIZE]
ith_index //= 2
q_block_current = ra.dot.remote(
q_block_current,
ra.subarray.remote(q_tree[ith_index, j], lower, upper))
q_result.objectids[i] = q_block_current
r = current_rs[0]
return q_result, ray.get(r)
<|endoftext|> | [
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steveYeah/PyBomb | pybomb/clients/game_client.py | GameClient.fetch | def fetch(self, id_, return_fields=None):
"""
Wrapper for fetching details of game by ID
:param id_: int
:param return_fields: tuple
:return: pybomb.clients.Response
"""
game_params = {"id": id_}
if return_fields is not None:
self._validate_return_fields(return_fields)
field_list = ",".join(return_fields)
game_params["field_list"] = field_list
response = self._query(game_params, direct=True)
return response | python | def fetch(self, id_, return_fields=None):
"""
Wrapper for fetching details of game by ID
:param id_: int
:param return_fields: tuple
:return: pybomb.clients.Response
"""
game_params = {"id": id_}
if return_fields is not None:
self._validate_return_fields(return_fields)
field_list = ",".join(return_fields)
game_params["field_list"] = field_list
response = self._query(game_params, direct=True)
return response | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Wrapper for fetching details of game by ID
:param id_: int
:param return_fields: tuple
:return: pybomb.clients.Response
### Response:
def fetch(self, id_, return_fields=None):
"""
Wrapper for fetching details of game by ID
:param id_: int
:param return_fields: tuple
:return: pybomb.clients.Response
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game_params = {"id": id_}
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self._validate_return_fields(return_fields)
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wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.add_event | def add_event(self, name, time, chan):
"""Action: add a single event."""
self.annot.add_event(name, time, chan=chan)
self.update_annotations() | python | def add_event(self, name, time, chan):
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Action: add a single event.
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def add_event(self, name, time, chan):
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Cadair/jupyter_environment_kernels | environment_kernels/activate_helper.py | escape_windows_cmd_string | def escape_windows_cmd_string(s):
"""Returns a string that is usable by the Windows cmd.exe.
The escaping is based on details here and emperical testing:
http://www.robvanderwoude.com/escapechars.php
"""
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s = s.replace('/?', '/.')
return s | python | def escape_windows_cmd_string(s):
"""Returns a string that is usable by the Windows cmd.exe.
The escaping is based on details here and emperical testing:
http://www.robvanderwoude.com/escapechars.php
"""
for c in '()%!^<>&|"':
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s = s.replace('/?', '/.')
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".",
"robvanderwoude",
".",
"com",
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"escapechars",
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] | train | https://github.com/Cadair/jupyter_environment_kernels/blob/3da304550b511bda7d5d39280379b5ca39bb31bc/environment_kernels/activate_helper.py#L158-L166 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Returns a string that is usable by the Windows cmd.exe.
The escaping is based on details here and emperical testing:
http://www.robvanderwoude.com/escapechars.php
### Response:
def escape_windows_cmd_string(s):
"""Returns a string that is usable by the Windows cmd.exe.
The escaping is based on details here and emperical testing:
http://www.robvanderwoude.com/escapechars.php
"""
for c in '()%!^<>&|"':
s = s.replace(c, '^' + c)
s = s.replace('/?', '/.')
return s
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proycon/clam | clam/common/auth.py | NonceMemory.cleanup | def cleanup(self):
"""Delete expired nonces"""
t = time.time()
for noncefile in glob(self.path + '/*.nonce'):
if os.path.getmtime(noncefile) + self.expiration > t:
os.unlink(noncefile) | python | def cleanup(self):
"""Delete expired nonces"""
t = time.time()
for noncefile in glob(self.path + '/*.nonce'):
if os.path.getmtime(noncefile) + self.expiration > t:
os.unlink(noncefile) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Delete expired nonces
### Response:
def cleanup(self):
"""Delete expired nonces"""
t = time.time()
for noncefile in glob(self.path + '/*.nonce'):
if os.path.getmtime(noncefile) + self.expiration > t:
os.unlink(noncefile)
<|endoftext|> | [
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raghakot/keras-vis | vis/utils/utils.py | find_layer_idx | def find_layer_idx(model, layer_name):
"""Looks up the layer index corresponding to `layer_name` from `model`.
Args:
model: The `keras.models.Model` instance.
layer_name: The name of the layer to lookup.
Returns:
The layer index if found. Raises an exception otherwise.
"""
layer_idx = None
for idx, layer in enumerate(model.layers):
if layer.name == layer_name:
layer_idx = idx
break
if layer_idx is None:
raise ValueError("No layer with name '{}' within the model".format(layer_name))
return layer_idx | python | def find_layer_idx(model, layer_name):
"""Looks up the layer index corresponding to `layer_name` from `model`.
Args:
model: The `keras.models.Model` instance.
layer_name: The name of the layer to lookup.
Returns:
The layer index if found. Raises an exception otherwise.
"""
layer_idx = None
for idx, layer in enumerate(model.layers):
if layer.name == layer_name:
layer_idx = idx
break
if layer_idx is None:
raise ValueError("No layer with name '{}' within the model".format(layer_name))
return layer_idx | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Looks up the layer index corresponding to `layer_name` from `model`.
Args:
model: The `keras.models.Model` instance.
layer_name: The name of the layer to lookup.
Returns:
The layer index if found. Raises an exception otherwise.
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def find_layer_idx(model, layer_name):
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Args:
model: The `keras.models.Model` instance.
layer_name: The name of the layer to lookup.
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acutesoftware/AIKIF | scripts/AI_CLI.py | AICLI.cmd_add | def cmd_add(self, txt):
"""
Enter add mode - all text entered now will be
processed as adding information until cancelled
"""
self.show_output('Adding ', txt)
self.raw.add(txt)
print(self.raw)
return 'Added ' + txt | python | def cmd_add(self, txt):
"""
Enter add mode - all text entered now will be
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"""
self.show_output('Adding ', txt)
self.raw.add(txt)
print(self.raw)
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Enter add mode - all text entered now will be
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### Response:
def cmd_add(self, txt):
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Enter add mode - all text entered now will be
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googleapis/dialogflow-python-client-v2 | samples/knowledge_base_management.py | list_knowledge_bases | def list_knowledge_bases(project_id):
"""Lists the Knowledge bases belonging to a project.
Args:
project_id: The GCP project linked with the agent."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
project_path = client.project_path(project_id)
print('Knowledge Bases for: {}'.format(project_id))
for knowledge_base in client.list_knowledge_bases(project_path):
print(' - Display Name: {}'.format(knowledge_base.display_name))
print(' - Knowledge ID: {}\n'.format(knowledge_base.name)) | python | def list_knowledge_bases(project_id):
"""Lists the Knowledge bases belonging to a project.
Args:
project_id: The GCP project linked with the agent."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
project_path = client.project_path(project_id)
print('Knowledge Bases for: {}'.format(project_id))
for knowledge_base in client.list_knowledge_bases(project_path):
print(' - Display Name: {}'.format(knowledge_base.display_name))
print(' - Knowledge ID: {}\n'.format(knowledge_base.name)) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Lists the Knowledge bases belonging to a project.
Args:
project_id: The GCP project linked with the agent.
### Response:
def list_knowledge_bases(project_id):
"""Lists the Knowledge bases belonging to a project.
Args:
project_id: The GCP project linked with the agent."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
project_path = client.project_path(project_id)
print('Knowledge Bases for: {}'.format(project_id))
for knowledge_base in client.list_knowledge_bases(project_path):
print(' - Display Name: {}'.format(knowledge_base.display_name))
print(' - Knowledge ID: {}\n'.format(knowledge_base.name))
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daviddrysdale/python-phonenumbers | python/phonenumbers/phonenumberutil.py | _is_viable_phone_number | def _is_viable_phone_number(number):
"""Checks to see if a string could possibly be a phone number.
At the moment, checks to see that the string begins with at least 2
digits, ignoring any punctuation commonly found in phone numbers. This
method does not require the number to be normalized in advance - but does
assume that leading non-number symbols have been removed, such as by the
method _extract_possible_number.
Arguments:
number -- string to be checked for viability as a phone number
Returns True if the number could be a phone number of some sort, otherwise
False
"""
if len(number) < _MIN_LENGTH_FOR_NSN:
return False
match = fullmatch(_VALID_PHONE_NUMBER_PATTERN, number)
return bool(match) | python | def _is_viable_phone_number(number):
"""Checks to see if a string could possibly be a phone number.
At the moment, checks to see that the string begins with at least 2
digits, ignoring any punctuation commonly found in phone numbers. This
method does not require the number to be normalized in advance - but does
assume that leading non-number symbols have been removed, such as by the
method _extract_possible_number.
Arguments:
number -- string to be checked for viability as a phone number
Returns True if the number could be a phone number of some sort, otherwise
False
"""
if len(number) < _MIN_LENGTH_FOR_NSN:
return False
match = fullmatch(_VALID_PHONE_NUMBER_PATTERN, number)
return bool(match) | [
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assume that leading non-number symbols have been removed, such as by the
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Returns True if the number could be a phone number of some sort, otherwise
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Checks to see if a string could possibly be a phone number.
At the moment, checks to see that the string begins with at least 2
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method does not require the number to be normalized in advance - but does
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number -- string to be checked for viability as a phone number
Returns True if the number could be a phone number of some sort, otherwise
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### Response:
def _is_viable_phone_number(number):
"""Checks to see if a string could possibly be a phone number.
At the moment, checks to see that the string begins with at least 2
digits, ignoring any punctuation commonly found in phone numbers. This
method does not require the number to be normalized in advance - but does
assume that leading non-number symbols have been removed, such as by the
method _extract_possible_number.
Arguments:
number -- string to be checked for viability as a phone number
Returns True if the number could be a phone number of some sort, otherwise
False
"""
if len(number) < _MIN_LENGTH_FOR_NSN:
return False
match = fullmatch(_VALID_PHONE_NUMBER_PATTERN, number)
return bool(match)
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saltstack/salt | salt/key.py | Key.finger_all | def finger_all(self, hash_type=None):
'''
Return fingerprints for all keys
'''
if hash_type is None:
hash_type = __opts__['hash_type']
ret = {}
for status, keys in six.iteritems(self.all_keys()):
ret[status] = {}
for key in keys:
if status == 'local':
path = os.path.join(self.opts['pki_dir'], key)
else:
path = os.path.join(self.opts['pki_dir'], status, key)
ret[status][key] = salt.utils.crypt.pem_finger(path, sum_type=hash_type)
return ret | python | def finger_all(self, hash_type=None):
'''
Return fingerprints for all keys
'''
if hash_type is None:
hash_type = __opts__['hash_type']
ret = {}
for status, keys in six.iteritems(self.all_keys()):
ret[status] = {}
for key in keys:
if status == 'local':
path = os.path.join(self.opts['pki_dir'], key)
else:
path = os.path.join(self.opts['pki_dir'], status, key)
ret[status][key] = salt.utils.crypt.pem_finger(path, sum_type=hash_type)
return ret | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Return fingerprints for all keys
### Response:
def finger_all(self, hash_type=None):
'''
Return fingerprints for all keys
'''
if hash_type is None:
hash_type = __opts__['hash_type']
ret = {}
for status, keys in six.iteritems(self.all_keys()):
ret[status] = {}
for key in keys:
if status == 'local':
path = os.path.join(self.opts['pki_dir'], key)
else:
path = os.path.join(self.opts['pki_dir'], status, key)
ret[status][key] = salt.utils.crypt.pem_finger(path, sum_type=hash_type)
return ret
<|endoftext|> | [
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jldantas/libmft | libmft/api.py | Datastream.add_from_datastream | def add_from_datastream(self, source_ds):
'''Add information from another datastream. Verifies if the datastream
added is correct and copy the relevant fields if necessary.'''
if source_ds.name != self.name:
raise DataStreamError("Data from a different stream 'f{source_ds.name}' cannot be add to this stream")
if self._data_runs is None:
raise DataStreamError("Cannot add data to a resident datastream.")
if self.cluster_count < source_ds.cluster_count:
self.cluster_count = source_ds.cluster_count
if self.size == 0 and source_ds.size:
self.size = source_ds.size
self.alloc_size = source_ds.alloc_size
if source_ds._data_runs:
self._data_runs += source_ds._data_runs
self._data_runs_sorted = False | python | def add_from_datastream(self, source_ds):
'''Add information from another datastream. Verifies if the datastream
added is correct and copy the relevant fields if necessary.'''
if source_ds.name != self.name:
raise DataStreamError("Data from a different stream 'f{source_ds.name}' cannot be add to this stream")
if self._data_runs is None:
raise DataStreamError("Cannot add data to a resident datastream.")
if self.cluster_count < source_ds.cluster_count:
self.cluster_count = source_ds.cluster_count
if self.size == 0 and source_ds.size:
self.size = source_ds.size
self.alloc_size = source_ds.alloc_size
if source_ds._data_runs:
self._data_runs += source_ds._data_runs
self._data_runs_sorted = False | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Add information from another datastream. Verifies if the datastream
added is correct and copy the relevant fields if necessary.
### Response:
def add_from_datastream(self, source_ds):
'''Add information from another datastream. Verifies if the datastream
added is correct and copy the relevant fields if necessary.'''
if source_ds.name != self.name:
raise DataStreamError("Data from a different stream 'f{source_ds.name}' cannot be add to this stream")
if self._data_runs is None:
raise DataStreamError("Cannot add data to a resident datastream.")
if self.cluster_count < source_ds.cluster_count:
self.cluster_count = source_ds.cluster_count
if self.size == 0 and source_ds.size:
self.size = source_ds.size
self.alloc_size = source_ds.alloc_size
if source_ds._data_runs:
self._data_runs += source_ds._data_runs
self._data_runs_sorted = False
<|endoftext|> | [
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jeroyang/cateye | cateye/cateye.py | correct | def correct(tokens, term_freq):
"""
Correct a list of tokens, according to the term_freq
"""
log = []
output = []
for token in tokens:
corrected = _correct(token, term_freq)
if corrected != token:
log.append((token, corrected))
output.append(corrected)
return output, log | python | def correct(tokens, term_freq):
"""
Correct a list of tokens, according to the term_freq
"""
log = []
output = []
for token in tokens:
corrected = _correct(token, term_freq)
if corrected != token:
log.append((token, corrected))
output.append(corrected)
return output, log | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Correct a list of tokens, according to the term_freq
### Response:
def correct(tokens, term_freq):
"""
Correct a list of tokens, according to the term_freq
"""
log = []
output = []
for token in tokens:
corrected = _correct(token, term_freq)
if corrected != token:
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"""Gets the transform information when the overlay is rendering on a component."""
fn = self.function_table.getOverlayTransformTrackedDeviceComponent
punDeviceIndex = TrackedDeviceIndex_t()
result = fn(ulOverlayHandle, byref(punDeviceIndex), pchComponentName, unComponentNameSize)
return result, punDeviceIndex | python | def getOverlayTransformTrackedDeviceComponent(self, ulOverlayHandle, pchComponentName, unComponentNameSize):
"""Gets the transform information when the overlay is rendering on a component."""
fn = self.function_table.getOverlayTransformTrackedDeviceComponent
punDeviceIndex = TrackedDeviceIndex_t()
result = fn(ulOverlayHandle, byref(punDeviceIndex), pchComponentName, unComponentNameSize)
return result, punDeviceIndex | [
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You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Gets the transform information when the overlay is rendering on a component.
### Response:
def getOverlayTransformTrackedDeviceComponent(self, ulOverlayHandle, pchComponentName, unComponentNameSize):
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lambdamusic/Ontospy | ontospy/ontodocs/misc/scripts/export_all.py | cli_run_viz | def cli_run_viz(source=None, outputpath="", theme="", verbose=False):
"""
This application is a wrapper on the main ontospy-viz script. It generates docs for all models in the local library. Using the Complex-html template..
@todo allow to pass a custom folder ..
> python -m ontospy.viz.scripts.export_all -o ~/Desktop/test/ --theme random
"""
if outputpath:
if not (os.path.exists(outputpath)) or not (os.path.isdir(outputpath)):
click.secho(
"WARNING: the -o option must include a valid directory path.",
fg="red")
sys.exit(0)
else:
from os.path import expanduser
home = expanduser("~")
outputpath = os.path.join(home, "ontospy-viz-multi")
if source:
source_folder = source[0]
if not os.path.isdir(source_folder):
click.secho(
"WARNING: '%s' is not a valid directory path." % source_folder,
fg="red")
sys.exit(0)
files_list = [
f for f in os.listdir(source_folder)
if os.path.isfile(os.path.join(source_folder, f))
]
click.secho(
"Exporting the directory: '%s'" % source_folder, fg="green")
click.secho("----------", fg="green")
else:
click.secho(
"Exporting the local library: '%s'" % get_home_location(),
fg="green")
click.secho("----------", fg="green")
files_list = get_localontologies()
source_folder = get_home_location()
report_pages = []
for onto_name in files_list:
full_uri = os.path.join(source_folder, onto_name)
if theme:
if theme == "random":
_theme = random_theme()
else:
_theme = theme
else:
_theme = BOOTSWATCH_THEME_DEFAULT
click.secho("Onto: <%s> Theme: '%s'" % (onto_name, _theme), fg="green")
printDebug("Loading graph...", dim=True)
g = Ontospy(os.path.join(source_folder, onto_name), verbose=verbose)
if g.sources:
# if Ontospy graph has no valid 'sources' = file passed was not valid RDF
printDebug("Building visualization...", dim=True)
onto_name_safe = slugify(unicode(onto_name))
onto_outputpath = os.path.join(outputpath, onto_name_safe)
# note: single static files output path
static_outputpath = os.path.join(outputpath, "static")
# v = KompleteViz(g, theme=_theme)
v = KompleteVizMultiModel(
g,
theme=_theme,
static_url="../static/",
output_path_static=static_outputpath)
try:
# note: onto_outputpath is wiped out each time as part of the build
url = v.build(onto_outputpath)
report_pages.append(
"<a href='%s/index.html' target='_blank'>%s</a> ('%s' theme)<br />"
% (onto_name_safe, onto_name, _theme))
except:
e = sys.exc_info()[0]
printDebug("Error: " + str(e), "red")
continue
# generate a report page
report_path = os.path.join(outputpath, "index.html")
html = """
<html>
<head>
<style media="screen">
a {font-size: 20px; padding: 15px; text-transform: capitalize; text-decoration: none;}
a:hover {text-decoration: underline;}
</style>
</head>
<body>
<h1>Ontospy-generated documentation:</h1>
%s
</body>
</html>
"""
with open(report_path, "w") as text_file:
text_file.write(html % ("".join([x for x in report_pages])))
# open report
webbrowser.open("file:///" + report_path)
raise SystemExit(1) | python | def cli_run_viz(source=None, outputpath="", theme="", verbose=False):
"""
This application is a wrapper on the main ontospy-viz script. It generates docs for all models in the local library. Using the Complex-html template..
@todo allow to pass a custom folder ..
> python -m ontospy.viz.scripts.export_all -o ~/Desktop/test/ --theme random
"""
if outputpath:
if not (os.path.exists(outputpath)) or not (os.path.isdir(outputpath)):
click.secho(
"WARNING: the -o option must include a valid directory path.",
fg="red")
sys.exit(0)
else:
from os.path import expanduser
home = expanduser("~")
outputpath = os.path.join(home, "ontospy-viz-multi")
if source:
source_folder = source[0]
if not os.path.isdir(source_folder):
click.secho(
"WARNING: '%s' is not a valid directory path." % source_folder,
fg="red")
sys.exit(0)
files_list = [
f for f in os.listdir(source_folder)
if os.path.isfile(os.path.join(source_folder, f))
]
click.secho(
"Exporting the directory: '%s'" % source_folder, fg="green")
click.secho("----------", fg="green")
else:
click.secho(
"Exporting the local library: '%s'" % get_home_location(),
fg="green")
click.secho("----------", fg="green")
files_list = get_localontologies()
source_folder = get_home_location()
report_pages = []
for onto_name in files_list:
full_uri = os.path.join(source_folder, onto_name)
if theme:
if theme == "random":
_theme = random_theme()
else:
_theme = theme
else:
_theme = BOOTSWATCH_THEME_DEFAULT
click.secho("Onto: <%s> Theme: '%s'" % (onto_name, _theme), fg="green")
printDebug("Loading graph...", dim=True)
g = Ontospy(os.path.join(source_folder, onto_name), verbose=verbose)
if g.sources:
# if Ontospy graph has no valid 'sources' = file passed was not valid RDF
printDebug("Building visualization...", dim=True)
onto_name_safe = slugify(unicode(onto_name))
onto_outputpath = os.path.join(outputpath, onto_name_safe)
# note: single static files output path
static_outputpath = os.path.join(outputpath, "static")
# v = KompleteViz(g, theme=_theme)
v = KompleteVizMultiModel(
g,
theme=_theme,
static_url="../static/",
output_path_static=static_outputpath)
try:
# note: onto_outputpath is wiped out each time as part of the build
url = v.build(onto_outputpath)
report_pages.append(
"<a href='%s/index.html' target='_blank'>%s</a> ('%s' theme)<br />"
% (onto_name_safe, onto_name, _theme))
except:
e = sys.exc_info()[0]
printDebug("Error: " + str(e), "red")
continue
# generate a report page
report_path = os.path.join(outputpath, "index.html")
html = """
<html>
<head>
<style media="screen">
a {font-size: 20px; padding: 15px; text-transform: capitalize; text-decoration: none;}
a:hover {text-decoration: underline;}
</style>
</head>
<body>
<h1>Ontospy-generated documentation:</h1>
%s
</body>
</html>
"""
with open(report_path, "w") as text_file:
text_file.write(html % ("".join([x for x in report_pages])))
# open report
webbrowser.open("file:///" + report_path)
raise SystemExit(1) | [
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] | This application is a wrapper on the main ontospy-viz script. It generates docs for all models in the local library. Using the Complex-html template..
@todo allow to pass a custom folder ..
> python -m ontospy.viz.scripts.export_all -o ~/Desktop/test/ --theme random | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
This application is a wrapper on the main ontospy-viz script. It generates docs for all models in the local library. Using the Complex-html template..
@todo allow to pass a custom folder ..
> python -m ontospy.viz.scripts.export_all -o ~/Desktop/test/ --theme random
### Response:
def cli_run_viz(source=None, outputpath="", theme="", verbose=False):
"""
This application is a wrapper on the main ontospy-viz script. It generates docs for all models in the local library. Using the Complex-html template..
@todo allow to pass a custom folder ..
> python -m ontospy.viz.scripts.export_all -o ~/Desktop/test/ --theme random
"""
if outputpath:
if not (os.path.exists(outputpath)) or not (os.path.isdir(outputpath)):
click.secho(
"WARNING: the -o option must include a valid directory path.",
fg="red")
sys.exit(0)
else:
from os.path import expanduser
home = expanduser("~")
outputpath = os.path.join(home, "ontospy-viz-multi")
if source:
source_folder = source[0]
if not os.path.isdir(source_folder):
click.secho(
"WARNING: '%s' is not a valid directory path." % source_folder,
fg="red")
sys.exit(0)
files_list = [
f for f in os.listdir(source_folder)
if os.path.isfile(os.path.join(source_folder, f))
]
click.secho(
"Exporting the directory: '%s'" % source_folder, fg="green")
click.secho("----------", fg="green")
else:
click.secho(
"Exporting the local library: '%s'" % get_home_location(),
fg="green")
click.secho("----------", fg="green")
files_list = get_localontologies()
source_folder = get_home_location()
report_pages = []
for onto_name in files_list:
full_uri = os.path.join(source_folder, onto_name)
if theme:
if theme == "random":
_theme = random_theme()
else:
_theme = theme
else:
_theme = BOOTSWATCH_THEME_DEFAULT
click.secho("Onto: <%s> Theme: '%s'" % (onto_name, _theme), fg="green")
printDebug("Loading graph...", dim=True)
g = Ontospy(os.path.join(source_folder, onto_name), verbose=verbose)
if g.sources:
# if Ontospy graph has no valid 'sources' = file passed was not valid RDF
printDebug("Building visualization...", dim=True)
onto_name_safe = slugify(unicode(onto_name))
onto_outputpath = os.path.join(outputpath, onto_name_safe)
# note: single static files output path
static_outputpath = os.path.join(outputpath, "static")
# v = KompleteViz(g, theme=_theme)
v = KompleteVizMultiModel(
g,
theme=_theme,
static_url="../static/",
output_path_static=static_outputpath)
try:
# note: onto_outputpath is wiped out each time as part of the build
url = v.build(onto_outputpath)
report_pages.append(
"<a href='%s/index.html' target='_blank'>%s</a> ('%s' theme)<br />"
% (onto_name_safe, onto_name, _theme))
except:
e = sys.exc_info()[0]
printDebug("Error: " + str(e), "red")
continue
# generate a report page
report_path = os.path.join(outputpath, "index.html")
html = """
<html>
<head>
<style media="screen">
a {font-size: 20px; padding: 15px; text-transform: capitalize; text-decoration: none;}
a:hover {text-decoration: underline;}
</style>
</head>
<body>
<h1>Ontospy-generated documentation:</h1>
%s
</body>
</html>
"""
with open(report_path, "w") as text_file:
text_file.write(html % ("".join([x for x in report_pages])))
# open report
webbrowser.open("file:///" + report_path)
raise SystemExit(1)
<|endoftext|> | [
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bcbio/bcbio-nextgen | bcbio/heterogeneity/bubbletree.py | _is_possible_loh | def _is_possible_loh(rec, vcf_rec, params, somatic_info, use_status=False, max_normal_depth=None):
"""Check if the VCF record is a het in the normal with sufficient support.
Only returns SNPs, since indels tend to have less precise frequency measurements.
"""
if _is_biallelic_snp(rec) and _passes_plus_germline(rec, use_status=use_status):
stats = _tumor_normal_stats(rec, somatic_info, vcf_rec)
depths = [tz.get_in([x, "depth"], stats) for x in ["normal", "tumor"]]
depths = [d for d in depths if d is not None]
normal_freq = tz.get_in(["normal", "freq"], stats)
tumor_freq = tz.get_in(["tumor", "freq"], stats)
if all([d > params["min_depth"] for d in depths]):
if max_normal_depth and tz.get_in(["normal", "depth"], stats, 0) > max_normal_depth:
return None
if normal_freq is not None:
if normal_freq >= params["min_freq"] and normal_freq <= params["max_freq"]:
return stats
elif (tumor_freq >= params["tumor_only"]["min_freq"] and
tumor_freq <= params["tumor_only"]["max_freq"]):
if (vcf_rec and not _has_population_germline(vcf_rec)) or is_population_germline(rec):
return stats | python | def _is_possible_loh(rec, vcf_rec, params, somatic_info, use_status=False, max_normal_depth=None):
"""Check if the VCF record is a het in the normal with sufficient support.
Only returns SNPs, since indels tend to have less precise frequency measurements.
"""
if _is_biallelic_snp(rec) and _passes_plus_germline(rec, use_status=use_status):
stats = _tumor_normal_stats(rec, somatic_info, vcf_rec)
depths = [tz.get_in([x, "depth"], stats) for x in ["normal", "tumor"]]
depths = [d for d in depths if d is not None]
normal_freq = tz.get_in(["normal", "freq"], stats)
tumor_freq = tz.get_in(["tumor", "freq"], stats)
if all([d > params["min_depth"] for d in depths]):
if max_normal_depth and tz.get_in(["normal", "depth"], stats, 0) > max_normal_depth:
return None
if normal_freq is not None:
if normal_freq >= params["min_freq"] and normal_freq <= params["max_freq"]:
return stats
elif (tumor_freq >= params["tumor_only"]["min_freq"] and
tumor_freq <= params["tumor_only"]["max_freq"]):
if (vcf_rec and not _has_population_germline(vcf_rec)) or is_population_germline(rec):
return stats | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Check if the VCF record is a het in the normal with sufficient support.
Only returns SNPs, since indels tend to have less precise frequency measurements.
### Response:
def _is_possible_loh(rec, vcf_rec, params, somatic_info, use_status=False, max_normal_depth=None):
"""Check if the VCF record is a het in the normal with sufficient support.
Only returns SNPs, since indels tend to have less precise frequency measurements.
"""
if _is_biallelic_snp(rec) and _passes_plus_germline(rec, use_status=use_status):
stats = _tumor_normal_stats(rec, somatic_info, vcf_rec)
depths = [tz.get_in([x, "depth"], stats) for x in ["normal", "tumor"]]
depths = [d for d in depths if d is not None]
normal_freq = tz.get_in(["normal", "freq"], stats)
tumor_freq = tz.get_in(["tumor", "freq"], stats)
if all([d > params["min_depth"] for d in depths]):
if max_normal_depth and tz.get_in(["normal", "depth"], stats, 0) > max_normal_depth:
return None
if normal_freq is not None:
if normal_freq >= params["min_freq"] and normal_freq <= params["max_freq"]:
return stats
elif (tumor_freq >= params["tumor_only"]["min_freq"] and
tumor_freq <= params["tumor_only"]["max_freq"]):
if (vcf_rec and not _has_population_germline(vcf_rec)) or is_population_germline(rec):
return stats
<|endoftext|> | [
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kmarekspartz/props | props/__init__.py | one_of | def one_of(*generators):
"""
Generates an arbitrary value of one of the specified generators.
This is a class factory, it makes a class which is a closure around the
specified generators.
"""
class OneOfGenerators(ArbitraryInterface):
"""
A closure class around the generators specified above, which
generates one of the generators.
"""
@classmethod
def arbitrary(cls):
"""
Generate one of the enclosed generators.
"""
return arbitrary(random.choice(generators))
OneOfGenerators.__name__ = ''.join([
'one_of(', ', '.join(generator.__name__ for generator in generators),
')'
])
return OneOfGenerators | python | def one_of(*generators):
"""
Generates an arbitrary value of one of the specified generators.
This is a class factory, it makes a class which is a closure around the
specified generators.
"""
class OneOfGenerators(ArbitraryInterface):
"""
A closure class around the generators specified above, which
generates one of the generators.
"""
@classmethod
def arbitrary(cls):
"""
Generate one of the enclosed generators.
"""
return arbitrary(random.choice(generators))
OneOfGenerators.__name__ = ''.join([
'one_of(', ', '.join(generator.__name__ for generator in generators),
')'
])
return OneOfGenerators | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Generates an arbitrary value of one of the specified generators.
This is a class factory, it makes a class which is a closure around the
specified generators.
### Response:
def one_of(*generators):
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Generates an arbitrary value of one of the specified generators.
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blockstack/blockstack-core | blockstack/lib/nameset/db.py | namedb_get_names_owned_by_address | def namedb_get_names_owned_by_address( cur, address, current_block ):
"""
Get the list of non-expired, non-revoked names owned by an address.
Only works if there is a *singular* address for the name.
"""
unexpired_fragment, unexpired_args = namedb_select_where_unexpired_names( current_block )
select_query = "SELECT name FROM name_records JOIN namespaces ON name_records.namespace_id = namespaces.namespace_id " + \
"WHERE name_records.address = ? AND name_records.revoked = 0 AND " + unexpired_fragment + ";"
args = (address,) + unexpired_args
name_rows = namedb_query_execute( cur, select_query, args )
names = []
for name_row in name_rows:
names.append( name_row['name'] )
if len(names) == 0:
return None
else:
return names | python | def namedb_get_names_owned_by_address( cur, address, current_block ):
"""
Get the list of non-expired, non-revoked names owned by an address.
Only works if there is a *singular* address for the name.
"""
unexpired_fragment, unexpired_args = namedb_select_where_unexpired_names( current_block )
select_query = "SELECT name FROM name_records JOIN namespaces ON name_records.namespace_id = namespaces.namespace_id " + \
"WHERE name_records.address = ? AND name_records.revoked = 0 AND " + unexpired_fragment + ";"
args = (address,) + unexpired_args
name_rows = namedb_query_execute( cur, select_query, args )
names = []
for name_row in name_rows:
names.append( name_row['name'] )
if len(names) == 0:
return None
else:
return names | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Get the list of non-expired, non-revoked names owned by an address.
Only works if there is a *singular* address for the name.
### Response:
def namedb_get_names_owned_by_address( cur, address, current_block ):
"""
Get the list of non-expired, non-revoked names owned by an address.
Only works if there is a *singular* address for the name.
"""
unexpired_fragment, unexpired_args = namedb_select_where_unexpired_names( current_block )
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"WHERE name_records.address = ? AND name_records.revoked = 0 AND " + unexpired_fragment + ";"
args = (address,) + unexpired_args
name_rows = namedb_query_execute( cur, select_query, args )
names = []
for name_row in name_rows:
names.append( name_row['name'] )
if len(names) == 0:
return None
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return names
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IvanMalison/okcupyd | okcupyd/question.py | UserQuestion.get_answer_id_for_question | def get_answer_id_for_question(self, question):
"""Get the answer_id corresponding to the answer given for question
by looking at this :class:`~.UserQuestion`'s answer_options.
The given :class:`~.Question` instance must have the same id as this
:class:`~.UserQuestion`.
That this method exists is admittedly somewhat weird. Unfortunately, it
seems to be the only way to retrieve this information.
"""
assert question.id == self.id
for answer_option in self.answer_options:
if answer_option.text == question.their_answer:
return answer_option.id | python | def get_answer_id_for_question(self, question):
"""Get the answer_id corresponding to the answer given for question
by looking at this :class:`~.UserQuestion`'s answer_options.
The given :class:`~.Question` instance must have the same id as this
:class:`~.UserQuestion`.
That this method exists is admittedly somewhat weird. Unfortunately, it
seems to be the only way to retrieve this information.
"""
assert question.id == self.id
for answer_option in self.answer_options:
if answer_option.text == question.their_answer:
return answer_option.id | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Get the answer_id corresponding to the answer given for question
by looking at this :class:`~.UserQuestion`'s answer_options.
The given :class:`~.Question` instance must have the same id as this
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That this method exists is admittedly somewhat weird. Unfortunately, it
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def get_answer_id_for_question(self, question):
"""Get the answer_id corresponding to the answer given for question
by looking at this :class:`~.UserQuestion`'s answer_options.
The given :class:`~.Question` instance must have the same id as this
:class:`~.UserQuestion`.
That this method exists is admittedly somewhat weird. Unfortunately, it
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assert question.id == self.id
for answer_option in self.answer_options:
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inasafe/inasafe | safe/metadata/utilities.py | serialize_dictionary | def serialize_dictionary(dictionary):
"""Function to stringify a dictionary recursively.
:param dictionary: The dictionary.
:type dictionary: dict
:return: The string.
:rtype: basestring
"""
string_value = {}
for k, v in list(dictionary.items()):
if isinstance(v, QUrl):
string_value[k] = v.toString()
elif isinstance(v, (QDate, QDateTime)):
string_value[k] = v.toString(Qt.ISODate)
elif isinstance(v, datetime):
string_value[k] = v.isoformat()
elif isinstance(v, date):
string_value[k] = v.isoformat()
elif isinstance(v, dict):
# Recursive call
string_value[k] = serialize_dictionary(v)
else:
string_value[k] = v
return string_value | python | def serialize_dictionary(dictionary):
"""Function to stringify a dictionary recursively.
:param dictionary: The dictionary.
:type dictionary: dict
:return: The string.
:rtype: basestring
"""
string_value = {}
for k, v in list(dictionary.items()):
if isinstance(v, QUrl):
string_value[k] = v.toString()
elif isinstance(v, (QDate, QDateTime)):
string_value[k] = v.toString(Qt.ISODate)
elif isinstance(v, datetime):
string_value[k] = v.isoformat()
elif isinstance(v, date):
string_value[k] = v.isoformat()
elif isinstance(v, dict):
# Recursive call
string_value[k] = serialize_dictionary(v)
else:
string_value[k] = v
return string_value | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Function to stringify a dictionary recursively.
:param dictionary: The dictionary.
:type dictionary: dict
:return: The string.
:rtype: basestring
### Response:
def serialize_dictionary(dictionary):
"""Function to stringify a dictionary recursively.
:param dictionary: The dictionary.
:type dictionary: dict
:return: The string.
:rtype: basestring
"""
string_value = {}
for k, v in list(dictionary.items()):
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string_value[k] = v.isoformat()
elif isinstance(v, date):
string_value[k] = v.isoformat()
elif isinstance(v, dict):
# Recursive call
string_value[k] = serialize_dictionary(v)
else:
string_value[k] = v
return string_value
<|endoftext|> | [
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ethpm/py-ethpm | ethpm/utils/contract.py | validate_minimal_contract_factory_data | def validate_minimal_contract_factory_data(contract_data: Dict[str, str]) -> None:
"""
Validate that contract data in a package contains at least an "abi" and
"deployment_bytecode" necessary to generate a deployable contract factory.
"""
if not all(key in contract_data.keys() for key in ("abi", "deployment_bytecode")):
raise InsufficientAssetsError(
"Minimum required contract data to generate a deployable "
"contract factory (abi & deployment_bytecode) not found."
) | python | def validate_minimal_contract_factory_data(contract_data: Dict[str, str]) -> None:
"""
Validate that contract data in a package contains at least an "abi" and
"deployment_bytecode" necessary to generate a deployable contract factory.
"""
if not all(key in contract_data.keys() for key in ("abi", "deployment_bytecode")):
raise InsufficientAssetsError(
"Minimum required contract data to generate a deployable "
"contract factory (abi & deployment_bytecode) not found."
) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Validate that contract data in a package contains at least an "abi" and
"deployment_bytecode" necessary to generate a deployable contract factory.
### Response:
def validate_minimal_contract_factory_data(contract_data: Dict[str, str]) -> None:
"""
Validate that contract data in a package contains at least an "abi" and
"deployment_bytecode" necessary to generate a deployable contract factory.
"""
if not all(key in contract_data.keys() for key in ("abi", "deployment_bytecode")):
raise InsufficientAssetsError(
"Minimum required contract data to generate a deployable "
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)
<|endoftext|> | [
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thespacedoctor/astrocalc | astrocalc/distances/converter.py | converter.redshift_to_distance | def redshift_to_distance(
self,
z,
WM=0.3,
WV=0.7,
H0=70.0):
"""*convert redshift to various distance measurements*
**Key Arguments:**
- ``z`` -- redshift measurement.
- ``WM`` -- Omega_matter. Default *0.3*
- ``WV`` -- Omega_vacuum. Default *0.7*
- ``H0`` -- Hubble constant. (km s-1 Mpc-1) Default *70.0*
**Return:**
- ``results`` -- result dictionary including
- ``dcmr_mpc`` -- co-moving radius distance
- ``da_mpc`` -- angular distance
- ``da_scale`` -- angular distance scale
- ``dl_mpc`` -- luminosity distance (usually use this one)
- ``dmod`` -- distance modulus (determined from luminosity distance)
.. todo::
- replace convert_redshift_to_distance in all other code
**Usage:**
.. code-block:: python
from astrocalc.distances import converter
c = converter(log=log)
dists = c.redshift_to_distance(
z=0.343
)
print "Distance Modulus: " + str(dists["dmod"]) + " mag"
print "Luminousity Distance: " + str(dists["dl_mpc"]) + " Mpc"
print "Angular Size Scale: " + str(dists["da_scale"]) + " kpc/arcsec"
print "Angular Size Distance: " + str(dists["da_mpc"]) + " Mpc"
print "Comoving Radial Distance: " + str(dists["dcmr_mpc"]) + " Mpc"
# OUTPUT :
# Distance Modulus: 41.27 mag
# Luminousity Distance: 1795.16 Mpc
# Angular Size Scale: 4.85 kpc/arcsec
# Angular Size Distance: 999.76 Mpc
# Comoving Radial Distance: 1339.68 Mpc
from astrocalc.distances import converter
c = converter(log=log)
dists = c.redshift_to_distance(
z=0.343,
WM=0.286,
WV=0.714,
H0=69.6
)
print "Distance Modulus: " + str(dists["dmod"]) + " mag"
print "Luminousity Distance: " + str(dists["dl_mpc"]) + " Mpc"
print "Angular Size Scale: " + str(dists["da_scale"]) + " kpc/arcsec"
print "Angular Size Distance: " + str(dists["da_mpc"]) + " Mpc"
print "Comoving Radial Distance: " + str(dists["dcmr_mpc"]) + " Mpc"
# OUTPUT :
# Distance Modulus: 41.29 mag
# Luminousity Distance: 1811.71 Mpc
# Angular Size Scale: 4.89 kpc/arcsec
# Angular Size Distance: 1008.97 Mpc
# Comoving Radial Distance: 1352.03 Mpc
"""
self.log.info('starting the ``redshift_to_distance`` method')
# VARIABLE
h = H0 / 100.0
WR = 4.165E-5 / (h * h) # Omega_radiation
WK = 1.0 - WM - WV - WR # Omega_curvature = 1 - Omega(Total)
c = 299792.458 # speed of light (km/s)
# Arbitrarily set the values of these variables to zero just so we can
# define them.
DCMR = 0.0 # comoving radial distance in units of c/H0
DCMR_Mpc = 0.0 # comoving radial distance in units of Mpc
DA = 0.0 # angular size distance in units of c/H0
DA_Mpc = 0.0 # angular size distance in units of Mpc
# scale at angular size distance in units of Kpc / arcsec
DA_scale = 0.0
DL = 0.0 # luminosity distance in units of c/H0
DL_Mpc = 0.0 # luminosity distance in units of Mpc
# Distance modulus determined from luminosity distance
DMOD = 0.0
a = 0.0 # 1/(1+z), the scale factor of the Universe
az = 1.0 / (1.0 + z) # 1/(1+z), for the given redshift
# Compute the integral over a=1/(1+z) from az to 1 in n steps
n = 1000
for i in range(n):
a = az + (1.0 - az) * (i + 0.5) / n
adot = math.sqrt(WK + (WM / a) + (WR / (math.pow(a, 2)))
+ (WV * math.pow(a, 2)))
DCMR = DCMR + 1.0 / (a * adot)
# comoving radial distance in units of c/H0
DCMR = (1.0 - az) * DCMR / n
# comoving radial distance in units of Mpc
DCMR_Mpc = (c / H0) * DCMR
# Tangental comoving radial distance
x = math.sqrt(abs(WK)) * DCMR
if x > 0.1:
if WK > 0.0:
ratio = 0.5 * (math.exp(x) - math.exp(-x)) / x
else:
ratio = math.sin(x) / x
else:
y = math.pow(x, 2)
if WK < 0.0:
y = -y
ratio = 1 + y / 6.0 + math.pow(y, 2) / 120.0
DA = az * ratio * DCMR # angular size distance in units of c/H0
DA_Mpc = (c / H0) * DA # angular size distance in units of Mpc
# scale at angular size distance in units of Kpc / arcsec
DA_scale = DA_Mpc / 206.264806
DL = DA / math.pow(az, 2) # luminosity distance in units of c/H0
DL_Mpc = (c / H0) * DL # luminosity distance in units of Mpc
# Distance modulus determined from luminosity distance
DMOD = 5 * math.log10(DL_Mpc * 1e6) - 5
# FIXING PRECISIONS
# PRECISION TEST
precision = len(repr(z).split(".")[-1])
DCMR_Mpc = "%0.*f" % (precision, DCMR_Mpc)
DA_Mpc = "%0.*f" % (precision, DA_Mpc)
DA_scale = "%0.*f" % (precision, DA_scale)
DL_Mpc = "%0.*f" % (precision, DL_Mpc)
DMOD = "%0.*f" % (precision, DMOD)
z = "%0.*f" % (precision, z)
results = \
{
"dcmr_mpc": float(DCMR_Mpc),
"da_mpc": float(DA_Mpc),
"da_scale": float(DA_scale),
"dl_mpc": float(DL_Mpc),
"dmod": float(DMOD),
"z": float(z)
}
self.log.info('completed the ``redshift_to_distance`` method')
return results | python | def redshift_to_distance(
self,
z,
WM=0.3,
WV=0.7,
H0=70.0):
"""*convert redshift to various distance measurements*
**Key Arguments:**
- ``z`` -- redshift measurement.
- ``WM`` -- Omega_matter. Default *0.3*
- ``WV`` -- Omega_vacuum. Default *0.7*
- ``H0`` -- Hubble constant. (km s-1 Mpc-1) Default *70.0*
**Return:**
- ``results`` -- result dictionary including
- ``dcmr_mpc`` -- co-moving radius distance
- ``da_mpc`` -- angular distance
- ``da_scale`` -- angular distance scale
- ``dl_mpc`` -- luminosity distance (usually use this one)
- ``dmod`` -- distance modulus (determined from luminosity distance)
.. todo::
- replace convert_redshift_to_distance in all other code
**Usage:**
.. code-block:: python
from astrocalc.distances import converter
c = converter(log=log)
dists = c.redshift_to_distance(
z=0.343
)
print "Distance Modulus: " + str(dists["dmod"]) + " mag"
print "Luminousity Distance: " + str(dists["dl_mpc"]) + " Mpc"
print "Angular Size Scale: " + str(dists["da_scale"]) + " kpc/arcsec"
print "Angular Size Distance: " + str(dists["da_mpc"]) + " Mpc"
print "Comoving Radial Distance: " + str(dists["dcmr_mpc"]) + " Mpc"
# OUTPUT :
# Distance Modulus: 41.27 mag
# Luminousity Distance: 1795.16 Mpc
# Angular Size Scale: 4.85 kpc/arcsec
# Angular Size Distance: 999.76 Mpc
# Comoving Radial Distance: 1339.68 Mpc
from astrocalc.distances import converter
c = converter(log=log)
dists = c.redshift_to_distance(
z=0.343,
WM=0.286,
WV=0.714,
H0=69.6
)
print "Distance Modulus: " + str(dists["dmod"]) + " mag"
print "Luminousity Distance: " + str(dists["dl_mpc"]) + " Mpc"
print "Angular Size Scale: " + str(dists["da_scale"]) + " kpc/arcsec"
print "Angular Size Distance: " + str(dists["da_mpc"]) + " Mpc"
print "Comoving Radial Distance: " + str(dists["dcmr_mpc"]) + " Mpc"
# OUTPUT :
# Distance Modulus: 41.29 mag
# Luminousity Distance: 1811.71 Mpc
# Angular Size Scale: 4.89 kpc/arcsec
# Angular Size Distance: 1008.97 Mpc
# Comoving Radial Distance: 1352.03 Mpc
"""
self.log.info('starting the ``redshift_to_distance`` method')
# VARIABLE
h = H0 / 100.0
WR = 4.165E-5 / (h * h) # Omega_radiation
WK = 1.0 - WM - WV - WR # Omega_curvature = 1 - Omega(Total)
c = 299792.458 # speed of light (km/s)
# Arbitrarily set the values of these variables to zero just so we can
# define them.
DCMR = 0.0 # comoving radial distance in units of c/H0
DCMR_Mpc = 0.0 # comoving radial distance in units of Mpc
DA = 0.0 # angular size distance in units of c/H0
DA_Mpc = 0.0 # angular size distance in units of Mpc
# scale at angular size distance in units of Kpc / arcsec
DA_scale = 0.0
DL = 0.0 # luminosity distance in units of c/H0
DL_Mpc = 0.0 # luminosity distance in units of Mpc
# Distance modulus determined from luminosity distance
DMOD = 0.0
a = 0.0 # 1/(1+z), the scale factor of the Universe
az = 1.0 / (1.0 + z) # 1/(1+z), for the given redshift
# Compute the integral over a=1/(1+z) from az to 1 in n steps
n = 1000
for i in range(n):
a = az + (1.0 - az) * (i + 0.5) / n
adot = math.sqrt(WK + (WM / a) + (WR / (math.pow(a, 2)))
+ (WV * math.pow(a, 2)))
DCMR = DCMR + 1.0 / (a * adot)
# comoving radial distance in units of c/H0
DCMR = (1.0 - az) * DCMR / n
# comoving radial distance in units of Mpc
DCMR_Mpc = (c / H0) * DCMR
# Tangental comoving radial distance
x = math.sqrt(abs(WK)) * DCMR
if x > 0.1:
if WK > 0.0:
ratio = 0.5 * (math.exp(x) - math.exp(-x)) / x
else:
ratio = math.sin(x) / x
else:
y = math.pow(x, 2)
if WK < 0.0:
y = -y
ratio = 1 + y / 6.0 + math.pow(y, 2) / 120.0
DA = az * ratio * DCMR # angular size distance in units of c/H0
DA_Mpc = (c / H0) * DA # angular size distance in units of Mpc
# scale at angular size distance in units of Kpc / arcsec
DA_scale = DA_Mpc / 206.264806
DL = DA / math.pow(az, 2) # luminosity distance in units of c/H0
DL_Mpc = (c / H0) * DL # luminosity distance in units of Mpc
# Distance modulus determined from luminosity distance
DMOD = 5 * math.log10(DL_Mpc * 1e6) - 5
# FIXING PRECISIONS
# PRECISION TEST
precision = len(repr(z).split(".")[-1])
DCMR_Mpc = "%0.*f" % (precision, DCMR_Mpc)
DA_Mpc = "%0.*f" % (precision, DA_Mpc)
DA_scale = "%0.*f" % (precision, DA_scale)
DL_Mpc = "%0.*f" % (precision, DL_Mpc)
DMOD = "%0.*f" % (precision, DMOD)
z = "%0.*f" % (precision, z)
results = \
{
"dcmr_mpc": float(DCMR_Mpc),
"da_mpc": float(DA_Mpc),
"da_scale": float(DA_scale),
"dl_mpc": float(DL_Mpc),
"dmod": float(DMOD),
"z": float(z)
}
self.log.info('completed the ``redshift_to_distance`` method')
return results | [
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] | *convert redshift to various distance measurements*
**Key Arguments:**
- ``z`` -- redshift measurement.
- ``WM`` -- Omega_matter. Default *0.3*
- ``WV`` -- Omega_vacuum. Default *0.7*
- ``H0`` -- Hubble constant. (km s-1 Mpc-1) Default *70.0*
**Return:**
- ``results`` -- result dictionary including
- ``dcmr_mpc`` -- co-moving radius distance
- ``da_mpc`` -- angular distance
- ``da_scale`` -- angular distance scale
- ``dl_mpc`` -- luminosity distance (usually use this one)
- ``dmod`` -- distance modulus (determined from luminosity distance)
.. todo::
- replace convert_redshift_to_distance in all other code
**Usage:**
.. code-block:: python
from astrocalc.distances import converter
c = converter(log=log)
dists = c.redshift_to_distance(
z=0.343
)
print "Distance Modulus: " + str(dists["dmod"]) + " mag"
print "Luminousity Distance: " + str(dists["dl_mpc"]) + " Mpc"
print "Angular Size Scale: " + str(dists["da_scale"]) + " kpc/arcsec"
print "Angular Size Distance: " + str(dists["da_mpc"]) + " Mpc"
print "Comoving Radial Distance: " + str(dists["dcmr_mpc"]) + " Mpc"
# OUTPUT :
# Distance Modulus: 41.27 mag
# Luminousity Distance: 1795.16 Mpc
# Angular Size Scale: 4.85 kpc/arcsec
# Angular Size Distance: 999.76 Mpc
# Comoving Radial Distance: 1339.68 Mpc
from astrocalc.distances import converter
c = converter(log=log)
dists = c.redshift_to_distance(
z=0.343,
WM=0.286,
WV=0.714,
H0=69.6
)
print "Distance Modulus: " + str(dists["dmod"]) + " mag"
print "Luminousity Distance: " + str(dists["dl_mpc"]) + " Mpc"
print "Angular Size Scale: " + str(dists["da_scale"]) + " kpc/arcsec"
print "Angular Size Distance: " + str(dists["da_mpc"]) + " Mpc"
print "Comoving Radial Distance: " + str(dists["dcmr_mpc"]) + " Mpc"
# OUTPUT :
# Distance Modulus: 41.29 mag
# Luminousity Distance: 1811.71 Mpc
# Angular Size Scale: 4.89 kpc/arcsec
# Angular Size Distance: 1008.97 Mpc
# Comoving Radial Distance: 1352.03 Mpc | [
"*",
"convert",
"redshift",
"to",
"various",
"distance",
"measurements",
"*"
] | train | https://github.com/thespacedoctor/astrocalc/blob/dfbebf9b86d7b2d2110c48a6a4f4194bf8885b86/astrocalc/distances/converter.py#L113-L265 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
*convert redshift to various distance measurements*
**Key Arguments:**
- ``z`` -- redshift measurement.
- ``WM`` -- Omega_matter. Default *0.3*
- ``WV`` -- Omega_vacuum. Default *0.7*
- ``H0`` -- Hubble constant. (km s-1 Mpc-1) Default *70.0*
**Return:**
- ``results`` -- result dictionary including
- ``dcmr_mpc`` -- co-moving radius distance
- ``da_mpc`` -- angular distance
- ``da_scale`` -- angular distance scale
- ``dl_mpc`` -- luminosity distance (usually use this one)
- ``dmod`` -- distance modulus (determined from luminosity distance)
.. todo::
- replace convert_redshift_to_distance in all other code
**Usage:**
.. code-block:: python
from astrocalc.distances import converter
c = converter(log=log)
dists = c.redshift_to_distance(
z=0.343
)
print "Distance Modulus: " + str(dists["dmod"]) + " mag"
print "Luminousity Distance: " + str(dists["dl_mpc"]) + " Mpc"
print "Angular Size Scale: " + str(dists["da_scale"]) + " kpc/arcsec"
print "Angular Size Distance: " + str(dists["da_mpc"]) + " Mpc"
print "Comoving Radial Distance: " + str(dists["dcmr_mpc"]) + " Mpc"
# OUTPUT :
# Distance Modulus: 41.27 mag
# Luminousity Distance: 1795.16 Mpc
# Angular Size Scale: 4.85 kpc/arcsec
# Angular Size Distance: 999.76 Mpc
# Comoving Radial Distance: 1339.68 Mpc
from astrocalc.distances import converter
c = converter(log=log)
dists = c.redshift_to_distance(
z=0.343,
WM=0.286,
WV=0.714,
H0=69.6
)
print "Distance Modulus: " + str(dists["dmod"]) + " mag"
print "Luminousity Distance: " + str(dists["dl_mpc"]) + " Mpc"
print "Angular Size Scale: " + str(dists["da_scale"]) + " kpc/arcsec"
print "Angular Size Distance: " + str(dists["da_mpc"]) + " Mpc"
print "Comoving Radial Distance: " + str(dists["dcmr_mpc"]) + " Mpc"
# OUTPUT :
# Distance Modulus: 41.29 mag
# Luminousity Distance: 1811.71 Mpc
# Angular Size Scale: 4.89 kpc/arcsec
# Angular Size Distance: 1008.97 Mpc
# Comoving Radial Distance: 1352.03 Mpc
### Response:
def redshift_to_distance(
self,
z,
WM=0.3,
WV=0.7,
H0=70.0):
"""*convert redshift to various distance measurements*
**Key Arguments:**
- ``z`` -- redshift measurement.
- ``WM`` -- Omega_matter. Default *0.3*
- ``WV`` -- Omega_vacuum. Default *0.7*
- ``H0`` -- Hubble constant. (km s-1 Mpc-1) Default *70.0*
**Return:**
- ``results`` -- result dictionary including
- ``dcmr_mpc`` -- co-moving radius distance
- ``da_mpc`` -- angular distance
- ``da_scale`` -- angular distance scale
- ``dl_mpc`` -- luminosity distance (usually use this one)
- ``dmod`` -- distance modulus (determined from luminosity distance)
.. todo::
- replace convert_redshift_to_distance in all other code
**Usage:**
.. code-block:: python
from astrocalc.distances import converter
c = converter(log=log)
dists = c.redshift_to_distance(
z=0.343
)
print "Distance Modulus: " + str(dists["dmod"]) + " mag"
print "Luminousity Distance: " + str(dists["dl_mpc"]) + " Mpc"
print "Angular Size Scale: " + str(dists["da_scale"]) + " kpc/arcsec"
print "Angular Size Distance: " + str(dists["da_mpc"]) + " Mpc"
print "Comoving Radial Distance: " + str(dists["dcmr_mpc"]) + " Mpc"
# OUTPUT :
# Distance Modulus: 41.27 mag
# Luminousity Distance: 1795.16 Mpc
# Angular Size Scale: 4.85 kpc/arcsec
# Angular Size Distance: 999.76 Mpc
# Comoving Radial Distance: 1339.68 Mpc
from astrocalc.distances import converter
c = converter(log=log)
dists = c.redshift_to_distance(
z=0.343,
WM=0.286,
WV=0.714,
H0=69.6
)
print "Distance Modulus: " + str(dists["dmod"]) + " mag"
print "Luminousity Distance: " + str(dists["dl_mpc"]) + " Mpc"
print "Angular Size Scale: " + str(dists["da_scale"]) + " kpc/arcsec"
print "Angular Size Distance: " + str(dists["da_mpc"]) + " Mpc"
print "Comoving Radial Distance: " + str(dists["dcmr_mpc"]) + " Mpc"
# OUTPUT :
# Distance Modulus: 41.29 mag
# Luminousity Distance: 1811.71 Mpc
# Angular Size Scale: 4.89 kpc/arcsec
# Angular Size Distance: 1008.97 Mpc
# Comoving Radial Distance: 1352.03 Mpc
"""
self.log.info('starting the ``redshift_to_distance`` method')
# VARIABLE
h = H0 / 100.0
WR = 4.165E-5 / (h * h) # Omega_radiation
WK = 1.0 - WM - WV - WR # Omega_curvature = 1 - Omega(Total)
c = 299792.458 # speed of light (km/s)
# Arbitrarily set the values of these variables to zero just so we can
# define them.
DCMR = 0.0 # comoving radial distance in units of c/H0
DCMR_Mpc = 0.0 # comoving radial distance in units of Mpc
DA = 0.0 # angular size distance in units of c/H0
DA_Mpc = 0.0 # angular size distance in units of Mpc
# scale at angular size distance in units of Kpc / arcsec
DA_scale = 0.0
DL = 0.0 # luminosity distance in units of c/H0
DL_Mpc = 0.0 # luminosity distance in units of Mpc
# Distance modulus determined from luminosity distance
DMOD = 0.0
a = 0.0 # 1/(1+z), the scale factor of the Universe
az = 1.0 / (1.0 + z) # 1/(1+z), for the given redshift
# Compute the integral over a=1/(1+z) from az to 1 in n steps
n = 1000
for i in range(n):
a = az + (1.0 - az) * (i + 0.5) / n
adot = math.sqrt(WK + (WM / a) + (WR / (math.pow(a, 2)))
+ (WV * math.pow(a, 2)))
DCMR = DCMR + 1.0 / (a * adot)
# comoving radial distance in units of c/H0
DCMR = (1.0 - az) * DCMR / n
# comoving radial distance in units of Mpc
DCMR_Mpc = (c / H0) * DCMR
# Tangental comoving radial distance
x = math.sqrt(abs(WK)) * DCMR
if x > 0.1:
if WK > 0.0:
ratio = 0.5 * (math.exp(x) - math.exp(-x)) / x
else:
ratio = math.sin(x) / x
else:
y = math.pow(x, 2)
if WK < 0.0:
y = -y
ratio = 1 + y / 6.0 + math.pow(y, 2) / 120.0
DA = az * ratio * DCMR # angular size distance in units of c/H0
DA_Mpc = (c / H0) * DA # angular size distance in units of Mpc
# scale at angular size distance in units of Kpc / arcsec
DA_scale = DA_Mpc / 206.264806
DL = DA / math.pow(az, 2) # luminosity distance in units of c/H0
DL_Mpc = (c / H0) * DL # luminosity distance in units of Mpc
# Distance modulus determined from luminosity distance
DMOD = 5 * math.log10(DL_Mpc * 1e6) - 5
# FIXING PRECISIONS
# PRECISION TEST
precision = len(repr(z).split(".")[-1])
DCMR_Mpc = "%0.*f" % (precision, DCMR_Mpc)
DA_Mpc = "%0.*f" % (precision, DA_Mpc)
DA_scale = "%0.*f" % (precision, DA_scale)
DL_Mpc = "%0.*f" % (precision, DL_Mpc)
DMOD = "%0.*f" % (precision, DMOD)
z = "%0.*f" % (precision, z)
results = \
{
"dcmr_mpc": float(DCMR_Mpc),
"da_mpc": float(DA_Mpc),
"da_scale": float(DA_scale),
"dl_mpc": float(DL_Mpc),
"dmod": float(DMOD),
"z": float(z)
}
self.log.info('completed the ``redshift_to_distance`` method')
return results
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sbg/sevenbridges-python | sevenbridges/http/client.py | config_vars | def config_vars(profiles, advance_access):
"""
Utility method to fetch config vars using ini section profile
:param profiles: profile name.
:param advance_access: advance_access flag.
:return:
"""
for profile in profiles:
try:
config = Config(profile, advance_access=advance_access)
url = config.api_endpoint
token = config.auth_token
proxies = config.proxies
aa = config.advance_access
return url, token, proxies, aa
except Exception:
pass
return None, None, None, None | python | def config_vars(profiles, advance_access):
"""
Utility method to fetch config vars using ini section profile
:param profiles: profile name.
:param advance_access: advance_access flag.
:return:
"""
for profile in profiles:
try:
config = Config(profile, advance_access=advance_access)
url = config.api_endpoint
token = config.auth_token
proxies = config.proxies
aa = config.advance_access
return url, token, proxies, aa
except Exception:
pass
return None, None, None, None | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Utility method to fetch config vars using ini section profile
:param profiles: profile name.
:param advance_access: advance_access flag.
:return:
### Response:
def config_vars(profiles, advance_access):
"""
Utility method to fetch config vars using ini section profile
:param profiles: profile name.
:param advance_access: advance_access flag.
:return:
"""
for profile in profiles:
try:
config = Config(profile, advance_access=advance_access)
url = config.api_endpoint
token = config.auth_token
proxies = config.proxies
aa = config.advance_access
return url, token, proxies, aa
except Exception:
pass
return None, None, None, None
<|endoftext|> | [
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twilio/twilio-python | twilio/rest/video/v1/composition/__init__.py | CompositionList.page | def page(self, status=values.unset, date_created_after=values.unset,
date_created_before=values.unset, room_sid=values.unset,
page_token=values.unset, page_number=values.unset,
page_size=values.unset):
"""
Retrieve a single page of CompositionInstance records from the API.
Request is executed immediately
:param CompositionInstance.Status status: Only show Compositions with the given status.
:param datetime date_created_after: Only show Compositions created on or after this ISO8601 date-time with timezone.
:param datetime date_created_before: Only show Compositions created before this ISO8601 date-time with timezone.
:param unicode room_sid: Only show Compositions with the given Room SID.
:param str page_token: PageToken provided by the API
:param int page_number: Page Number, this value is simply for client state
:param int page_size: Number of records to return, defaults to 50
:returns: Page of CompositionInstance
:rtype: twilio.rest.video.v1.composition.CompositionPage
"""
params = values.of({
'Status': status,
'DateCreatedAfter': serialize.iso8601_datetime(date_created_after),
'DateCreatedBefore': serialize.iso8601_datetime(date_created_before),
'RoomSid': room_sid,
'PageToken': page_token,
'Page': page_number,
'PageSize': page_size,
})
response = self._version.page(
'GET',
self._uri,
params=params,
)
return CompositionPage(self._version, response, self._solution) | python | def page(self, status=values.unset, date_created_after=values.unset,
date_created_before=values.unset, room_sid=values.unset,
page_token=values.unset, page_number=values.unset,
page_size=values.unset):
"""
Retrieve a single page of CompositionInstance records from the API.
Request is executed immediately
:param CompositionInstance.Status status: Only show Compositions with the given status.
:param datetime date_created_after: Only show Compositions created on or after this ISO8601 date-time with timezone.
:param datetime date_created_before: Only show Compositions created before this ISO8601 date-time with timezone.
:param unicode room_sid: Only show Compositions with the given Room SID.
:param str page_token: PageToken provided by the API
:param int page_number: Page Number, this value is simply for client state
:param int page_size: Number of records to return, defaults to 50
:returns: Page of CompositionInstance
:rtype: twilio.rest.video.v1.composition.CompositionPage
"""
params = values.of({
'Status': status,
'DateCreatedAfter': serialize.iso8601_datetime(date_created_after),
'DateCreatedBefore': serialize.iso8601_datetime(date_created_before),
'RoomSid': room_sid,
'PageToken': page_token,
'Page': page_number,
'PageSize': page_size,
})
response = self._version.page(
'GET',
self._uri,
params=params,
)
return CompositionPage(self._version, response, self._solution) | [
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:param CompositionInstance.Status status: Only show Compositions with the given status.
:param datetime date_created_after: Only show Compositions created on or after this ISO8601 date-time with timezone.
:param datetime date_created_before: Only show Compositions created before this ISO8601 date-time with timezone.
:param unicode room_sid: Only show Compositions with the given Room SID.
:param str page_token: PageToken provided by the API
:param int page_number: Page Number, this value is simply for client state
:param int page_size: Number of records to return, defaults to 50
:returns: Page of CompositionInstance
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Retrieve a single page of CompositionInstance records from the API.
Request is executed immediately
:param CompositionInstance.Status status: Only show Compositions with the given status.
:param datetime date_created_after: Only show Compositions created on or after this ISO8601 date-time with timezone.
:param datetime date_created_before: Only show Compositions created before this ISO8601 date-time with timezone.
:param unicode room_sid: Only show Compositions with the given Room SID.
:param str page_token: PageToken provided by the API
:param int page_number: Page Number, this value is simply for client state
:param int page_size: Number of records to return, defaults to 50
:returns: Page of CompositionInstance
:rtype: twilio.rest.video.v1.composition.CompositionPage
### Response:
def page(self, status=values.unset, date_created_after=values.unset,
date_created_before=values.unset, room_sid=values.unset,
page_token=values.unset, page_number=values.unset,
page_size=values.unset):
"""
Retrieve a single page of CompositionInstance records from the API.
Request is executed immediately
:param CompositionInstance.Status status: Only show Compositions with the given status.
:param datetime date_created_after: Only show Compositions created on or after this ISO8601 date-time with timezone.
:param datetime date_created_before: Only show Compositions created before this ISO8601 date-time with timezone.
:param unicode room_sid: Only show Compositions with the given Room SID.
:param str page_token: PageToken provided by the API
:param int page_number: Page Number, this value is simply for client state
:param int page_size: Number of records to return, defaults to 50
:returns: Page of CompositionInstance
:rtype: twilio.rest.video.v1.composition.CompositionPage
"""
params = values.of({
'Status': status,
'DateCreatedAfter': serialize.iso8601_datetime(date_created_after),
'DateCreatedBefore': serialize.iso8601_datetime(date_created_before),
'RoomSid': room_sid,
'PageToken': page_token,
'Page': page_number,
'PageSize': page_size,
})
response = self._version.page(
'GET',
self._uri,
params=params,
)
return CompositionPage(self._version, response, self._solution)
<|endoftext|> | [
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nerdvegas/rez | src/rez/vendor/sortedcontainers/sortedset.py | SortedSet.copy | def copy(self):
"""Create a shallow copy of the sorted set."""
return self._fromset(set(self._set), key=self._key) | python | def copy(self):
"""Create a shallow copy of the sorted set."""
return self._fromset(set(self._set), key=self._key) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Create a shallow copy of the sorted set.
### Response:
def copy(self):
"""Create a shallow copy of the sorted set."""
return self._fromset(set(self._set), key=self._key)
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SeleniumHQ/selenium | py/selenium/webdriver/remote/webdriver.py | WebDriver.find_element | def find_element(self, by=By.ID, value=None):
"""
Find an element given a By strategy and locator. Prefer the find_element_by_* methods when
possible.
:Usage:
::
element = driver.find_element(By.ID, 'foo')
:rtype: WebElement
"""
if self.w3c:
if by == By.ID:
by = By.CSS_SELECTOR
value = '[id="%s"]' % value
elif by == By.TAG_NAME:
by = By.CSS_SELECTOR
elif by == By.CLASS_NAME:
by = By.CSS_SELECTOR
value = ".%s" % value
elif by == By.NAME:
by = By.CSS_SELECTOR
value = '[name="%s"]' % value
return self.execute(Command.FIND_ELEMENT, {
'using': by,
'value': value})['value'] | python | def find_element(self, by=By.ID, value=None):
"""
Find an element given a By strategy and locator. Prefer the find_element_by_* methods when
possible.
:Usage:
::
element = driver.find_element(By.ID, 'foo')
:rtype: WebElement
"""
if self.w3c:
if by == By.ID:
by = By.CSS_SELECTOR
value = '[id="%s"]' % value
elif by == By.TAG_NAME:
by = By.CSS_SELECTOR
elif by == By.CLASS_NAME:
by = By.CSS_SELECTOR
value = ".%s" % value
elif by == By.NAME:
by = By.CSS_SELECTOR
value = '[name="%s"]' % value
return self.execute(Command.FIND_ELEMENT, {
'using': by,
'value': value})['value'] | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Find an element given a By strategy and locator. Prefer the find_element_by_* methods when
possible.
:Usage:
::
element = driver.find_element(By.ID, 'foo')
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def find_element(self, by=By.ID, value=None):
"""
Find an element given a By strategy and locator. Prefer the find_element_by_* methods when
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:Usage:
::
element = driver.find_element(By.ID, 'foo')
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by = By.CSS_SELECTOR
value = ".%s" % value
elif by == By.NAME:
by = By.CSS_SELECTOR
value = '[name="%s"]' % value
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sanoma/django-arctic | arctic/templatetags/arctic_pagination_tags.py | arctic_paginate | def arctic_paginate(parser, token):
"""
Renders a Page object with pagination bar.
Example::
{% arctic_paginate page_obj paginator=page_obj.paginator range=10 %}
Named Parameters::
range - The size of the pagination bar (ie, if set to 10 then, at most,
10 page numbers will display at any given time) Defaults to
None, which shows all pages.
show_first_last - Accepts "true" or "false". Determines whether or not
to show the first and last page links. Defaults to
"false"
"""
bits = token.split_contents()
if len(bits) < 2:
raise TemplateSyntaxError(
"'%s' takes at least one argument"
" (Page object reference)" % bits[0]
)
page = parser.compile_filter(bits[1])
kwargs = {}
bits = bits[2:]
kwarg_re = re.compile(r"(\w+)=(.+)")
if len(bits):
for bit in bits:
match = kwarg_re.match(bit)
if not match:
raise TemplateSyntaxError(
"Malformed arguments to bootstrap_pagination paginate tag"
)
name, value = match.groups()
kwargs[name] = parser.compile_filter(value)
return PaginationNode(page, kwargs) | python | def arctic_paginate(parser, token):
"""
Renders a Page object with pagination bar.
Example::
{% arctic_paginate page_obj paginator=page_obj.paginator range=10 %}
Named Parameters::
range - The size of the pagination bar (ie, if set to 10 then, at most,
10 page numbers will display at any given time) Defaults to
None, which shows all pages.
show_first_last - Accepts "true" or "false". Determines whether or not
to show the first and last page links. Defaults to
"false"
"""
bits = token.split_contents()
if len(bits) < 2:
raise TemplateSyntaxError(
"'%s' takes at least one argument"
" (Page object reference)" % bits[0]
)
page = parser.compile_filter(bits[1])
kwargs = {}
bits = bits[2:]
kwarg_re = re.compile(r"(\w+)=(.+)")
if len(bits):
for bit in bits:
match = kwarg_re.match(bit)
if not match:
raise TemplateSyntaxError(
"Malformed arguments to bootstrap_pagination paginate tag"
)
name, value = match.groups()
kwargs[name] = parser.compile_filter(value)
return PaginationNode(page, kwargs) | [
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10 page numbers will display at any given time) Defaults to
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show_first_last - Accepts "true" or "false". Determines whether or not
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Renders a Page object with pagination bar.
Example::
{% arctic_paginate page_obj paginator=page_obj.paginator range=10 %}
Named Parameters::
range - The size of the pagination bar (ie, if set to 10 then, at most,
10 page numbers will display at any given time) Defaults to
None, which shows all pages.
show_first_last - Accepts "true" or "false". Determines whether or not
to show the first and last page links. Defaults to
"false"
### Response:
def arctic_paginate(parser, token):
"""
Renders a Page object with pagination bar.
Example::
{% arctic_paginate page_obj paginator=page_obj.paginator range=10 %}
Named Parameters::
range - The size of the pagination bar (ie, if set to 10 then, at most,
10 page numbers will display at any given time) Defaults to
None, which shows all pages.
show_first_last - Accepts "true" or "false". Determines whether or not
to show the first and last page links. Defaults to
"false"
"""
bits = token.split_contents()
if len(bits) < 2:
raise TemplateSyntaxError(
"'%s' takes at least one argument"
" (Page object reference)" % bits[0]
)
page = parser.compile_filter(bits[1])
kwargs = {}
bits = bits[2:]
kwarg_re = re.compile(r"(\w+)=(.+)")
if len(bits):
for bit in bits:
match = kwarg_re.match(bit)
if not match:
raise TemplateSyntaxError(
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)
name, value = match.groups()
kwargs[name] = parser.compile_filter(value)
return PaginationNode(page, kwargs)
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SpamScope/mail-parser | mailparser/mailparser.py | MailParser.from_string | def from_string(cls, s):
"""
Init a new object from a string.
Args:
s (string): raw email
Returns:
Instance of MailParser
"""
log.debug("Parsing email from string")
message = email.message_from_string(s)
return cls(message) | python | def from_string(cls, s):
"""
Init a new object from a string.
Args:
s (string): raw email
Returns:
Instance of MailParser
"""
log.debug("Parsing email from string")
message = email.message_from_string(s)
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Init a new object from a string.
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s (string): raw email
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Metatab/metapack | metapack/package/core.py | PackageBuilder.create_nv_link | def create_nv_link(self):
"""After a save(), write a link to the saved file using a non-versioned name"""
from os.path import abspath, islink
from os import unlink, symlink
nv_name = self.doc.as_version(None)
from_path = abspath(self._last_write_path or self.package_path.path)
to_path = join(dirname(from_path), nv_name + self.type_suffix)
if islink(to_path):
unlink(to_path)
symlink(from_path, to_path) | python | def create_nv_link(self):
"""After a save(), write a link to the saved file using a non-versioned name"""
from os.path import abspath, islink
from os import unlink, symlink
nv_name = self.doc.as_version(None)
from_path = abspath(self._last_write_path or self.package_path.path)
to_path = join(dirname(from_path), nv_name + self.type_suffix)
if islink(to_path):
unlink(to_path)
symlink(from_path, to_path) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
After a save(), write a link to the saved file using a non-versioned name
### Response:
def create_nv_link(self):
"""After a save(), write a link to the saved file using a non-versioned name"""
from os.path import abspath, islink
from os import unlink, symlink
nv_name = self.doc.as_version(None)
from_path = abspath(self._last_write_path or self.package_path.path)
to_path = join(dirname(from_path), nv_name + self.type_suffix)
if islink(to_path):
unlink(to_path)
symlink(from_path, to_path)
<|endoftext|> | [
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"""
Intends to get the bound field from the form regarding the field name
:param form: Django Form: django form instance
:param field_name: str: name of the field in form instance
:return: Django Form bound field
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field = form.fields[field_name]
field = field.get_bound_field(form, field_name)
return field | python | def get_form_bound_field(form, field_name):
"""
Intends to get the bound field from the form regarding the field name
:param form: Django Form: django form instance
:param field_name: str: name of the field in form instance
:return: Django Form bound field
"""
field = form.fields[field_name]
field = field.get_bound_field(form, field_name)
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You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
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dnanexus/dx-toolkit | src/python/dxpy/bindings/dxjob.py | DXJob.wait_on_done | def wait_on_done(self, interval=2, timeout=3600*24*7, **kwargs):
'''
:param interval: Number of seconds between queries to the job's state
:type interval: integer
:param timeout: Maximum amount of time to wait, in seconds, until the job is done running
:type timeout: integer
:raises: :exc:`~dxpy.exceptions.DXError` if the timeout is reached before the job has finished running, or :exc:`dxpy.exceptions.DXJobFailureError` if the job fails
Waits until the job has finished running.
'''
elapsed = 0
while True:
state = self._get_state(**kwargs)
if state == "done":
break
if state == "failed":
desc = self.describe(**kwargs)
err_msg = "Job has failed because of {failureReason}: {failureMessage}".format(**desc)
if desc.get("failureFrom") != None and desc["failureFrom"]["id"] != desc["id"]:
err_msg += " (failure from {id})".format(id=desc['failureFrom']['id'])
raise DXJobFailureError(err_msg)
if state == "terminated":
raise DXJobFailureError("Job was terminated.")
if elapsed >= timeout or elapsed < 0:
raise DXJobFailureError("Reached timeout while waiting for the job to finish")
time.sleep(interval)
elapsed += interval | python | def wait_on_done(self, interval=2, timeout=3600*24*7, **kwargs):
'''
:param interval: Number of seconds between queries to the job's state
:type interval: integer
:param timeout: Maximum amount of time to wait, in seconds, until the job is done running
:type timeout: integer
:raises: :exc:`~dxpy.exceptions.DXError` if the timeout is reached before the job has finished running, or :exc:`dxpy.exceptions.DXJobFailureError` if the job fails
Waits until the job has finished running.
'''
elapsed = 0
while True:
state = self._get_state(**kwargs)
if state == "done":
break
if state == "failed":
desc = self.describe(**kwargs)
err_msg = "Job has failed because of {failureReason}: {failureMessage}".format(**desc)
if desc.get("failureFrom") != None and desc["failureFrom"]["id"] != desc["id"]:
err_msg += " (failure from {id})".format(id=desc['failureFrom']['id'])
raise DXJobFailureError(err_msg)
if state == "terminated":
raise DXJobFailureError("Job was terminated.")
if elapsed >= timeout or elapsed < 0:
raise DXJobFailureError("Reached timeout while waiting for the job to finish")
time.sleep(interval)
elapsed += interval | [
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:type interval: integer
:param timeout: Maximum amount of time to wait, in seconds, until the job is done running
:type timeout: integer
:raises: :exc:`~dxpy.exceptions.DXError` if the timeout is reached before the job has finished running, or :exc:`dxpy.exceptions.DXJobFailureError` if the job fails
Waits until the job has finished running. | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
:param interval: Number of seconds between queries to the job's state
:type interval: integer
:param timeout: Maximum amount of time to wait, in seconds, until the job is done running
:type timeout: integer
:raises: :exc:`~dxpy.exceptions.DXError` if the timeout is reached before the job has finished running, or :exc:`dxpy.exceptions.DXJobFailureError` if the job fails
Waits until the job has finished running.
### Response:
def wait_on_done(self, interval=2, timeout=3600*24*7, **kwargs):
'''
:param interval: Number of seconds between queries to the job's state
:type interval: integer
:param timeout: Maximum amount of time to wait, in seconds, until the job is done running
:type timeout: integer
:raises: :exc:`~dxpy.exceptions.DXError` if the timeout is reached before the job has finished running, or :exc:`dxpy.exceptions.DXJobFailureError` if the job fails
Waits until the job has finished running.
'''
elapsed = 0
while True:
state = self._get_state(**kwargs)
if state == "done":
break
if state == "failed":
desc = self.describe(**kwargs)
err_msg = "Job has failed because of {failureReason}: {failureMessage}".format(**desc)
if desc.get("failureFrom") != None and desc["failureFrom"]["id"] != desc["id"]:
err_msg += " (failure from {id})".format(id=desc['failureFrom']['id'])
raise DXJobFailureError(err_msg)
if state == "terminated":
raise DXJobFailureError("Job was terminated.")
if elapsed >= timeout or elapsed < 0:
raise DXJobFailureError("Reached timeout while waiting for the job to finish")
time.sleep(interval)
elapsed += interval
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googlesamples/assistant-sdk-python | google-assistant-sdk/googlesamples/assistant/grpc/audio_helpers.py | ConversationStream.stop_recording | def stop_recording(self):
"""Stop recording from the audio source."""
self._stop_recording.set()
with self._source_lock:
self._source.stop()
self._recording = False | python | def stop_recording(self):
"""Stop recording from the audio source."""
self._stop_recording.set()
with self._source_lock:
self._source.stop()
self._recording = False | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Stop recording from the audio source.
### Response:
def stop_recording(self):
"""Stop recording from the audio source."""
self._stop_recording.set()
with self._source_lock:
self._source.stop()
self._recording = False
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PmagPy/PmagPy | SPD/lib/lib_arai_plot_statistics.py | fancy_SCAT | def fancy_SCAT(points, low_bound, high_bound, x_max, y_max):
"""
runs SCAT test and returns 'Pass' or 'Fail'
"""
# iterate through all relevant points and see if any of them fall outside of your SCAT box
# {'points_arai': [(x,y),(x,y)], 'points_ptrm': [(x,y),(x,y)], ...}
SCAT = 'Pass'
SCATs = {'SCAT_arai': 'Pass', 'SCAT_ptrm': 'Pass', 'SCAT_tail': 'Pass'}
for point_type in points:
#print 'point_type', point_type
for point in points[point_type]:
#print 'point', point
result = in_SCAT_box(point[0], point[1], low_bound, high_bound, x_max, y_max)
if not result:
# print "SCAT TEST FAILED"
x = 'SCAT' + point_type[6:]
#print 'lib point type', point_type
#print 'xxxx', x
SCATs[x] = 'Fail'
SCAT = 'Fail'
return SCAT, SCATs | python | def fancy_SCAT(points, low_bound, high_bound, x_max, y_max):
"""
runs SCAT test and returns 'Pass' or 'Fail'
"""
# iterate through all relevant points and see if any of them fall outside of your SCAT box
# {'points_arai': [(x,y),(x,y)], 'points_ptrm': [(x,y),(x,y)], ...}
SCAT = 'Pass'
SCATs = {'SCAT_arai': 'Pass', 'SCAT_ptrm': 'Pass', 'SCAT_tail': 'Pass'}
for point_type in points:
#print 'point_type', point_type
for point in points[point_type]:
#print 'point', point
result = in_SCAT_box(point[0], point[1], low_bound, high_bound, x_max, y_max)
if not result:
# print "SCAT TEST FAILED"
x = 'SCAT' + point_type[6:]
#print 'lib point type', point_type
#print 'xxxx', x
SCATs[x] = 'Fail'
SCAT = 'Fail'
return SCAT, SCATs | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
runs SCAT test and returns 'Pass' or 'Fail'
### Response:
def fancy_SCAT(points, low_bound, high_bound, x_max, y_max):
"""
runs SCAT test and returns 'Pass' or 'Fail'
"""
# iterate through all relevant points and see if any of them fall outside of your SCAT box
# {'points_arai': [(x,y),(x,y)], 'points_ptrm': [(x,y),(x,y)], ...}
SCAT = 'Pass'
SCATs = {'SCAT_arai': 'Pass', 'SCAT_ptrm': 'Pass', 'SCAT_tail': 'Pass'}
for point_type in points:
#print 'point_type', point_type
for point in points[point_type]:
#print 'point', point
result = in_SCAT_box(point[0], point[1], low_bound, high_bound, x_max, y_max)
if not result:
# print "SCAT TEST FAILED"
x = 'SCAT' + point_type[6:]
#print 'lib point type', point_type
#print 'xxxx', x
SCATs[x] = 'Fail'
SCAT = 'Fail'
return SCAT, SCATs
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BetterWorks/django-anonymizer | anonymizer/base.py | DjangoFaker.date | def date(self, field=None, val=None):
"""
Like datetime, but truncated to be a date only
"""
return self.datetime(field=field, val=val).date() | python | def date(self, field=None, val=None):
"""
Like datetime, but truncated to be a date only
"""
return self.datetime(field=field, val=val).date() | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Like datetime, but truncated to be a date only
### Response:
def date(self, field=None, val=None):
"""
Like datetime, but truncated to be a date only
"""
return self.datetime(field=field, val=val).date()
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aliyun/aliyun-log-python-sdk | aliyun/log/logclient.py | LogClient.get_begin_cursor | def get_begin_cursor(self, project_name, logstore_name, shard_id):
""" Get begin cursor from log service for batch pull logs
Unsuccessful opertaion will cause an LogException.
:type project_name: string
:param project_name: the Project name
:type logstore_name: string
:param logstore_name: the logstore name
:type shard_id: int
:param shard_id: the shard id
:return: GetLogsResponse
:raise: LogException
"""
return self.get_cursor(project_name, logstore_name, shard_id, "begin") | python | def get_begin_cursor(self, project_name, logstore_name, shard_id):
""" Get begin cursor from log service for batch pull logs
Unsuccessful opertaion will cause an LogException.
:type project_name: string
:param project_name: the Project name
:type logstore_name: string
:param logstore_name: the logstore name
:type shard_id: int
:param shard_id: the shard id
:return: GetLogsResponse
:raise: LogException
"""
return self.get_cursor(project_name, logstore_name, shard_id, "begin") | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Get begin cursor from log service for batch pull logs
Unsuccessful opertaion will cause an LogException.
:type project_name: string
:param project_name: the Project name
:type logstore_name: string
:param logstore_name: the logstore name
:type shard_id: int
:param shard_id: the shard id
:return: GetLogsResponse
:raise: LogException
### Response:
def get_begin_cursor(self, project_name, logstore_name, shard_id):
""" Get begin cursor from log service for batch pull logs
Unsuccessful opertaion will cause an LogException.
:type project_name: string
:param project_name: the Project name
:type logstore_name: string
:param logstore_name: the logstore name
:type shard_id: int
:param shard_id: the shard id
:return: GetLogsResponse
:raise: LogException
"""
return self.get_cursor(project_name, logstore_name, shard_id, "begin")
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apple/turicreate | deps/src/libxml2-2.9.1/python/libxml2.py | xmlNode.newNs | def newNs(self, href, prefix):
"""Creation of a new Namespace. This function will refuse to
create a namespace with a similar prefix than an existing
one present on this node. We use href==None in the case of
an element creation where the namespace was not defined. """
ret = libxml2mod.xmlNewNs(self._o, href, prefix)
if ret is None:raise treeError('xmlNewNs() failed')
__tmp = xmlNs(_obj=ret)
return __tmp | python | def newNs(self, href, prefix):
"""Creation of a new Namespace. This function will refuse to
create a namespace with a similar prefix than an existing
one present on this node. We use href==None in the case of
an element creation where the namespace was not defined. """
ret = libxml2mod.xmlNewNs(self._o, href, prefix)
if ret is None:raise treeError('xmlNewNs() failed')
__tmp = xmlNs(_obj=ret)
return __tmp | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Creation of a new Namespace. This function will refuse to
create a namespace with a similar prefix than an existing
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def newNs(self, href, prefix):
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one present on this node. We use href==None in the case of
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ret = libxml2mod.xmlNewNs(self._o, href, prefix)
if ret is None:raise treeError('xmlNewNs() failed')
__tmp = xmlNs(_obj=ret)
return __tmp
<|endoftext|> | [
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OSSOS/MOP | src/ossos/core/ossos/gui/models/workload.py | WorkUnitProvider.get_workunit | def get_workunit(self, ignore_list=None):
"""
Gets a new unit of work.
Args:
ignore_list: list(str)
A list of filenames which should be ignored. Defaults to None.
Returns:
new_workunit: WorkUnit
A new unit of work that has not yet been processed. A lock on
it has been acquired.
Raises:
NoAvailableWorkException
There is no more work available.
"""
if ignore_list is None:
ignore_list = []
potential_files = self.get_potential_files(ignore_list)
while len(potential_files) > 0:
potential_file = self.select_potential_file(potential_files)
potential_files.remove(potential_file)
if self._filter(potential_file):
continue
if self.directory_context.get_file_size(potential_file) == 0:
continue
if self.progress_manager.is_done(potential_file):
self._done.append(potential_file)
continue
else:
try:
self.progress_manager.lock(potential_file)
except FileLockedException:
continue
self._already_fetched.append(potential_file)
return self.builder.build_workunit(
self.directory_context.get_full_path(potential_file))
logger.info("No eligible workunits remain to be fetched.")
raise NoAvailableWorkException() | python | def get_workunit(self, ignore_list=None):
"""
Gets a new unit of work.
Args:
ignore_list: list(str)
A list of filenames which should be ignored. Defaults to None.
Returns:
new_workunit: WorkUnit
A new unit of work that has not yet been processed. A lock on
it has been acquired.
Raises:
NoAvailableWorkException
There is no more work available.
"""
if ignore_list is None:
ignore_list = []
potential_files = self.get_potential_files(ignore_list)
while len(potential_files) > 0:
potential_file = self.select_potential_file(potential_files)
potential_files.remove(potential_file)
if self._filter(potential_file):
continue
if self.directory_context.get_file_size(potential_file) == 0:
continue
if self.progress_manager.is_done(potential_file):
self._done.append(potential_file)
continue
else:
try:
self.progress_manager.lock(potential_file)
except FileLockedException:
continue
self._already_fetched.append(potential_file)
return self.builder.build_workunit(
self.directory_context.get_full_path(potential_file))
logger.info("No eligible workunits remain to be fetched.")
raise NoAvailableWorkException() | [
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Args:
ignore_list: list(str)
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Returns:
new_workunit: WorkUnit
A new unit of work that has not yet been processed. A lock on
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Gets a new unit of work.
Args:
ignore_list: list(str)
A list of filenames which should be ignored. Defaults to None.
Returns:
new_workunit: WorkUnit
A new unit of work that has not yet been processed. A lock on
it has been acquired.
Raises:
NoAvailableWorkException
There is no more work available.
### Response:
def get_workunit(self, ignore_list=None):
"""
Gets a new unit of work.
Args:
ignore_list: list(str)
A list of filenames which should be ignored. Defaults to None.
Returns:
new_workunit: WorkUnit
A new unit of work that has not yet been processed. A lock on
it has been acquired.
Raises:
NoAvailableWorkException
There is no more work available.
"""
if ignore_list is None:
ignore_list = []
potential_files = self.get_potential_files(ignore_list)
while len(potential_files) > 0:
potential_file = self.select_potential_file(potential_files)
potential_files.remove(potential_file)
if self._filter(potential_file):
continue
if self.directory_context.get_file_size(potential_file) == 0:
continue
if self.progress_manager.is_done(potential_file):
self._done.append(potential_file)
continue
else:
try:
self.progress_manager.lock(potential_file)
except FileLockedException:
continue
self._already_fetched.append(potential_file)
return self.builder.build_workunit(
self.directory_context.get_full_path(potential_file))
logger.info("No eligible workunits remain to be fetched.")
raise NoAvailableWorkException()
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basler/pypylon | scripts/builddoxy2swig/doxy2swig/doxy2swig.py | Doxy2SWIG.handle_typical_memberdefs_no_overload | def handle_typical_memberdefs_no_overload(self, signature, memberdef_nodes):
"""Produce standard documentation for memberdef_nodes."""
for n in memberdef_nodes:
self.add_text(['\n', '%feature("docstring") ', signature, ' "', '\n'])
if self.with_function_signature:
self.add_line_with_subsequent_indent(self.get_function_signature(n))
self.subnode_parse(n, pieces=[], ignore=['definition', 'name'])
self.add_text(['";', '\n']) | python | def handle_typical_memberdefs_no_overload(self, signature, memberdef_nodes):
"""Produce standard documentation for memberdef_nodes."""
for n in memberdef_nodes:
self.add_text(['\n', '%feature("docstring") ', signature, ' "', '\n'])
if self.with_function_signature:
self.add_line_with_subsequent_indent(self.get_function_signature(n))
self.subnode_parse(n, pieces=[], ignore=['definition', 'name'])
self.add_text(['";', '\n']) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Produce standard documentation for memberdef_nodes.
### Response:
def handle_typical_memberdefs_no_overload(self, signature, memberdef_nodes):
"""Produce standard documentation for memberdef_nodes."""
for n in memberdef_nodes:
self.add_text(['\n', '%feature("docstring") ', signature, ' "', '\n'])
if self.with_function_signature:
self.add_line_with_subsequent_indent(self.get_function_signature(n))
self.subnode_parse(n, pieces=[], ignore=['definition', 'name'])
self.add_text(['";', '\n'])
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phaethon/kamene | kamene/packet.py | ls | def ls(obj=None):
"""List available layers, or infos on a given layer"""
if obj is None:
import builtins
all = builtins.__dict__.copy()
all.update(globals())
objlst = sorted(conf.layers, key=lambda x:x.__name__)
for o in objlst:
print("%-10s : %s" %(o.__name__,o.name))
else:
if isinstance(obj, type) and issubclass(obj, Packet):
for f in obj.fields_desc:
print("%-10s : %-20s = (%s)" % (f.name, f.__class__.__name__, repr(f.default)))
elif isinstance(obj, Packet):
for f in obj.fields_desc:
print("%-10s : %-20s = %-15s (%s)" % (f.name, f.__class__.__name__, repr(getattr(obj,f.name)), repr(f.default)))
if not isinstance(obj.payload, NoPayload):
print("--")
ls(obj.payload)
else:
print("Not a packet class. Type 'ls()' to list packet classes.") | python | def ls(obj=None):
"""List available layers, or infos on a given layer"""
if obj is None:
import builtins
all = builtins.__dict__.copy()
all.update(globals())
objlst = sorted(conf.layers, key=lambda x:x.__name__)
for o in objlst:
print("%-10s : %s" %(o.__name__,o.name))
else:
if isinstance(obj, type) and issubclass(obj, Packet):
for f in obj.fields_desc:
print("%-10s : %-20s = (%s)" % (f.name, f.__class__.__name__, repr(f.default)))
elif isinstance(obj, Packet):
for f in obj.fields_desc:
print("%-10s : %-20s = %-15s (%s)" % (f.name, f.__class__.__name__, repr(getattr(obj,f.name)), repr(f.default)))
if not isinstance(obj.payload, NoPayload):
print("--")
ls(obj.payload)
else:
print("Not a packet class. Type 'ls()' to list packet classes.") | [
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] | List available layers, or infos on a given layer | [
"List",
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] | train | https://github.com/phaethon/kamene/blob/11d4064844f4f68ac5d7546f5633ac7d02082914/kamene/packet.py#L1250-L1273 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
List available layers, or infos on a given layer
### Response:
def ls(obj=None):
"""List available layers, or infos on a given layer"""
if obj is None:
import builtins
all = builtins.__dict__.copy()
all.update(globals())
objlst = sorted(conf.layers, key=lambda x:x.__name__)
for o in objlst:
print("%-10s : %s" %(o.__name__,o.name))
else:
if isinstance(obj, type) and issubclass(obj, Packet):
for f in obj.fields_desc:
print("%-10s : %-20s = (%s)" % (f.name, f.__class__.__name__, repr(f.default)))
elif isinstance(obj, Packet):
for f in obj.fields_desc:
print("%-10s : %-20s = %-15s (%s)" % (f.name, f.__class__.__name__, repr(getattr(obj,f.name)), repr(f.default)))
if not isinstance(obj.payload, NoPayload):
print("--")
ls(obj.payload)
else:
print("Not a packet class. Type 'ls()' to list packet classes.")
<|endoftext|> | [
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pgmpy/pgmpy | pgmpy/readwrite/XMLBIF.py | XMLBIFWriter.get_definition | def get_definition(self):
"""
Add Definition to XMLBIF
Return
------
dict: dict of type {variable: definition tag}
Examples
--------
>>> writer = XMLBIFWriter(model)
>>> writer.get_definition()
{'hear-bark': <Element DEFINITION at 0x7f1d48977408>,
'family-out': <Element DEFINITION at 0x7f1d489773c8>,
'dog-out': <Element DEFINITION at 0x7f1d48977388>,
'bowel-problem': <Element DEFINITION at 0x7f1d48977348>,
'light-on': <Element DEFINITION at 0x7f1d48977448>}
"""
cpds = self.model.get_cpds()
cpds.sort(key=lambda x: x.variable)
definition_tag = {}
for cpd in cpds:
definition_tag[cpd.variable] = etree.SubElement(self.network, "DEFINITION")
etree.SubElement(definition_tag[cpd.variable], "FOR").text = cpd.variable
for child in sorted(cpd.variables[:0:-1]):
etree.SubElement(definition_tag[cpd.variable], "GIVEN").text = child
return definition_tag | python | def get_definition(self):
"""
Add Definition to XMLBIF
Return
------
dict: dict of type {variable: definition tag}
Examples
--------
>>> writer = XMLBIFWriter(model)
>>> writer.get_definition()
{'hear-bark': <Element DEFINITION at 0x7f1d48977408>,
'family-out': <Element DEFINITION at 0x7f1d489773c8>,
'dog-out': <Element DEFINITION at 0x7f1d48977388>,
'bowel-problem': <Element DEFINITION at 0x7f1d48977348>,
'light-on': <Element DEFINITION at 0x7f1d48977448>}
"""
cpds = self.model.get_cpds()
cpds.sort(key=lambda x: x.variable)
definition_tag = {}
for cpd in cpds:
definition_tag[cpd.variable] = etree.SubElement(self.network, "DEFINITION")
etree.SubElement(definition_tag[cpd.variable], "FOR").text = cpd.variable
for child in sorted(cpd.variables[:0:-1]):
etree.SubElement(definition_tag[cpd.variable], "GIVEN").text = child
return definition_tag | [
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Return
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dict: dict of type {variable: definition tag}
Examples
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>>> writer.get_definition()
{'hear-bark': <Element DEFINITION at 0x7f1d48977408>,
'family-out': <Element DEFINITION at 0x7f1d489773c8>,
'dog-out': <Element DEFINITION at 0x7f1d48977388>,
'bowel-problem': <Element DEFINITION at 0x7f1d48977348>,
'light-on': <Element DEFINITION at 0x7f1d48977448>} | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Add Definition to XMLBIF
Return
------
dict: dict of type {variable: definition tag}
Examples
--------
>>> writer = XMLBIFWriter(model)
>>> writer.get_definition()
{'hear-bark': <Element DEFINITION at 0x7f1d48977408>,
'family-out': <Element DEFINITION at 0x7f1d489773c8>,
'dog-out': <Element DEFINITION at 0x7f1d48977388>,
'bowel-problem': <Element DEFINITION at 0x7f1d48977348>,
'light-on': <Element DEFINITION at 0x7f1d48977448>}
### Response:
def get_definition(self):
"""
Add Definition to XMLBIF
Return
------
dict: dict of type {variable: definition tag}
Examples
--------
>>> writer = XMLBIFWriter(model)
>>> writer.get_definition()
{'hear-bark': <Element DEFINITION at 0x7f1d48977408>,
'family-out': <Element DEFINITION at 0x7f1d489773c8>,
'dog-out': <Element DEFINITION at 0x7f1d48977388>,
'bowel-problem': <Element DEFINITION at 0x7f1d48977348>,
'light-on': <Element DEFINITION at 0x7f1d48977448>}
"""
cpds = self.model.get_cpds()
cpds.sort(key=lambda x: x.variable)
definition_tag = {}
for cpd in cpds:
definition_tag[cpd.variable] = etree.SubElement(self.network, "DEFINITION")
etree.SubElement(definition_tag[cpd.variable], "FOR").text = cpd.variable
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dhermes/bezier | scripts/post_process_journal.py | post_process_travis_macos | def post_process_travis_macos(journal_filename):
"""Post-process a generated journal file on Travis macOS.
Args:
journal_filename (str): The name of the journal file.
"""
travis_build_dir = os.environ.get("TRAVIS_BUILD_DIR", "")
with open(journal_filename, "r") as file_obj:
content = file_obj.read()
processed = content.replace(travis_build_dir, "${TRAVIS_BUILD_DIR}")
with open(journal_filename, "w") as file_obj:
file_obj.write(processed) | python | def post_process_travis_macos(journal_filename):
"""Post-process a generated journal file on Travis macOS.
Args:
journal_filename (str): The name of the journal file.
"""
travis_build_dir = os.environ.get("TRAVIS_BUILD_DIR", "")
with open(journal_filename, "r") as file_obj:
content = file_obj.read()
processed = content.replace(travis_build_dir, "${TRAVIS_BUILD_DIR}")
with open(journal_filename, "w") as file_obj:
file_obj.write(processed) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Post-process a generated journal file on Travis macOS.
Args:
journal_filename (str): The name of the journal file.
### Response:
def post_process_travis_macos(journal_filename):
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journal_filename (str): The name of the journal file.
"""
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src-d/lookout-sdk | python/lookout/sdk/grpc/log_fields.py | LogFieldsContext.pack_metadata | def pack_metadata(self) -> List[Tuple[str, Any]]:
"""Packs the log fields and the invocation metadata into a new metadata
The log fields are added in the new metadata with the key
`LOG_FIELDS_KEY_META`.
"""
metadata = [(k, v) for k, v in self._invocation_metadata.items()
if k != LOG_FIELDS_KEY_META]
metadata.append((LOG_FIELDS_KEY_META, self._log_fields.dumps()))
return metadata | python | def pack_metadata(self) -> List[Tuple[str, Any]]:
"""Packs the log fields and the invocation metadata into a new metadata
The log fields are added in the new metadata with the key
`LOG_FIELDS_KEY_META`.
"""
metadata = [(k, v) for k, v in self._invocation_metadata.items()
if k != LOG_FIELDS_KEY_META]
metadata.append((LOG_FIELDS_KEY_META, self._log_fields.dumps()))
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Packs the log fields and the invocation metadata into a new metadata
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gwastro/pycbc | pycbc/types/array.py | Array._return | def _return(self, ary):
"""Wrap the ary to return an Array type """
if isinstance(ary, Array):
return ary
return Array(ary, copy=False) | python | def _return(self, ary):
"""Wrap the ary to return an Array type """
if isinstance(ary, Array):
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You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Wrap the ary to return an Array type
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Kronuz/pyScss | scss/compiler.py | Compilation._at_variables | def _at_variables(self, calculator, rule, scope, block):
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_rule.unparsed_contents = block.unparsed_contents
_rule.namespace = rule.namespace
_rule.properties = []
self.manage_children(_rule, scope) | python | def _at_variables(self, calculator, rule, scope, block):
"""
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warn_deprecated(
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_rule.unparsed_contents = block.unparsed_contents
_rule.namespace = rule.namespace
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"Implements"
] | train | https://github.com/Kronuz/pyScss/blob/fb32b317f6e2b4b4aad2b86a74844658ac4aa11e/scss/compiler.py#L1011-L1023 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Implements @variables and @vars
### Response:
def _at_variables(self, calculator, rule, scope, block):
"""
Implements @variables and @vars
"""
warn_deprecated(
rule,
"@variables and @vars are deprecated. "
"Just assign variables at top-level.")
_rule = rule.copy()
_rule.unparsed_contents = block.unparsed_contents
_rule.namespace = rule.namespace
_rule.properties = []
self.manage_children(_rule, scope)
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yamins81/tabular | tabular/io.py | parsetypes | def parsetypes(dtype):
"""
Parse the types from a structured numpy dtype object.
Return list of string representations of types from a structured numpy
dtype object, e.g. ['int', 'float', 'str'].
Used by :func:`tabular.io.saveSV` to write out type information in the
header.
**Parameters**
**dtype** : numpy dtype object
Structured numpy dtype object to parse.
**Returns**
**out** : list of strings
List of strings corresponding to numpy types::
[dtype[i].name.strip('1234567890').rstrip('ing') \
for i in range(len(dtype))]
"""
return [dtype[i].name.strip('1234567890').rstrip('ing')
for i in range(len(dtype))] | python | def parsetypes(dtype):
"""
Parse the types from a structured numpy dtype object.
Return list of string representations of types from a structured numpy
dtype object, e.g. ['int', 'float', 'str'].
Used by :func:`tabular.io.saveSV` to write out type information in the
header.
**Parameters**
**dtype** : numpy dtype object
Structured numpy dtype object to parse.
**Returns**
**out** : list of strings
List of strings corresponding to numpy types::
[dtype[i].name.strip('1234567890').rstrip('ing') \
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"""
return [dtype[i].name.strip('1234567890').rstrip('ing')
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Structured numpy dtype object to parse.
**Returns**
**out** : list of strings
List of strings corresponding to numpy types::
[dtype[i].name.strip('1234567890').rstrip('ing') \
for i in range(len(dtype))] | [
"Parse",
"the",
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"from",
"a",
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"dtype",
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"."
] | train | https://github.com/yamins81/tabular/blob/1caf091c8c395960a9ad7078f95158b533cc52dd/tabular/io.py#L1808-L1835 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Parse the types from a structured numpy dtype object.
Return list of string representations of types from a structured numpy
dtype object, e.g. ['int', 'float', 'str'].
Used by :func:`tabular.io.saveSV` to write out type information in the
header.
**Parameters**
**dtype** : numpy dtype object
Structured numpy dtype object to parse.
**Returns**
**out** : list of strings
List of strings corresponding to numpy types::
[dtype[i].name.strip('1234567890').rstrip('ing') \
for i in range(len(dtype))]
### Response:
def parsetypes(dtype):
"""
Parse the types from a structured numpy dtype object.
Return list of string representations of types from a structured numpy
dtype object, e.g. ['int', 'float', 'str'].
Used by :func:`tabular.io.saveSV` to write out type information in the
header.
**Parameters**
**dtype** : numpy dtype object
Structured numpy dtype object to parse.
**Returns**
**out** : list of strings
List of strings corresponding to numpy types::
[dtype[i].name.strip('1234567890').rstrip('ing') \
for i in range(len(dtype))]
"""
return [dtype[i].name.strip('1234567890').rstrip('ing')
for i in range(len(dtype))]
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janpipek/physt | physt/plotting/vega.py | _create_axes | def _create_axes(hist: HistogramBase, vega: dict, kwargs: dict):
"""Create axes in the figure."""
xlabel = kwargs.pop("xlabel", hist.axis_names[0])
ylabel = kwargs.pop("ylabel", hist.axis_names[1] if len(hist.axis_names) >= 2 else None)
vega["axes"] = [
{"orient": "bottom", "scale": "xscale", "title": xlabel},
{"orient": "left", "scale": "yscale", "title": ylabel}
] | python | def _create_axes(hist: HistogramBase, vega: dict, kwargs: dict):
"""Create axes in the figure."""
xlabel = kwargs.pop("xlabel", hist.axis_names[0])
ylabel = kwargs.pop("ylabel", hist.axis_names[1] if len(hist.axis_names) >= 2 else None)
vega["axes"] = [
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"Create",
"axes",
"in",
"the",
"figure",
"."
] | train | https://github.com/janpipek/physt/blob/6dd441b073514e7728235f50b2352d56aacf38d4/physt/plotting/vega.py#L673-L680 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Create axes in the figure.
### Response:
def _create_axes(hist: HistogramBase, vega: dict, kwargs: dict):
"""Create axes in the figure."""
xlabel = kwargs.pop("xlabel", hist.axis_names[0])
ylabel = kwargs.pop("ylabel", hist.axis_names[1] if len(hist.axis_names) >= 2 else None)
vega["axes"] = [
{"orient": "bottom", "scale": "xscale", "title": xlabel},
{"orient": "left", "scale": "yscale", "title": ylabel}
]
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sethmlarson/virtualbox-python | virtualbox/library.py | IInternalSessionControl.on_network_adapter_change | def on_network_adapter_change(self, network_adapter, change_adapter):
"""Triggered when settings of a network adapter of the
associated virtual machine have changed.
in network_adapter of type :class:`INetworkAdapter`
in change_adapter of type bool
raises :class:`VBoxErrorInvalidVmState`
Session state prevents operation.
raises :class:`VBoxErrorInvalidObjectState`
Session type prevents operation.
"""
if not isinstance(network_adapter, INetworkAdapter):
raise TypeError("network_adapter can only be an instance of type INetworkAdapter")
if not isinstance(change_adapter, bool):
raise TypeError("change_adapter can only be an instance of type bool")
self._call("onNetworkAdapterChange",
in_p=[network_adapter, change_adapter]) | python | def on_network_adapter_change(self, network_adapter, change_adapter):
"""Triggered when settings of a network adapter of the
associated virtual machine have changed.
in network_adapter of type :class:`INetworkAdapter`
in change_adapter of type bool
raises :class:`VBoxErrorInvalidVmState`
Session state prevents operation.
raises :class:`VBoxErrorInvalidObjectState`
Session type prevents operation.
"""
if not isinstance(network_adapter, INetworkAdapter):
raise TypeError("network_adapter can only be an instance of type INetworkAdapter")
if not isinstance(change_adapter, bool):
raise TypeError("change_adapter can only be an instance of type bool")
self._call("onNetworkAdapterChange",
in_p=[network_adapter, change_adapter]) | [
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"network",
"adapter",
"of",
"the",
"associated",
"virtual",
"machine",
"have",
"changed",
"."
] | train | https://github.com/sethmlarson/virtualbox-python/blob/706c8e3f6e3aee17eb06458e73cbb4bc2d37878b/virtualbox/library.py#L28324-L28344 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Triggered when settings of a network adapter of the
associated virtual machine have changed.
in network_adapter of type :class:`INetworkAdapter`
in change_adapter of type bool
raises :class:`VBoxErrorInvalidVmState`
Session state prevents operation.
raises :class:`VBoxErrorInvalidObjectState`
Session type prevents operation.
### Response:
def on_network_adapter_change(self, network_adapter, change_adapter):
"""Triggered when settings of a network adapter of the
associated virtual machine have changed.
in network_adapter of type :class:`INetworkAdapter`
in change_adapter of type bool
raises :class:`VBoxErrorInvalidVmState`
Session state prevents operation.
raises :class:`VBoxErrorInvalidObjectState`
Session type prevents operation.
"""
if not isinstance(network_adapter, INetworkAdapter):
raise TypeError("network_adapter can only be an instance of type INetworkAdapter")
if not isinstance(change_adapter, bool):
raise TypeError("change_adapter can only be an instance of type bool")
self._call("onNetworkAdapterChange",
in_p=[network_adapter, change_adapter])
<|endoftext|> | [
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datadotworld/data.world-py | datadotworld/client/_swagger/apis/insights_api.py | InsightsApi.get_insight | def get_insight(self, project_owner, project_id, id, **kwargs):
"""
Retrieve an insight
Retrieve an insight.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_insight(project_owner, project_id, id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str project_owner: User name and unique identifier of the creator of a project. For example, in the URL: [https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs](https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs), `government` is the unique identifier of the owner. (required)
:param str project_id: User name and unique identifier of the project. For example, in the URL: [https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs](https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs), `how-to-add-depth-to-your-data-with-the-us-census-acs` is the unique identifier of the owner. (required)
:param str id: Insight unique identifier. (required)
:return: InsightSummaryResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_insight_with_http_info(project_owner, project_id, id, **kwargs)
else:
(data) = self.get_insight_with_http_info(project_owner, project_id, id, **kwargs)
return data | python | def get_insight(self, project_owner, project_id, id, **kwargs):
"""
Retrieve an insight
Retrieve an insight.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_insight(project_owner, project_id, id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str project_owner: User name and unique identifier of the creator of a project. For example, in the URL: [https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs](https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs), `government` is the unique identifier of the owner. (required)
:param str project_id: User name and unique identifier of the project. For example, in the URL: [https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs](https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs), `how-to-add-depth-to-your-data-with-the-us-census-acs` is the unique identifier of the owner. (required)
:param str id: Insight unique identifier. (required)
:return: InsightSummaryResponse
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"""
kwargs['_return_http_data_only'] = True
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return self.get_insight_with_http_info(project_owner, project_id, id, **kwargs)
else:
(data) = self.get_insight_with_http_info(project_owner, project_id, id, **kwargs)
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Retrieve an insight
Retrieve an insight.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_insight(project_owner, project_id, id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str project_owner: User name and unique identifier of the creator of a project. For example, in the URL: [https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs](https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs), `government` is the unique identifier of the owner. (required)
:param str project_id: User name and unique identifier of the project. For example, in the URL: [https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs](https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs), `how-to-add-depth-to-your-data-with-the-us-census-acs` is the unique identifier of the owner. (required)
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### Response:
def get_insight(self, project_owner, project_id, id, **kwargs):
"""
Retrieve an insight
Retrieve an insight.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_insight(project_owner, project_id, id, callback=callback_function)
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:param str project_owner: User name and unique identifier of the creator of a project. For example, in the URL: [https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs](https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs), `government` is the unique identifier of the owner. (required)
:param str project_id: User name and unique identifier of the project. For example, in the URL: [https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs](https://data.world/government/how-to-add-depth-to-your-data-with-the-us-census-acs), `how-to-add-depth-to-your-data-with-the-us-census-acs` is the unique identifier of the owner. (required)
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"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_insight_with_http_info(project_owner, project_id, id, **kwargs)
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return data
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tensorflow/tensor2tensor | tensor2tensor/models/video/next_frame_glow.py | get_cond_latents | def get_cond_latents(all_latents=None, hparams=None):
"""Get z^{cond}_{t} given z^{1..t-1}.
Args:
all_latents: list of list of tensors,
outer-size equals no.of time_steps-1
inner-size equals hparams.n_levels.
hparams: See next_frame_glow_hparams.
Returns:
cond_latents: conditional latents at time-step t.
"""
cond_latents = None
if hparams.gen_mode == "conditional":
if hparams.latent_dist_encoder in ["conv_net", "conv3d_net"]:
num_cond_latents = (hparams.num_cond_latents +
int(hparams.cond_first_frame))
if len(all_latents) >= num_cond_latents:
cond_latents = all_latents[-hparams.num_cond_latents:]
if hparams.cond_first_frame:
cond_latents = [all_latents[0]] + cond_latents
elif hparams.latent_dist_encoder in ["pointwise", "conv_lstm"]:
if all_latents:
cond_latents = all_latents[-1]
if hparams.gen_mode == "conditional":
global_step = tf.train.get_or_create_global_step()
condition = tf.greater(global_step, hparams.pretrain_steps)
else:
condition = tf.constant(False, dtype=tf.bool)
return condition, cond_latents | python | def get_cond_latents(all_latents=None, hparams=None):
"""Get z^{cond}_{t} given z^{1..t-1}.
Args:
all_latents: list of list of tensors,
outer-size equals no.of time_steps-1
inner-size equals hparams.n_levels.
hparams: See next_frame_glow_hparams.
Returns:
cond_latents: conditional latents at time-step t.
"""
cond_latents = None
if hparams.gen_mode == "conditional":
if hparams.latent_dist_encoder in ["conv_net", "conv3d_net"]:
num_cond_latents = (hparams.num_cond_latents +
int(hparams.cond_first_frame))
if len(all_latents) >= num_cond_latents:
cond_latents = all_latents[-hparams.num_cond_latents:]
if hparams.cond_first_frame:
cond_latents = [all_latents[0]] + cond_latents
elif hparams.latent_dist_encoder in ["pointwise", "conv_lstm"]:
if all_latents:
cond_latents = all_latents[-1]
if hparams.gen_mode == "conditional":
global_step = tf.train.get_or_create_global_step()
condition = tf.greater(global_step, hparams.pretrain_steps)
else:
condition = tf.constant(False, dtype=tf.bool)
return condition, cond_latents | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Get z^{cond}_{t} given z^{1..t-1}.
Args:
all_latents: list of list of tensors,
outer-size equals no.of time_steps-1
inner-size equals hparams.n_levels.
hparams: See next_frame_glow_hparams.
Returns:
cond_latents: conditional latents at time-step t.
### Response:
def get_cond_latents(all_latents=None, hparams=None):
"""Get z^{cond}_{t} given z^{1..t-1}.
Args:
all_latents: list of list of tensors,
outer-size equals no.of time_steps-1
inner-size equals hparams.n_levels.
hparams: See next_frame_glow_hparams.
Returns:
cond_latents: conditional latents at time-step t.
"""
cond_latents = None
if hparams.gen_mode == "conditional":
if hparams.latent_dist_encoder in ["conv_net", "conv3d_net"]:
num_cond_latents = (hparams.num_cond_latents +
int(hparams.cond_first_frame))
if len(all_latents) >= num_cond_latents:
cond_latents = all_latents[-hparams.num_cond_latents:]
if hparams.cond_first_frame:
cond_latents = [all_latents[0]] + cond_latents
elif hparams.latent_dist_encoder in ["pointwise", "conv_lstm"]:
if all_latents:
cond_latents = all_latents[-1]
if hparams.gen_mode == "conditional":
global_step = tf.train.get_or_create_global_step()
condition = tf.greater(global_step, hparams.pretrain_steps)
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<|endoftext|> | [
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openid/python-openid | openid/message.py | Message._fixNS | def _fixNS(self, namespace):
"""Convert an input value into the internally used values of
this object
@param namespace: The string or constant to convert
@type namespace: str or unicode or BARE_NS or OPENID_NS
"""
if namespace == OPENID_NS:
if self._openid_ns_uri is None:
raise UndefinedOpenIDNamespace('OpenID namespace not set')
else:
namespace = self._openid_ns_uri
if namespace != BARE_NS and type(namespace) not in [str, unicode]:
raise TypeError(
"Namespace must be BARE_NS, OPENID_NS or a string. got %r"
% (namespace,))
if namespace != BARE_NS and ':' not in namespace:
fmt = 'OpenID 2.0 namespace identifiers SHOULD be URIs. Got %r'
warnings.warn(fmt % (namespace,), DeprecationWarning)
if namespace == 'sreg':
fmt = 'Using %r instead of "sreg" as namespace'
warnings.warn(fmt % (SREG_URI,), DeprecationWarning,)
return SREG_URI
return namespace | python | def _fixNS(self, namespace):
"""Convert an input value into the internally used values of
this object
@param namespace: The string or constant to convert
@type namespace: str or unicode or BARE_NS or OPENID_NS
"""
if namespace == OPENID_NS:
if self._openid_ns_uri is None:
raise UndefinedOpenIDNamespace('OpenID namespace not set')
else:
namespace = self._openid_ns_uri
if namespace != BARE_NS and type(namespace) not in [str, unicode]:
raise TypeError(
"Namespace must be BARE_NS, OPENID_NS or a string. got %r"
% (namespace,))
if namespace != BARE_NS and ':' not in namespace:
fmt = 'OpenID 2.0 namespace identifiers SHOULD be URIs. Got %r'
warnings.warn(fmt % (namespace,), DeprecationWarning)
if namespace == 'sreg':
fmt = 'Using %r instead of "sreg" as namespace'
warnings.warn(fmt % (SREG_URI,), DeprecationWarning,)
return SREG_URI
return namespace | [
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] | train | https://github.com/openid/python-openid/blob/f7e13536f0d1828d3cef5ae7a7b55cabadff37fc/openid/message.py#L369-L396 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Convert an input value into the internally used values of
this object
@param namespace: The string or constant to convert
@type namespace: str or unicode or BARE_NS or OPENID_NS
### Response:
def _fixNS(self, namespace):
"""Convert an input value into the internally used values of
this object
@param namespace: The string or constant to convert
@type namespace: str or unicode or BARE_NS or OPENID_NS
"""
if namespace == OPENID_NS:
if self._openid_ns_uri is None:
raise UndefinedOpenIDNamespace('OpenID namespace not set')
else:
namespace = self._openid_ns_uri
if namespace != BARE_NS and type(namespace) not in [str, unicode]:
raise TypeError(
"Namespace must be BARE_NS, OPENID_NS or a string. got %r"
% (namespace,))
if namespace != BARE_NS and ':' not in namespace:
fmt = 'OpenID 2.0 namespace identifiers SHOULD be URIs. Got %r'
warnings.warn(fmt % (namespace,), DeprecationWarning)
if namespace == 'sreg':
fmt = 'Using %r instead of "sreg" as namespace'
warnings.warn(fmt % (SREG_URI,), DeprecationWarning,)
return SREG_URI
return namespace
<|endoftext|> | [
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senaite/senaite.core | bika/lims/setuphandlers.py | setup_handler | def setup_handler(context):
"""SENAITE setup handler
"""
if context.readDataFile("bika.lims_various.txt") is None:
return
logger.info("SENAITE setup handler [BEGIN]")
portal = context.getSite()
# Run Installers
remove_default_content(portal)
hide_navbar_items(portal)
reindex_content_structure(portal)
setup_groups(portal)
setup_catalog_mappings(portal)
setup_core_catalogs(portal)
# Setting up all LIMS catalogs defined in catalog folder
setup_catalogs(portal, getCatalogDefinitions())
# Run after all catalogs have been setup
setup_auditlog_catalog(portal)
logger.info("SENAITE setup handler [DONE]") | python | def setup_handler(context):
"""SENAITE setup handler
"""
if context.readDataFile("bika.lims_various.txt") is None:
return
logger.info("SENAITE setup handler [BEGIN]")
portal = context.getSite()
# Run Installers
remove_default_content(portal)
hide_navbar_items(portal)
reindex_content_structure(portal)
setup_groups(portal)
setup_catalog_mappings(portal)
setup_core_catalogs(portal)
# Setting up all LIMS catalogs defined in catalog folder
setup_catalogs(portal, getCatalogDefinitions())
# Run after all catalogs have been setup
setup_auditlog_catalog(portal)
logger.info("SENAITE setup handler [DONE]") | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
SENAITE setup handler
### Response:
def setup_handler(context):
"""SENAITE setup handler
"""
if context.readDataFile("bika.lims_various.txt") is None:
return
logger.info("SENAITE setup handler [BEGIN]")
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reindex_content_structure(portal)
setup_groups(portal)
setup_catalog_mappings(portal)
setup_core_catalogs(portal)
# Setting up all LIMS catalogs defined in catalog folder
setup_catalogs(portal, getCatalogDefinitions())
# Run after all catalogs have been setup
setup_auditlog_catalog(portal)
logger.info("SENAITE setup handler [DONE]")
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Microsoft/LightGBM | python-package/lightgbm/basic.py | Dataset.__init_from_list_np2d | def __init_from_list_np2d(self, mats, params_str, ref_dataset):
"""Initialize data from a list of 2-D numpy matrices."""
ncol = mats[0].shape[1]
nrow = np.zeros((len(mats),), np.int32)
if mats[0].dtype == np.float64:
ptr_data = (ctypes.POINTER(ctypes.c_double) * len(mats))()
else:
ptr_data = (ctypes.POINTER(ctypes.c_float) * len(mats))()
holders = []
type_ptr_data = None
for i, mat in enumerate(mats):
if len(mat.shape) != 2:
raise ValueError('Input numpy.ndarray must be 2 dimensional')
if mat.shape[1] != ncol:
raise ValueError('Input arrays must have same number of columns')
nrow[i] = mat.shape[0]
if mat.dtype == np.float32 or mat.dtype == np.float64:
mats[i] = np.array(mat.reshape(mat.size), dtype=mat.dtype, copy=False)
else:
# change non-float data to float data, need to copy
mats[i] = np.array(mat.reshape(mat.size), dtype=np.float32)
chunk_ptr_data, chunk_type_ptr_data, holder = c_float_array(mats[i])
if type_ptr_data is not None and chunk_type_ptr_data != type_ptr_data:
raise ValueError('Input chunks must have same type')
ptr_data[i] = chunk_ptr_data
type_ptr_data = chunk_type_ptr_data
holders.append(holder)
self.handle = ctypes.c_void_p()
_safe_call(_LIB.LGBM_DatasetCreateFromMats(
ctypes.c_int(len(mats)),
ctypes.cast(ptr_data, ctypes.POINTER(ctypes.POINTER(ctypes.c_double))),
ctypes.c_int(type_ptr_data),
nrow.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
ctypes.c_int(ncol),
ctypes.c_int(C_API_IS_ROW_MAJOR),
c_str(params_str),
ref_dataset,
ctypes.byref(self.handle)))
return self | python | def __init_from_list_np2d(self, mats, params_str, ref_dataset):
"""Initialize data from a list of 2-D numpy matrices."""
ncol = mats[0].shape[1]
nrow = np.zeros((len(mats),), np.int32)
if mats[0].dtype == np.float64:
ptr_data = (ctypes.POINTER(ctypes.c_double) * len(mats))()
else:
ptr_data = (ctypes.POINTER(ctypes.c_float) * len(mats))()
holders = []
type_ptr_data = None
for i, mat in enumerate(mats):
if len(mat.shape) != 2:
raise ValueError('Input numpy.ndarray must be 2 dimensional')
if mat.shape[1] != ncol:
raise ValueError('Input arrays must have same number of columns')
nrow[i] = mat.shape[0]
if mat.dtype == np.float32 or mat.dtype == np.float64:
mats[i] = np.array(mat.reshape(mat.size), dtype=mat.dtype, copy=False)
else:
# change non-float data to float data, need to copy
mats[i] = np.array(mat.reshape(mat.size), dtype=np.float32)
chunk_ptr_data, chunk_type_ptr_data, holder = c_float_array(mats[i])
if type_ptr_data is not None and chunk_type_ptr_data != type_ptr_data:
raise ValueError('Input chunks must have same type')
ptr_data[i] = chunk_ptr_data
type_ptr_data = chunk_type_ptr_data
holders.append(holder)
self.handle = ctypes.c_void_p()
_safe_call(_LIB.LGBM_DatasetCreateFromMats(
ctypes.c_int(len(mats)),
ctypes.cast(ptr_data, ctypes.POINTER(ctypes.POINTER(ctypes.c_double))),
ctypes.c_int(type_ptr_data),
nrow.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
ctypes.c_int(ncol),
ctypes.c_int(C_API_IS_ROW_MAJOR),
c_str(params_str),
ref_dataset,
ctypes.byref(self.handle)))
return self | [
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] | Initialize data from a list of 2-D numpy matrices. | [
"Initialize",
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] | train | https://github.com/Microsoft/LightGBM/blob/8d2ec69f4f685b0ab1c4624d59ee2d3287bb3147/python-package/lightgbm/basic.py#L872-L917 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Initialize data from a list of 2-D numpy matrices.
### Response:
def __init_from_list_np2d(self, mats, params_str, ref_dataset):
"""Initialize data from a list of 2-D numpy matrices."""
ncol = mats[0].shape[1]
nrow = np.zeros((len(mats),), np.int32)
if mats[0].dtype == np.float64:
ptr_data = (ctypes.POINTER(ctypes.c_double) * len(mats))()
else:
ptr_data = (ctypes.POINTER(ctypes.c_float) * len(mats))()
holders = []
type_ptr_data = None
for i, mat in enumerate(mats):
if len(mat.shape) != 2:
raise ValueError('Input numpy.ndarray must be 2 dimensional')
if mat.shape[1] != ncol:
raise ValueError('Input arrays must have same number of columns')
nrow[i] = mat.shape[0]
if mat.dtype == np.float32 or mat.dtype == np.float64:
mats[i] = np.array(mat.reshape(mat.size), dtype=mat.dtype, copy=False)
else:
# change non-float data to float data, need to copy
mats[i] = np.array(mat.reshape(mat.size), dtype=np.float32)
chunk_ptr_data, chunk_type_ptr_data, holder = c_float_array(mats[i])
if type_ptr_data is not None and chunk_type_ptr_data != type_ptr_data:
raise ValueError('Input chunks must have same type')
ptr_data[i] = chunk_ptr_data
type_ptr_data = chunk_type_ptr_data
holders.append(holder)
self.handle = ctypes.c_void_p()
_safe_call(_LIB.LGBM_DatasetCreateFromMats(
ctypes.c_int(len(mats)),
ctypes.cast(ptr_data, ctypes.POINTER(ctypes.POINTER(ctypes.c_double))),
ctypes.c_int(type_ptr_data),
nrow.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
ctypes.c_int(ncol),
ctypes.c_int(C_API_IS_ROW_MAJOR),
c_str(params_str),
ref_dataset,
ctypes.byref(self.handle)))
return self
<|endoftext|> | [
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paylogic/halogen | halogen/types.py | ISOUTCDateTime.format_as_utc | def format_as_utc(self, value):
"""Format UTC times."""
if isinstance(value, datetime.datetime):
if value.tzinfo is not None:
value = value.astimezone(pytz.UTC)
value = value.replace(microsecond=0)
return value.isoformat().replace('+00:00', 'Z') | python | def format_as_utc(self, value):
"""Format UTC times."""
if isinstance(value, datetime.datetime):
if value.tzinfo is not None:
value = value.astimezone(pytz.UTC)
value = value.replace(microsecond=0)
return value.isoformat().replace('+00:00', 'Z') | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Format UTC times.
### Response:
def format_as_utc(self, value):
"""Format UTC times."""
if isinstance(value, datetime.datetime):
if value.tzinfo is not None:
value = value.astimezone(pytz.UTC)
value = value.replace(microsecond=0)
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WZBSocialScienceCenter/tmtoolkit | tmtoolkit/topicmod/visualize.py | plot_topic_word_heatmap | def plot_topic_word_heatmap(fig, ax, topic_word_distrib, vocab,
which_topics=None, which_topic_indices=None,
which_words=None, which_word_indices=None,
xaxislabel=None, yaxislabel=None,
**kwargs):
"""
Plot a heatmap for a topic-word distribution `topic_word_distrib` to a matplotlib Figure `fig` and Axes `ax`
using `vocab` as vocabulary on the x-axis and topics from 1 to `n_topics=doc_topic_distrib.shape[1]` on
the y-axis.
A subset of words from `vocab` can be specified either directly with a sequence `which_words` or
`which_document_indices` containing a sequence of word indices in `vocab`.
A subset of topics can be specified either with a sequence `which_topics` containing sequence of numbers between
[1, n_topics] or `which_topic_indices` which is a number between [0, n_topics-1]
Additional arguments can be passed via `kwargs` to `plot_heatmap`.
Please note that it is almost always necessary to select a subset of your topic-word distribution with the
`which_words` or `which_topics` parameters, as otherwise the amount of data to be plotted will be too high
to give a reasonable picture.
"""
if which_topics is not None and which_topic_indices is not None:
raise ValueError('only `which_topics` or `which_topic_indices` can be set, not both')
if which_words is not None and which_word_indices is not None:
raise ValueError('only `which_words` or `which_word_indices` can be set, not both')
if which_topics is not None:
which_topic_indices = np.array(which_topics) - 1
if which_words is not None:
which_word_indices = np.where(np.isin(vocab, which_words))[0]
select_distrib_subset = False
topic_labels = np.array(range(1, topic_word_distrib.shape[0]+1))
if which_topic_indices is not None:
select_distrib_subset = True
topic_labels = topic_labels[which_topic_indices]
if which_word_indices is not None:
select_distrib_subset = True
vocab = np.array(vocab)[which_word_indices]
if select_distrib_subset:
topic_word_distrib = mat2d_window_from_indices(topic_word_distrib, which_topic_indices, which_word_indices)
return plot_heatmap(fig, ax, topic_word_distrib,
xaxislabel=xaxislabel or 'vocab',
yaxislabel=yaxislabel or 'topic',
xticklabels=vocab,
yticklabels=topic_labels,
**kwargs) | python | def plot_topic_word_heatmap(fig, ax, topic_word_distrib, vocab,
which_topics=None, which_topic_indices=None,
which_words=None, which_word_indices=None,
xaxislabel=None, yaxislabel=None,
**kwargs):
"""
Plot a heatmap for a topic-word distribution `topic_word_distrib` to a matplotlib Figure `fig` and Axes `ax`
using `vocab` as vocabulary on the x-axis and topics from 1 to `n_topics=doc_topic_distrib.shape[1]` on
the y-axis.
A subset of words from `vocab` can be specified either directly with a sequence `which_words` or
`which_document_indices` containing a sequence of word indices in `vocab`.
A subset of topics can be specified either with a sequence `which_topics` containing sequence of numbers between
[1, n_topics] or `which_topic_indices` which is a number between [0, n_topics-1]
Additional arguments can be passed via `kwargs` to `plot_heatmap`.
Please note that it is almost always necessary to select a subset of your topic-word distribution with the
`which_words` or `which_topics` parameters, as otherwise the amount of data to be plotted will be too high
to give a reasonable picture.
"""
if which_topics is not None and which_topic_indices is not None:
raise ValueError('only `which_topics` or `which_topic_indices` can be set, not both')
if which_words is not None and which_word_indices is not None:
raise ValueError('only `which_words` or `which_word_indices` can be set, not both')
if which_topics is not None:
which_topic_indices = np.array(which_topics) - 1
if which_words is not None:
which_word_indices = np.where(np.isin(vocab, which_words))[0]
select_distrib_subset = False
topic_labels = np.array(range(1, topic_word_distrib.shape[0]+1))
if which_topic_indices is not None:
select_distrib_subset = True
topic_labels = topic_labels[which_topic_indices]
if which_word_indices is not None:
select_distrib_subset = True
vocab = np.array(vocab)[which_word_indices]
if select_distrib_subset:
topic_word_distrib = mat2d_window_from_indices(topic_word_distrib, which_topic_indices, which_word_indices)
return plot_heatmap(fig, ax, topic_word_distrib,
xaxislabel=xaxislabel or 'vocab',
yaxislabel=yaxislabel or 'topic',
xticklabels=vocab,
yticklabels=topic_labels,
**kwargs) | [
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A subset of words from `vocab` can be specified either directly with a sequence `which_words` or
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A subset of topics can be specified either with a sequence `which_topics` containing sequence of numbers between
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Additional arguments can be passed via `kwargs` to `plot_heatmap`.
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] | train | https://github.com/WZBSocialScienceCenter/tmtoolkit/blob/ca8b9d072e37ccc82b533f47d48bd9755722305b/tmtoolkit/topicmod/visualize.py#L182-L232 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Plot a heatmap for a topic-word distribution `topic_word_distrib` to a matplotlib Figure `fig` and Axes `ax`
using `vocab` as vocabulary on the x-axis and topics from 1 to `n_topics=doc_topic_distrib.shape[1]` on
the y-axis.
A subset of words from `vocab` can be specified either directly with a sequence `which_words` or
`which_document_indices` containing a sequence of word indices in `vocab`.
A subset of topics can be specified either with a sequence `which_topics` containing sequence of numbers between
[1, n_topics] or `which_topic_indices` which is a number between [0, n_topics-1]
Additional arguments can be passed via `kwargs` to `plot_heatmap`.
Please note that it is almost always necessary to select a subset of your topic-word distribution with the
`which_words` or `which_topics` parameters, as otherwise the amount of data to be plotted will be too high
to give a reasonable picture.
### Response:
def plot_topic_word_heatmap(fig, ax, topic_word_distrib, vocab,
which_topics=None, which_topic_indices=None,
which_words=None, which_word_indices=None,
xaxislabel=None, yaxislabel=None,
**kwargs):
"""
Plot a heatmap for a topic-word distribution `topic_word_distrib` to a matplotlib Figure `fig` and Axes `ax`
using `vocab` as vocabulary on the x-axis and topics from 1 to `n_topics=doc_topic_distrib.shape[1]` on
the y-axis.
A subset of words from `vocab` can be specified either directly with a sequence `which_words` or
`which_document_indices` containing a sequence of word indices in `vocab`.
A subset of topics can be specified either with a sequence `which_topics` containing sequence of numbers between
[1, n_topics] or `which_topic_indices` which is a number between [0, n_topics-1]
Additional arguments can be passed via `kwargs` to `plot_heatmap`.
Please note that it is almost always necessary to select a subset of your topic-word distribution with the
`which_words` or `which_topics` parameters, as otherwise the amount of data to be plotted will be too high
to give a reasonable picture.
"""
if which_topics is not None and which_topic_indices is not None:
raise ValueError('only `which_topics` or `which_topic_indices` can be set, not both')
if which_words is not None and which_word_indices is not None:
raise ValueError('only `which_words` or `which_word_indices` can be set, not both')
if which_topics is not None:
which_topic_indices = np.array(which_topics) - 1
if which_words is not None:
which_word_indices = np.where(np.isin(vocab, which_words))[0]
select_distrib_subset = False
topic_labels = np.array(range(1, topic_word_distrib.shape[0]+1))
if which_topic_indices is not None:
select_distrib_subset = True
topic_labels = topic_labels[which_topic_indices]
if which_word_indices is not None:
select_distrib_subset = True
vocab = np.array(vocab)[which_word_indices]
if select_distrib_subset:
topic_word_distrib = mat2d_window_from_indices(topic_word_distrib, which_topic_indices, which_word_indices)
return plot_heatmap(fig, ax, topic_word_distrib,
xaxislabel=xaxislabel or 'vocab',
yaxislabel=yaxislabel or 'topic',
xticklabels=vocab,
yticklabels=topic_labels,
**kwargs)
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ajk8/microcache | microcache/__init__.py | Microcache.temporarily_disabled | def temporarily_disabled(self):
"""
Temporarily disable the cache (useful for testing)
"""
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self.disable(clear_cache=False)
try:
yield
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"""
Temporarily disable the cache (useful for testing)
"""
old_setting = self.options.enabled
self.disable(clear_cache=False)
try:
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finally:
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Temporarily disable the cache (useful for testing)
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tensorflow/tensor2tensor | tensor2tensor/models/evolved_transformer.py | add_evolved_transformer_hparams | def add_evolved_transformer_hparams(hparams):
"""Add Evolved Transformer hparams.
Note: These are for the Adam optimizer, not the Adafactor optimizer used in
the paper.
Args:
hparams: Current hparams.
Returns:
hparams updated with Evolved Transformer values.
"""
# Evolved Transformer "layers" are twice as deep as Transformer, so roughly
# halve the number that we use. These numbers are taken from
# arxiv.org/abs/1901.11117 .
hparams.num_encoder_layers = 3
hparams.num_decoder_layers = 4
# Learning rate and decay scheme that mimics the transformer Adam config,
# but with cosine decay instead of rsqrt.
hparams.learning_rate_constant /= hparams.learning_rate_warmup_steps ** 0.5
hparams.learning_rate_schedule = (
"constant*linear_warmup*single_cycle_cos_decay*rsqrt_hidden_size")
# The current infrastructure does not support exposing
# `train_steps` to the decay functions, and so we are hard coding the decay
# steps here to match the default number of train steps used in `t2t_trainer`.
# TODO(davidso): Thread `train_steps` through to decay functions so we do not
# have to worry about a `learning_rate_decay_steps` mismatch.
hparams.learning_rate_decay_steps = 250000
return hparams | python | def add_evolved_transformer_hparams(hparams):
"""Add Evolved Transformer hparams.
Note: These are for the Adam optimizer, not the Adafactor optimizer used in
the paper.
Args:
hparams: Current hparams.
Returns:
hparams updated with Evolved Transformer values.
"""
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# halve the number that we use. These numbers are taken from
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hparams.num_encoder_layers = 3
hparams.num_decoder_layers = 4
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You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
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Add Evolved Transformer hparams.
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hparams updated with Evolved Transformer values.
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def add_evolved_transformer_hparams(hparams):
"""Add Evolved Transformer hparams.
Note: These are for the Adam optimizer, not the Adafactor optimizer used in
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Args:
hparams: Current hparams.
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hparams updated with Evolved Transformer values.
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orbeckst/RecSQL | recsql/sqlarray.py | KRingbuffer._prune | def _prune(self):
"""Primitive way to keep dict in sync with RB."""
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for k in delkeys: # necessary because dict is changed during iterations
super(KRingbuffer,self).__delitem__(k) | python | def _prune(self):
"""Primitive way to keep dict in sync with RB."""
delkeys = [k for k in self.keys() if k not in self.__ringbuffer]
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You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Primitive way to keep dict in sync with RB.
### Response:
def _prune(self):
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miguelgrinberg/python-engineio | engineio/asyncio_server.py | AsyncServer.start_background_task | def start_background_task(self, target, *args, **kwargs):
"""Start a background task using the appropriate async model.
This is a utility function that applications can use to start a
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:param target: the target function to execute.
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return asyncio.ensure_future(target(*args, **kwargs)) | python | def start_background_task(self, target, *args, **kwargs):
"""Start a background task using the appropriate async model.
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:param args: arguments to pass to the function.
:param kwargs: keyword arguments to pass to the function.
The return value is a ``asyncio.Task`` object.
"""
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Start a background task using the appropriate async model.
This is a utility function that applications can use to start a
background task using the method that is compatible with the
selected async mode.
:param target: the target function to execute.
:param args: arguments to pass to the function.
:param kwargs: keyword arguments to pass to the function.
The return value is a ``asyncio.Task`` object.
### Response:
def start_background_task(self, target, *args, **kwargs):
"""Start a background task using the appropriate async model.
This is a utility function that applications can use to start a
background task using the method that is compatible with the
selected async mode.
:param target: the target function to execute.
:param args: arguments to pass to the function.
:param kwargs: keyword arguments to pass to the function.
The return value is a ``asyncio.Task`` object.
"""
return asyncio.ensure_future(target(*args, **kwargs))
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lago-project/lago | lago/build.py | Build.normalize_build_spec | def normalize_build_spec(self, build_spec):
"""
Convert a build spec into a list of Command tuples.
After running this command, self.build_cmds should hold all
the commands that should be run on the disk in self.disk_path.
Args:
build_spec (dict): The buildspec part from the init file
"""
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if not cmd:
continue
cmd_name = cmd.keys()[0]
cmd_options = cmd.values()[0]
cmd_handler = self.get_cmd_handler(cmd_name)
self.build_cmds.append(cmd_handler(cmd_options)) | python | def normalize_build_spec(self, build_spec):
"""
Convert a build spec into a list of Command tuples.
After running this command, self.build_cmds should hold all
the commands that should be run on the disk in self.disk_path.
Args:
build_spec (dict): The buildspec part from the init file
"""
for cmd in build_spec:
if not cmd:
continue
cmd_name = cmd.keys()[0]
cmd_options = cmd.values()[0]
cmd_handler = self.get_cmd_handler(cmd_name)
self.build_cmds.append(cmd_handler(cmd_options)) | [
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] | train | https://github.com/lago-project/lago/blob/5b8970f7687e063e4619066d5b8093ca997678c9/lago/build.py#L120-L135 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Convert a build spec into a list of Command tuples.
After running this command, self.build_cmds should hold all
the commands that should be run on the disk in self.disk_path.
Args:
build_spec (dict): The buildspec part from the init file
### Response:
def normalize_build_spec(self, build_spec):
"""
Convert a build spec into a list of Command tuples.
After running this command, self.build_cmds should hold all
the commands that should be run on the disk in self.disk_path.
Args:
build_spec (dict): The buildspec part from the init file
"""
for cmd in build_spec:
if not cmd:
continue
cmd_name = cmd.keys()[0]
cmd_options = cmd.values()[0]
cmd_handler = self.get_cmd_handler(cmd_name)
self.build_cmds.append(cmd_handler(cmd_options))
<|endoftext|> | [
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exekias/droplet | droplet/commands.py | run | def run(cmd, background=False):
"""
Executes the given command
If background flag is True the command will run in background
and this method will return a :class:`Popen` object
If background is False (default) the command will run in this thread
and this method will return stdout.
A CommandException will be raised if command fails
"""
logger.debug('Running command: %s' % cmd)
if background:
return subprocess.Popen(cmd, shell=True, close_fds=True)
else:
(status, output) = commands.getstatusoutput(cmd)
if status != 0:
logger.error("Command failed: %s" % cmd)
if output:
logger.debug('OUTPUT:\n' + output)
if status != 0:
raise CommandException(status, output)
return output | python | def run(cmd, background=False):
"""
Executes the given command
If background flag is True the command will run in background
and this method will return a :class:`Popen` object
If background is False (default) the command will run in this thread
and this method will return stdout.
A CommandException will be raised if command fails
"""
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if background:
return subprocess.Popen(cmd, shell=True, close_fds=True)
else:
(status, output) = commands.getstatusoutput(cmd)
if status != 0:
logger.error("Command failed: %s" % cmd)
if output:
logger.debug('OUTPUT:\n' + output)
if status != 0:
raise CommandException(status, output)
return output | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Executes the given command
If background flag is True the command will run in background
and this method will return a :class:`Popen` object
If background is False (default) the command will run in this thread
and this method will return stdout.
A CommandException will be raised if command fails
### Response:
def run(cmd, background=False):
"""
Executes the given command
If background flag is True the command will run in background
and this method will return a :class:`Popen` object
If background is False (default) the command will run in this thread
and this method will return stdout.
A CommandException will be raised if command fails
"""
logger.debug('Running command: %s' % cmd)
if background:
return subprocess.Popen(cmd, shell=True, close_fds=True)
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(status, output) = commands.getstatusoutput(cmd)
if status != 0:
logger.error("Command failed: %s" % cmd)
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hanguokai/youku | youku/youku_schemas.py | YoukuSchemas.playlist_category | def playlist_category(self):
"""doc: http://open.youku.com/docs/doc?id=94
"""
url = 'https://openapi.youku.com/v2/schemas/playlist/category.json'
r = requests.get(url)
check_error(r)
return r.json() | python | def playlist_category(self):
"""doc: http://open.youku.com/docs/doc?id=94
"""
url = 'https://openapi.youku.com/v2/schemas/playlist/category.json'
r = requests.get(url)
check_error(r)
return r.json() | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
doc: http://open.youku.com/docs/doc?id=94
### Response:
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check_error(r)
return r.json()
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apache/incubator-mxnet | example/gluon/embedding_learning/train.py | train | def train(epochs, ctx):
"""Training function."""
if isinstance(ctx, mx.Context):
ctx = [ctx]
net.initialize(mx.init.Xavier(magnitude=2), ctx=ctx)
opt_options = {'learning_rate': opt.lr, 'wd': opt.wd}
if opt.optimizer == 'sgd':
opt_options['momentum'] = 0.9
if opt.optimizer == 'adam':
opt_options['epsilon'] = 1e-7
trainer = gluon.Trainer(net.collect_params(), opt.optimizer,
opt_options,
kvstore=opt.kvstore)
if opt.lr_beta > 0.0:
# Jointly train class-specific beta.
# See "sampling matters in deep embedding learning" paper for details.
beta.initialize(mx.init.Constant(opt.beta), ctx=ctx)
trainer_beta = gluon.Trainer([beta], 'sgd',
{'learning_rate': opt.lr_beta, 'momentum': 0.9},
kvstore=opt.kvstore)
loss = MarginLoss(margin=opt.margin, nu=opt.nu)
best_val = 0.0
for epoch in range(epochs):
tic = time.time()
prev_loss, cumulative_loss = 0.0, 0.0
# Learning rate schedule.
trainer.set_learning_rate(get_lr(opt.lr, epoch, steps, opt.factor))
logging.info('Epoch %d learning rate=%f', epoch, trainer.learning_rate)
if opt.lr_beta > 0.0:
trainer_beta.set_learning_rate(get_lr(opt.lr_beta, epoch, steps, opt.factor))
logging.info('Epoch %d beta learning rate=%f', epoch, trainer_beta.learning_rate)
# Inner training loop.
for i in range(200):
batch = train_data.next()
data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
Ls = []
with ag.record():
for x, y in zip(data, label):
a_indices, anchors, positives, negatives, _ = net(x)
if opt.lr_beta > 0.0:
L = loss(anchors, positives, negatives, beta, y[a_indices])
else:
L = loss(anchors, positives, negatives, opt.beta, None)
# Store the loss and do backward after we have done forward
# on all GPUs for better speed on multiple GPUs.
Ls.append(L)
cumulative_loss += nd.mean(L).asscalar()
for L in Ls:
L.backward()
# Update.
trainer.step(batch.data[0].shape[0])
if opt.lr_beta > 0.0:
trainer_beta.step(batch.data[0].shape[0])
if (i+1) % opt.log_interval == 0:
logging.info('[Epoch %d, Iter %d] training loss=%f' % (
epoch, i+1, cumulative_loss - prev_loss))
prev_loss = cumulative_loss
logging.info('[Epoch %d] training loss=%f'%(epoch, cumulative_loss))
logging.info('[Epoch %d] time cost: %f'%(epoch, time.time()-tic))
names, val_accs = test(ctx)
for name, val_acc in zip(names, val_accs):
logging.info('[Epoch %d] validation: %s=%f'%(epoch, name, val_acc))
if val_accs[0] > best_val:
best_val = val_accs[0]
logging.info('Saving %s.' % opt.save_model_prefix)
net.save_parameters('%s.params' % opt.save_model_prefix)
return best_val | python | def train(epochs, ctx):
"""Training function."""
if isinstance(ctx, mx.Context):
ctx = [ctx]
net.initialize(mx.init.Xavier(magnitude=2), ctx=ctx)
opt_options = {'learning_rate': opt.lr, 'wd': opt.wd}
if opt.optimizer == 'sgd':
opt_options['momentum'] = 0.9
if opt.optimizer == 'adam':
opt_options['epsilon'] = 1e-7
trainer = gluon.Trainer(net.collect_params(), opt.optimizer,
opt_options,
kvstore=opt.kvstore)
if opt.lr_beta > 0.0:
# Jointly train class-specific beta.
# See "sampling matters in deep embedding learning" paper for details.
beta.initialize(mx.init.Constant(opt.beta), ctx=ctx)
trainer_beta = gluon.Trainer([beta], 'sgd',
{'learning_rate': opt.lr_beta, 'momentum': 0.9},
kvstore=opt.kvstore)
loss = MarginLoss(margin=opt.margin, nu=opt.nu)
best_val = 0.0
for epoch in range(epochs):
tic = time.time()
prev_loss, cumulative_loss = 0.0, 0.0
# Learning rate schedule.
trainer.set_learning_rate(get_lr(opt.lr, epoch, steps, opt.factor))
logging.info('Epoch %d learning rate=%f', epoch, trainer.learning_rate)
if opt.lr_beta > 0.0:
trainer_beta.set_learning_rate(get_lr(opt.lr_beta, epoch, steps, opt.factor))
logging.info('Epoch %d beta learning rate=%f', epoch, trainer_beta.learning_rate)
# Inner training loop.
for i in range(200):
batch = train_data.next()
data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
Ls = []
with ag.record():
for x, y in zip(data, label):
a_indices, anchors, positives, negatives, _ = net(x)
if opt.lr_beta > 0.0:
L = loss(anchors, positives, negatives, beta, y[a_indices])
else:
L = loss(anchors, positives, negatives, opt.beta, None)
# Store the loss and do backward after we have done forward
# on all GPUs for better speed on multiple GPUs.
Ls.append(L)
cumulative_loss += nd.mean(L).asscalar()
for L in Ls:
L.backward()
# Update.
trainer.step(batch.data[0].shape[0])
if opt.lr_beta > 0.0:
trainer_beta.step(batch.data[0].shape[0])
if (i+1) % opt.log_interval == 0:
logging.info('[Epoch %d, Iter %d] training loss=%f' % (
epoch, i+1, cumulative_loss - prev_loss))
prev_loss = cumulative_loss
logging.info('[Epoch %d] training loss=%f'%(epoch, cumulative_loss))
logging.info('[Epoch %d] time cost: %f'%(epoch, time.time()-tic))
names, val_accs = test(ctx)
for name, val_acc in zip(names, val_accs):
logging.info('[Epoch %d] validation: %s=%f'%(epoch, name, val_acc))
if val_accs[0] > best_val:
best_val = val_accs[0]
logging.info('Saving %s.' % opt.save_model_prefix)
net.save_parameters('%s.params' % opt.save_model_prefix)
return best_val | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Training function.
### Response:
def train(epochs, ctx):
"""Training function."""
if isinstance(ctx, mx.Context):
ctx = [ctx]
net.initialize(mx.init.Xavier(magnitude=2), ctx=ctx)
opt_options = {'learning_rate': opt.lr, 'wd': opt.wd}
if opt.optimizer == 'sgd':
opt_options['momentum'] = 0.9
if opt.optimizer == 'adam':
opt_options['epsilon'] = 1e-7
trainer = gluon.Trainer(net.collect_params(), opt.optimizer,
opt_options,
kvstore=opt.kvstore)
if opt.lr_beta > 0.0:
# Jointly train class-specific beta.
# See "sampling matters in deep embedding learning" paper for details.
beta.initialize(mx.init.Constant(opt.beta), ctx=ctx)
trainer_beta = gluon.Trainer([beta], 'sgd',
{'learning_rate': opt.lr_beta, 'momentum': 0.9},
kvstore=opt.kvstore)
loss = MarginLoss(margin=opt.margin, nu=opt.nu)
best_val = 0.0
for epoch in range(epochs):
tic = time.time()
prev_loss, cumulative_loss = 0.0, 0.0
# Learning rate schedule.
trainer.set_learning_rate(get_lr(opt.lr, epoch, steps, opt.factor))
logging.info('Epoch %d learning rate=%f', epoch, trainer.learning_rate)
if opt.lr_beta > 0.0:
trainer_beta.set_learning_rate(get_lr(opt.lr_beta, epoch, steps, opt.factor))
logging.info('Epoch %d beta learning rate=%f', epoch, trainer_beta.learning_rate)
# Inner training loop.
for i in range(200):
batch = train_data.next()
data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
Ls = []
with ag.record():
for x, y in zip(data, label):
a_indices, anchors, positives, negatives, _ = net(x)
if opt.lr_beta > 0.0:
L = loss(anchors, positives, negatives, beta, y[a_indices])
else:
L = loss(anchors, positives, negatives, opt.beta, None)
# Store the loss and do backward after we have done forward
# on all GPUs for better speed on multiple GPUs.
Ls.append(L)
cumulative_loss += nd.mean(L).asscalar()
for L in Ls:
L.backward()
# Update.
trainer.step(batch.data[0].shape[0])
if opt.lr_beta > 0.0:
trainer_beta.step(batch.data[0].shape[0])
if (i+1) % opt.log_interval == 0:
logging.info('[Epoch %d, Iter %d] training loss=%f' % (
epoch, i+1, cumulative_loss - prev_loss))
prev_loss = cumulative_loss
logging.info('[Epoch %d] training loss=%f'%(epoch, cumulative_loss))
logging.info('[Epoch %d] time cost: %f'%(epoch, time.time()-tic))
names, val_accs = test(ctx)
for name, val_acc in zip(names, val_accs):
logging.info('[Epoch %d] validation: %s=%f'%(epoch, name, val_acc))
if val_accs[0] > best_val:
best_val = val_accs[0]
logging.info('Saving %s.' % opt.save_model_prefix)
net.save_parameters('%s.params' % opt.save_model_prefix)
return best_val
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gsi-upm/soil | soil/web/__init__.py | ModularServer.launch | def launch(self, port=None):
""" Run the app. """
if port is not None:
self.port = port
url = 'http://127.0.0.1:{PORT}'.format(PORT=self.port)
print('Interface starting at {url}'.format(url=url))
self.listen(self.port)
# webbrowser.open(url)
tornado.ioloop.IOLoop.instance().start() | python | def launch(self, port=None):
""" Run the app. """
if port is not None:
self.port = port
url = 'http://127.0.0.1:{PORT}'.format(PORT=self.port)
print('Interface starting at {url}'.format(url=url))
self.listen(self.port)
# webbrowser.open(url)
tornado.ioloop.IOLoop.instance().start() | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Run the app.
### Response:
def launch(self, port=None):
""" Run the app. """
if port is not None:
self.port = port
url = 'http://127.0.0.1:{PORT}'.format(PORT=self.port)
print('Interface starting at {url}'.format(url=url))
self.listen(self.port)
# webbrowser.open(url)
tornado.ioloop.IOLoop.instance().start()
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user-cont/conu | conu/backend/k8s/backend.py | K8sBackend.delete_namespace | def delete_namespace(self, name):
"""
Delete namespace with specific name
:param name: str, namespace to delete
:return: None
"""
self.core_api.delete_namespace(name, client.V1DeleteOptions())
logger.info("Deleting namespace: %s", name) | python | def delete_namespace(self, name):
"""
Delete namespace with specific name
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Delete namespace with specific name
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### Response:
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quantmind/dynts | dynts/api/timeseries.py | TimeSeries.series | def series(self):
'''Generator of single series data (no dates are included).'''
data = self.values()
if len(data):
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else:
raise StopIteration | python | def series(self):
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Generator of single series data (no dates are included).
### Response:
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'''Generator of single series data (no dates are included).'''
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biosignalsnotebooks/biosignalsnotebooks | biosignalsnotebooks/build/lib/biosignalsnotebooks/visualise.py | opensignals_style | def opensignals_style(figure_list, grid_plot=None, toolbar="right"):
"""
-----
Brief
-----
Function used to automatically apply the OpenSignals graphical style to Bokeh plots.
-----------
Description
-----------
OpenSignals has its own graphical style and Bokeh plots function in an object oriented way.
This function allows to apply the Opensignals graphical style to a set of Bokeh figures objects given as inputs.
----------
Parameters
----------
figure_list : bokeh figure/s
The base object/s where the graphical functions will be applied.
grid_plot : bokeh gridplot
Contains the layout structure, where multiple bokeh figures are represented.
toolbar : str
String defining the toolbar position.
"""
for fig in figure_list:
fig.background_fill_color = (242, 242, 242)
fig.toolbar.active_scroll = fig.select_one(WheelZoomTool)
# Removal of unnecessary tools.
figure_tools = fig.tools
for tool in range(len(figure_tools) - 1, -1, -1):
if not isinstance(figure_tools[tool], (type(PanTool()), type(BoxZoomTool()),
type(WheelZoomTool()), type(ResetTool()))):
del figure_tools[tool]
fig.sizing_mode = 'scale_width'
fig.height = 200
fig.toolbar.logo = None
fig.toolbar_location = toolbar
fig.xgrid.grid_line_color = (150, 150, 150)
fig.ygrid.grid_line_color = (150, 150, 150)
fig.xgrid.grid_line_dash = [2, 2]
fig.xaxis.major_tick_line_color = "white"
fig.xaxis.minor_tick_line_color = "white"
fig.xaxis.axis_line_color = "white"
fig.yaxis.major_tick_in = 0
fig.yaxis.major_tick_out = 0
fig.yaxis.major_tick_line_color = "white"
fig.yaxis.minor_tick_line_color = "white"
fig.yaxis.minor_tick_in = 0
fig.yaxis.minor_tick_out = 0
fig.yaxis.axis_line_color = (150, 150, 150)
fig.yaxis.axis_line_dash = [2, 2]
fig.yaxis.major_label_text_color = (88, 88, 88)
fig.xaxis.major_label_text_color = (88, 88, 88)
fig.ygrid.grid_line_dash = [2, 2]
if isinstance(grid_plot, list):
if grid_plot:
for g_plot in grid_plot:
g_plot.sizing_mode = 'scale_width'
g_plot.height = 600 | python | def opensignals_style(figure_list, grid_plot=None, toolbar="right"):
"""
-----
Brief
-----
Function used to automatically apply the OpenSignals graphical style to Bokeh plots.
-----------
Description
-----------
OpenSignals has its own graphical style and Bokeh plots function in an object oriented way.
This function allows to apply the Opensignals graphical style to a set of Bokeh figures objects given as inputs.
----------
Parameters
----------
figure_list : bokeh figure/s
The base object/s where the graphical functions will be applied.
grid_plot : bokeh gridplot
Contains the layout structure, where multiple bokeh figures are represented.
toolbar : str
String defining the toolbar position.
"""
for fig in figure_list:
fig.background_fill_color = (242, 242, 242)
fig.toolbar.active_scroll = fig.select_one(WheelZoomTool)
# Removal of unnecessary tools.
figure_tools = fig.tools
for tool in range(len(figure_tools) - 1, -1, -1):
if not isinstance(figure_tools[tool], (type(PanTool()), type(BoxZoomTool()),
type(WheelZoomTool()), type(ResetTool()))):
del figure_tools[tool]
fig.sizing_mode = 'scale_width'
fig.height = 200
fig.toolbar.logo = None
fig.toolbar_location = toolbar
fig.xgrid.grid_line_color = (150, 150, 150)
fig.ygrid.grid_line_color = (150, 150, 150)
fig.xgrid.grid_line_dash = [2, 2]
fig.xaxis.major_tick_line_color = "white"
fig.xaxis.minor_tick_line_color = "white"
fig.xaxis.axis_line_color = "white"
fig.yaxis.major_tick_in = 0
fig.yaxis.major_tick_out = 0
fig.yaxis.major_tick_line_color = "white"
fig.yaxis.minor_tick_line_color = "white"
fig.yaxis.minor_tick_in = 0
fig.yaxis.minor_tick_out = 0
fig.yaxis.axis_line_color = (150, 150, 150)
fig.yaxis.axis_line_dash = [2, 2]
fig.yaxis.major_label_text_color = (88, 88, 88)
fig.xaxis.major_label_text_color = (88, 88, 88)
fig.ygrid.grid_line_dash = [2, 2]
if isinstance(grid_plot, list):
if grid_plot:
for g_plot in grid_plot:
g_plot.sizing_mode = 'scale_width'
g_plot.height = 600 | [
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Brief
-----
Function used to automatically apply the OpenSignals graphical style to Bokeh plots.
-----------
Description
-----------
OpenSignals has its own graphical style and Bokeh plots function in an object oriented way.
This function allows to apply the Opensignals graphical style to a set of Bokeh figures objects given as inputs.
----------
Parameters
----------
figure_list : bokeh figure/s
The base object/s where the graphical functions will be applied.
grid_plot : bokeh gridplot
Contains the layout structure, where multiple bokeh figures are represented.
toolbar : str
String defining the toolbar position. | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
-----
Brief
-----
Function used to automatically apply the OpenSignals graphical style to Bokeh plots.
-----------
Description
-----------
OpenSignals has its own graphical style and Bokeh plots function in an object oriented way.
This function allows to apply the Opensignals graphical style to a set of Bokeh figures objects given as inputs.
----------
Parameters
----------
figure_list : bokeh figure/s
The base object/s where the graphical functions will be applied.
grid_plot : bokeh gridplot
Contains the layout structure, where multiple bokeh figures are represented.
toolbar : str
String defining the toolbar position.
### Response:
def opensignals_style(figure_list, grid_plot=None, toolbar="right"):
"""
-----
Brief
-----
Function used to automatically apply the OpenSignals graphical style to Bokeh plots.
-----------
Description
-----------
OpenSignals has its own graphical style and Bokeh plots function in an object oriented way.
This function allows to apply the Opensignals graphical style to a set of Bokeh figures objects given as inputs.
----------
Parameters
----------
figure_list : bokeh figure/s
The base object/s where the graphical functions will be applied.
grid_plot : bokeh gridplot
Contains the layout structure, where multiple bokeh figures are represented.
toolbar : str
String defining the toolbar position.
"""
for fig in figure_list:
fig.background_fill_color = (242, 242, 242)
fig.toolbar.active_scroll = fig.select_one(WheelZoomTool)
# Removal of unnecessary tools.
figure_tools = fig.tools
for tool in range(len(figure_tools) - 1, -1, -1):
if not isinstance(figure_tools[tool], (type(PanTool()), type(BoxZoomTool()),
type(WheelZoomTool()), type(ResetTool()))):
del figure_tools[tool]
fig.sizing_mode = 'scale_width'
fig.height = 200
fig.toolbar.logo = None
fig.toolbar_location = toolbar
fig.xgrid.grid_line_color = (150, 150, 150)
fig.ygrid.grid_line_color = (150, 150, 150)
fig.xgrid.grid_line_dash = [2, 2]
fig.xaxis.major_tick_line_color = "white"
fig.xaxis.minor_tick_line_color = "white"
fig.xaxis.axis_line_color = "white"
fig.yaxis.major_tick_in = 0
fig.yaxis.major_tick_out = 0
fig.yaxis.major_tick_line_color = "white"
fig.yaxis.minor_tick_line_color = "white"
fig.yaxis.minor_tick_in = 0
fig.yaxis.minor_tick_out = 0
fig.yaxis.axis_line_color = (150, 150, 150)
fig.yaxis.axis_line_dash = [2, 2]
fig.yaxis.major_label_text_color = (88, 88, 88)
fig.xaxis.major_label_text_color = (88, 88, 88)
fig.ygrid.grid_line_dash = [2, 2]
if isinstance(grid_plot, list):
if grid_plot:
for g_plot in grid_plot:
g_plot.sizing_mode = 'scale_width'
g_plot.height = 600
<|endoftext|> | [
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twilio/twilio-python | twilio/rest/monitor/v1/alert.py | AlertList.page | def page(self, log_level=values.unset, start_date=values.unset,
end_date=values.unset, page_token=values.unset,
page_number=values.unset, page_size=values.unset):
"""
Retrieve a single page of AlertInstance records from the API.
Request is executed immediately
:param unicode log_level: Only show alerts for this log-level.
:param date start_date: Only show Alerts on or after this date.
:param date end_date: Only show Alerts on or before this date.
:param str page_token: PageToken provided by the API
:param int page_number: Page Number, this value is simply for client state
:param int page_size: Number of records to return, defaults to 50
:returns: Page of AlertInstance
:rtype: twilio.rest.monitor.v1.alert.AlertPage
"""
params = values.of({
'LogLevel': log_level,
'StartDate': serialize.iso8601_date(start_date),
'EndDate': serialize.iso8601_date(end_date),
'PageToken': page_token,
'Page': page_number,
'PageSize': page_size,
})
response = self._version.page(
'GET',
self._uri,
params=params,
)
return AlertPage(self._version, response, self._solution) | python | def page(self, log_level=values.unset, start_date=values.unset,
end_date=values.unset, page_token=values.unset,
page_number=values.unset, page_size=values.unset):
"""
Retrieve a single page of AlertInstance records from the API.
Request is executed immediately
:param unicode log_level: Only show alerts for this log-level.
:param date start_date: Only show Alerts on or after this date.
:param date end_date: Only show Alerts on or before this date.
:param str page_token: PageToken provided by the API
:param int page_number: Page Number, this value is simply for client state
:param int page_size: Number of records to return, defaults to 50
:returns: Page of AlertInstance
:rtype: twilio.rest.monitor.v1.alert.AlertPage
"""
params = values.of({
'LogLevel': log_level,
'StartDate': serialize.iso8601_date(start_date),
'EndDate': serialize.iso8601_date(end_date),
'PageToken': page_token,
'Page': page_number,
'PageSize': page_size,
})
response = self._version.page(
'GET',
self._uri,
params=params,
)
return AlertPage(self._version, response, self._solution) | [
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:param date start_date: Only show Alerts on or after this date.
:param date end_date: Only show Alerts on or before this date.
:param str page_token: PageToken provided by the API
:param int page_number: Page Number, this value is simply for client state
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Retrieve a single page of AlertInstance records from the API.
Request is executed immediately
:param unicode log_level: Only show alerts for this log-level.
:param date start_date: Only show Alerts on or after this date.
:param date end_date: Only show Alerts on or before this date.
:param str page_token: PageToken provided by the API
:param int page_number: Page Number, this value is simply for client state
:param int page_size: Number of records to return, defaults to 50
:returns: Page of AlertInstance
:rtype: twilio.rest.monitor.v1.alert.AlertPage
### Response:
def page(self, log_level=values.unset, start_date=values.unset,
end_date=values.unset, page_token=values.unset,
page_number=values.unset, page_size=values.unset):
"""
Retrieve a single page of AlertInstance records from the API.
Request is executed immediately
:param unicode log_level: Only show alerts for this log-level.
:param date start_date: Only show Alerts on or after this date.
:param date end_date: Only show Alerts on or before this date.
:param str page_token: PageToken provided by the API
:param int page_number: Page Number, this value is simply for client state
:param int page_size: Number of records to return, defaults to 50
:returns: Page of AlertInstance
:rtype: twilio.rest.monitor.v1.alert.AlertPage
"""
params = values.of({
'LogLevel': log_level,
'StartDate': serialize.iso8601_date(start_date),
'EndDate': serialize.iso8601_date(end_date),
'PageToken': page_token,
'Page': page_number,
'PageSize': page_size,
})
response = self._version.page(
'GET',
self._uri,
params=params,
)
return AlertPage(self._version, response, self._solution)
<|endoftext|> | [
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coderholic/pyradio | pyradio/player.py | VlcPlayer._format_volume_string | def _format_volume_string(self, volume_string):
""" format vlc's volume """
self.actual_volume = int(volume_string.split(self.volume_string)[1].split(',')[0].split()[0])
return '[Vol: {}%] '.format(int(100 * self.actual_volume / self.max_volume)) | python | def _format_volume_string(self, volume_string):
""" format vlc's volume """
self.actual_volume = int(volume_string.split(self.volume_string)[1].split(',')[0].split()[0])
return '[Vol: {}%] '.format(int(100 * self.actual_volume / self.max_volume)) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
format vlc's volume
### Response:
def _format_volume_string(self, volume_string):
""" format vlc's volume """
self.actual_volume = int(volume_string.split(self.volume_string)[1].split(',')[0].split()[0])
return '[Vol: {}%] '.format(int(100 * self.actual_volume / self.max_volume))
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pkgw/pwkit | pwkit/io.py | Path.read_lines | def read_lines (self, mode='rt', noexistok=False, **kwargs):
"""Generate a sequence of lines from the file pointed to by this path, by
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with self.open (mode=mode, **kwargs) as f:
for line in f:
yield line
except IOError as e:
if e.errno != 2 or not noexistok:
raise | python | def read_lines (self, mode='rt', noexistok=False, **kwargs):
"""Generate a sequence of lines from the file pointed to by this path, by
opening as a regular file and iterating over it. The lines therefore
contain their newline characters. If *noexistok*, a nonexistent file
will result in an empty sequence rather than an exception. *kwargs*
are passed to :meth:`Path.open`.
"""
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with self.open (mode=mode, **kwargs) as f:
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Generate a sequence of lines from the file pointed to by this path, by
opening as a regular file and iterating over it. The lines therefore
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def read_lines (self, mode='rt', noexistok=False, **kwargs):
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python-constraint/python-constraint | constraint/__init__.py | Problem.getSolution | def getSolution(self):
"""
Find and return a solution to the problem
Example:
>>> problem = Problem()
>>> problem.getSolution() is None
True
>>> problem.addVariables(["a"], [42])
>>> problem.getSolution()
{'a': 42}
@return: Solution for the problem
@rtype: dictionary mapping variables to values
"""
domains, constraints, vconstraints = self._getArgs()
if not domains:
return None
return self._solver.getSolution(domains, constraints, vconstraints) | python | def getSolution(self):
"""
Find and return a solution to the problem
Example:
>>> problem = Problem()
>>> problem.getSolution() is None
True
>>> problem.addVariables(["a"], [42])
>>> problem.getSolution()
{'a': 42}
@return: Solution for the problem
@rtype: dictionary mapping variables to values
"""
domains, constraints, vconstraints = self._getArgs()
if not domains:
return None
return self._solver.getSolution(domains, constraints, vconstraints) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Find and return a solution to the problem
Example:
>>> problem = Problem()
>>> problem.getSolution() is None
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>>> problem.addVariables(["a"], [42])
>>> problem.getSolution()
{'a': 42}
@return: Solution for the problem
@rtype: dictionary mapping variables to values
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def getSolution(self):
"""
Find and return a solution to the problem
Example:
>>> problem = Problem()
>>> problem.getSolution() is None
True
>>> problem.addVariables(["a"], [42])
>>> problem.getSolution()
{'a': 42}
@return: Solution for the problem
@rtype: dictionary mapping variables to values
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gagneurlab/concise | concise/utils/position.py | extract_landmarks | def extract_landmarks(gtf, landmarks=ALL_LANDMARKS):
"""Given an gene annotation GFF/GTF file,
# Arguments
gtf: File path or a loaded `pd.DataFrame` with columns:
seqname, feature, start, end, strand
landmarks: list or a dictionary of landmark extractors (function or name)
# Note
When landmark extractor names are used, they have to be implemented in
the module `concise.preprocessing.position`
# Returns
Dictionary of pd.DataFrames with landmark positions
(columns: seqname, position, strand)
"""
if isinstance(gtf, str):
_logger.info("Reading gtf file..")
gtf = read_gtf(gtf)
_logger.info("Done")
_logger.info("Running landmark extractors..")
# landmarks to a dictionary with a function
assert isinstance(landmarks, (list, tuple, set, dict))
if isinstance(landmarks, dict):
landmarks = {k: _get_fun(v) for k, v in landmarks.items()}
else:
landmarks = {_to_string(fn_str): _get_fun(fn_str)
for fn_str in landmarks}
r = {k: _validate_pos(v(gtf)) for k, v in landmarks.items()}
_logger.info("Done!")
return r | python | def extract_landmarks(gtf, landmarks=ALL_LANDMARKS):
"""Given an gene annotation GFF/GTF file,
# Arguments
gtf: File path or a loaded `pd.DataFrame` with columns:
seqname, feature, start, end, strand
landmarks: list or a dictionary of landmark extractors (function or name)
# Note
When landmark extractor names are used, they have to be implemented in
the module `concise.preprocessing.position`
# Returns
Dictionary of pd.DataFrames with landmark positions
(columns: seqname, position, strand)
"""
if isinstance(gtf, str):
_logger.info("Reading gtf file..")
gtf = read_gtf(gtf)
_logger.info("Done")
_logger.info("Running landmark extractors..")
# landmarks to a dictionary with a function
assert isinstance(landmarks, (list, tuple, set, dict))
if isinstance(landmarks, dict):
landmarks = {k: _get_fun(v) for k, v in landmarks.items()}
else:
landmarks = {_to_string(fn_str): _get_fun(fn_str)
for fn_str in landmarks}
r = {k: _validate_pos(v(gtf)) for k, v in landmarks.items()}
_logger.info("Done!")
return r | [
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gtf: File path or a loaded `pd.DataFrame` with columns:
seqname, feature, start, end, strand
landmarks: list or a dictionary of landmark extractors (function or name)
# Note
When landmark extractor names are used, they have to be implemented in
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# Returns
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Given an gene annotation GFF/GTF file,
# Arguments
gtf: File path or a loaded `pd.DataFrame` with columns:
seqname, feature, start, end, strand
landmarks: list or a dictionary of landmark extractors (function or name)
# Note
When landmark extractor names are used, they have to be implemented in
the module `concise.preprocessing.position`
# Returns
Dictionary of pd.DataFrames with landmark positions
(columns: seqname, position, strand)
### Response:
def extract_landmarks(gtf, landmarks=ALL_LANDMARKS):
"""Given an gene annotation GFF/GTF file,
# Arguments
gtf: File path or a loaded `pd.DataFrame` with columns:
seqname, feature, start, end, strand
landmarks: list or a dictionary of landmark extractors (function or name)
# Note
When landmark extractor names are used, they have to be implemented in
the module `concise.preprocessing.position`
# Returns
Dictionary of pd.DataFrames with landmark positions
(columns: seqname, position, strand)
"""
if isinstance(gtf, str):
_logger.info("Reading gtf file..")
gtf = read_gtf(gtf)
_logger.info("Done")
_logger.info("Running landmark extractors..")
# landmarks to a dictionary with a function
assert isinstance(landmarks, (list, tuple, set, dict))
if isinstance(landmarks, dict):
landmarks = {k: _get_fun(v) for k, v in landmarks.items()}
else:
landmarks = {_to_string(fn_str): _get_fun(fn_str)
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r = {k: _validate_pos(v(gtf)) for k, v in landmarks.items()}
_logger.info("Done!")
return r
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