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signaturit/python-sdk | signaturit_sdk/signaturit_client.py | SignaturitClient.remove_user | def remove_user(self, user_id):
"""
Remove a user from your team
:param user_id: Id of user
"""
url = self.TEAM_USERS_ID_URL % user_id
connection = Connection(self.token)
connection.set_url(self.production, url)
return connection.delete_request() | python | def remove_user(self, user_id):
"""
Remove a user from your team
:param user_id: Id of user
"""
url = self.TEAM_USERS_ID_URL % user_id
connection = Connection(self.token)
connection.set_url(self.production, url)
return connection.delete_request() | [
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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:
Remove a user from your team
:param user_id: Id of user
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def remove_user(self, user_id):
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jilljenn/tryalgo | tryalgo/laser_mirrors.py | solve | def solve(succ, orien, i, direc):
"""Can a laser leaving mirror i in direction direc reach exit ?
:param i: mirror index
:param direc: direction leaving mirror i
:param orient: orient[i]=orientation of mirror i
:param succ: succ[i][direc]=succ mirror reached
when leaving i in direction direc
"""
assert orien[i] is not None
j = succ[i][direc]
if j is None: # basic case
return False
if j == len(orien) - 1:
return True
if orien[j] is None: # try both orientations
for x in [0, 1]:
orien[j] = x
if solve(succ, orien, j, reflex[direc][x]):
return True
orien[j] = None
return False
else:
return solve(succ, orien, j, reflex[direc][orien[j]]) | python | def solve(succ, orien, i, direc):
"""Can a laser leaving mirror i in direction direc reach exit ?
:param i: mirror index
:param direc: direction leaving mirror i
:param orient: orient[i]=orientation of mirror i
:param succ: succ[i][direc]=succ mirror reached
when leaving i in direction direc
"""
assert orien[i] is not None
j = succ[i][direc]
if j is None: # basic case
return False
if j == len(orien) - 1:
return True
if orien[j] is None: # try both orientations
for x in [0, 1]:
orien[j] = x
if solve(succ, orien, j, reflex[direc][x]):
return True
orien[j] = None
return False
else:
return solve(succ, orien, j, reflex[direc][orien[j]]) | [
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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:
Can a laser leaving mirror i in direction direc reach exit ?
:param i: mirror index
:param direc: direction leaving mirror i
:param orient: orient[i]=orientation of mirror i
:param succ: succ[i][direc]=succ mirror reached
when leaving i in direction direc
### Response:
def solve(succ, orien, i, direc):
"""Can a laser leaving mirror i in direction direc reach exit ?
:param i: mirror index
:param direc: direction leaving mirror i
:param orient: orient[i]=orientation of mirror i
:param succ: succ[i][direc]=succ mirror reached
when leaving i in direction direc
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assert orien[i] is not None
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return False
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return True
if orien[j] is None: # try both orientations
for x in [0, 1]:
orien[j] = x
if solve(succ, orien, j, reflex[direc][x]):
return True
orien[j] = None
return False
else:
return solve(succ, orien, j, reflex[direc][orien[j]])
<|endoftext|> | [
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fprimex/zdesk | zdesk/zdesk_api.py | ZendeskAPI.community_post_create | def community_post_create(self, data, **kwargs):
"https://developer.zendesk.com/rest_api/docs/help_center/posts#create-post"
api_path = "/api/v2/community/posts.json"
return self.call(api_path, method="POST", data=data, **kwargs) | python | def community_post_create(self, data, **kwargs):
"https://developer.zendesk.com/rest_api/docs/help_center/posts#create-post"
api_path = "/api/v2/community/posts.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:
https://developer.zendesk.com/rest_api/docs/help_center/posts#create-post
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Sanji-IO/sanji | sanji/message.py | Message.to_dict | def to_dict(self):
"""
to_dict will clean all protected and private properties
"""
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"""
to_dict will clean all protected and private properties
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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:
to_dict will clean all protected and private properties
### Response:
def to_dict(self):
"""
to_dict will clean all protected and private properties
"""
return dict(
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non-Jedi/gyr | gyr/matrix_objects.py | Event.user | def user(self):
"""Creates a User object when requested."""
try:
return self._user
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self._user = MatrixUser(self.mxid, self.Api(identity=self.mxid))
return self._user | python | def user(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:
Creates a User object when requested.
### Response:
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"""
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return (float(value) - self.min_raw_value) / self.max_raw_value | python | def _input_to_raw_value(self, value: int) -> float:
"""
Convert the value read from evdev to a 0.0 to 1.0 range.
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ga4gh/ga4gh-server | ga4gh/server/datarepo.py | AbstractDataRepository.getOntologyByName | def getOntologyByName(self, name):
"""
Returns an ontology by name
"""
if name not in self._ontologyNameMap:
raise exceptions.OntologyNameNotFoundException(name)
return self._ontologyNameMap[name] | python | def getOntologyByName(self, name):
"""
Returns an ontology by name
"""
if name not in self._ontologyNameMap:
raise exceptions.OntologyNameNotFoundException(name)
return self._ontologyNameMap[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:
Returns an ontology by name
### Response:
def getOntologyByName(self, name):
"""
Returns an ontology by name
"""
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AgeOfLearning/coeus-unity-python-framework | coeus_unity/commands.py | await_any_transforms_exist | def await_any_transforms_exist(cli, transform_paths, does_exist=DEFAULT_TRANSFORM_EXISTS, timeout_seconds=DEFAULT_TIMEOUT_SECONDS):
"""
Waits for a transform to exist based on does_exist.
:param cli:
:param transform_paths: An array of transform paths [...]
:param does_exist: Whether or not to await for exist state (True | False)
:param timeout_seconds: How long until this returns with failure
:return: bool
"""
message_payload = {
"transform_paths": transform_paths,
"do_exist": does_exist,
"match_mode": "Any",
"timeout": timeout_seconds
}
msg = message.Message("await.unity.transform.exists", message_payload)
cli.send_message(msg)
response = cli.read_message()
verify_response(response)
return bool(response['payload']['success']) | python | def await_any_transforms_exist(cli, transform_paths, does_exist=DEFAULT_TRANSFORM_EXISTS, timeout_seconds=DEFAULT_TIMEOUT_SECONDS):
"""
Waits for a transform to exist based on does_exist.
:param cli:
:param transform_paths: An array of transform paths [...]
:param does_exist: Whether or not to await for exist state (True | False)
:param timeout_seconds: How long until this returns with failure
:return: bool
"""
message_payload = {
"transform_paths": transform_paths,
"do_exist": does_exist,
"match_mode": "Any",
"timeout": timeout_seconds
}
msg = message.Message("await.unity.transform.exists", message_payload)
cli.send_message(msg)
response = cli.read_message()
verify_response(response)
return bool(response['payload']['success']) | [
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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:
Waits for a transform to exist based on does_exist.
:param cli:
:param transform_paths: An array of transform paths [...]
:param does_exist: Whether or not to await for exist state (True | False)
:param timeout_seconds: How long until this returns with failure
:return: bool
### Response:
def await_any_transforms_exist(cli, transform_paths, does_exist=DEFAULT_TRANSFORM_EXISTS, timeout_seconds=DEFAULT_TIMEOUT_SECONDS):
"""
Waits for a transform to exist based on does_exist.
:param cli:
:param transform_paths: An array of transform paths [...]
:param does_exist: Whether or not to await for exist state (True | False)
:param timeout_seconds: How long until this returns with failure
:return: bool
"""
message_payload = {
"transform_paths": transform_paths,
"do_exist": does_exist,
"match_mode": "Any",
"timeout": timeout_seconds
}
msg = message.Message("await.unity.transform.exists", message_payload)
cli.send_message(msg)
response = cli.read_message()
verify_response(response)
return bool(response['payload']['success'])
<|endoftext|> | [
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buildbot/buildbot | master/buildbot/plugins/db.py | _PluginDB.add_namespace | def add_namespace(self, namespace, interface=None, check_extras=True,
load_now=False):
"""
register given namespace in global database of plugins
in case it's already registered, return the registration
"""
tempo = self._namespaces.get(namespace)
if tempo is None:
tempo = _Plugins(namespace, interface, check_extras)
self._namespaces[namespace] = tempo
if load_now:
tempo.load()
return tempo | python | def add_namespace(self, namespace, interface=None, check_extras=True,
load_now=False):
"""
register given namespace in global database of plugins
in case it's already registered, return the registration
"""
tempo = self._namespaces.get(namespace)
if tempo is None:
tempo = _Plugins(namespace, interface, check_extras)
self._namespaces[namespace] = tempo
if load_now:
tempo.load()
return tempo | [
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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:
register given namespace in global database of plugins
in case it's already registered, return the registration
### Response:
def add_namespace(self, namespace, interface=None, check_extras=True,
load_now=False):
"""
register given namespace in global database of plugins
in case it's already registered, return the registration
"""
tempo = self._namespaces.get(namespace)
if tempo is None:
tempo = _Plugins(namespace, interface, check_extras)
self._namespaces[namespace] = tempo
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<|endoftext|> | [
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KelSolaar/Umbra | umbra/ui/visual_accelerators.py | highlight_current_line | def highlight_current_line(editor):
"""
Highlights given editor current line.
:param editor: Document editor.
:type editor: QWidget
:return: Method success.
:rtype: bool
"""
format = editor.language.theme.get("accelerator.line")
if not format:
return False
extra_selections = editor.extraSelections() or []
if not editor.isReadOnly():
selection = QTextEdit.ExtraSelection()
selection.format.setBackground(format.background())
selection.format.setProperty(QTextFormat.FullWidthSelection, True)
selection.cursor = editor.textCursor()
selection.cursor.clearSelection()
extra_selections.append(selection)
editor.setExtraSelections(extra_selections)
return True | python | def highlight_current_line(editor):
"""
Highlights given editor current line.
:param editor: Document editor.
:type editor: QWidget
:return: Method success.
:rtype: bool
"""
format = editor.language.theme.get("accelerator.line")
if not format:
return False
extra_selections = editor.extraSelections() or []
if not editor.isReadOnly():
selection = QTextEdit.ExtraSelection()
selection.format.setBackground(format.background())
selection.format.setProperty(QTextFormat.FullWidthSelection, True)
selection.cursor = editor.textCursor()
selection.cursor.clearSelection()
extra_selections.append(selection)
editor.setExtraSelections(extra_selections)
return True | [
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:param editor: Document editor.
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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:
Highlights given editor current line.
:param editor: Document editor.
:type editor: QWidget
:return: Method success.
:rtype: bool
### Response:
def highlight_current_line(editor):
"""
Highlights given editor current line.
:param editor: Document editor.
:type editor: QWidget
:return: Method success.
:rtype: bool
"""
format = editor.language.theme.get("accelerator.line")
if not format:
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if not editor.isReadOnly():
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selection.format.setBackground(format.background())
selection.format.setProperty(QTextFormat.FullWidthSelection, True)
selection.cursor = editor.textCursor()
selection.cursor.clearSelection()
extra_selections.append(selection)
editor.setExtraSelections(extra_selections)
return True
<|endoftext|> | [
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bwohlberg/sporco | sporco/cnvrep.py | Pcn | def Pcn(x, dsz, Nv, dimN=2, dimC=1, crp=False, zm=False):
"""Constraint set projection for convolutional dictionary update
problem.
Parameters
----------
x : array_like
Input array
dsz : tuple
Filter support size(s), specified using the same format as the `dsz`
parameter of :func:`bcrop`
Nv : tuple
Sizes of problem spatial indices
dimN : int, optional (default 2)
Number of problem spatial indices
dimC : int, optional (default 1)
Number of problem channel indices
crp : bool, optional (default False)
Flag indicating whether the result should be cropped to the support
of the largest filter in the dictionary.
zm : bool, optional (default False)
Flag indicating whether the projection function should include
filter mean subtraction
Returns
-------
y : ndarray
Projection of input onto constraint set
"""
if crp:
def zpadfn(x):
return x
else:
def zpadfn(x):
return zpad(x, Nv)
if zm:
def zmeanfn(x):
return zeromean(x, dsz, dimN)
else:
def zmeanfn(x):
return x
return normalise(zmeanfn(zpadfn(bcrop(x, dsz, dimN))), dimN + dimC) | python | def Pcn(x, dsz, Nv, dimN=2, dimC=1, crp=False, zm=False):
"""Constraint set projection for convolutional dictionary update
problem.
Parameters
----------
x : array_like
Input array
dsz : tuple
Filter support size(s), specified using the same format as the `dsz`
parameter of :func:`bcrop`
Nv : tuple
Sizes of problem spatial indices
dimN : int, optional (default 2)
Number of problem spatial indices
dimC : int, optional (default 1)
Number of problem channel indices
crp : bool, optional (default False)
Flag indicating whether the result should be cropped to the support
of the largest filter in the dictionary.
zm : bool, optional (default False)
Flag indicating whether the projection function should include
filter mean subtraction
Returns
-------
y : ndarray
Projection of input onto constraint set
"""
if crp:
def zpadfn(x):
return x
else:
def zpadfn(x):
return zpad(x, Nv)
if zm:
def zmeanfn(x):
return zeromean(x, dsz, dimN)
else:
def zmeanfn(x):
return x
return normalise(zmeanfn(zpadfn(bcrop(x, dsz, dimN))), dimN + dimC) | [
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Input array
dsz : tuple
Filter support size(s), specified using the same format as the `dsz`
parameter of :func:`bcrop`
Nv : tuple
Sizes of problem spatial indices
dimN : int, optional (default 2)
Number of problem spatial indices
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Number of problem channel indices
crp : bool, optional (default False)
Flag indicating whether the result should be cropped to the support
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zm : bool, optional (default False)
Flag indicating whether the projection function should include
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Returns
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y : ndarray
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] | train | https://github.com/bwohlberg/sporco/blob/8946a04331106f4e39904fbdf2dc7351900baa04/sporco/cnvrep.py#L842-L886 | <|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:
Constraint set projection for convolutional dictionary update
problem.
Parameters
----------
x : array_like
Input array
dsz : tuple
Filter support size(s), specified using the same format as the `dsz`
parameter of :func:`bcrop`
Nv : tuple
Sizes of problem spatial indices
dimN : int, optional (default 2)
Number of problem spatial indices
dimC : int, optional (default 1)
Number of problem channel indices
crp : bool, optional (default False)
Flag indicating whether the result should be cropped to the support
of the largest filter in the dictionary.
zm : bool, optional (default False)
Flag indicating whether the projection function should include
filter mean subtraction
Returns
-------
y : ndarray
Projection of input onto constraint set
### Response:
def Pcn(x, dsz, Nv, dimN=2, dimC=1, crp=False, zm=False):
"""Constraint set projection for convolutional dictionary update
problem.
Parameters
----------
x : array_like
Input array
dsz : tuple
Filter support size(s), specified using the same format as the `dsz`
parameter of :func:`bcrop`
Nv : tuple
Sizes of problem spatial indices
dimN : int, optional (default 2)
Number of problem spatial indices
dimC : int, optional (default 1)
Number of problem channel indices
crp : bool, optional (default False)
Flag indicating whether the result should be cropped to the support
of the largest filter in the dictionary.
zm : bool, optional (default False)
Flag indicating whether the projection function should include
filter mean subtraction
Returns
-------
y : ndarray
Projection of input onto constraint set
"""
if crp:
def zpadfn(x):
return x
else:
def zpadfn(x):
return zpad(x, Nv)
if zm:
def zmeanfn(x):
return zeromean(x, dsz, dimN)
else:
def zmeanfn(x):
return x
return normalise(zmeanfn(zpadfn(bcrop(x, dsz, dimN))), dimN + dimC)
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AmesCornish/buttersink | buttersink/Store.py | Volume.writeInfo | def writeInfo(self, stream):
""" Write information about diffs into a file stream for use later. """
for (fromUUID, size) in Diff.theKnownSizes[self.uuid].iteritems():
self.writeInfoLine(stream, fromUUID, size) | python | def writeInfo(self, stream):
""" Write information about diffs into a file stream for use later. """
for (fromUUID, size) in Diff.theKnownSizes[self.uuid].iteritems():
self.writeInfoLine(stream, fromUUID, size) | [
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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:
Write information about diffs into a file stream for use later.
### Response:
def writeInfo(self, stream):
""" Write information about diffs into a file stream for use later. """
for (fromUUID, size) in Diff.theKnownSizes[self.uuid].iteritems():
self.writeInfoLine(stream, fromUUID, size)
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uranusjr/django-gunicorn | djgunicorn/config.py | post_worker_init | def post_worker_init(worker):
"""Hook into Gunicorn to display message after launching.
This mimics the behaviour of Django's stock runserver command.
"""
quit_command = 'CTRL-BREAK' if sys.platform == 'win32' else 'CONTROL-C'
sys.stdout.write(
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"Starting development server at {urls}\n"
"Quit the server with {quit_command}.\n".format(
djangover=django.get_version(),
gunicornver=gunicorn.__version__,
settings=os.environ.get('DJANGO_SETTINGS_MODULE'),
urls=', '.join('http://{0}/'.format(b) for b in worker.cfg.bind),
quit_command=quit_command,
),
) | python | def post_worker_init(worker):
"""Hook into Gunicorn to display message after launching.
This mimics the behaviour of Django's stock runserver command.
"""
quit_command = 'CTRL-BREAK' if sys.platform == 'win32' else 'CONTROL-C'
sys.stdout.write(
"Django version {djangover}, Gunicorn version {gunicornver}, "
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djangover=django.get_version(),
gunicornver=gunicorn.__version__,
settings=os.environ.get('DJANGO_SETTINGS_MODULE'),
urls=', '.join('http://{0}/'.format(b) for b in worker.cfg.bind),
quit_command=quit_command,
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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:
Hook into Gunicorn to display message after launching.
This mimics the behaviour of Django's stock runserver command.
### Response:
def post_worker_init(worker):
"""Hook into Gunicorn to display message after launching.
This mimics the behaviour of Django's stock runserver command.
"""
quit_command = 'CTRL-BREAK' if sys.platform == 'win32' else 'CONTROL-C'
sys.stdout.write(
"Django version {djangover}, Gunicorn version {gunicornver}, "
"using settings {settings!r}\n"
"Starting development server at {urls}\n"
"Quit the server with {quit_command}.\n".format(
djangover=django.get_version(),
gunicornver=gunicorn.__version__,
settings=os.environ.get('DJANGO_SETTINGS_MODULE'),
urls=', '.join('http://{0}/'.format(b) for b in worker.cfg.bind),
quit_command=quit_command,
),
)
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xenon-middleware/pyxenon | xenon/server.py | find_free_port | def find_free_port():
"""Finds a free port."""
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
sock.bind(('', 0))
return sock.getsockname()[1] | python | def find_free_port():
"""Finds a free port."""
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
sock.bind(('', 0))
return sock.getsockname()[1] | [
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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:
Finds a free port.
### Response:
def find_free_port():
"""Finds a free port."""
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
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dixudx/rtcclient | rtcclient/query.py | Query.runSavedQueryByUrl | def runSavedQueryByUrl(self, saved_query_url, returned_properties=None):
"""Query workitems using the saved query url
:param saved_query_url: the saved query url
:param returned_properties: the returned properties that you want.
Refer to :class:`rtcclient.client.RTCClient` for more explanations
:return: a :class:`list` that contains the queried
:class:`rtcclient.workitem.Workitem` objects
:rtype: list
"""
try:
if "=" not in saved_query_url:
raise exception.BadValue()
saved_query_id = saved_query_url.split("=")[-1]
if not saved_query_id:
raise exception.BadValue()
except:
error_msg = "No saved query id is found in the url"
self.log.error(error_msg)
raise exception.BadValue(error_msg)
return self._runSavedQuery(saved_query_id,
returned_properties=returned_properties) | python | def runSavedQueryByUrl(self, saved_query_url, returned_properties=None):
"""Query workitems using the saved query url
:param saved_query_url: the saved query url
:param returned_properties: the returned properties that you want.
Refer to :class:`rtcclient.client.RTCClient` for more explanations
:return: a :class:`list` that contains the queried
:class:`rtcclient.workitem.Workitem` objects
:rtype: list
"""
try:
if "=" not in saved_query_url:
raise exception.BadValue()
saved_query_id = saved_query_url.split("=")[-1]
if not saved_query_id:
raise exception.BadValue()
except:
error_msg = "No saved query id is found in the url"
self.log.error(error_msg)
raise exception.BadValue(error_msg)
return self._runSavedQuery(saved_query_id,
returned_properties=returned_properties) | [
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:param returned_properties: the returned properties that you want.
Refer to :class:`rtcclient.client.RTCClient` for more explanations
:return: a :class:`list` that contains the queried
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:rtype: list | [
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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:
Query workitems using the saved query url
:param saved_query_url: the saved query url
:param returned_properties: the returned properties that you want.
Refer to :class:`rtcclient.client.RTCClient` for more explanations
:return: a :class:`list` that contains the queried
:class:`rtcclient.workitem.Workitem` objects
:rtype: list
### Response:
def runSavedQueryByUrl(self, saved_query_url, returned_properties=None):
"""Query workitems using the saved query url
:param saved_query_url: the saved query url
:param returned_properties: the returned properties that you want.
Refer to :class:`rtcclient.client.RTCClient` for more explanations
:return: a :class:`list` that contains the queried
:class:`rtcclient.workitem.Workitem` objects
:rtype: list
"""
try:
if "=" not in saved_query_url:
raise exception.BadValue()
saved_query_id = saved_query_url.split("=")[-1]
if not saved_query_id:
raise exception.BadValue()
except:
error_msg = "No saved query id is found in the url"
self.log.error(error_msg)
raise exception.BadValue(error_msg)
return self._runSavedQuery(saved_query_id,
returned_properties=returned_properties)
<|endoftext|> | [
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mgraffg/EvoDAG | EvoDAG/base.py | EvoDAG.predict | def predict(self, v=None, X=None):
"""In classification this returns the classes, in
regression it is equivalent to the decision function"""
if X is None:
X = v
v = None
m = self.model(v=v)
return m.predict(X) | python | def predict(self, v=None, X=None):
"""In classification this returns the classes, in
regression it is equivalent to the decision function"""
if X is None:
X = v
v = None
m = self.model(v=v)
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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:
In classification this returns the classes, in
regression it is equivalent to the decision function
### Response:
def predict(self, v=None, X=None):
"""In classification this returns the classes, in
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newville/wxmplot | examples/tifffile.py | _replace_by | def _replace_by(module_function, warn=False):
"""Try replace decorated function by module.function."""
def decorate(func, module_function=module_function, warn=warn):
sys.path.append(os.path.dirname(__file__))
try:
module, function = module_function.split('.')
func, oldfunc = getattr(__import__(module), function), func
globals()['__old_' + func.__name__] = oldfunc
except Exception:
if warn:
warnings.warn("failed to import %s" % module_function)
sys.path.pop()
return func
return decorate | python | def _replace_by(module_function, warn=False):
"""Try replace decorated function by module.function."""
def decorate(func, module_function=module_function, warn=warn):
sys.path.append(os.path.dirname(__file__))
try:
module, function = module_function.split('.')
func, oldfunc = getattr(__import__(module), function), func
globals()['__old_' + func.__name__] = oldfunc
except Exception:
if warn:
warnings.warn("failed to import %s" % module_function)
sys.path.pop()
return func
return decorate | [
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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:
Try replace decorated function by module.function.
### Response:
def _replace_by(module_function, warn=False):
"""Try replace decorated function by module.function."""
def decorate(func, module_function=module_function, warn=warn):
sys.path.append(os.path.dirname(__file__))
try:
module, function = module_function.split('.')
func, oldfunc = getattr(__import__(module), function), func
globals()['__old_' + func.__name__] = oldfunc
except Exception:
if warn:
warnings.warn("failed to import %s" % module_function)
sys.path.pop()
return func
return decorate
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mbedmicro/pyOCD | pyocd/utility/conversion.py | u64_to_hex16le | def u64_to_hex16le(val):
"""! @brief Create 16-digit hexadecimal string from 64-bit register value"""
return ''.join("%02x" % (x & 0xFF) for x in (
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val >> 24,
val >> 32,
val >> 40,
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val >> 56,
)) | python | def u64_to_hex16le(val):
"""! @brief Create 16-digit hexadecimal string from 64-bit register value"""
return ''.join("%02x" % (x & 0xFF) for x in (
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val >> 8,
val >> 16,
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val >> 40,
val >> 48,
val >> 56,
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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 Create 16-digit hexadecimal string from 64-bit register value
### Response:
def u64_to_hex16le(val):
"""! @brief Create 16-digit hexadecimal string from 64-bit register value"""
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jsvine/spectra | spectra/grapefruit.py | Color.RgbToWebSafe | def RgbToWebSafe(r, g, b, alt=False):
'''Convert the color from RGB to 'web safe' RGB
Parameters:
:r:
The Red component value [0...1]
:g:
The Green component value [0...1]
:b:
The Blue component value [0...1]
:alt:
If True, use the alternative color instead of the nearest one.
Can be used for dithering.
Returns:
The color as an (r, g, b) tuple in the range:
the range:
r[0...1],
g[0...1],
b[0...1]
>>> '(%g, %g, %g)' % Color.RgbToWebSafe(1, 0.55, 0.0)
'(1, 0.6, 0)'
'''
webSafeComponent = Color._WebSafeComponent
return tuple((webSafeComponent(v, alt) for v in (r, g, b))) | python | def RgbToWebSafe(r, g, b, alt=False):
'''Convert the color from RGB to 'web safe' RGB
Parameters:
:r:
The Red component value [0...1]
:g:
The Green component value [0...1]
:b:
The Blue component value [0...1]
:alt:
If True, use the alternative color instead of the nearest one.
Can be used for dithering.
Returns:
The color as an (r, g, b) tuple in the range:
the range:
r[0...1],
g[0...1],
b[0...1]
>>> '(%g, %g, %g)' % Color.RgbToWebSafe(1, 0.55, 0.0)
'(1, 0.6, 0)'
'''
webSafeComponent = Color._WebSafeComponent
return tuple((webSafeComponent(v, alt) for v in (r, g, b))) | [
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'(1, 0.6, 0)' | [
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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:
Convert the color from RGB to 'web safe' RGB
Parameters:
:r:
The Red component value [0...1]
:g:
The Green component value [0...1]
:b:
The Blue component value [0...1]
:alt:
If True, use the alternative color instead of the nearest one.
Can be used for dithering.
Returns:
The color as an (r, g, b) tuple in the range:
the range:
r[0...1],
g[0...1],
b[0...1]
>>> '(%g, %g, %g)' % Color.RgbToWebSafe(1, 0.55, 0.0)
'(1, 0.6, 0)'
### Response:
def RgbToWebSafe(r, g, b, alt=False):
'''Convert the color from RGB to 'web safe' RGB
Parameters:
:r:
The Red component value [0...1]
:g:
The Green component value [0...1]
:b:
The Blue component value [0...1]
:alt:
If True, use the alternative color instead of the nearest one.
Can be used for dithering.
Returns:
The color as an (r, g, b) tuple in the range:
the range:
r[0...1],
g[0...1],
b[0...1]
>>> '(%g, %g, %g)' % Color.RgbToWebSafe(1, 0.55, 0.0)
'(1, 0.6, 0)'
'''
webSafeComponent = Color._WebSafeComponent
return tuple((webSafeComponent(v, alt) for v in (r, g, b)))
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aio-libs/aiohttp | aiohttp/web_protocol.py | RequestHandler.handle_error | def handle_error(self,
request: BaseRequest,
status: int=500,
exc: Optional[BaseException]=None,
message: Optional[str]=None) -> StreamResponse:
"""Handle errors.
Returns HTTP response with specific status code. Logs additional
information. It always closes current connection."""
self.log_exception("Error handling request", exc_info=exc)
ct = 'text/plain'
if status == HTTPStatus.INTERNAL_SERVER_ERROR:
title = '{0.value} {0.phrase}'.format(
HTTPStatus.INTERNAL_SERVER_ERROR
)
msg = HTTPStatus.INTERNAL_SERVER_ERROR.description
tb = None
if self.debug:
with suppress(Exception):
tb = traceback.format_exc()
if 'text/html' in request.headers.get('Accept', ''):
if tb:
tb = html_escape(tb)
msg = '<h2>Traceback:</h2>\n<pre>{}</pre>'.format(tb)
message = (
"<html><head>"
"<title>{title}</title>"
"</head><body>\n<h1>{title}</h1>"
"\n{msg}\n</body></html>\n"
).format(title=title, msg=msg)
ct = 'text/html'
else:
if tb:
msg = tb
message = title + '\n\n' + msg
resp = Response(status=status, text=message, content_type=ct)
resp.force_close()
# some data already got sent, connection is broken
if request.writer.output_size > 0 or self.transport is None:
self.force_close()
return resp | python | def handle_error(self,
request: BaseRequest,
status: int=500,
exc: Optional[BaseException]=None,
message: Optional[str]=None) -> StreamResponse:
"""Handle errors.
Returns HTTP response with specific status code. Logs additional
information. It always closes current connection."""
self.log_exception("Error handling request", exc_info=exc)
ct = 'text/plain'
if status == HTTPStatus.INTERNAL_SERVER_ERROR:
title = '{0.value} {0.phrase}'.format(
HTTPStatus.INTERNAL_SERVER_ERROR
)
msg = HTTPStatus.INTERNAL_SERVER_ERROR.description
tb = None
if self.debug:
with suppress(Exception):
tb = traceback.format_exc()
if 'text/html' in request.headers.get('Accept', ''):
if tb:
tb = html_escape(tb)
msg = '<h2>Traceback:</h2>\n<pre>{}</pre>'.format(tb)
message = (
"<html><head>"
"<title>{title}</title>"
"</head><body>\n<h1>{title}</h1>"
"\n{msg}\n</body></html>\n"
).format(title=title, msg=msg)
ct = 'text/html'
else:
if tb:
msg = tb
message = title + '\n\n' + msg
resp = Response(status=status, text=message, content_type=ct)
resp.force_close()
# some data already got sent, connection is broken
if request.writer.output_size > 0 or self.transport is None:
self.force_close()
return resp | [
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Returns HTTP response with specific status code. Logs additional
information. It always closes current connection. | [
"Handle",
"errors",
"."
] | train | https://github.com/aio-libs/aiohttp/blob/9504fe2affaaff673fa4f3754c1c44221f8ba47d/aiohttp/web_protocol.py#L511-L556 | <|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:
Handle errors.
Returns HTTP response with specific status code. Logs additional
information. It always closes current connection.
### Response:
def handle_error(self,
request: BaseRequest,
status: int=500,
exc: Optional[BaseException]=None,
message: Optional[str]=None) -> StreamResponse:
"""Handle errors.
Returns HTTP response with specific status code. Logs additional
information. It always closes current connection."""
self.log_exception("Error handling request", exc_info=exc)
ct = 'text/plain'
if status == HTTPStatus.INTERNAL_SERVER_ERROR:
title = '{0.value} {0.phrase}'.format(
HTTPStatus.INTERNAL_SERVER_ERROR
)
msg = HTTPStatus.INTERNAL_SERVER_ERROR.description
tb = None
if self.debug:
with suppress(Exception):
tb = traceback.format_exc()
if 'text/html' in request.headers.get('Accept', ''):
if tb:
tb = html_escape(tb)
msg = '<h2>Traceback:</h2>\n<pre>{}</pre>'.format(tb)
message = (
"<html><head>"
"<title>{title}</title>"
"</head><body>\n<h1>{title}</h1>"
"\n{msg}\n</body></html>\n"
).format(title=title, msg=msg)
ct = 'text/html'
else:
if tb:
msg = tb
message = title + '\n\n' + msg
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jbloomlab/phydms | phydmslib/models.py | ExpCM._update_B | def _update_B(self):
"""Update `B`."""
for param in self.freeparams:
if param == 'mu':
continue
paramval = getattr(self, param)
if isinstance(paramval, float):
self.B[param] = broadcastMatrixMultiply(self.Ainv,
broadcastMatrixMultiply(self.dPrxy[param], self.A))
else:
assert isinstance(paramval, numpy.ndarray) and paramval.ndim == 1
for j in range(paramval.shape[0]):
self.B[param][j] = broadcastMatrixMultiply(self.Ainv,
broadcastMatrixMultiply(self.dPrxy[param][j],
self.A)) | python | def _update_B(self):
"""Update `B`."""
for param in self.freeparams:
if param == 'mu':
continue
paramval = getattr(self, param)
if isinstance(paramval, float):
self.B[param] = broadcastMatrixMultiply(self.Ainv,
broadcastMatrixMultiply(self.dPrxy[param], self.A))
else:
assert isinstance(paramval, numpy.ndarray) and paramval.ndim == 1
for j in range(paramval.shape[0]):
self.B[param][j] = broadcastMatrixMultiply(self.Ainv,
broadcastMatrixMultiply(self.dPrxy[param][j],
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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:
Update `B`.
### Response:
def _update_B(self):
"""Update `B`."""
for param in self.freeparams:
if param == 'mu':
continue
paramval = getattr(self, param)
if isinstance(paramval, float):
self.B[param] = broadcastMatrixMultiply(self.Ainv,
broadcastMatrixMultiply(self.dPrxy[param], self.A))
else:
assert isinstance(paramval, numpy.ndarray) and paramval.ndim == 1
for j in range(paramval.shape[0]):
self.B[param][j] = broadcastMatrixMultiply(self.Ainv,
broadcastMatrixMultiply(self.dPrxy[param][j],
self.A))
<|endoftext|> | [
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tornadoweb/tornado | tornado/http1connection.py | HTTP1Connection.write_headers | def write_headers(
self,
start_line: Union[httputil.RequestStartLine, httputil.ResponseStartLine],
headers: httputil.HTTPHeaders,
chunk: bytes = None,
) -> "Future[None]":
"""Implements `.HTTPConnection.write_headers`."""
lines = []
if self.is_client:
assert isinstance(start_line, httputil.RequestStartLine)
self._request_start_line = start_line
lines.append(utf8("%s %s HTTP/1.1" % (start_line[0], start_line[1])))
# Client requests with a non-empty body must have either a
# Content-Length or a Transfer-Encoding.
self._chunking_output = (
start_line.method in ("POST", "PUT", "PATCH")
and "Content-Length" not in headers
and (
"Transfer-Encoding" not in headers
or headers["Transfer-Encoding"] == "chunked"
)
)
else:
assert isinstance(start_line, httputil.ResponseStartLine)
assert self._request_start_line is not None
assert self._request_headers is not None
self._response_start_line = start_line
lines.append(utf8("HTTP/1.1 %d %s" % (start_line[1], start_line[2])))
self._chunking_output = (
# TODO: should this use
# self._request_start_line.version or
# start_line.version?
self._request_start_line.version == "HTTP/1.1"
# 1xx, 204 and 304 responses have no body (not even a zero-length
# body), and so should not have either Content-Length or
# Transfer-Encoding headers.
and start_line.code not in (204, 304)
and (start_line.code < 100 or start_line.code >= 200)
# No need to chunk the output if a Content-Length is specified.
and "Content-Length" not in headers
# Applications are discouraged from touching Transfer-Encoding,
# but if they do, leave it alone.
and "Transfer-Encoding" not in headers
)
# If connection to a 1.1 client will be closed, inform client
if (
self._request_start_line.version == "HTTP/1.1"
and self._disconnect_on_finish
):
headers["Connection"] = "close"
# If a 1.0 client asked for keep-alive, add the header.
if (
self._request_start_line.version == "HTTP/1.0"
and self._request_headers.get("Connection", "").lower() == "keep-alive"
):
headers["Connection"] = "Keep-Alive"
if self._chunking_output:
headers["Transfer-Encoding"] = "chunked"
if not self.is_client and (
self._request_start_line.method == "HEAD"
or cast(httputil.ResponseStartLine, start_line).code == 304
):
self._expected_content_remaining = 0
elif "Content-Length" in headers:
self._expected_content_remaining = int(headers["Content-Length"])
else:
self._expected_content_remaining = None
# TODO: headers are supposed to be of type str, but we still have some
# cases that let bytes slip through. Remove these native_str calls when those
# are fixed.
header_lines = (
native_str(n) + ": " + native_str(v) for n, v in headers.get_all()
)
lines.extend(l.encode("latin1") for l in header_lines)
for line in lines:
if b"\n" in line:
raise ValueError("Newline in header: " + repr(line))
future = None
if self.stream.closed():
future = self._write_future = Future()
future.set_exception(iostream.StreamClosedError())
future.exception()
else:
future = self._write_future = Future()
data = b"\r\n".join(lines) + b"\r\n\r\n"
if chunk:
data += self._format_chunk(chunk)
self._pending_write = self.stream.write(data)
future_add_done_callback(self._pending_write, self._on_write_complete)
return future | python | def write_headers(
self,
start_line: Union[httputil.RequestStartLine, httputil.ResponseStartLine],
headers: httputil.HTTPHeaders,
chunk: bytes = None,
) -> "Future[None]":
"""Implements `.HTTPConnection.write_headers`."""
lines = []
if self.is_client:
assert isinstance(start_line, httputil.RequestStartLine)
self._request_start_line = start_line
lines.append(utf8("%s %s HTTP/1.1" % (start_line[0], start_line[1])))
# Client requests with a non-empty body must have either a
# Content-Length or a Transfer-Encoding.
self._chunking_output = (
start_line.method in ("POST", "PUT", "PATCH")
and "Content-Length" not in headers
and (
"Transfer-Encoding" not in headers
or headers["Transfer-Encoding"] == "chunked"
)
)
else:
assert isinstance(start_line, httputil.ResponseStartLine)
assert self._request_start_line is not None
assert self._request_headers is not None
self._response_start_line = start_line
lines.append(utf8("HTTP/1.1 %d %s" % (start_line[1], start_line[2])))
self._chunking_output = (
# TODO: should this use
# self._request_start_line.version or
# start_line.version?
self._request_start_line.version == "HTTP/1.1"
# 1xx, 204 and 304 responses have no body (not even a zero-length
# body), and so should not have either Content-Length or
# Transfer-Encoding headers.
and start_line.code not in (204, 304)
and (start_line.code < 100 or start_line.code >= 200)
# No need to chunk the output if a Content-Length is specified.
and "Content-Length" not in headers
# Applications are discouraged from touching Transfer-Encoding,
# but if they do, leave it alone.
and "Transfer-Encoding" not in headers
)
# If connection to a 1.1 client will be closed, inform client
if (
self._request_start_line.version == "HTTP/1.1"
and self._disconnect_on_finish
):
headers["Connection"] = "close"
# If a 1.0 client asked for keep-alive, add the header.
if (
self._request_start_line.version == "HTTP/1.0"
and self._request_headers.get("Connection", "").lower() == "keep-alive"
):
headers["Connection"] = "Keep-Alive"
if self._chunking_output:
headers["Transfer-Encoding"] = "chunked"
if not self.is_client and (
self._request_start_line.method == "HEAD"
or cast(httputil.ResponseStartLine, start_line).code == 304
):
self._expected_content_remaining = 0
elif "Content-Length" in headers:
self._expected_content_remaining = int(headers["Content-Length"])
else:
self._expected_content_remaining = None
# TODO: headers are supposed to be of type str, but we still have some
# cases that let bytes slip through. Remove these native_str calls when those
# are fixed.
header_lines = (
native_str(n) + ": " + native_str(v) for n, v in headers.get_all()
)
lines.extend(l.encode("latin1") for l in header_lines)
for line in lines:
if b"\n" in line:
raise ValueError("Newline in header: " + repr(line))
future = None
if self.stream.closed():
future = self._write_future = Future()
future.set_exception(iostream.StreamClosedError())
future.exception()
else:
future = self._write_future = Future()
data = b"\r\n".join(lines) + b"\r\n\r\n"
if chunk:
data += self._format_chunk(chunk)
self._pending_write = self.stream.write(data)
future_add_done_callback(self._pending_write, self._on_write_complete)
return future | [
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] | Implements `.HTTPConnection.write_headers`. | [
"Implements",
".",
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"."
] | train | https://github.com/tornadoweb/tornado/blob/b8b481770bcdb333a69afde5cce7eaa449128326/tornado/http1connection.py#L376-L465 | <|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 `.HTTPConnection.write_headers`.
### Response:
def write_headers(
self,
start_line: Union[httputil.RequestStartLine, httputil.ResponseStartLine],
headers: httputil.HTTPHeaders,
chunk: bytes = None,
) -> "Future[None]":
"""Implements `.HTTPConnection.write_headers`."""
lines = []
if self.is_client:
assert isinstance(start_line, httputil.RequestStartLine)
self._request_start_line = start_line
lines.append(utf8("%s %s HTTP/1.1" % (start_line[0], start_line[1])))
# Client requests with a non-empty body must have either a
# Content-Length or a Transfer-Encoding.
self._chunking_output = (
start_line.method in ("POST", "PUT", "PATCH")
and "Content-Length" not in headers
and (
"Transfer-Encoding" not in headers
or headers["Transfer-Encoding"] == "chunked"
)
)
else:
assert isinstance(start_line, httputil.ResponseStartLine)
assert self._request_start_line is not None
assert self._request_headers is not None
self._response_start_line = start_line
lines.append(utf8("HTTP/1.1 %d %s" % (start_line[1], start_line[2])))
self._chunking_output = (
# TODO: should this use
# self._request_start_line.version or
# start_line.version?
self._request_start_line.version == "HTTP/1.1"
# 1xx, 204 and 304 responses have no body (not even a zero-length
# body), and so should not have either Content-Length or
# Transfer-Encoding headers.
and start_line.code not in (204, 304)
and (start_line.code < 100 or start_line.code >= 200)
# No need to chunk the output if a Content-Length is specified.
and "Content-Length" not in headers
# Applications are discouraged from touching Transfer-Encoding,
# but if they do, leave it alone.
and "Transfer-Encoding" not in headers
)
# If connection to a 1.1 client will be closed, inform client
if (
self._request_start_line.version == "HTTP/1.1"
and self._disconnect_on_finish
):
headers["Connection"] = "close"
# If a 1.0 client asked for keep-alive, add the header.
if (
self._request_start_line.version == "HTTP/1.0"
and self._request_headers.get("Connection", "").lower() == "keep-alive"
):
headers["Connection"] = "Keep-Alive"
if self._chunking_output:
headers["Transfer-Encoding"] = "chunked"
if not self.is_client and (
self._request_start_line.method == "HEAD"
or cast(httputil.ResponseStartLine, start_line).code == 304
):
self._expected_content_remaining = 0
elif "Content-Length" in headers:
self._expected_content_remaining = int(headers["Content-Length"])
else:
self._expected_content_remaining = None
# TODO: headers are supposed to be of type str, but we still have some
# cases that let bytes slip through. Remove these native_str calls when those
# are fixed.
header_lines = (
native_str(n) + ": " + native_str(v) for n, v in headers.get_all()
)
lines.extend(l.encode("latin1") for l in header_lines)
for line in lines:
if b"\n" in line:
raise ValueError("Newline in header: " + repr(line))
future = None
if self.stream.closed():
future = self._write_future = Future()
future.set_exception(iostream.StreamClosedError())
future.exception()
else:
future = self._write_future = Future()
data = b"\r\n".join(lines) + b"\r\n\r\n"
if chunk:
data += self._format_chunk(chunk)
self._pending_write = self.stream.write(data)
future_add_done_callback(self._pending_write, self._on_write_complete)
return future
<|endoftext|> | [
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chaoss/grimoirelab-sortinghat | sortinghat/parser.py | create_organizations_parser | def create_organizations_parser(stream):
"""Create an organizations parser for the given stream.
Factory function that creates an organizations parser for the
given stream. The stream is only used to guess the type of the
required parser.
:param stream: stream used to guess the type of the parser
:returns: an organizations parser
:raise InvalidFormatError: raised when no one of the available
parsers can parse the given stream
"""
import sortinghat.parsing as parsing
# First, try with default parser
for p in parsing.SORTINGHAT_ORGS_PARSERS:
klass = parsing.SORTINGHAT_ORGS_PARSERS[p]
parser = klass()
if parser.check(stream):
return parser
raise InvalidFormatError(cause=INVALID_FORMAT_MSG) | python | def create_organizations_parser(stream):
"""Create an organizations parser for the given stream.
Factory function that creates an organizations parser for the
given stream. The stream is only used to guess the type of the
required parser.
:param stream: stream used to guess the type of the parser
:returns: an organizations parser
:raise InvalidFormatError: raised when no one of the available
parsers can parse the given stream
"""
import sortinghat.parsing as parsing
# First, try with default parser
for p in parsing.SORTINGHAT_ORGS_PARSERS:
klass = parsing.SORTINGHAT_ORGS_PARSERS[p]
parser = klass()
if parser.check(stream):
return parser
raise InvalidFormatError(cause=INVALID_FORMAT_MSG) | [
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:returns: an organizations parser
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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 an organizations parser for the given stream.
Factory function that creates an organizations parser for the
given stream. The stream is only used to guess the type of the
required parser.
:param stream: stream used to guess the type of the parser
:returns: an organizations parser
:raise InvalidFormatError: raised when no one of the available
parsers can parse the given stream
### Response:
def create_organizations_parser(stream):
"""Create an organizations parser for the given stream.
Factory function that creates an organizations parser for the
given stream. The stream is only used to guess the type of the
required parser.
:param stream: stream used to guess the type of the parser
:returns: an organizations parser
:raise InvalidFormatError: raised when no one of the available
parsers can parse the given stream
"""
import sortinghat.parsing as parsing
# First, try with default parser
for p in parsing.SORTINGHAT_ORGS_PARSERS:
klass = parsing.SORTINGHAT_ORGS_PARSERS[p]
parser = klass()
if parser.check(stream):
return parser
raise InvalidFormatError(cause=INVALID_FORMAT_MSG)
<|endoftext|> | [
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jkwill87/teletype | teletype/components/config.py | set_char | def set_char(key, value):
""" Updates charters used to render components.
"""
global _chars
category = _get_char_category(key)
if not category:
raise KeyError
_chars[category][key] = value | python | def set_char(key, value):
""" Updates charters used to render components.
"""
global _chars
category = _get_char_category(key)
if not category:
raise KeyError
_chars[category][key] = 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:
Updates charters used to render components.
### Response:
def set_char(key, value):
""" Updates charters used to render components.
"""
global _chars
category = _get_char_category(key)
if not category:
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cockroachdb/cockroachdb-python | cockroachdb/sqlalchemy/transaction.py | run_transaction | def run_transaction(transactor, callback):
"""Run a transaction with retries.
``callback()`` will be called with one argument to execute the
transaction. ``callback`` may be called more than once; it should have
no side effects other than writes to the database on the given
connection. ``callback`` should not call ``commit()` or ``rollback()``;
these will be called automatically.
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* `sqlalchemy.engine.Connection`: the same connection is passed to the callback.
* `sqlalchemy.engine.Engine`: a connection is created and passed to the callback.
* `sqlalchemy.orm.sessionmaker`: a session is created and passed to the callback.
"""
if isinstance(transactor, sqlalchemy.engine.Connection):
return _txn_retry_loop(transactor, callback)
elif isinstance(transactor, sqlalchemy.engine.Engine):
with transactor.connect() as connection:
return _txn_retry_loop(connection, callback)
elif isinstance(transactor, sqlalchemy.orm.sessionmaker):
session = transactor(autocommit=True)
return _txn_retry_loop(session, callback)
else:
raise TypeError("don't know how to run a transaction on %s", type(transactor)) | python | def run_transaction(transactor, callback):
"""Run a transaction with retries.
``callback()`` will be called with one argument to execute the
transaction. ``callback`` may be called more than once; it should have
no side effects other than writes to the database on the given
connection. ``callback`` should not call ``commit()` or ``rollback()``;
these will be called automatically.
The ``transactor`` argument may be one of the following types:
* `sqlalchemy.engine.Connection`: the same connection is passed to the callback.
* `sqlalchemy.engine.Engine`: a connection is created and passed to the callback.
* `sqlalchemy.orm.sessionmaker`: a session is created and passed to the callback.
"""
if isinstance(transactor, sqlalchemy.engine.Connection):
return _txn_retry_loop(transactor, callback)
elif isinstance(transactor, sqlalchemy.engine.Engine):
with transactor.connect() as connection:
return _txn_retry_loop(connection, callback)
elif isinstance(transactor, sqlalchemy.orm.sessionmaker):
session = transactor(autocommit=True)
return _txn_retry_loop(session, callback)
else:
raise TypeError("don't know how to run a transaction on %s", type(transactor)) | [
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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 a transaction with retries.
``callback()`` will be called with one argument to execute the
transaction. ``callback`` may be called more than once; it should have
no side effects other than writes to the database on the given
connection. ``callback`` should not call ``commit()` or ``rollback()``;
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* `sqlalchemy.orm.sessionmaker`: a session is created and passed to the callback.
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def run_transaction(transactor, callback):
"""Run a transaction with retries.
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"""
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elif isinstance(transactor, sqlalchemy.engine.Engine):
with transactor.connect() as connection:
return _txn_retry_loop(connection, callback)
elif isinstance(transactor, sqlalchemy.orm.sessionmaker):
session = transactor(autocommit=True)
return _txn_retry_loop(session, callback)
else:
raise TypeError("don't know how to run a transaction on %s", type(transactor))
<|endoftext|> | [
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inveniosoftware/invenio-collections | invenio_collections/ext.py | _AppState.collections | def collections(self, values):
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# if cache server is configured, save collection list
if self.cache:
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self.app.config['COLLECTIONS_CACHE_KEY'], values) | python | def collections(self, values):
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# if cache server is configured, save collection list
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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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Set list of collections.
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oceanprotocol/squid-py | squid_py/ocean/ocean_assets.py | OceanAssets.create | def create(self, metadata, publisher_account,
service_descriptors=None, providers=None,
use_secret_store=True):
"""
Register an asset in both the keeper's DIDRegistry (on-chain) and in the Metadata store (
Aquarius).
:param metadata: dict conforming to the Metadata accepted by Ocean Protocol.
:param publisher_account: Account of the publisher registering this asset
:param service_descriptors: list of ServiceDescriptor tuples of length 2.
The first item must be one of ServiceTypes and the second
item is a dict of parameters and values required by the service
:param providers: list of addresses of providers of this asset (a provider is
an ethereum account that is authorized to provide asset services)
:return: DDO instance
"""
assert isinstance(metadata, dict), f'Expected metadata of type dict, got {type(metadata)}'
if not metadata or not Metadata.validate(metadata):
raise OceanInvalidMetadata('Metadata seems invalid. Please make sure'
' the required metadata values are filled in.')
# copy metadata so we don't change the original
metadata_copy = copy.deepcopy(metadata)
# Create a DDO object
did = DID.did()
logger.debug(f'Generating new did: {did}')
# Check if it's already registered first!
if did in self._get_aquarius().list_assets():
raise OceanDIDAlreadyExist(
f'Asset id {did} is already registered to another asset.')
ddo = DDO(did)
# Add public key and authentication
ddo.add_public_key(did, publisher_account.address)
ddo.add_authentication(did, PUBLIC_KEY_TYPE_RSA)
# Setup metadata service
# First replace `files` with encrypted `files`
assert metadata_copy['base'][
'files'], 'files is required in the metadata base attributes.'
assert Metadata.validate(metadata), 'metadata seems invalid.'
logger.debug('Encrypting content urls in the metadata.')
brizo = BrizoProvider.get_brizo()
if not use_secret_store:
encrypt_endpoint = brizo.get_encrypt_endpoint(self._config)
files_encrypted = brizo.encrypt_files_dict(
metadata_copy['base']['files'],
encrypt_endpoint,
ddo.asset_id,
publisher_account.address,
self._keeper.sign_hash(ddo.asset_id, publisher_account)
)
else:
files_encrypted = self._get_secret_store(publisher_account) \
.encrypt_document(
did_to_id(did),
json.dumps(metadata_copy['base']['files']),
)
metadata_copy['base']['checksum'] = ddo.generate_checksum(did, metadata)
ddo.add_proof(metadata_copy['base']['checksum'], publisher_account, self._keeper)
# only assign if the encryption worked
if files_encrypted:
logger.info(f'Content urls encrypted successfully {files_encrypted}')
index = 0
for file in metadata_copy['base']['files']:
file['index'] = index
index = index + 1
del file['url']
metadata_copy['base']['encryptedFiles'] = files_encrypted
else:
raise AssertionError('Encrypting the files failed. Make sure the secret store is'
' setup properly in your config file.')
# DDO url and `Metadata` service
ddo_service_endpoint = self._get_aquarius().get_service_endpoint(did)
metadata_service_desc = ServiceDescriptor.metadata_service_descriptor(metadata_copy,
ddo_service_endpoint)
if not service_descriptors:
service_descriptors = [ServiceDescriptor.authorization_service_descriptor(
self._config.secret_store_url)]
brizo = BrizoProvider.get_brizo()
service_descriptors += [ServiceDescriptor.access_service_descriptor(
metadata[MetadataBase.KEY]['price'],
brizo.get_consume_endpoint(self._config),
brizo.get_service_endpoint(self._config),
3600,
self._keeper.escrow_access_secretstore_template.address
)]
else:
service_types = set(map(lambda x: x[0], service_descriptors))
if ServiceTypes.AUTHORIZATION not in service_types:
service_descriptors += [ServiceDescriptor.authorization_service_descriptor(
self._config.secret_store_url)]
else:
brizo = BrizoProvider.get_brizo()
service_descriptors += [ServiceDescriptor.access_service_descriptor(
metadata[MetadataBase.KEY]['price'],
brizo.get_consume_endpoint(self._config),
brizo.get_service_endpoint(self._config),
3600,
self._keeper.escrow_access_secretstore_template.address
)]
# Add all services to ddo
service_descriptors = service_descriptors + [metadata_service_desc]
for service in ServiceFactory.build_services(did, service_descriptors):
ddo.add_service(service)
logger.debug(
f'Generated ddo and services, DID is {ddo.did},'
f' metadata service @{ddo_service_endpoint}, '
f'`Access` service initialize @{ddo.services[0].endpoints.service}.')
response = None
# register on-chain
registered_on_chain = self._keeper.did_registry.register(
did,
checksum=Web3Provider.get_web3().sha3(text=metadata_copy['base']['checksum']),
url=ddo_service_endpoint,
account=publisher_account,
providers=providers
)
if registered_on_chain is False:
logger.warning(f'Registering {did} on-chain failed.')
return None
logger.info(f'DDO with DID {did} successfully registered on chain.')
try:
# publish the new ddo in ocean-db/Aquarius
response = self._get_aquarius().publish_asset_ddo(ddo)
logger.debug('Asset/ddo published successfully in aquarius.')
except ValueError as ve:
raise ValueError(f'Invalid value to publish in the metadata: {str(ve)}')
except Exception as e:
logger.error(f'Publish asset in aquarius failed: {str(e)}')
if not response:
return None
return ddo | python | def create(self, metadata, publisher_account,
service_descriptors=None, providers=None,
use_secret_store=True):
"""
Register an asset in both the keeper's DIDRegistry (on-chain) and in the Metadata store (
Aquarius).
:param metadata: dict conforming to the Metadata accepted by Ocean Protocol.
:param publisher_account: Account of the publisher registering this asset
:param service_descriptors: list of ServiceDescriptor tuples of length 2.
The first item must be one of ServiceTypes and the second
item is a dict of parameters and values required by the service
:param providers: list of addresses of providers of this asset (a provider is
an ethereum account that is authorized to provide asset services)
:return: DDO instance
"""
assert isinstance(metadata, dict), f'Expected metadata of type dict, got {type(metadata)}'
if not metadata or not Metadata.validate(metadata):
raise OceanInvalidMetadata('Metadata seems invalid. Please make sure'
' the required metadata values are filled in.')
# copy metadata so we don't change the original
metadata_copy = copy.deepcopy(metadata)
# Create a DDO object
did = DID.did()
logger.debug(f'Generating new did: {did}')
# Check if it's already registered first!
if did in self._get_aquarius().list_assets():
raise OceanDIDAlreadyExist(
f'Asset id {did} is already registered to another asset.')
ddo = DDO(did)
# Add public key and authentication
ddo.add_public_key(did, publisher_account.address)
ddo.add_authentication(did, PUBLIC_KEY_TYPE_RSA)
# Setup metadata service
# First replace `files` with encrypted `files`
assert metadata_copy['base'][
'files'], 'files is required in the metadata base attributes.'
assert Metadata.validate(metadata), 'metadata seems invalid.'
logger.debug('Encrypting content urls in the metadata.')
brizo = BrizoProvider.get_brizo()
if not use_secret_store:
encrypt_endpoint = brizo.get_encrypt_endpoint(self._config)
files_encrypted = brizo.encrypt_files_dict(
metadata_copy['base']['files'],
encrypt_endpoint,
ddo.asset_id,
publisher_account.address,
self._keeper.sign_hash(ddo.asset_id, publisher_account)
)
else:
files_encrypted = self._get_secret_store(publisher_account) \
.encrypt_document(
did_to_id(did),
json.dumps(metadata_copy['base']['files']),
)
metadata_copy['base']['checksum'] = ddo.generate_checksum(did, metadata)
ddo.add_proof(metadata_copy['base']['checksum'], publisher_account, self._keeper)
# only assign if the encryption worked
if files_encrypted:
logger.info(f'Content urls encrypted successfully {files_encrypted}')
index = 0
for file in metadata_copy['base']['files']:
file['index'] = index
index = index + 1
del file['url']
metadata_copy['base']['encryptedFiles'] = files_encrypted
else:
raise AssertionError('Encrypting the files failed. Make sure the secret store is'
' setup properly in your config file.')
# DDO url and `Metadata` service
ddo_service_endpoint = self._get_aquarius().get_service_endpoint(did)
metadata_service_desc = ServiceDescriptor.metadata_service_descriptor(metadata_copy,
ddo_service_endpoint)
if not service_descriptors:
service_descriptors = [ServiceDescriptor.authorization_service_descriptor(
self._config.secret_store_url)]
brizo = BrizoProvider.get_brizo()
service_descriptors += [ServiceDescriptor.access_service_descriptor(
metadata[MetadataBase.KEY]['price'],
brizo.get_consume_endpoint(self._config),
brizo.get_service_endpoint(self._config),
3600,
self._keeper.escrow_access_secretstore_template.address
)]
else:
service_types = set(map(lambda x: x[0], service_descriptors))
if ServiceTypes.AUTHORIZATION not in service_types:
service_descriptors += [ServiceDescriptor.authorization_service_descriptor(
self._config.secret_store_url)]
else:
brizo = BrizoProvider.get_brizo()
service_descriptors += [ServiceDescriptor.access_service_descriptor(
metadata[MetadataBase.KEY]['price'],
brizo.get_consume_endpoint(self._config),
brizo.get_service_endpoint(self._config),
3600,
self._keeper.escrow_access_secretstore_template.address
)]
# Add all services to ddo
service_descriptors = service_descriptors + [metadata_service_desc]
for service in ServiceFactory.build_services(did, service_descriptors):
ddo.add_service(service)
logger.debug(
f'Generated ddo and services, DID is {ddo.did},'
f' metadata service @{ddo_service_endpoint}, '
f'`Access` service initialize @{ddo.services[0].endpoints.service}.')
response = None
# register on-chain
registered_on_chain = self._keeper.did_registry.register(
did,
checksum=Web3Provider.get_web3().sha3(text=metadata_copy['base']['checksum']),
url=ddo_service_endpoint,
account=publisher_account,
providers=providers
)
if registered_on_chain is False:
logger.warning(f'Registering {did} on-chain failed.')
return None
logger.info(f'DDO with DID {did} successfully registered on chain.')
try:
# publish the new ddo in ocean-db/Aquarius
response = self._get_aquarius().publish_asset_ddo(ddo)
logger.debug('Asset/ddo published successfully in aquarius.')
except ValueError as ve:
raise ValueError(f'Invalid value to publish in the metadata: {str(ve)}')
except Exception as e:
logger.error(f'Publish asset in aquarius failed: {str(e)}')
if not response:
return None
return ddo | [
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] | Register an asset in both the keeper's DIDRegistry (on-chain) and in the Metadata store (
Aquarius).
:param metadata: dict conforming to the Metadata accepted by Ocean Protocol.
:param publisher_account: Account of the publisher registering this asset
:param service_descriptors: list of ServiceDescriptor tuples of length 2.
The first item must be one of ServiceTypes and the second
item is a dict of parameters and values required by the service
:param providers: list of addresses of providers of this asset (a provider is
an ethereum account that is authorized to provide asset services)
:return: DDO instance | [
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] | train | https://github.com/oceanprotocol/squid-py/blob/43a5b7431627e4c9ab7382ed9eb8153e96ed4483/squid_py/ocean/ocean_assets.py#L56-L195 | <|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:
Register an asset in both the keeper's DIDRegistry (on-chain) and in the Metadata store (
Aquarius).
:param metadata: dict conforming to the Metadata accepted by Ocean Protocol.
:param publisher_account: Account of the publisher registering this asset
:param service_descriptors: list of ServiceDescriptor tuples of length 2.
The first item must be one of ServiceTypes and the second
item is a dict of parameters and values required by the service
:param providers: list of addresses of providers of this asset (a provider is
an ethereum account that is authorized to provide asset services)
:return: DDO instance
### Response:
def create(self, metadata, publisher_account,
service_descriptors=None, providers=None,
use_secret_store=True):
"""
Register an asset in both the keeper's DIDRegistry (on-chain) and in the Metadata store (
Aquarius).
:param metadata: dict conforming to the Metadata accepted by Ocean Protocol.
:param publisher_account: Account of the publisher registering this asset
:param service_descriptors: list of ServiceDescriptor tuples of length 2.
The first item must be one of ServiceTypes and the second
item is a dict of parameters and values required by the service
:param providers: list of addresses of providers of this asset (a provider is
an ethereum account that is authorized to provide asset services)
:return: DDO instance
"""
assert isinstance(metadata, dict), f'Expected metadata of type dict, got {type(metadata)}'
if not metadata or not Metadata.validate(metadata):
raise OceanInvalidMetadata('Metadata seems invalid. Please make sure'
' the required metadata values are filled in.')
# copy metadata so we don't change the original
metadata_copy = copy.deepcopy(metadata)
# Create a DDO object
did = DID.did()
logger.debug(f'Generating new did: {did}')
# Check if it's already registered first!
if did in self._get_aquarius().list_assets():
raise OceanDIDAlreadyExist(
f'Asset id {did} is already registered to another asset.')
ddo = DDO(did)
# Add public key and authentication
ddo.add_public_key(did, publisher_account.address)
ddo.add_authentication(did, PUBLIC_KEY_TYPE_RSA)
# Setup metadata service
# First replace `files` with encrypted `files`
assert metadata_copy['base'][
'files'], 'files is required in the metadata base attributes.'
assert Metadata.validate(metadata), 'metadata seems invalid.'
logger.debug('Encrypting content urls in the metadata.')
brizo = BrizoProvider.get_brizo()
if not use_secret_store:
encrypt_endpoint = brizo.get_encrypt_endpoint(self._config)
files_encrypted = brizo.encrypt_files_dict(
metadata_copy['base']['files'],
encrypt_endpoint,
ddo.asset_id,
publisher_account.address,
self._keeper.sign_hash(ddo.asset_id, publisher_account)
)
else:
files_encrypted = self._get_secret_store(publisher_account) \
.encrypt_document(
did_to_id(did),
json.dumps(metadata_copy['base']['files']),
)
metadata_copy['base']['checksum'] = ddo.generate_checksum(did, metadata)
ddo.add_proof(metadata_copy['base']['checksum'], publisher_account, self._keeper)
# only assign if the encryption worked
if files_encrypted:
logger.info(f'Content urls encrypted successfully {files_encrypted}')
index = 0
for file in metadata_copy['base']['files']:
file['index'] = index
index = index + 1
del file['url']
metadata_copy['base']['encryptedFiles'] = files_encrypted
else:
raise AssertionError('Encrypting the files failed. Make sure the secret store is'
' setup properly in your config file.')
# DDO url and `Metadata` service
ddo_service_endpoint = self._get_aquarius().get_service_endpoint(did)
metadata_service_desc = ServiceDescriptor.metadata_service_descriptor(metadata_copy,
ddo_service_endpoint)
if not service_descriptors:
service_descriptors = [ServiceDescriptor.authorization_service_descriptor(
self._config.secret_store_url)]
brizo = BrizoProvider.get_brizo()
service_descriptors += [ServiceDescriptor.access_service_descriptor(
metadata[MetadataBase.KEY]['price'],
brizo.get_consume_endpoint(self._config),
brizo.get_service_endpoint(self._config),
3600,
self._keeper.escrow_access_secretstore_template.address
)]
else:
service_types = set(map(lambda x: x[0], service_descriptors))
if ServiceTypes.AUTHORIZATION not in service_types:
service_descriptors += [ServiceDescriptor.authorization_service_descriptor(
self._config.secret_store_url)]
else:
brizo = BrizoProvider.get_brizo()
service_descriptors += [ServiceDescriptor.access_service_descriptor(
metadata[MetadataBase.KEY]['price'],
brizo.get_consume_endpoint(self._config),
brizo.get_service_endpoint(self._config),
3600,
self._keeper.escrow_access_secretstore_template.address
)]
# Add all services to ddo
service_descriptors = service_descriptors + [metadata_service_desc]
for service in ServiceFactory.build_services(did, service_descriptors):
ddo.add_service(service)
logger.debug(
f'Generated ddo and services, DID is {ddo.did},'
f' metadata service @{ddo_service_endpoint}, '
f'`Access` service initialize @{ddo.services[0].endpoints.service}.')
response = None
# register on-chain
registered_on_chain = self._keeper.did_registry.register(
did,
checksum=Web3Provider.get_web3().sha3(text=metadata_copy['base']['checksum']),
url=ddo_service_endpoint,
account=publisher_account,
providers=providers
)
if registered_on_chain is False:
logger.warning(f'Registering {did} on-chain failed.')
return None
logger.info(f'DDO with DID {did} successfully registered on chain.')
try:
# publish the new ddo in ocean-db/Aquarius
response = self._get_aquarius().publish_asset_ddo(ddo)
logger.debug('Asset/ddo published successfully in aquarius.')
except ValueError as ve:
raise ValueError(f'Invalid value to publish in the metadata: {str(ve)}')
except Exception as e:
logger.error(f'Publish asset in aquarius failed: {str(e)}')
if not response:
return None
return ddo
<|endoftext|> | [
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StackStorm/pybind | pybind/slxos/v17r_1_01a/cfm_state/__init__.py | cfm_state._set_dmm | def _set_dmm(self, v, load=False):
"""
Setter method for dmm, mapped from YANG variable /cfm_state/dmm (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_dmm is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_dmm() directly.
YANG Description: CFM Y1731 DM Details
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=dmm.dmm, is_container='container', presence=False, yang_name="dmm", rest_name="dmm", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'dot1ag-dmm', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-dot1ag-operational', defining_module='brocade-dot1ag-operational', yang_type='container', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """dmm must be of a type compatible with container""",
'defined-type': "container",
'generated-type': """YANGDynClass(base=dmm.dmm, is_container='container', presence=False, yang_name="dmm", rest_name="dmm", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'dot1ag-dmm', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-dot1ag-operational', defining_module='brocade-dot1ag-operational', yang_type='container', is_config=False)""",
})
self.__dmm = t
if hasattr(self, '_set'):
self._set() | python | def _set_dmm(self, v, load=False):
"""
Setter method for dmm, mapped from YANG variable /cfm_state/dmm (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_dmm is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_dmm() directly.
YANG Description: CFM Y1731 DM Details
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=dmm.dmm, is_container='container', presence=False, yang_name="dmm", rest_name="dmm", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'dot1ag-dmm', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-dot1ag-operational', defining_module='brocade-dot1ag-operational', yang_type='container', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """dmm must be of a type compatible with container""",
'defined-type': "container",
'generated-type': """YANGDynClass(base=dmm.dmm, is_container='container', presence=False, yang_name="dmm", rest_name="dmm", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'dot1ag-dmm', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-dot1ag-operational', defining_module='brocade-dot1ag-operational', yang_type='container', is_config=False)""",
})
self.__dmm = t
if hasattr(self, '_set'):
self._set() | [
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If this variable is read-only (config: false) in the
source YANG file, then _set_dmm is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_dmm() directly.
YANG Description: CFM Y1731 DM Details | [
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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:
Setter method for dmm, mapped from YANG variable /cfm_state/dmm (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_dmm is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_dmm() directly.
YANG Description: CFM Y1731 DM Details
### Response:
def _set_dmm(self, v, load=False):
"""
Setter method for dmm, mapped from YANG variable /cfm_state/dmm (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_dmm is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_dmm() directly.
YANG Description: CFM Y1731 DM Details
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=dmm.dmm, is_container='container', presence=False, yang_name="dmm", rest_name="dmm", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'dot1ag-dmm', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-dot1ag-operational', defining_module='brocade-dot1ag-operational', yang_type='container', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """dmm must be of a type compatible with container""",
'defined-type': "container",
'generated-type': """YANGDynClass(base=dmm.dmm, is_container='container', presence=False, yang_name="dmm", rest_name="dmm", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'dot1ag-dmm', u'cli-suppress-show-path': None}}, namespace='urn:brocade.com:mgmt:brocade-dot1ag-operational', defining_module='brocade-dot1ag-operational', yang_type='container', is_config=False)""",
})
self.__dmm = t
if hasattr(self, '_set'):
self._set()
<|endoftext|> | [
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ktbyers/netmiko | netmiko/cisco/cisco_ios.py | InLineTransfer.config_md5 | def config_md5(self, source_config):
"""Compute MD5 hash of file."""
file_contents = source_config + "\n" # Cisco IOS automatically adds this
file_contents = file_contents.encode("UTF-8")
return hashlib.md5(file_contents).hexdigest() | python | def config_md5(self, source_config):
"""Compute MD5 hash of file."""
file_contents = source_config + "\n" # Cisco IOS automatically adds this
file_contents = file_contents.encode("UTF-8")
return hashlib.md5(file_contents).hexdigest() | [
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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:
Compute MD5 hash of file.
### Response:
def config_md5(self, source_config):
"""Compute MD5 hash of file."""
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modlinltd/django-advanced-filters | advanced_filters/forms.py | AdvancedFilterQueryForm._build_query_dict | def _build_query_dict(self, formdata=None):
"""
Take submitted data from form and create a query dict to be
used in a Q object (or filter)
"""
if self.is_valid() and formdata is None:
formdata = self.cleaned_data
key = "{field}__{operator}".format(**formdata)
if formdata['operator'] == "isnull":
return {key: None}
elif formdata['operator'] == "istrue":
return {formdata['field']: True}
elif formdata['operator'] == "isfalse":
return {formdata['field']: False}
return {key: formdata['value']} | python | def _build_query_dict(self, formdata=None):
"""
Take submitted data from form and create a query dict to be
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"""
if self.is_valid() and formdata is None:
formdata = self.cleaned_data
key = "{field}__{operator}".format(**formdata)
if formdata['operator'] == "isnull":
return {key: None}
elif formdata['operator'] == "istrue":
return {formdata['field']: True}
elif formdata['operator'] == "isfalse":
return {formdata['field']: False}
return {key: formdata['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:
Take submitted data from form and create a query dict to be
used in a Q object (or filter)
### Response:
def _build_query_dict(self, formdata=None):
"""
Take submitted data from form and create a query dict to be
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elif formdata['operator'] == "istrue":
return {formdata['field']: True}
elif formdata['operator'] == "isfalse":
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return {key: formdata['value']}
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huyingxi/Synonyms | synonyms/utils.py | deaccent | def deaccent(text):
"""
Remove accentuation from the given string. Input text is either a unicode string or utf8 encoded bytestring.
Return input string with accents removed, as unicode.
>>> deaccent("Šéf chomutovských komunistů dostal poštou bílý prášek")
u'Sef chomutovskych komunistu dostal postou bily prasek'
"""
if not isinstance(text, unicode):
# assume utf8 for byte strings, use default (strict) error handling
text = text.decode('utf8')
norm = unicodedata.normalize("NFD", text)
result = u('').join(ch for ch in norm if unicodedata.category(ch) != 'Mn')
return unicodedata.normalize("NFC", result) | python | def deaccent(text):
"""
Remove accentuation from the given string. Input text is either a unicode string or utf8 encoded bytestring.
Return input string with accents removed, as unicode.
>>> deaccent("Šéf chomutovských komunistů dostal poštou bílý prášek")
u'Sef chomutovskych komunistu dostal postou bily prasek'
"""
if not isinstance(text, unicode):
# assume utf8 for byte strings, use default (strict) error handling
text = text.decode('utf8')
norm = unicodedata.normalize("NFD", text)
result = u('').join(ch for ch in norm if unicodedata.category(ch) != 'Mn')
return unicodedata.normalize("NFC", result) | [
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u'Sef chomutovskych komunistu dostal postou bily prasek' | [
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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:
Remove accentuation from the given string. Input text is either a unicode string or utf8 encoded bytestring.
Return input string with accents removed, as unicode.
>>> deaccent("Šéf chomutovských komunistů dostal poštou bílý prášek")
u'Sef chomutovskych komunistu dostal postou bily prasek'
### Response:
def deaccent(text):
"""
Remove accentuation from the given string. Input text is either a unicode string or utf8 encoded bytestring.
Return input string with accents removed, as unicode.
>>> deaccent("Šéf chomutovských komunistů dostal poštou bílý prášek")
u'Sef chomutovskych komunistu dostal postou bily prasek'
"""
if not isinstance(text, unicode):
# assume utf8 for byte strings, use default (strict) error handling
text = text.decode('utf8')
norm = unicodedata.normalize("NFD", text)
result = u('').join(ch for ch in norm if unicodedata.category(ch) != 'Mn')
return unicodedata.normalize("NFC", result)
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pantsbuild/pants | src/python/pants/pantsd/service/scheduler_service.py | SchedulerService.run | def run(self):
"""Main service entrypoint."""
while not self._state.is_terminating:
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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:
Main service entrypoint.
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Kozea/pygal | pygal/util.py | round_to_float | def round_to_float(number, precision):
"""Round a float to a precision"""
rounded = Decimal(str(floor((number + precision / 2) // precision))
) * Decimal(str(precision))
return float(rounded) | python | def round_to_float(number, precision):
"""Round a float to a precision"""
rounded = Decimal(str(floor((number + precision / 2) // precision))
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return float(rounded) | [
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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:
Round a float to a precision
### Response:
def round_to_float(number, precision):
"""Round a float to a precision"""
rounded = Decimal(str(floor((number + precision / 2) // precision))
) * Decimal(str(precision))
return float(rounded)
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LogicalDash/LiSE | LiSE/LiSE/cache.py | NodeContentsCache.truncate_loc | def truncate_loc(self, character, location, branch, turn, tick):
"""Remove future data about a particular location
Return True if I deleted anything, False otherwise.
"""
r = False
branches_turns = self.branches[character, location][branch]
branches_turns.truncate(turn)
if turn in branches_turns:
bttrn = branches_turns[turn]
if bttrn.future(tick):
bttrn.truncate(tick)
r = True
keyses = self.keys[character, location]
for keysbranches in keyses.values():
if branch not in keysbranches:
continue
keysbranch = keysbranches[branch]
if keysbranch.future(turn):
keysbranch.truncate(turn)
r = True
if turn in keysbranch:
keysbranchturn = keysbranch[turn]
if keysbranchturn.future(tick):
keysbranchturn.truncate(tick)
r = True
if branch in self.settings:
for sets in (self.settings, self.presettings):
sets_branch = sets[branch]
if turn in sets_branch:
sets_turn = sets_branch[turn]
for tic, setting in list(sets_turn.future(tick).items()):
if setting[:2] == (character, location):
del sets_turn[tic]
r = True
if not sets_turn:
del sets_branch[turn]
assert r, "Found an empty cache when I didn't delete anything"
for trn, tics in list(sets_branch.future(turn).items()):
for tic, setting in list(tics.future(tick).items()):
if setting[:2] == (character, location):
del tics[tic]
r = True
if not tics:
del sets_branch[trn]
assert r, "Found an empty cache when I didn't delete anything"
if not sets_branch:
del sets[branch]
assert r, "Found an empty cache when I didn't delete anything"
self.shallowest = OrderedDict()
return r | python | def truncate_loc(self, character, location, branch, turn, tick):
"""Remove future data about a particular location
Return True if I deleted anything, False otherwise.
"""
r = False
branches_turns = self.branches[character, location][branch]
branches_turns.truncate(turn)
if turn in branches_turns:
bttrn = branches_turns[turn]
if bttrn.future(tick):
bttrn.truncate(tick)
r = True
keyses = self.keys[character, location]
for keysbranches in keyses.values():
if branch not in keysbranches:
continue
keysbranch = keysbranches[branch]
if keysbranch.future(turn):
keysbranch.truncate(turn)
r = True
if turn in keysbranch:
keysbranchturn = keysbranch[turn]
if keysbranchturn.future(tick):
keysbranchturn.truncate(tick)
r = True
if branch in self.settings:
for sets in (self.settings, self.presettings):
sets_branch = sets[branch]
if turn in sets_branch:
sets_turn = sets_branch[turn]
for tic, setting in list(sets_turn.future(tick).items()):
if setting[:2] == (character, location):
del sets_turn[tic]
r = True
if not sets_turn:
del sets_branch[turn]
assert r, "Found an empty cache when I didn't delete anything"
for trn, tics in list(sets_branch.future(turn).items()):
for tic, setting in list(tics.future(tick).items()):
if setting[:2] == (character, location):
del tics[tic]
r = True
if not tics:
del sets_branch[trn]
assert r, "Found an empty cache when I didn't delete anything"
if not sets_branch:
del sets[branch]
assert r, "Found an empty cache when I didn't delete anything"
self.shallowest = OrderedDict()
return r | [
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Return True if I deleted anything, False otherwise. | [
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] | train | https://github.com/LogicalDash/LiSE/blob/fe6fd4f0a7c1780e065f4c9babb9bc443af6bb84/LiSE/LiSE/cache.py#L635-L686 | <|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:
Remove future data about a particular location
Return True if I deleted anything, False otherwise.
### Response:
def truncate_loc(self, character, location, branch, turn, tick):
"""Remove future data about a particular location
Return True if I deleted anything, False otherwise.
"""
r = False
branches_turns = self.branches[character, location][branch]
branches_turns.truncate(turn)
if turn in branches_turns:
bttrn = branches_turns[turn]
if bttrn.future(tick):
bttrn.truncate(tick)
r = True
keyses = self.keys[character, location]
for keysbranches in keyses.values():
if branch not in keysbranches:
continue
keysbranch = keysbranches[branch]
if keysbranch.future(turn):
keysbranch.truncate(turn)
r = True
if turn in keysbranch:
keysbranchturn = keysbranch[turn]
if keysbranchturn.future(tick):
keysbranchturn.truncate(tick)
r = True
if branch in self.settings:
for sets in (self.settings, self.presettings):
sets_branch = sets[branch]
if turn in sets_branch:
sets_turn = sets_branch[turn]
for tic, setting in list(sets_turn.future(tick).items()):
if setting[:2] == (character, location):
del sets_turn[tic]
r = True
if not sets_turn:
del sets_branch[turn]
assert r, "Found an empty cache when I didn't delete anything"
for trn, tics in list(sets_branch.future(turn).items()):
for tic, setting in list(tics.future(tick).items()):
if setting[:2] == (character, location):
del tics[tic]
r = True
if not tics:
del sets_branch[trn]
assert r, "Found an empty cache when I didn't delete anything"
if not sets_branch:
del sets[branch]
assert r, "Found an empty cache when I didn't delete anything"
self.shallowest = OrderedDict()
return r
<|endoftext|> | [
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rigetti/grove | grove/tomography/state_tomography.py | state_tomography_programs | def state_tomography_programs(state_prep, qubits=None,
rotation_generator=tomography.default_rotations):
"""
Yield tomographic sequences that prepare a state with Quil program `state_prep` and then append
tomographic rotations on the specified `qubits`. If `qubits is None`, it assumes all qubits in
the program should be tomographically rotated.
:param Program state_prep: The program to prepare the state to be tomographed.
:param list|NoneType qubits: A list of Qubits or Numbers, to perform the tomography on. If
`None`, performs it on all in state_prep.
:param generator rotation_generator: A generator that yields tomography rotations to perform.
:return: Program for state tomography.
:rtype: Program
"""
if qubits is None:
qubits = state_prep.get_qubits()
for tomography_program in rotation_generator(*qubits):
state_tomography_program = Program(Pragma("PRESERVE_BLOCK"))
state_tomography_program.inst(state_prep)
state_tomography_program.inst(tomography_program)
state_tomography_program.inst(Pragma("END_PRESERVE_BLOCK"))
yield state_tomography_program | python | def state_tomography_programs(state_prep, qubits=None,
rotation_generator=tomography.default_rotations):
"""
Yield tomographic sequences that prepare a state with Quil program `state_prep` and then append
tomographic rotations on the specified `qubits`. If `qubits is None`, it assumes all qubits in
the program should be tomographically rotated.
:param Program state_prep: The program to prepare the state to be tomographed.
:param list|NoneType qubits: A list of Qubits or Numbers, to perform the tomography on. If
`None`, performs it on all in state_prep.
:param generator rotation_generator: A generator that yields tomography rotations to perform.
:return: Program for state tomography.
:rtype: Program
"""
if qubits is None:
qubits = state_prep.get_qubits()
for tomography_program in rotation_generator(*qubits):
state_tomography_program = Program(Pragma("PRESERVE_BLOCK"))
state_tomography_program.inst(state_prep)
state_tomography_program.inst(tomography_program)
state_tomography_program.inst(Pragma("END_PRESERVE_BLOCK"))
yield state_tomography_program | [
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tomographic rotations on the specified `qubits`. If `qubits is None`, it assumes all qubits in
the program should be tomographically rotated.
:param Program state_prep: The program to prepare the state to be tomographed.
:param list|NoneType qubits: A list of Qubits or Numbers, to perform the tomography on. If
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:param generator rotation_generator: A generator that yields tomography rotations to perform.
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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:
Yield tomographic sequences that prepare a state with Quil program `state_prep` and then append
tomographic rotations on the specified `qubits`. If `qubits is None`, it assumes all qubits in
the program should be tomographically rotated.
:param Program state_prep: The program to prepare the state to be tomographed.
:param list|NoneType qubits: A list of Qubits or Numbers, to perform the tomography on. If
`None`, performs it on all in state_prep.
:param generator rotation_generator: A generator that yields tomography rotations to perform.
:return: Program for state tomography.
:rtype: Program
### Response:
def state_tomography_programs(state_prep, qubits=None,
rotation_generator=tomography.default_rotations):
"""
Yield tomographic sequences that prepare a state with Quil program `state_prep` and then append
tomographic rotations on the specified `qubits`. If `qubits is None`, it assumes all qubits in
the program should be tomographically rotated.
:param Program state_prep: The program to prepare the state to be tomographed.
:param list|NoneType qubits: A list of Qubits or Numbers, to perform the tomography on. If
`None`, performs it on all in state_prep.
:param generator rotation_generator: A generator that yields tomography rotations to perform.
:return: Program for state tomography.
:rtype: Program
"""
if qubits is None:
qubits = state_prep.get_qubits()
for tomography_program in rotation_generator(*qubits):
state_tomography_program = Program(Pragma("PRESERVE_BLOCK"))
state_tomography_program.inst(state_prep)
state_tomography_program.inst(tomography_program)
state_tomography_program.inst(Pragma("END_PRESERVE_BLOCK"))
yield state_tomography_program
<|endoftext|> | [
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sdispater/orator | orator/migrations/database_migration_repository.py | DatabaseMigrationRepository.log | def log(self, file, batch):
"""
Log that a migration was run.
:type file: str
:type batch: int
"""
record = {"migration": file, "batch": batch}
self.table().insert(**record) | python | def log(self, file, batch):
"""
Log that a migration was run.
:type file: str
:type batch: int
"""
record = {"migration": file, "batch": batch}
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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:
Log that a migration was run.
:type file: str
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Log that a migration was run.
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getsentry/sentry-python | sentry_sdk/utils.py | event_hint_with_exc_info | def event_hint_with_exc_info(exc_info=None):
# type: (ExcInfo) -> Dict[str, Optional[ExcInfo]]
"""Creates a hint with the exc info filled in."""
if exc_info is None:
exc_info = sys.exc_info()
else:
exc_info = exc_info_from_error(exc_info)
if exc_info[0] is None:
exc_info = None
return {"exc_info": exc_info} | python | def event_hint_with_exc_info(exc_info=None):
# type: (ExcInfo) -> Dict[str, Optional[ExcInfo]]
"""Creates a hint with the exc info filled in."""
if exc_info is None:
exc_info = sys.exc_info()
else:
exc_info = exc_info_from_error(exc_info)
if exc_info[0] is None:
exc_info = None
return {"exc_info": exc_info} | [
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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:
Creates a hint with the exc info filled in.
### Response:
def event_hint_with_exc_info(exc_info=None):
# type: (ExcInfo) -> Dict[str, Optional[ExcInfo]]
"""Creates a hint with the exc info filled in."""
if exc_info is None:
exc_info = sys.exc_info()
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riga/law | law/cli/software.py | remove_software_cache | def remove_software_cache(sw_dir=None):
"""
Removes the software cache directory at *sw_dir* which is evaluated with :py:func:`get_sw_dir`.
"""
sw_dir = get_sw_dir(sw_dir)
if os.path.exists(sw_dir):
shutil.rmtree(sw_dir) | python | def remove_software_cache(sw_dir=None):
"""
Removes the software cache directory at *sw_dir* which is evaluated with :py:func:`get_sw_dir`.
"""
sw_dir = get_sw_dir(sw_dir)
if os.path.exists(sw_dir):
shutil.rmtree(sw_dir) | [
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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 the software cache directory at *sw_dir* which is evaluated with :py:func:`get_sw_dir`.
### Response:
def remove_software_cache(sw_dir=None):
"""
Removes the software cache directory at *sw_dir* which is evaluated with :py:func:`get_sw_dir`.
"""
sw_dir = get_sw_dir(sw_dir)
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wecatch/app-turbo | turbo/util.py | to_basestring | def to_basestring(value):
"""Converts a string argument to a subclass of basestring.
In python2, byte and unicode strings are mostly interchangeable,
so functions that deal with a user-supplied argument in combination
with ascii string constants can use either and should return the type
the user supplied. In python3, the two types are not interchangeable,
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"""
if isinstance(value, _BASESTRING_TYPES):
return value
if not isinstance(value, bytes):
raise TypeError(
"Expected bytes, unicode, or None; got %r" % type(value)
)
return value.decode("utf-8") | python | def to_basestring(value):
"""Converts a string argument to a subclass of basestring.
In python2, byte and unicode strings are mostly interchangeable,
so functions that deal with a user-supplied argument in combination
with ascii string constants can use either and should return the type
the user supplied. In python3, the two types are not interchangeable,
so this method is needed to convert byte strings to unicode.
"""
if isinstance(value, _BASESTRING_TYPES):
return value
if not isinstance(value, bytes):
raise TypeError(
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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:
Converts a string argument to a subclass of basestring.
In python2, byte and unicode strings are mostly interchangeable,
so functions that deal with a user-supplied argument in combination
with ascii string constants can use either and should return the type
the user supplied. In python3, the two types are not interchangeable,
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### Response:
def to_basestring(value):
"""Converts a string argument to a subclass of basestring.
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ga4gh/ga4gh-server | ga4gh/server/datarepo.py | SqlDataRepository.insertDataset | def insertDataset(self, dataset):
"""
Inserts the specified dataset into this repository.
"""
try:
models.Dataset.create(
id=dataset.getId(),
name=dataset.getLocalId(),
description=dataset.getDescription(),
attributes=json.dumps(dataset.getAttributes()))
except Exception:
raise exceptions.DuplicateNameException(
dataset.getLocalId()) | python | def insertDataset(self, dataset):
"""
Inserts the specified dataset into this repository.
"""
try:
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id=dataset.getId(),
name=dataset.getLocalId(),
description=dataset.getDescription(),
attributes=json.dumps(dataset.getAttributes()))
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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:
Inserts the specified dataset into this repository.
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3ll3d00d/vibe | backend/src/analyser/common/targetcontroller.py | TargetController.storeFromHinge | def storeFromHinge(self, name, hingePoints):
"""
Stores a new item in the cache if it is allowed in.
:param name: the name.
:param hingePoints: the hinge points.
:return: true if it is stored.
"""
if name not in self._cache:
if self._valid(hingePoints):
self._cache[name] = {'name': name, 'type': 'hinge', 'hinge': hingePoints}
self.writeCache()
return True
return False | python | def storeFromHinge(self, name, hingePoints):
"""
Stores a new item in the cache if it is allowed in.
:param name: the name.
:param hingePoints: the hinge points.
:return: true if it is stored.
"""
if name not in self._cache:
if self._valid(hingePoints):
self._cache[name] = {'name': name, 'type': 'hinge', 'hinge': hingePoints}
self.writeCache()
return True
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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:
Stores a new item in the cache if it is allowed in.
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Desiiii/weeb.py | weeb/client.py | Client.generate_waifu_insult | async def generate_waifu_insult(self, avatar):
"""Generate a waifu insult image.
This function is a coroutine.
Parameters:
avatar: str - http/s url pointing to an image, has to have proper headers and be a direct link to an image
Return Type: image data"""
if not isinstance(avatar, str):
raise TypeError("type of 'avatar' must be str.")
async with aiohttp.ClientSession() as session:
async with session.post("https://api.weeb.sh/auto-image/waifu-insult", headers=self.__headers, data={"avatar": avatar}) as resp:
if resp.status == 200:
return await resp.read()
else:
raise Exception((await resp.json())['message']) | python | async def generate_waifu_insult(self, avatar):
"""Generate a waifu insult image.
This function is a coroutine.
Parameters:
avatar: str - http/s url pointing to an image, has to have proper headers and be a direct link to an image
Return Type: image data"""
if not isinstance(avatar, str):
raise TypeError("type of 'avatar' must be str.")
async with aiohttp.ClientSession() as session:
async with session.post("https://api.weeb.sh/auto-image/waifu-insult", headers=self.__headers, data={"avatar": avatar}) as resp:
if resp.status == 200:
return await resp.read()
else:
raise Exception((await resp.json())['message']) | [
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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 waifu insult image.
This function is a coroutine.
Parameters:
avatar: str - http/s url pointing to an image, has to have proper headers and be a direct link to an image
Return Type: image data
### Response:
async def generate_waifu_insult(self, avatar):
"""Generate a waifu insult image.
This function is a coroutine.
Parameters:
avatar: str - http/s url pointing to an image, has to have proper headers and be a direct link to an image
Return Type: image data"""
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pmorissette/bt | bt/core.py | StrategyBase.close | def close(self, child):
"""
Close a child position - alias for rebalance(0, child). This will also
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Args:
* child (str): Child, specified by name.
"""
c = self.children[child]
# flatten if children not None
if c.children is not None and len(c.children) != 0:
c.flatten()
if c.value != 0. and not np.isnan(c.value):
c.allocate(-c.value) | python | def close(self, child):
"""
Close a child position - alias for rebalance(0, child). This will also
flatten (close out all) the child's children.
Args:
* child (str): Child, specified by name.
"""
c = self.children[child]
# flatten if children not None
if c.children is not None and len(c.children) != 0:
c.flatten()
if c.value != 0. and not np.isnan(c.value):
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flaviogrossi/sockjs-cyclone | sockjs/cyclone/sessioncontainer.py | SessionContainer.add | def add(self, session):
""" Add session to the container.
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if session.expiry is not None:
self._queue.push(session) | python | def add(self, session):
""" Add session to the container.
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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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mailgun/talon | talon/quotations.py | remove_initial_spaces_and_mark_message_lines | def remove_initial_spaces_and_mark_message_lines(lines):
"""
Removes the initial spaces in each line before marking message lines.
This ensures headers can be identified if they are indented with spaces.
"""
i = 0
while i < len(lines):
lines[i] = lines[i].lstrip(' ')
i += 1
return mark_message_lines(lines) | python | def remove_initial_spaces_and_mark_message_lines(lines):
"""
Removes the initial spaces in each line before marking message lines.
This ensures headers can be identified if they are indented with spaces.
"""
i = 0
while i < len(lines):
lines[i] = lines[i].lstrip(' ')
i += 1
return mark_message_lines(lines) | [
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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 the initial spaces in each line before marking message lines.
This ensures headers can be identified if they are indented with spaces.
### Response:
def remove_initial_spaces_and_mark_message_lines(lines):
"""
Removes the initial spaces in each line before marking message lines.
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"""
i = 0
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/train_model.py | GAG.build_char_states | def build_char_states(self, char_embed, is_training, reuse, char_ids, char_lengths):
"""Build char embedding network for the QA model."""
max_char_length = self.cfg.max_char_length
inputs = dropout(tf.nn.embedding_lookup(char_embed, char_ids),
self.cfg.dropout, is_training)
inputs = tf.reshape(
inputs, shape=[max_char_length, -1, self.cfg.char_embed_dim])
char_lengths = tf.reshape(char_lengths, shape=[-1])
with tf.variable_scope('char_encoding', reuse=reuse):
cell_fw = XGRUCell(hidden_dim=self.cfg.char_embed_dim)
cell_bw = XGRUCell(hidden_dim=self.cfg.char_embed_dim)
_, (left_right, right_left) = tf.nn.bidirectional_dynamic_rnn(
cell_fw=cell_fw,
cell_bw=cell_bw,
sequence_length=char_lengths,
inputs=inputs,
time_major=True,
dtype=tf.float32
)
left_right = tf.reshape(left_right, shape=[-1, self.cfg.char_embed_dim])
right_left = tf.reshape(right_left, shape=[-1, self.cfg.char_embed_dim])
states = tf.concat([left_right, right_left], axis=1)
out_shape = tf.shape(char_ids)[1:3]
out_shape = tf.concat([out_shape, tf.constant(
value=[self.cfg.char_embed_dim * 2], dtype=tf.int32)], axis=0)
return tf.reshape(states, shape=out_shape) | python | def build_char_states(self, char_embed, is_training, reuse, char_ids, char_lengths):
"""Build char embedding network for the QA model."""
max_char_length = self.cfg.max_char_length
inputs = dropout(tf.nn.embedding_lookup(char_embed, char_ids),
self.cfg.dropout, is_training)
inputs = tf.reshape(
inputs, shape=[max_char_length, -1, self.cfg.char_embed_dim])
char_lengths = tf.reshape(char_lengths, shape=[-1])
with tf.variable_scope('char_encoding', reuse=reuse):
cell_fw = XGRUCell(hidden_dim=self.cfg.char_embed_dim)
cell_bw = XGRUCell(hidden_dim=self.cfg.char_embed_dim)
_, (left_right, right_left) = tf.nn.bidirectional_dynamic_rnn(
cell_fw=cell_fw,
cell_bw=cell_bw,
sequence_length=char_lengths,
inputs=inputs,
time_major=True,
dtype=tf.float32
)
left_right = tf.reshape(left_right, shape=[-1, self.cfg.char_embed_dim])
right_left = tf.reshape(right_left, shape=[-1, self.cfg.char_embed_dim])
states = tf.concat([left_right, right_left], axis=1)
out_shape = tf.shape(char_ids)[1:3]
out_shape = tf.concat([out_shape, tf.constant(
value=[self.cfg.char_embed_dim * 2], dtype=tf.int32)], axis=0)
return tf.reshape(states, shape=out_shape) | [
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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:
Build char embedding network for the QA model.
### Response:
def build_char_states(self, char_embed, is_training, reuse, char_ids, char_lengths):
"""Build char embedding network for the QA model."""
max_char_length = self.cfg.max_char_length
inputs = dropout(tf.nn.embedding_lookup(char_embed, char_ids),
self.cfg.dropout, is_training)
inputs = tf.reshape(
inputs, shape=[max_char_length, -1, self.cfg.char_embed_dim])
char_lengths = tf.reshape(char_lengths, shape=[-1])
with tf.variable_scope('char_encoding', reuse=reuse):
cell_fw = XGRUCell(hidden_dim=self.cfg.char_embed_dim)
cell_bw = XGRUCell(hidden_dim=self.cfg.char_embed_dim)
_, (left_right, right_left) = tf.nn.bidirectional_dynamic_rnn(
cell_fw=cell_fw,
cell_bw=cell_bw,
sequence_length=char_lengths,
inputs=inputs,
time_major=True,
dtype=tf.float32
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left_right = tf.reshape(left_right, shape=[-1, self.cfg.char_embed_dim])
right_left = tf.reshape(right_left, shape=[-1, self.cfg.char_embed_dim])
states = tf.concat([left_right, right_left], axis=1)
out_shape = tf.shape(char_ids)[1:3]
out_shape = tf.concat([out_shape, tf.constant(
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return tf.reshape(states, shape=out_shape)
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richardkiss/pycoin | pycoin/ecdsa/intstream.py | _from_bytes | def _from_bytes(bytes, byteorder="big", signed=False):
"""This is the same functionality as ``int.from_bytes`` in python 3"""
return int.from_bytes(bytes, byteorder=byteorder, signed=signed) | python | def _from_bytes(bytes, byteorder="big", signed=False):
"""This is the same functionality as ``int.from_bytes`` in python 3"""
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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 is the same functionality as ``int.from_bytes`` in python 3
### Response:
def _from_bytes(bytes, byteorder="big", signed=False):
"""This is the same functionality as ``int.from_bytes`` in python 3"""
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DLR-RM/RAFCON | source/rafcon/gui/mygaphas/tools.py | ConnectionTool._handle_temporary_connection | def _handle_temporary_connection(self, old_sink, new_sink, of_target=True):
"""Connect connection to new_sink
If new_sink is set, the connection origin or target will be set to new_sink. The connection to old_sink is
being removed.
:param gaphas.aspect.ConnectionSink old_sink: Old sink (if existing)
:param gaphas.aspect.ConnectionSink new_sink: New sink (if existing)
:param bool of_target: Whether the origin or target will be reconnected
:return:
"""
def sink_set_and_differs(sink_a, sink_b):
if not sink_a:
return False
if not sink_b:
return True
if sink_a.port != sink_b.port:
return True
return False
if sink_set_and_differs(old_sink, new_sink):
sink_port_v = old_sink.port.port_v
self._disconnect_temporarily(sink_port_v, target=of_target)
if sink_set_and_differs(new_sink, old_sink):
sink_port_v = new_sink.port.port_v
self._connect_temporarily(sink_port_v, target=of_target) | python | def _handle_temporary_connection(self, old_sink, new_sink, of_target=True):
"""Connect connection to new_sink
If new_sink is set, the connection origin or target will be set to new_sink. The connection to old_sink is
being removed.
:param gaphas.aspect.ConnectionSink old_sink: Old sink (if existing)
:param gaphas.aspect.ConnectionSink new_sink: New sink (if existing)
:param bool of_target: Whether the origin or target will be reconnected
:return:
"""
def sink_set_and_differs(sink_a, sink_b):
if not sink_a:
return False
if not sink_b:
return True
if sink_a.port != sink_b.port:
return True
return False
if sink_set_and_differs(old_sink, new_sink):
sink_port_v = old_sink.port.port_v
self._disconnect_temporarily(sink_port_v, target=of_target)
if sink_set_and_differs(new_sink, old_sink):
sink_port_v = new_sink.port.port_v
self._connect_temporarily(sink_port_v, target=of_target) | [
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If new_sink is set, the connection origin or target will be set to new_sink. The connection to old_sink is
being removed.
:param gaphas.aspect.ConnectionSink old_sink: Old sink (if existing)
:param gaphas.aspect.ConnectionSink new_sink: New sink (if existing)
:param bool of_target: Whether the origin or target will be reconnected
:return: | [
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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:
Connect connection to new_sink
If new_sink is set, the connection origin or target will be set to new_sink. The connection to old_sink is
being removed.
:param gaphas.aspect.ConnectionSink old_sink: Old sink (if existing)
:param gaphas.aspect.ConnectionSink new_sink: New sink (if existing)
:param bool of_target: Whether the origin or target will be reconnected
:return:
### Response:
def _handle_temporary_connection(self, old_sink, new_sink, of_target=True):
"""Connect connection to new_sink
If new_sink is set, the connection origin or target will be set to new_sink. The connection to old_sink is
being removed.
:param gaphas.aspect.ConnectionSink old_sink: Old sink (if existing)
:param gaphas.aspect.ConnectionSink new_sink: New sink (if existing)
:param bool of_target: Whether the origin or target will be reconnected
:return:
"""
def sink_set_and_differs(sink_a, sink_b):
if not sink_a:
return False
if not sink_b:
return True
if sink_a.port != sink_b.port:
return True
return False
if sink_set_and_differs(old_sink, new_sink):
sink_port_v = old_sink.port.port_v
self._disconnect_temporarily(sink_port_v, target=of_target)
if sink_set_and_differs(new_sink, old_sink):
sink_port_v = new_sink.port.port_v
self._connect_temporarily(sink_port_v, target=of_target)
<|endoftext|> | [
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jonathf/chaospy | chaospy/poly/shaping.py | transpose | def transpose(vari):
"""
Transpose a shapeable quantety.
Args:
vari (chaospy.poly.base.Poly, numpy.ndarray):
Quantety of interest.
Returns:
(chaospy.poly.base.Poly, numpy.ndarray):
Same type as ``vari``.
Examples:
>>> P = chaospy.reshape(chaospy.prange(4), (2,2))
>>> print(P)
[[1, q0], [q0^2, q0^3]]
>>> print(chaospy.transpose(P))
[[1, q0^2], [q0, q0^3]]
"""
if isinstance(vari, Poly):
core = vari.A.copy()
for key in vari.keys:
core[key] = transpose(core[key])
return Poly(core, vari.dim, vari.shape[::-1], vari.dtype)
return numpy.transpose(vari) | python | def transpose(vari):
"""
Transpose a shapeable quantety.
Args:
vari (chaospy.poly.base.Poly, numpy.ndarray):
Quantety of interest.
Returns:
(chaospy.poly.base.Poly, numpy.ndarray):
Same type as ``vari``.
Examples:
>>> P = chaospy.reshape(chaospy.prange(4), (2,2))
>>> print(P)
[[1, q0], [q0^2, q0^3]]
>>> print(chaospy.transpose(P))
[[1, q0^2], [q0, q0^3]]
"""
if isinstance(vari, Poly):
core = vari.A.copy()
for key in vari.keys:
core[key] = transpose(core[key])
return Poly(core, vari.dim, vari.shape[::-1], vari.dtype)
return numpy.transpose(vari) | [
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Args:
vari (chaospy.poly.base.Poly, numpy.ndarray):
Quantety of interest.
Returns:
(chaospy.poly.base.Poly, numpy.ndarray):
Same type as ``vari``.
Examples:
>>> P = chaospy.reshape(chaospy.prange(4), (2,2))
>>> print(P)
[[1, q0], [q0^2, q0^3]]
>>> print(chaospy.transpose(P))
[[1, q0^2], [q0, q0^3]] | [
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] | train | https://github.com/jonathf/chaospy/blob/25ecfa7bf5608dc10c0b31d142ded0e3755f5d74/chaospy/poly/shaping.py#L114-L139 | <|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:
Transpose a shapeable quantety.
Args:
vari (chaospy.poly.base.Poly, numpy.ndarray):
Quantety of interest.
Returns:
(chaospy.poly.base.Poly, numpy.ndarray):
Same type as ``vari``.
Examples:
>>> P = chaospy.reshape(chaospy.prange(4), (2,2))
>>> print(P)
[[1, q0], [q0^2, q0^3]]
>>> print(chaospy.transpose(P))
[[1, q0^2], [q0, q0^3]]
### Response:
def transpose(vari):
"""
Transpose a shapeable quantety.
Args:
vari (chaospy.poly.base.Poly, numpy.ndarray):
Quantety of interest.
Returns:
(chaospy.poly.base.Poly, numpy.ndarray):
Same type as ``vari``.
Examples:
>>> P = chaospy.reshape(chaospy.prange(4), (2,2))
>>> print(P)
[[1, q0], [q0^2, q0^3]]
>>> print(chaospy.transpose(P))
[[1, q0^2], [q0, q0^3]]
"""
if isinstance(vari, Poly):
core = vari.A.copy()
for key in vari.keys:
core[key] = transpose(core[key])
return Poly(core, vari.dim, vari.shape[::-1], vari.dtype)
return numpy.transpose(vari)
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pycampers/zproc | zproc/state/state.py | State.fork | def fork(self, server_address: str = None, *, namespace: str = None) -> "State":
r"""
"Forks" this State object.
Takes the same args as the :py:class:`State` constructor,
except that they automatically default to the values provided during the creation of this State object.
If no args are provided to this function,
then it shall create a new :py:class:`State` object
that follows the exact same semantics as this one.
This is preferred over ``copy()``\ -ing a :py:class:`State` object.
Useful when one needs to access 2 or more namespaces from the same code.
"""
if server_address is None:
server_address = self.server_address
if namespace is None:
namespace = self.namespace
return self.__class__(server_address, namespace=namespace) | python | def fork(self, server_address: str = None, *, namespace: str = None) -> "State":
r"""
"Forks" this State object.
Takes the same args as the :py:class:`State` constructor,
except that they automatically default to the values provided during the creation of this State object.
If no args are provided to this function,
then it shall create a new :py:class:`State` object
that follows the exact same semantics as this one.
This is preferred over ``copy()``\ -ing a :py:class:`State` object.
Useful when one needs to access 2 or more namespaces from the same code.
"""
if server_address is None:
server_address = self.server_address
if namespace is None:
namespace = self.namespace
return self.__class__(server_address, namespace=namespace) | [
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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:
r"""
"Forks" this State object.
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except that they automatically default to the values provided during the creation of this State object.
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then it shall create a new :py:class:`State` object
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Useful when one needs to access 2 or more namespaces from the same code.
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def fork(self, server_address: str = None, *, namespace: str = None) -> "State":
r"""
"Forks" this State object.
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except that they automatically default to the values provided during the creation of this State object.
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Useful when one needs to access 2 or more namespaces from the same code.
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if server_address is None:
server_address = self.server_address
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<|endoftext|> | [
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"""This is a utility function that creates the Program
Builder in an Environment if it is not there already.
If it is already there, we return the existing one.
"""
try:
program = env['BUILDERS']['Program']
except KeyError:
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emitter = '$PROGEMITTER',
prefix = '$PROGPREFIX',
suffix = '$PROGSUFFIX',
src_suffix = '$OBJSUFFIX',
src_builder = 'Object',
target_scanner = ProgramScanner)
env['BUILDERS']['Program'] = program
return program | python | def createProgBuilder(env):
"""This is a utility function that creates the Program
Builder in an Environment if it is not there already.
If it is already there, we return the existing one.
"""
try:
program = env['BUILDERS']['Program']
except KeyError:
import SCons.Defaults
program = SCons.Builder.Builder(action = SCons.Defaults.LinkAction,
emitter = '$PROGEMITTER',
prefix = '$PROGPREFIX',
suffix = '$PROGSUFFIX',
src_suffix = '$OBJSUFFIX',
src_builder = 'Object',
target_scanner = ProgramScanner)
env['BUILDERS']['Program'] = program
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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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This is a utility function that creates the Program
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suffix = '$PROGSUFFIX',
src_suffix = '$OBJSUFFIX',
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lingthio/Flask-User | flask_user/user_manager__views.py | UserManager__Views.invite_user_view | def invite_user_view(self):
""" Allows users to send invitations to register an account """
invite_user_form = self.InviteUserFormClass(request.form)
if request.method == 'POST' and invite_user_form.validate():
# Find User and UserEmail by email
email = invite_user_form.email.data
user, user_email = self.db_manager.get_user_and_user_email_by_email(email)
if user:
flash("User with that email has already registered", "error")
return redirect(url_for('user.invite_user'))
# Add UserInvitation
user_invitation = self.db_manager.add_user_invitation(
email=email,
invited_by_user_id=current_user.id)
self.db_manager.commit()
try:
# Send invite_user email
self.email_manager.send_invite_user_email(current_user, user_invitation)
except Exception as e:
# delete new UserInvitation object if send fails
self.db_manager.delete_object(user_invitation)
self.db_manager.commit()
raise
# Send sent_invitation signal
signals \
.user_sent_invitation \
.send(current_app._get_current_object(), user_invitation=user_invitation,
form=invite_user_form)
# Flash a system message
flash(_('Invitation has been sent.'), 'success')
# Redirect
safe_next_url = self._get_safe_next_url('next', self.USER_AFTER_INVITE_ENDPOINT)
return redirect(safe_next_url)
self.prepare_domain_translations()
return render_template(self.USER_INVITE_USER_TEMPLATE, form=invite_user_form) | python | def invite_user_view(self):
""" Allows users to send invitations to register an account """
invite_user_form = self.InviteUserFormClass(request.form)
if request.method == 'POST' and invite_user_form.validate():
# Find User and UserEmail by email
email = invite_user_form.email.data
user, user_email = self.db_manager.get_user_and_user_email_by_email(email)
if user:
flash("User with that email has already registered", "error")
return redirect(url_for('user.invite_user'))
# Add UserInvitation
user_invitation = self.db_manager.add_user_invitation(
email=email,
invited_by_user_id=current_user.id)
self.db_manager.commit()
try:
# Send invite_user email
self.email_manager.send_invite_user_email(current_user, user_invitation)
except Exception as e:
# delete new UserInvitation object if send fails
self.db_manager.delete_object(user_invitation)
self.db_manager.commit()
raise
# Send sent_invitation signal
signals \
.user_sent_invitation \
.send(current_app._get_current_object(), user_invitation=user_invitation,
form=invite_user_form)
# Flash a system message
flash(_('Invitation has been sent.'), 'success')
# Redirect
safe_next_url = self._get_safe_next_url('next', self.USER_AFTER_INVITE_ENDPOINT)
return redirect(safe_next_url)
self.prepare_domain_translations()
return render_template(self.USER_INVITE_USER_TEMPLATE, form=invite_user_form) | [
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] | train | https://github.com/lingthio/Flask-User/blob/a379fa0a281789618c484b459cb41236779b95b1/flask_user/user_manager__views.py#L313-L355 | <|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:
Allows users to send invitations to register an account
### Response:
def invite_user_view(self):
""" Allows users to send invitations to register an account """
invite_user_form = self.InviteUserFormClass(request.form)
if request.method == 'POST' and invite_user_form.validate():
# Find User and UserEmail by email
email = invite_user_form.email.data
user, user_email = self.db_manager.get_user_and_user_email_by_email(email)
if user:
flash("User with that email has already registered", "error")
return redirect(url_for('user.invite_user'))
# Add UserInvitation
user_invitation = self.db_manager.add_user_invitation(
email=email,
invited_by_user_id=current_user.id)
self.db_manager.commit()
try:
# Send invite_user email
self.email_manager.send_invite_user_email(current_user, user_invitation)
except Exception as e:
# delete new UserInvitation object if send fails
self.db_manager.delete_object(user_invitation)
self.db_manager.commit()
raise
# Send sent_invitation signal
signals \
.user_sent_invitation \
.send(current_app._get_current_object(), user_invitation=user_invitation,
form=invite_user_form)
# Flash a system message
flash(_('Invitation has been sent.'), 'success')
# Redirect
safe_next_url = self._get_safe_next_url('next', self.USER_AFTER_INVITE_ENDPOINT)
return redirect(safe_next_url)
self.prepare_domain_translations()
return render_template(self.USER_INVITE_USER_TEMPLATE, form=invite_user_form)
<|endoftext|> | [
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wakatime/wakatime | wakatime/packages/pygments/formatters/__init__.py | load_formatter_from_file | def load_formatter_from_file(filename, formattername="CustomFormatter",
**options):
"""Load a formatter from a file.
This method expects a file located relative to the current working
directory, which contains a class named CustomFormatter. By default,
it expects the Formatter to be named CustomFormatter; you can specify
your own class name as the second argument to this function.
Users should be very careful with the input, because this method
is equivalent to running eval on the input file.
Raises ClassNotFound if there are any problems importing the Formatter.
.. versionadded:: 2.2
"""
try:
# This empty dict will contain the namespace for the exec'd file
custom_namespace = {}
exec(open(filename, 'rb').read(), custom_namespace)
# Retrieve the class `formattername` from that namespace
if formattername not in custom_namespace:
raise ClassNotFound('no valid %s class found in %s' %
(formattername, filename))
formatter_class = custom_namespace[formattername]
# And finally instantiate it with the options
return formatter_class(**options)
except IOError as err:
raise ClassNotFound('cannot read %s' % filename)
except ClassNotFound as err:
raise
except Exception as err:
raise ClassNotFound('error when loading custom formatter: %s' % err) | python | def load_formatter_from_file(filename, formattername="CustomFormatter",
**options):
"""Load a formatter from a file.
This method expects a file located relative to the current working
directory, which contains a class named CustomFormatter. By default,
it expects the Formatter to be named CustomFormatter; you can specify
your own class name as the second argument to this function.
Users should be very careful with the input, because this method
is equivalent to running eval on the input file.
Raises ClassNotFound if there are any problems importing the Formatter.
.. versionadded:: 2.2
"""
try:
# This empty dict will contain the namespace for the exec'd file
custom_namespace = {}
exec(open(filename, 'rb').read(), custom_namespace)
# Retrieve the class `formattername` from that namespace
if formattername not in custom_namespace:
raise ClassNotFound('no valid %s class found in %s' %
(formattername, filename))
formatter_class = custom_namespace[formattername]
# And finally instantiate it with the options
return formatter_class(**options)
except IOError as err:
raise ClassNotFound('cannot read %s' % filename)
except ClassNotFound as err:
raise
except Exception as err:
raise ClassNotFound('error when loading custom formatter: %s' % err) | [
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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:
Load a formatter from a file.
This method expects a file located relative to the current working
directory, which contains a class named CustomFormatter. By default,
it expects the Formatter to be named CustomFormatter; you can specify
your own class name as the second argument to this function.
Users should be very careful with the input, because this method
is equivalent to running eval on the input file.
Raises ClassNotFound if there are any problems importing the Formatter.
.. versionadded:: 2.2
### Response:
def load_formatter_from_file(filename, formattername="CustomFormatter",
**options):
"""Load a formatter from a file.
This method expects a file located relative to the current working
directory, which contains a class named CustomFormatter. By default,
it expects the Formatter to be named CustomFormatter; you can specify
your own class name as the second argument to this function.
Users should be very careful with the input, because this method
is equivalent to running eval on the input file.
Raises ClassNotFound if there are any problems importing the Formatter.
.. versionadded:: 2.2
"""
try:
# This empty dict will contain the namespace for the exec'd file
custom_namespace = {}
exec(open(filename, 'rb').read(), custom_namespace)
# Retrieve the class `formattername` from that namespace
if formattername not in custom_namespace:
raise ClassNotFound('no valid %s class found in %s' %
(formattername, filename))
formatter_class = custom_namespace[formattername]
# And finally instantiate it with the options
return formatter_class(**options)
except IOError as err:
raise ClassNotFound('cannot read %s' % filename)
except ClassNotFound as err:
raise
except Exception as err:
raise ClassNotFound('error when loading custom formatter: %s' % err)
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markfinger/assembla | assembla/lib.py | assembla_filter | def assembla_filter(func):
"""
Filters :data for the objects in it which possess attributes equal in
name/value to a key/value in kwargs.
Each key/value combination in kwargs is compared against the object, so
multiple keyword arguments can be passed in to constrain the filtering.
"""
@wraps(func)
def wrapper(class_instance, **kwargs):
# Get the result
extra_params = kwargs.get('extra_params', None)
if extra_params:
del kwargs['extra_params']
results = func(class_instance, extra_params)
# Filter the result
if kwargs:
results = filter(
# Find the objects who have an equal number of matching attr/value
# combinations as `len(kwargs)`
lambda obj: len(kwargs) == len(
filter(
lambda boolean: boolean,
[obj.get(attr_name) == value
for attr_name, value in kwargs.iteritems()]
)
),
results
)
return results
return wrapper | python | def assembla_filter(func):
"""
Filters :data for the objects in it which possess attributes equal in
name/value to a key/value in kwargs.
Each key/value combination in kwargs is compared against the object, so
multiple keyword arguments can be passed in to constrain the filtering.
"""
@wraps(func)
def wrapper(class_instance, **kwargs):
# Get the result
extra_params = kwargs.get('extra_params', None)
if extra_params:
del kwargs['extra_params']
results = func(class_instance, extra_params)
# Filter the result
if kwargs:
results = filter(
# Find the objects who have an equal number of matching attr/value
# combinations as `len(kwargs)`
lambda obj: len(kwargs) == len(
filter(
lambda boolean: boolean,
[obj.get(attr_name) == value
for attr_name, value in kwargs.iteritems()]
)
),
results
)
return results
return wrapper | [
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Each key/value combination in kwargs is compared against the object, so
multiple keyword arguments can be passed in to constrain the filtering. | [
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] | train | https://github.com/markfinger/assembla/blob/967a77a5ba718df94f60e832b6e0cf14c72426aa/assembla/lib.py#L43-L76 | <|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:
Filters :data for the objects in it which possess attributes equal in
name/value to a key/value in kwargs.
Each key/value combination in kwargs is compared against the object, so
multiple keyword arguments can be passed in to constrain the filtering.
### Response:
def assembla_filter(func):
"""
Filters :data for the objects in it which possess attributes equal in
name/value to a key/value in kwargs.
Each key/value combination in kwargs is compared against the object, so
multiple keyword arguments can be passed in to constrain the filtering.
"""
@wraps(func)
def wrapper(class_instance, **kwargs):
# Get the result
extra_params = kwargs.get('extra_params', None)
if extra_params:
del kwargs['extra_params']
results = func(class_instance, extra_params)
# Filter the result
if kwargs:
results = filter(
# Find the objects who have an equal number of matching attr/value
# combinations as `len(kwargs)`
lambda obj: len(kwargs) == len(
filter(
lambda boolean: boolean,
[obj.get(attr_name) == value
for attr_name, value in kwargs.iteritems()]
)
),
results
)
return results
return wrapper
<|endoftext|> | [
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bcbio/bcbio-nextgen | bcbio/variation/genotype.py | batch_for_variantcall | def batch_for_variantcall(samples):
"""Prepare a set of samples for parallel variant calling.
CWL input target that groups samples into batches and variant callers
for parallel processing.
If doing joint calling, with `tools_on: [gvcf]`, split the sample into
individuals instead of combining into a batch.
"""
sample_order = [dd.get_sample_name(utils.to_single_data(x)) for x in samples]
to_process, extras = _dup_samples_by_variantcaller(samples, require_bam=False)
batch_groups = collections.defaultdict(list)
to_process = [utils.to_single_data(x) for x in to_process]
for data in cwlutils.samples_to_records(to_process):
vc = get_variantcaller(data, require_bam=False)
batches = dd.get_batches(data) or dd.get_sample_name(data)
if not isinstance(batches, (list, tuple)):
batches = [batches]
for b in batches:
batch_groups[(b, vc)].append(utils.deepish_copy(data))
batches = []
for cur_group in batch_groups.values():
joint_calling = any([is_joint(d) for d in cur_group])
if joint_calling:
for d in cur_group:
batches.append([d])
else:
batches.append(cur_group)
def by_original_order(xs):
return (min([sample_order.index(dd.get_sample_name(x)) for x in xs]),
min([dd.get_variantcaller_order(x) for x in xs]))
return sorted(batches + extras, key=by_original_order) | python | def batch_for_variantcall(samples):
"""Prepare a set of samples for parallel variant calling.
CWL input target that groups samples into batches and variant callers
for parallel processing.
If doing joint calling, with `tools_on: [gvcf]`, split the sample into
individuals instead of combining into a batch.
"""
sample_order = [dd.get_sample_name(utils.to_single_data(x)) for x in samples]
to_process, extras = _dup_samples_by_variantcaller(samples, require_bam=False)
batch_groups = collections.defaultdict(list)
to_process = [utils.to_single_data(x) for x in to_process]
for data in cwlutils.samples_to_records(to_process):
vc = get_variantcaller(data, require_bam=False)
batches = dd.get_batches(data) or dd.get_sample_name(data)
if not isinstance(batches, (list, tuple)):
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joint_calling = any([is_joint(d) for d in cur_group])
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def by_original_order(xs):
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] | Prepare a set of samples for parallel variant calling.
CWL input target that groups samples into batches and variant callers
for parallel processing.
If doing joint calling, with `tools_on: [gvcf]`, split the sample into
individuals instead of combining into a batch. | [
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] | train | https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/genotype.py#L241-L272 | <|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:
Prepare a set of samples for parallel variant calling.
CWL input target that groups samples into batches and variant callers
for parallel processing.
If doing joint calling, with `tools_on: [gvcf]`, split the sample into
individuals instead of combining into a batch.
### Response:
def batch_for_variantcall(samples):
"""Prepare a set of samples for parallel variant calling.
CWL input target that groups samples into batches and variant callers
for parallel processing.
If doing joint calling, with `tools_on: [gvcf]`, split the sample into
individuals instead of combining into a batch.
"""
sample_order = [dd.get_sample_name(utils.to_single_data(x)) for x in samples]
to_process, extras = _dup_samples_by_variantcaller(samples, require_bam=False)
batch_groups = collections.defaultdict(list)
to_process = [utils.to_single_data(x) for x in to_process]
for data in cwlutils.samples_to_records(to_process):
vc = get_variantcaller(data, require_bam=False)
batches = dd.get_batches(data) or dd.get_sample_name(data)
if not isinstance(batches, (list, tuple)):
batches = [batches]
for b in batches:
batch_groups[(b, vc)].append(utils.deepish_copy(data))
batches = []
for cur_group in batch_groups.values():
joint_calling = any([is_joint(d) for d in cur_group])
if joint_calling:
for d in cur_group:
batches.append([d])
else:
batches.append(cur_group)
def by_original_order(xs):
return (min([sample_order.index(dd.get_sample_name(x)) for x in xs]),
min([dd.get_variantcaller_order(x) for x in xs]))
return sorted(batches + extras, key=by_original_order)
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biocore-ntnu/epic | epic/matrixes/matrixes.py | create_matrixes | def create_matrixes(chip, input, df, args):
# type: (Iterable[pd.DataFrame], Iterable[pd.DataFrame], pd.DataFrame, Namespace) -> List[pd.DataFrame]
"Creates matrixes which can be written to file as is (matrix) or as bedGraph."
genome = args.chromosome_sizes
chip = put_dfs_in_chromosome_dict(chip)
input = put_dfs_in_chromosome_dict(input)
all_chromosomes = natsorted(set(list(chip.keys()) + list(input.keys())))
# print("df1\n", df, file=sys.stderr)
islands = enriched_bins(df, args)
# print("islands1\n", islands, file=sys.stderr)
logging.info("Creating matrixes from count data.")
dfms = Parallel(n_jobs=args.number_cores)(delayed(_create_matrixes)(
chromosome, chip, input, islands, genome[chromosome],
args.window_size) for chromosome in all_chromosomes)
return dfms | python | def create_matrixes(chip, input, df, args):
# type: (Iterable[pd.DataFrame], Iterable[pd.DataFrame], pd.DataFrame, Namespace) -> List[pd.DataFrame]
"Creates matrixes which can be written to file as is (matrix) or as bedGraph."
genome = args.chromosome_sizes
chip = put_dfs_in_chromosome_dict(chip)
input = put_dfs_in_chromosome_dict(input)
all_chromosomes = natsorted(set(list(chip.keys()) + list(input.keys())))
# print("df1\n", df, file=sys.stderr)
islands = enriched_bins(df, args)
# print("islands1\n", islands, file=sys.stderr)
logging.info("Creating matrixes from count data.")
dfms = Parallel(n_jobs=args.number_cores)(delayed(_create_matrixes)(
chromosome, chip, input, islands, genome[chromosome],
args.window_size) for chromosome in all_chromosomes)
return dfms | [
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### Instruction:
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### Task:
Creates matrixes which can be written to file as is (matrix) or as bedGraph.
### Response:
def create_matrixes(chip, input, df, args):
# type: (Iterable[pd.DataFrame], Iterable[pd.DataFrame], pd.DataFrame, Namespace) -> List[pd.DataFrame]
"Creates matrixes which can be written to file as is (matrix) or as bedGraph."
genome = args.chromosome_sizes
chip = put_dfs_in_chromosome_dict(chip)
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all_chromosomes = natsorted(set(list(chip.keys()) + list(input.keys())))
# print("df1\n", df, file=sys.stderr)
islands = enriched_bins(df, args)
# print("islands1\n", islands, file=sys.stderr)
logging.info("Creating matrixes from count data.")
dfms = Parallel(n_jobs=args.number_cores)(delayed(_create_matrixes)(
chromosome, chip, input, islands, genome[chromosome],
args.window_size) for chromosome in all_chromosomes)
return dfms
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linkedin/Zopkio | zopkio/deployer.py | Deployer.hangup | def hangup(self, unique_id, configs=None):
"""
Issue a signal to hangup the specified process
:Parameter unique_id: the name of the process
"""
self._send_signal(unique_id, signal.SIGHUP, configs) | python | def hangup(self, unique_id, configs=None):
"""
Issue a signal to hangup the specified process
:Parameter unique_id: the name of the process
"""
self._send_signal(unique_id, signal.SIGHUP, configs) | [
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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:
Issue a signal to hangup the specified process
:Parameter unique_id: the name of the process
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def hangup(self, unique_id, configs=None):
"""
Issue a signal to hangup the specified process
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pantsbuild/pants | src/python/pants/backend/python/tasks/resolve_requirements_task_base.py | ResolveRequirementsTaskBase.merged_pex | def merged_pex(cls, path, pex_info, interpreter, pexes, interpeter_constraints=None):
"""Yields a pex builder at path with the given pexes already merged.
:rtype: :class:`pex.pex_builder.PEXBuilder`
"""
pex_paths = [pex.path() for pex in pexes if pex]
if pex_paths:
pex_info = pex_info.copy()
pex_info.merge_pex_path(':'.join(pex_paths))
with safe_concurrent_creation(path) as safe_path:
builder = PEXBuilder(safe_path, interpreter, pex_info=pex_info)
if interpeter_constraints:
for constraint in interpeter_constraints:
builder.add_interpreter_constraint(constraint)
yield builder | python | def merged_pex(cls, path, pex_info, interpreter, pexes, interpeter_constraints=None):
"""Yields a pex builder at path with the given pexes already merged.
:rtype: :class:`pex.pex_builder.PEXBuilder`
"""
pex_paths = [pex.path() for pex in pexes if pex]
if pex_paths:
pex_info = pex_info.copy()
pex_info.merge_pex_path(':'.join(pex_paths))
with safe_concurrent_creation(path) as safe_path:
builder = PEXBuilder(safe_path, interpreter, pex_info=pex_info)
if interpeter_constraints:
for constraint in interpeter_constraints:
builder.add_interpreter_constraint(constraint)
yield builder | [
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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:
Yields a pex builder at path with the given pexes already merged.
:rtype: :class:`pex.pex_builder.PEXBuilder`
### Response:
def merged_pex(cls, path, pex_info, interpreter, pexes, interpeter_constraints=None):
"""Yields a pex builder at path with the given pexes already merged.
:rtype: :class:`pex.pex_builder.PEXBuilder`
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pex_paths = [pex.path() for pex in pexes if pex]
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pex_info.merge_pex_path(':'.join(pex_paths))
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builder = PEXBuilder(safe_path, interpreter, pex_info=pex_info)
if interpeter_constraints:
for constraint in interpeter_constraints:
builder.add_interpreter_constraint(constraint)
yield builder
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infobloxopen/infoblox-client | infoblox_client/connector.py | Connector.create_object | def create_object(self, obj_type, payload, return_fields=None):
"""Create an Infoblox object of type 'obj_type'
Args:
obj_type (str): Infoblox object type,
e.g. 'network', 'range', etc.
payload (dict): Payload with data to send
return_fields (list): List of fields to be returned
Returns:
The object reference of the newly create object
Raises:
InfobloxException
"""
self._validate_obj_type_or_die(obj_type)
query_params = self._build_query_params(return_fields=return_fields)
url = self._construct_url(obj_type, query_params)
opts = self._get_request_options(data=payload)
self._log_request('post', url, opts)
if(self.session.cookies):
# the first 'get' or 'post' action will generate a cookie
# after that, we don't need to re-authenticate
self.session.auth = None
r = self.session.post(url, **opts)
self._validate_authorized(r)
if r.status_code != requests.codes.CREATED:
response = utils.safe_json_load(r.content)
already_assigned = 'is assigned to another network view'
if response and already_assigned in response.get('text'):
exception = ib_ex.InfobloxMemberAlreadyAssigned
else:
exception = ib_ex.InfobloxCannotCreateObject
raise exception(
response=response,
obj_type=obj_type,
content=r.content,
args=payload,
code=r.status_code)
return self._parse_reply(r) | python | def create_object(self, obj_type, payload, return_fields=None):
"""Create an Infoblox object of type 'obj_type'
Args:
obj_type (str): Infoblox object type,
e.g. 'network', 'range', etc.
payload (dict): Payload with data to send
return_fields (list): List of fields to be returned
Returns:
The object reference of the newly create object
Raises:
InfobloxException
"""
self._validate_obj_type_or_die(obj_type)
query_params = self._build_query_params(return_fields=return_fields)
url = self._construct_url(obj_type, query_params)
opts = self._get_request_options(data=payload)
self._log_request('post', url, opts)
if(self.session.cookies):
# the first 'get' or 'post' action will generate a cookie
# after that, we don't need to re-authenticate
self.session.auth = None
r = self.session.post(url, **opts)
self._validate_authorized(r)
if r.status_code != requests.codes.CREATED:
response = utils.safe_json_load(r.content)
already_assigned = 'is assigned to another network view'
if response and already_assigned in response.get('text'):
exception = ib_ex.InfobloxMemberAlreadyAssigned
else:
exception = ib_ex.InfobloxCannotCreateObject
raise exception(
response=response,
obj_type=obj_type,
content=r.content,
args=payload,
code=r.status_code)
return self._parse_reply(r) | [
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Args:
obj_type (str): Infoblox object type,
e.g. 'network', 'range', etc.
payload (dict): Payload with data to send
return_fields (list): List of fields to be returned
Returns:
The object reference of the newly create object
Raises:
InfobloxException | [
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] | train | https://github.com/infobloxopen/infoblox-client/blob/edeec62db1935784c728731b2ae7cf0fcc9bf84d/infoblox_client/connector.py#L345-L387 | <|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 an Infoblox object of type 'obj_type'
Args:
obj_type (str): Infoblox object type,
e.g. 'network', 'range', etc.
payload (dict): Payload with data to send
return_fields (list): List of fields to be returned
Returns:
The object reference of the newly create object
Raises:
InfobloxException
### Response:
def create_object(self, obj_type, payload, return_fields=None):
"""Create an Infoblox object of type 'obj_type'
Args:
obj_type (str): Infoblox object type,
e.g. 'network', 'range', etc.
payload (dict): Payload with data to send
return_fields (list): List of fields to be returned
Returns:
The object reference of the newly create object
Raises:
InfobloxException
"""
self._validate_obj_type_or_die(obj_type)
query_params = self._build_query_params(return_fields=return_fields)
url = self._construct_url(obj_type, query_params)
opts = self._get_request_options(data=payload)
self._log_request('post', url, opts)
if(self.session.cookies):
# the first 'get' or 'post' action will generate a cookie
# after that, we don't need to re-authenticate
self.session.auth = None
r = self.session.post(url, **opts)
self._validate_authorized(r)
if r.status_code != requests.codes.CREATED:
response = utils.safe_json_load(r.content)
already_assigned = 'is assigned to another network view'
if response and already_assigned in response.get('text'):
exception = ib_ex.InfobloxMemberAlreadyAssigned
else:
exception = ib_ex.InfobloxCannotCreateObject
raise exception(
response=response,
obj_type=obj_type,
content=r.content,
args=payload,
code=r.status_code)
return self._parse_reply(r)
<|endoftext|> | [
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deepmind/sonnet | sonnet/python/modules/basic.py | BatchReshape.transpose | def transpose(self, name=None):
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name=name) | python | def transpose(self, name=None):
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Returns transpose batch reshape.
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saltstack/salt | salt/modules/git.py | stash | def stash(cwd,
action='save',
opts='',
git_opts='',
user=None,
password=None,
ignore_retcode=False,
output_encoding=None):
'''
Interface to `git-stash(1)`_, returns the stdout from the git command
cwd
The path to the git checkout
opts
Any additional options to add to the command line, in a single string.
Use this to complete the ``git stash`` command by adding the remaining
arguments (i.e. ``'save <stash comment>'``, ``'apply stash@{2}'``,
``'show'``, etc.). Omitting this argument will simply run ``git
stash``.
git_opts
Any additional options to add to git command itself (not the ``stash``
subcommand), in a single string. This is useful for passing ``-c`` to
run git with temporary changes to the git configuration.
.. versionadded:: 2017.7.0
.. note::
This is only supported in git 1.7.2 and newer.
user
User under which to run the git command. By default, the command is run
by the user under which the minion is running.
password
Windows only. Required when specifying ``user``. This parameter will be
ignored on non-Windows platforms.
.. versionadded:: 2016.3.4
ignore_retcode : False
If ``True``, do not log an error to the minion log if the git command
returns a nonzero exit status.
.. versionadded:: 2015.8.0
output_encoding
Use this option to specify which encoding to use to decode the output
from any git commands which are run. This should not be needed in most
cases.
.. note::
This should only be needed if the files in the repository were
created with filenames using an encoding other than UTF-8 to handle
Unicode characters.
.. versionadded:: 2018.3.1
.. _`git-stash(1)`: http://git-scm.com/docs/git-stash
CLI Examples:
.. code-block:: bash
salt myminion git.stash /path/to/repo save opts='work in progress'
salt myminion git.stash /path/to/repo apply opts='stash@{1}'
salt myminion git.stash /path/to/repo drop opts='stash@{1}'
salt myminion git.stash /path/to/repo list
'''
cwd = _expand_path(cwd, user)
command = ['git'] + _format_git_opts(git_opts)
command.extend(['stash', action])
command.extend(_format_opts(opts))
return _git_run(command,
cwd=cwd,
user=user,
password=password,
ignore_retcode=ignore_retcode,
output_encoding=output_encoding)['stdout'] | python | def stash(cwd,
action='save',
opts='',
git_opts='',
user=None,
password=None,
ignore_retcode=False,
output_encoding=None):
'''
Interface to `git-stash(1)`_, returns the stdout from the git command
cwd
The path to the git checkout
opts
Any additional options to add to the command line, in a single string.
Use this to complete the ``git stash`` command by adding the remaining
arguments (i.e. ``'save <stash comment>'``, ``'apply stash@{2}'``,
``'show'``, etc.). Omitting this argument will simply run ``git
stash``.
git_opts
Any additional options to add to git command itself (not the ``stash``
subcommand), in a single string. This is useful for passing ``-c`` to
run git with temporary changes to the git configuration.
.. versionadded:: 2017.7.0
.. note::
This is only supported in git 1.7.2 and newer.
user
User under which to run the git command. By default, the command is run
by the user under which the minion is running.
password
Windows only. Required when specifying ``user``. This parameter will be
ignored on non-Windows platforms.
.. versionadded:: 2016.3.4
ignore_retcode : False
If ``True``, do not log an error to the minion log if the git command
returns a nonzero exit status.
.. versionadded:: 2015.8.0
output_encoding
Use this option to specify which encoding to use to decode the output
from any git commands which are run. This should not be needed in most
cases.
.. note::
This should only be needed if the files in the repository were
created with filenames using an encoding other than UTF-8 to handle
Unicode characters.
.. versionadded:: 2018.3.1
.. _`git-stash(1)`: http://git-scm.com/docs/git-stash
CLI Examples:
.. code-block:: bash
salt myminion git.stash /path/to/repo save opts='work in progress'
salt myminion git.stash /path/to/repo apply opts='stash@{1}'
salt myminion git.stash /path/to/repo drop opts='stash@{1}'
salt myminion git.stash /path/to/repo list
'''
cwd = _expand_path(cwd, user)
command = ['git'] + _format_git_opts(git_opts)
command.extend(['stash', action])
command.extend(_format_opts(opts))
return _git_run(command,
cwd=cwd,
user=user,
password=password,
ignore_retcode=ignore_retcode,
output_encoding=output_encoding)['stdout'] | [
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The path to the git checkout
opts
Any additional options to add to the command line, in a single string.
Use this to complete the ``git stash`` command by adding the remaining
arguments (i.e. ``'save <stash comment>'``, ``'apply stash@{2}'``,
``'show'``, etc.). Omitting this argument will simply run ``git
stash``.
git_opts
Any additional options to add to git command itself (not the ``stash``
subcommand), in a single string. This is useful for passing ``-c`` to
run git with temporary changes to the git configuration.
.. versionadded:: 2017.7.0
.. note::
This is only supported in git 1.7.2 and newer.
user
User under which to run the git command. By default, the command is run
by the user under which the minion is running.
password
Windows only. Required when specifying ``user``. This parameter will be
ignored on non-Windows platforms.
.. versionadded:: 2016.3.4
ignore_retcode : False
If ``True``, do not log an error to the minion log if the git command
returns a nonzero exit status.
.. versionadded:: 2015.8.0
output_encoding
Use this option to specify which encoding to use to decode the output
from any git commands which are run. This should not be needed in most
cases.
.. note::
This should only be needed if the files in the repository were
created with filenames using an encoding other than UTF-8 to handle
Unicode characters.
.. versionadded:: 2018.3.1
.. _`git-stash(1)`: http://git-scm.com/docs/git-stash
CLI Examples:
.. code-block:: bash
salt myminion git.stash /path/to/repo save opts='work in progress'
salt myminion git.stash /path/to/repo apply opts='stash@{1}'
salt myminion git.stash /path/to/repo drop opts='stash@{1}'
salt myminion git.stash /path/to/repo list | [
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] | train | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/git.py#L4544-L4623 | <|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:
Interface to `git-stash(1)`_, returns the stdout from the git command
cwd
The path to the git checkout
opts
Any additional options to add to the command line, in a single string.
Use this to complete the ``git stash`` command by adding the remaining
arguments (i.e. ``'save <stash comment>'``, ``'apply stash@{2}'``,
``'show'``, etc.). Omitting this argument will simply run ``git
stash``.
git_opts
Any additional options to add to git command itself (not the ``stash``
subcommand), in a single string. This is useful for passing ``-c`` to
run git with temporary changes to the git configuration.
.. versionadded:: 2017.7.0
.. note::
This is only supported in git 1.7.2 and newer.
user
User under which to run the git command. By default, the command is run
by the user under which the minion is running.
password
Windows only. Required when specifying ``user``. This parameter will be
ignored on non-Windows platforms.
.. versionadded:: 2016.3.4
ignore_retcode : False
If ``True``, do not log an error to the minion log if the git command
returns a nonzero exit status.
.. versionadded:: 2015.8.0
output_encoding
Use this option to specify which encoding to use to decode the output
from any git commands which are run. This should not be needed in most
cases.
.. note::
This should only be needed if the files in the repository were
created with filenames using an encoding other than UTF-8 to handle
Unicode characters.
.. versionadded:: 2018.3.1
.. _`git-stash(1)`: http://git-scm.com/docs/git-stash
CLI Examples:
.. code-block:: bash
salt myminion git.stash /path/to/repo save opts='work in progress'
salt myminion git.stash /path/to/repo apply opts='stash@{1}'
salt myminion git.stash /path/to/repo drop opts='stash@{1}'
salt myminion git.stash /path/to/repo list
### Response:
def stash(cwd,
action='save',
opts='',
git_opts='',
user=None,
password=None,
ignore_retcode=False,
output_encoding=None):
'''
Interface to `git-stash(1)`_, returns the stdout from the git command
cwd
The path to the git checkout
opts
Any additional options to add to the command line, in a single string.
Use this to complete the ``git stash`` command by adding the remaining
arguments (i.e. ``'save <stash comment>'``, ``'apply stash@{2}'``,
``'show'``, etc.). Omitting this argument will simply run ``git
stash``.
git_opts
Any additional options to add to git command itself (not the ``stash``
subcommand), in a single string. This is useful for passing ``-c`` to
run git with temporary changes to the git configuration.
.. versionadded:: 2017.7.0
.. note::
This is only supported in git 1.7.2 and newer.
user
User under which to run the git command. By default, the command is run
by the user under which the minion is running.
password
Windows only. Required when specifying ``user``. This parameter will be
ignored on non-Windows platforms.
.. versionadded:: 2016.3.4
ignore_retcode : False
If ``True``, do not log an error to the minion log if the git command
returns a nonzero exit status.
.. versionadded:: 2015.8.0
output_encoding
Use this option to specify which encoding to use to decode the output
from any git commands which are run. This should not be needed in most
cases.
.. note::
This should only be needed if the files in the repository were
created with filenames using an encoding other than UTF-8 to handle
Unicode characters.
.. versionadded:: 2018.3.1
.. _`git-stash(1)`: http://git-scm.com/docs/git-stash
CLI Examples:
.. code-block:: bash
salt myminion git.stash /path/to/repo save opts='work in progress'
salt myminion git.stash /path/to/repo apply opts='stash@{1}'
salt myminion git.stash /path/to/repo drop opts='stash@{1}'
salt myminion git.stash /path/to/repo list
'''
cwd = _expand_path(cwd, user)
command = ['git'] + _format_git_opts(git_opts)
command.extend(['stash', action])
command.extend(_format_opts(opts))
return _git_run(command,
cwd=cwd,
user=user,
password=password,
ignore_retcode=ignore_retcode,
output_encoding=output_encoding)['stdout']
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osrg/ryu | ryu/cmd/of_config_cli.py | Cmd.do_list_cap | def do_list_cap(self, line):
"""list_cap <peer>
"""
def f(p, args):
for i in p.netconf.server_capabilities:
print(i)
self._request(line, f) | python | def do_list_cap(self, line):
"""list_cap <peer>
"""
def f(p, args):
for i in p.netconf.server_capabilities:
print(i)
self._request(line, 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:
list_cap <peer>
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def do_list_cap(self, line):
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"""
def f(p, args):
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acoomans/flask-autodoc | examples/factory/blog/frontend.py | post_post | def post_post():
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Form Data: title, content, authorid.
"""
authorid = request.form.get('authorid', None)
Post(request.form['title'],
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users[authorid])
return redirect("/posts") | python | def post_post():
"""Create a new post.
Form Data: title, content, authorid.
"""
authorid = request.form.get('authorid', 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.
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Create a new post.
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dedupeio/dedupe | dedupe/api.py | GazetteerMatching.match | def match(self, messy_data, threshold=0.5, n_matches=1, generator=False): # pragma: no cover
"""Identifies pairs of records that refer to the same entity, returns
tuples containing a set of record ids and a confidence score as a float
between 0 and 1. The record_ids within each set should refer to the
same entity and the confidence score is the estimated probability that
the records refer to the same entity.
This method should only used for small to moderately sized datasets
for larger data, use matchBlocks
Arguments:
messy_data -- Dictionary of records from messy dataset, where the
keys are record_ids and the values are dictionaries with
the keys being field names
threshold -- Number between 0 and 1 (default is .5). We will consider
records as potential duplicates if the predicted
probability of being a duplicate is above the threshold.
Lowering the number will increase recall, raising it
will increase precision
n_matches -- Maximum number of possible matches from the canonical
record set to match against each record in the messy
record set
"""
blocked_pairs = self._blockData(messy_data)
clusters = self.matchBlocks(blocked_pairs, threshold, n_matches)
clusters = (cluster for cluster in clusters if len(cluster))
if generator:
return clusters
else:
return list(clusters) | python | def match(self, messy_data, threshold=0.5, n_matches=1, generator=False): # pragma: no cover
"""Identifies pairs of records that refer to the same entity, returns
tuples containing a set of record ids and a confidence score as a float
between 0 and 1. The record_ids within each set should refer to the
same entity and the confidence score is the estimated probability that
the records refer to the same entity.
This method should only used for small to moderately sized datasets
for larger data, use matchBlocks
Arguments:
messy_data -- Dictionary of records from messy dataset, where the
keys are record_ids and the values are dictionaries with
the keys being field names
threshold -- Number between 0 and 1 (default is .5). We will consider
records as potential duplicates if the predicted
probability of being a duplicate is above the threshold.
Lowering the number will increase recall, raising it
will increase precision
n_matches -- Maximum number of possible matches from the canonical
record set to match against each record in the messy
record set
"""
blocked_pairs = self._blockData(messy_data)
clusters = self.matchBlocks(blocked_pairs, threshold, n_matches)
clusters = (cluster for cluster in clusters if len(cluster))
if generator:
return clusters
else:
return list(clusters) | [
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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:
Identifies pairs of records that refer to the same entity, returns
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def match(self, messy_data, threshold=0.5, n_matches=1, generator=False): # pragma: no cover
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messy_data -- Dictionary of records from messy dataset, where the
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threshold -- Number between 0 and 1 (default is .5). We will consider
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apache/spark | python/pyspark/sql/functions.py | when | def when(condition, value):
"""Evaluates a list of conditions and returns one of multiple possible result expressions.
If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions.
:param condition: a boolean :class:`Column` expression.
:param value: a literal value, or a :class:`Column` expression.
>>> df.select(when(df['age'] == 2, 3).otherwise(4).alias("age")).collect()
[Row(age=3), Row(age=4)]
>>> df.select(when(df.age == 2, df.age + 1).alias("age")).collect()
[Row(age=3), Row(age=None)]
"""
sc = SparkContext._active_spark_context
if not isinstance(condition, Column):
raise TypeError("condition should be a Column")
v = value._jc if isinstance(value, Column) else value
jc = sc._jvm.functions.when(condition._jc, v)
return Column(jc) | python | def when(condition, value):
"""Evaluates a list of conditions and returns one of multiple possible result expressions.
If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions.
:param condition: a boolean :class:`Column` expression.
:param value: a literal value, or a :class:`Column` expression.
>>> df.select(when(df['age'] == 2, 3).otherwise(4).alias("age")).collect()
[Row(age=3), Row(age=4)]
>>> df.select(when(df.age == 2, df.age + 1).alias("age")).collect()
[Row(age=3), Row(age=None)]
"""
sc = SparkContext._active_spark_context
if not isinstance(condition, Column):
raise TypeError("condition should be a Column")
v = value._jc if isinstance(value, Column) else value
jc = sc._jvm.functions.when(condition._jc, v)
return Column(jc) | [
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:param value: a literal value, or a :class:`Column` expression.
>>> df.select(when(df['age'] == 2, 3).otherwise(4).alias("age")).collect()
[Row(age=3), Row(age=4)]
>>> df.select(when(df.age == 2, df.age + 1).alias("age")).collect()
[Row(age=3), Row(age=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:
Evaluates a list of conditions and returns one of multiple possible result expressions.
If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions.
:param condition: a boolean :class:`Column` expression.
:param value: a literal value, or a :class:`Column` expression.
>>> df.select(when(df['age'] == 2, 3).otherwise(4).alias("age")).collect()
[Row(age=3), Row(age=4)]
>>> df.select(when(df.age == 2, df.age + 1).alias("age")).collect()
[Row(age=3), Row(age=None)]
### Response:
def when(condition, value):
"""Evaluates a list of conditions and returns one of multiple possible result expressions.
If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions.
:param condition: a boolean :class:`Column` expression.
:param value: a literal value, or a :class:`Column` expression.
>>> df.select(when(df['age'] == 2, 3).otherwise(4).alias("age")).collect()
[Row(age=3), Row(age=4)]
>>> df.select(when(df.age == 2, df.age + 1).alias("age")).collect()
[Row(age=3), Row(age=None)]
"""
sc = SparkContext._active_spark_context
if not isinstance(condition, Column):
raise TypeError("condition should be a Column")
v = value._jc if isinstance(value, Column) else value
jc = sc._jvm.functions.when(condition._jc, v)
return Column(jc)
<|endoftext|> | [
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Pipoline/rocket-python | rocketchat/api.py | RocketChatAPI.get_user_info | def get_user_info(self, user_id, **kwargs):
"""
Retrieves information about a user,
the result is only limited to what the callee has access to view.
:param user_id:
:param kwargs:
:return:
"""
return GetUserInfo(settings=self.settings, **kwargs).call(
user_id=user_id,
**kwargs
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"""
Retrieves information about a user,
the result is only limited to what the callee has access to view.
:param user_id:
:param kwargs:
:return:
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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:
Retrieves information about a user,
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eqcorrscan/EQcorrscan | eqcorrscan/utils/mag_calc.py | calc_b_value | def calc_b_value(magnitudes, completeness, max_mag=None, plotvar=True):
"""
Calculate the b-value for a range of completeness magnitudes.
Calculates a power-law fit to given magnitudes for each completeness
magnitude. Plots the b-values and residuals for the fitted catalogue
against the completeness values. Computes fits using numpy.polyfit,
which uses a least-squares technique.
:type magnitudes: list
:param magnitudes: Magnitudes to compute the b-value for.
:type completeness: list
:param completeness: list of completeness values to compute b-values for.
:type max_mag: float
:param max_mag: Maximum magnitude to attempt to fit in magnitudes.
:type plotvar: bool
:param plotvar: Turn plotting on or off.
:rtype: list
:return: List of tuples of (completeness, b-value, residual,\
number of magnitudes used)
.. rubric:: Example
>>> from obspy.clients.fdsn import Client
>>> from obspy import UTCDateTime
>>> from eqcorrscan.utils.mag_calc import calc_b_value
>>> client = Client('IRIS')
>>> t1 = UTCDateTime('2012-03-26T00:00:00')
>>> t2 = t1 + (3 * 86400)
>>> catalog = client.get_events(starttime=t1, endtime=t2, minmagnitude=3)
>>> magnitudes = [event.magnitudes[0].mag for event in catalog]
>>> b_values = calc_b_value(magnitudes, completeness=np.arange(3, 7, 0.2),
... plotvar=False)
>>> round(b_values[4][1])
1.0
>>> # We can set a maximum magnitude:
>>> b_values = calc_b_value(magnitudes, completeness=np.arange(3, 7, 0.2),
... plotvar=False, max_mag=5)
>>> round(b_values[4][1])
1.0
"""
b_values = []
# Calculate the cdf for all magnitudes
counts = Counter(magnitudes)
cdf = np.zeros(len(counts))
mag_steps = np.zeros(len(counts))
for i, magnitude in enumerate(sorted(counts.keys(), reverse=True)):
mag_steps[i] = magnitude
if i > 0:
cdf[i] = cdf[i - 1] + counts[magnitude]
else:
cdf[i] = counts[magnitude]
if not max_mag:
max_mag = max(magnitudes)
for m_c in completeness:
if m_c >= max_mag or m_c >= max(magnitudes):
warnings.warn('Not computing completeness at %s, above max_mag' %
str(m_c))
break
complete_mags = []
complete_freq = []
for i, mag in enumerate(mag_steps):
if mag >= m_c <= max_mag:
complete_mags.append(mag)
complete_freq.append(np.log10(cdf[i]))
if len(complete_mags) < 4:
warnings.warn('Not computing completeness above ' + str(m_c) +
', fewer than 4 events')
break
fit = np.polyfit(complete_mags, complete_freq, 1, full=True)
# Calculate the residuals according to the Wiemer & Wys 2000 definition
predicted_freqs = [fit[0][1] - abs(fit[0][0] * M)
for M in complete_mags]
r = 100 - ((np.sum([abs(complete_freq[i] - predicted_freqs[i])
for i in range(len(complete_freq))]) * 100) /
np.sum(complete_freq))
b_values.append((m_c, abs(fit[0][0]), r, str(len(complete_mags))))
if plotvar:
fig, ax1 = plt.subplots()
b_vals = ax1.scatter(list(zip(*b_values))[0], list(zip(*b_values))[1],
c='k')
resid = ax1.scatter(list(zip(*b_values))[0],
[100 - b for b in list(zip(*b_values))[2]], c='r')
ax1.set_ylabel('b-value and residual')
plt.xlabel('Completeness magnitude')
ax2 = ax1.twinx()
ax2.set_ylabel('Number of events used in fit')
n_ev = ax2.scatter(list(zip(*b_values))[0], list(zip(*b_values))[3],
c='g')
fig.legend((b_vals, resid, n_ev),
('b-values', 'residuals', 'number of events'),
'lower right')
ax1.set_title('Possible completeness values')
plt.show()
return b_values | python | def calc_b_value(magnitudes, completeness, max_mag=None, plotvar=True):
"""
Calculate the b-value for a range of completeness magnitudes.
Calculates a power-law fit to given magnitudes for each completeness
magnitude. Plots the b-values and residuals for the fitted catalogue
against the completeness values. Computes fits using numpy.polyfit,
which uses a least-squares technique.
:type magnitudes: list
:param magnitudes: Magnitudes to compute the b-value for.
:type completeness: list
:param completeness: list of completeness values to compute b-values for.
:type max_mag: float
:param max_mag: Maximum magnitude to attempt to fit in magnitudes.
:type plotvar: bool
:param plotvar: Turn plotting on or off.
:rtype: list
:return: List of tuples of (completeness, b-value, residual,\
number of magnitudes used)
.. rubric:: Example
>>> from obspy.clients.fdsn import Client
>>> from obspy import UTCDateTime
>>> from eqcorrscan.utils.mag_calc import calc_b_value
>>> client = Client('IRIS')
>>> t1 = UTCDateTime('2012-03-26T00:00:00')
>>> t2 = t1 + (3 * 86400)
>>> catalog = client.get_events(starttime=t1, endtime=t2, minmagnitude=3)
>>> magnitudes = [event.magnitudes[0].mag for event in catalog]
>>> b_values = calc_b_value(magnitudes, completeness=np.arange(3, 7, 0.2),
... plotvar=False)
>>> round(b_values[4][1])
1.0
>>> # We can set a maximum magnitude:
>>> b_values = calc_b_value(magnitudes, completeness=np.arange(3, 7, 0.2),
... plotvar=False, max_mag=5)
>>> round(b_values[4][1])
1.0
"""
b_values = []
# Calculate the cdf for all magnitudes
counts = Counter(magnitudes)
cdf = np.zeros(len(counts))
mag_steps = np.zeros(len(counts))
for i, magnitude in enumerate(sorted(counts.keys(), reverse=True)):
mag_steps[i] = magnitude
if i > 0:
cdf[i] = cdf[i - 1] + counts[magnitude]
else:
cdf[i] = counts[magnitude]
if not max_mag:
max_mag = max(magnitudes)
for m_c in completeness:
if m_c >= max_mag or m_c >= max(magnitudes):
warnings.warn('Not computing completeness at %s, above max_mag' %
str(m_c))
break
complete_mags = []
complete_freq = []
for i, mag in enumerate(mag_steps):
if mag >= m_c <= max_mag:
complete_mags.append(mag)
complete_freq.append(np.log10(cdf[i]))
if len(complete_mags) < 4:
warnings.warn('Not computing completeness above ' + str(m_c) +
', fewer than 4 events')
break
fit = np.polyfit(complete_mags, complete_freq, 1, full=True)
# Calculate the residuals according to the Wiemer & Wys 2000 definition
predicted_freqs = [fit[0][1] - abs(fit[0][0] * M)
for M in complete_mags]
r = 100 - ((np.sum([abs(complete_freq[i] - predicted_freqs[i])
for i in range(len(complete_freq))]) * 100) /
np.sum(complete_freq))
b_values.append((m_c, abs(fit[0][0]), r, str(len(complete_mags))))
if plotvar:
fig, ax1 = plt.subplots()
b_vals = ax1.scatter(list(zip(*b_values))[0], list(zip(*b_values))[1],
c='k')
resid = ax1.scatter(list(zip(*b_values))[0],
[100 - b for b in list(zip(*b_values))[2]], c='r')
ax1.set_ylabel('b-value and residual')
plt.xlabel('Completeness magnitude')
ax2 = ax1.twinx()
ax2.set_ylabel('Number of events used in fit')
n_ev = ax2.scatter(list(zip(*b_values))[0], list(zip(*b_values))[3],
c='g')
fig.legend((b_vals, resid, n_ev),
('b-values', 'residuals', 'number of events'),
'lower right')
ax1.set_title('Possible completeness values')
plt.show()
return b_values | [
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"b_values"
] | Calculate the b-value for a range of completeness magnitudes.
Calculates a power-law fit to given magnitudes for each completeness
magnitude. Plots the b-values and residuals for the fitted catalogue
against the completeness values. Computes fits using numpy.polyfit,
which uses a least-squares technique.
:type magnitudes: list
:param magnitudes: Magnitudes to compute the b-value for.
:type completeness: list
:param completeness: list of completeness values to compute b-values for.
:type max_mag: float
:param max_mag: Maximum magnitude to attempt to fit in magnitudes.
:type plotvar: bool
:param plotvar: Turn plotting on or off.
:rtype: list
:return: List of tuples of (completeness, b-value, residual,\
number of magnitudes used)
.. rubric:: Example
>>> from obspy.clients.fdsn import Client
>>> from obspy import UTCDateTime
>>> from eqcorrscan.utils.mag_calc import calc_b_value
>>> client = Client('IRIS')
>>> t1 = UTCDateTime('2012-03-26T00:00:00')
>>> t2 = t1 + (3 * 86400)
>>> catalog = client.get_events(starttime=t1, endtime=t2, minmagnitude=3)
>>> magnitudes = [event.magnitudes[0].mag for event in catalog]
>>> b_values = calc_b_value(magnitudes, completeness=np.arange(3, 7, 0.2),
... plotvar=False)
>>> round(b_values[4][1])
1.0
>>> # We can set a maximum magnitude:
>>> b_values = calc_b_value(magnitudes, completeness=np.arange(3, 7, 0.2),
... plotvar=False, max_mag=5)
>>> round(b_values[4][1])
1.0 | [
"Calculate",
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] | train | https://github.com/eqcorrscan/EQcorrscan/blob/3121b4aca801ee5d38f56ca297ce1c0f9515d9ff/eqcorrscan/utils/mag_calc.py#L132-L228 | <|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:
Calculate the b-value for a range of completeness magnitudes.
Calculates a power-law fit to given magnitudes for each completeness
magnitude. Plots the b-values and residuals for the fitted catalogue
against the completeness values. Computes fits using numpy.polyfit,
which uses a least-squares technique.
:type magnitudes: list
:param magnitudes: Magnitudes to compute the b-value for.
:type completeness: list
:param completeness: list of completeness values to compute b-values for.
:type max_mag: float
:param max_mag: Maximum magnitude to attempt to fit in magnitudes.
:type plotvar: bool
:param plotvar: Turn plotting on or off.
:rtype: list
:return: List of tuples of (completeness, b-value, residual,\
number of magnitudes used)
.. rubric:: Example
>>> from obspy.clients.fdsn import Client
>>> from obspy import UTCDateTime
>>> from eqcorrscan.utils.mag_calc import calc_b_value
>>> client = Client('IRIS')
>>> t1 = UTCDateTime('2012-03-26T00:00:00')
>>> t2 = t1 + (3 * 86400)
>>> catalog = client.get_events(starttime=t1, endtime=t2, minmagnitude=3)
>>> magnitudes = [event.magnitudes[0].mag for event in catalog]
>>> b_values = calc_b_value(magnitudes, completeness=np.arange(3, 7, 0.2),
... plotvar=False)
>>> round(b_values[4][1])
1.0
>>> # We can set a maximum magnitude:
>>> b_values = calc_b_value(magnitudes, completeness=np.arange(3, 7, 0.2),
... plotvar=False, max_mag=5)
>>> round(b_values[4][1])
1.0
### Response:
def calc_b_value(magnitudes, completeness, max_mag=None, plotvar=True):
"""
Calculate the b-value for a range of completeness magnitudes.
Calculates a power-law fit to given magnitudes for each completeness
magnitude. Plots the b-values and residuals for the fitted catalogue
against the completeness values. Computes fits using numpy.polyfit,
which uses a least-squares technique.
:type magnitudes: list
:param magnitudes: Magnitudes to compute the b-value for.
:type completeness: list
:param completeness: list of completeness values to compute b-values for.
:type max_mag: float
:param max_mag: Maximum magnitude to attempt to fit in magnitudes.
:type plotvar: bool
:param plotvar: Turn plotting on or off.
:rtype: list
:return: List of tuples of (completeness, b-value, residual,\
number of magnitudes used)
.. rubric:: Example
>>> from obspy.clients.fdsn import Client
>>> from obspy import UTCDateTime
>>> from eqcorrscan.utils.mag_calc import calc_b_value
>>> client = Client('IRIS')
>>> t1 = UTCDateTime('2012-03-26T00:00:00')
>>> t2 = t1 + (3 * 86400)
>>> catalog = client.get_events(starttime=t1, endtime=t2, minmagnitude=3)
>>> magnitudes = [event.magnitudes[0].mag for event in catalog]
>>> b_values = calc_b_value(magnitudes, completeness=np.arange(3, 7, 0.2),
... plotvar=False)
>>> round(b_values[4][1])
1.0
>>> # We can set a maximum magnitude:
>>> b_values = calc_b_value(magnitudes, completeness=np.arange(3, 7, 0.2),
... plotvar=False, max_mag=5)
>>> round(b_values[4][1])
1.0
"""
b_values = []
# Calculate the cdf for all magnitudes
counts = Counter(magnitudes)
cdf = np.zeros(len(counts))
mag_steps = np.zeros(len(counts))
for i, magnitude in enumerate(sorted(counts.keys(), reverse=True)):
mag_steps[i] = magnitude
if i > 0:
cdf[i] = cdf[i - 1] + counts[magnitude]
else:
cdf[i] = counts[magnitude]
if not max_mag:
max_mag = max(magnitudes)
for m_c in completeness:
if m_c >= max_mag or m_c >= max(magnitudes):
warnings.warn('Not computing completeness at %s, above max_mag' %
str(m_c))
break
complete_mags = []
complete_freq = []
for i, mag in enumerate(mag_steps):
if mag >= m_c <= max_mag:
complete_mags.append(mag)
complete_freq.append(np.log10(cdf[i]))
if len(complete_mags) < 4:
warnings.warn('Not computing completeness above ' + str(m_c) +
', fewer than 4 events')
break
fit = np.polyfit(complete_mags, complete_freq, 1, full=True)
# Calculate the residuals according to the Wiemer & Wys 2000 definition
predicted_freqs = [fit[0][1] - abs(fit[0][0] * M)
for M in complete_mags]
r = 100 - ((np.sum([abs(complete_freq[i] - predicted_freqs[i])
for i in range(len(complete_freq))]) * 100) /
np.sum(complete_freq))
b_values.append((m_c, abs(fit[0][0]), r, str(len(complete_mags))))
if plotvar:
fig, ax1 = plt.subplots()
b_vals = ax1.scatter(list(zip(*b_values))[0], list(zip(*b_values))[1],
c='k')
resid = ax1.scatter(list(zip(*b_values))[0],
[100 - b for b in list(zip(*b_values))[2]], c='r')
ax1.set_ylabel('b-value and residual')
plt.xlabel('Completeness magnitude')
ax2 = ax1.twinx()
ax2.set_ylabel('Number of events used in fit')
n_ev = ax2.scatter(list(zip(*b_values))[0], list(zip(*b_values))[3],
c='g')
fig.legend((b_vals, resid, n_ev),
('b-values', 'residuals', 'number of events'),
'lower right')
ax1.set_title('Possible completeness values')
plt.show()
return b_values
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carpedm20/fbchat | fbchat/_client.py | Client.setDefaultThread | def setDefaultThread(self, thread_id, thread_type):
"""
Sets default thread to send messages to
:param thread_id: User/Group ID to default to. See :ref:`intro_threads`
:param thread_type: See :ref:`intro_threads`
:type thread_type: models.ThreadType
"""
self._default_thread_id = thread_id
self._default_thread_type = thread_type | python | def setDefaultThread(self, thread_id, thread_type):
"""
Sets default thread to send messages to
:param thread_id: User/Group ID to default to. See :ref:`intro_threads`
:param thread_type: See :ref:`intro_threads`
:type thread_type: models.ThreadType
"""
self._default_thread_id = thread_id
self._default_thread_type = thread_type | [
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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:
Sets default thread to send messages to
:param thread_id: User/Group ID to default to. See :ref:`intro_threads`
:param thread_type: See :ref:`intro_threads`
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:param thread_id: User/Group ID to default to. See :ref:`intro_threads`
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SKA-ScienceDataProcessor/integration-prototype | sip/execution_control/configuration_db/sip_config_db/scheduling/workflow_definitions.py | get_workflow | def get_workflow(workflow_id: str, workflow_version: str) -> dict:
"""Get a workflow definition from the Configuration Database.
Args:
workflow_id (str): Workflow identifier
workflow_version (str): Workflow version
Returns:
dict, Workflow definition dictionary
"""
name = "workflow_definitions:{}:{}".format(workflow_id, workflow_version)
workflow = DB.get_hash_dict(name)
workflow['stages'] = ast.literal_eval(workflow['stages'])
return workflow | python | def get_workflow(workflow_id: str, workflow_version: str) -> dict:
"""Get a workflow definition from the Configuration Database.
Args:
workflow_id (str): Workflow identifier
workflow_version (str): Workflow version
Returns:
dict, Workflow definition dictionary
"""
name = "workflow_definitions:{}:{}".format(workflow_id, workflow_version)
workflow = DB.get_hash_dict(name)
workflow['stages'] = ast.literal_eval(workflow['stages'])
return workflow | [
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### Instruction:
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### Task:
Get a workflow definition from the Configuration Database.
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workflow_id (str): Workflow identifier
workflow_version (str): Workflow version
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dict, Workflow definition dictionary
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workflow_id (str): Workflow identifier
workflow_version (str): Workflow version
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brocade/pynos | pynos/versions/ver_6/ver_6_0_1/yang/brocade_common_def.py | brocade_common_def.router_fabric_virtual_gateway_address_family_ipv6_enable_global | def router_fabric_virtual_gateway_address_family_ipv6_enable_global(self, **kwargs):
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config = ET.Element("config")
router = ET.SubElement(config, "router", xmlns="urn:brocade.com:mgmt:brocade-common-def")
fabric_virtual_gateway = ET.SubElement(router, "fabric-virtual-gateway", xmlns="urn:brocade.com:mgmt:brocade-anycast-gateway")
address_family = ET.SubElement(fabric_virtual_gateway, "address-family")
ipv6 = ET.SubElement(address_family, "ipv6")
enable_global = ET.SubElement(ipv6, "enable_global")
callback = kwargs.pop('callback', self._callback)
return callback(config) | python | def router_fabric_virtual_gateway_address_family_ipv6_enable_global(self, **kwargs):
"""Auto Generated Code
"""
config = ET.Element("config")
router = ET.SubElement(config, "router", xmlns="urn:brocade.com:mgmt:brocade-common-def")
fabric_virtual_gateway = ET.SubElement(router, "fabric-virtual-gateway", xmlns="urn:brocade.com:mgmt:brocade-anycast-gateway")
address_family = ET.SubElement(fabric_virtual_gateway, "address-family")
ipv6 = ET.SubElement(address_family, "ipv6")
enable_global = ET.SubElement(ipv6, "enable_global")
callback = kwargs.pop('callback', self._callback)
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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:
Auto Generated Code
### Response:
def router_fabric_virtual_gateway_address_family_ipv6_enable_global(self, **kwargs):
"""Auto Generated Code
"""
config = ET.Element("config")
router = ET.SubElement(config, "router", xmlns="urn:brocade.com:mgmt:brocade-common-def")
fabric_virtual_gateway = ET.SubElement(router, "fabric-virtual-gateway", xmlns="urn:brocade.com:mgmt:brocade-anycast-gateway")
address_family = ET.SubElement(fabric_virtual_gateway, "address-family")
ipv6 = ET.SubElement(address_family, "ipv6")
enable_global = ET.SubElement(ipv6, "enable_global")
callback = kwargs.pop('callback', self._callback)
return callback(config)
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stevearc/dynamo3 | dynamo3/connection.py | DynamoDBConnection.put_item | def put_item(self, tablename, item, expected=None, returns=NONE,
return_capacity=None, expect_or=False, **kwargs):
"""
Store an item, overwriting existing data
This uses the older version of the DynamoDB API.
See also: :meth:`~.put_item2`.
Parameters
----------
tablename : str
Name of the table to write
item : dict
Item data
expected : dict, optional
DEPRECATED (use **kwargs instead).
If present, will check the values in Dynamo before performing the
write. If values do not match, will raise an exception. (Using None
as a value checks that the field does not exist).
returns : {NONE, ALL_OLD}, optional
If ALL_OLD, will return any data that was overwritten (default
NONE)
return_capacity : {NONE, INDEXES, TOTAL}, optional
INDEXES will return the consumed capacity for indexes, TOTAL will
return the consumed capacity for the table and the indexes.
(default NONE)
expect_or : bool, optional
If True, the **kwargs conditionals will be OR'd together. If False,
they will be AND'd. (default False).
**kwargs : dict, optional
Conditional filter on the PUT. Same format as the kwargs for
:meth:`~.scan`.
"""
keywords = {}
if kwargs:
keywords['Expected'] = encode_query_kwargs(self.dynamizer, kwargs)
if len(keywords['Expected']) > 1:
keywords['ConditionalOperator'] = 'OR' if expect_or else 'AND'
elif expected is not None:
keywords['Expected'] = build_expected(self.dynamizer, expected)
keywords['ReturnConsumedCapacity'] = \
self._default_capacity(return_capacity)
item = self.dynamizer.encode_keys(item)
ret = self.call('put_item', TableName=tablename, Item=item,
ReturnValues=returns, **keywords)
if ret:
return Result(self.dynamizer, ret, 'Attributes') | python | def put_item(self, tablename, item, expected=None, returns=NONE,
return_capacity=None, expect_or=False, **kwargs):
"""
Store an item, overwriting existing data
This uses the older version of the DynamoDB API.
See also: :meth:`~.put_item2`.
Parameters
----------
tablename : str
Name of the table to write
item : dict
Item data
expected : dict, optional
DEPRECATED (use **kwargs instead).
If present, will check the values in Dynamo before performing the
write. If values do not match, will raise an exception. (Using None
as a value checks that the field does not exist).
returns : {NONE, ALL_OLD}, optional
If ALL_OLD, will return any data that was overwritten (default
NONE)
return_capacity : {NONE, INDEXES, TOTAL}, optional
INDEXES will return the consumed capacity for indexes, TOTAL will
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(default NONE)
expect_or : bool, optional
If True, the **kwargs conditionals will be OR'd together. If False,
they will be AND'd. (default False).
**kwargs : dict, optional
Conditional filter on the PUT. Same format as the kwargs for
:meth:`~.scan`.
"""
keywords = {}
if kwargs:
keywords['Expected'] = encode_query_kwargs(self.dynamizer, kwargs)
if len(keywords['Expected']) > 1:
keywords['ConditionalOperator'] = 'OR' if expect_or else 'AND'
elif expected is not None:
keywords['Expected'] = build_expected(self.dynamizer, expected)
keywords['ReturnConsumedCapacity'] = \
self._default_capacity(return_capacity)
item = self.dynamizer.encode_keys(item)
ret = self.call('put_item', TableName=tablename, Item=item,
ReturnValues=returns, **keywords)
if ret:
return Result(self.dynamizer, ret, 'Attributes') | [
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Name of the table to write
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Item data
expected : dict, optional
DEPRECATED (use **kwargs instead).
If present, will check the values in Dynamo before performing the
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] | train | https://github.com/stevearc/dynamo3/blob/f897c40ece28586272dbcab8f0d99a14a1831dda/dynamo3/connection.py#L466-L513 | <|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:
Store an item, overwriting existing data
This uses the older version of the DynamoDB API.
See also: :meth:`~.put_item2`.
Parameters
----------
tablename : str
Name of the table to write
item : dict
Item data
expected : dict, optional
DEPRECATED (use **kwargs instead).
If present, will check the values in Dynamo before performing the
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Conditional filter on the PUT. Same format as the kwargs for
:meth:`~.scan`.
### Response:
def put_item(self, tablename, item, expected=None, returns=NONE,
return_capacity=None, expect_or=False, **kwargs):
"""
Store an item, overwriting existing data
This uses the older version of the DynamoDB API.
See also: :meth:`~.put_item2`.
Parameters
----------
tablename : str
Name of the table to write
item : dict
Item data
expected : dict, optional
DEPRECATED (use **kwargs instead).
If present, will check the values in Dynamo before performing the
write. If values do not match, will raise an exception. (Using None
as a value checks that the field does not exist).
returns : {NONE, ALL_OLD}, optional
If ALL_OLD, will return any data that was overwritten (default
NONE)
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expect_or : bool, optional
If True, the **kwargs conditionals will be OR'd together. If False,
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**kwargs : dict, optional
Conditional filter on the PUT. Same format as the kwargs for
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"""
keywords = {}
if kwargs:
keywords['Expected'] = encode_query_kwargs(self.dynamizer, kwargs)
if len(keywords['Expected']) > 1:
keywords['ConditionalOperator'] = 'OR' if expect_or else 'AND'
elif expected is not None:
keywords['Expected'] = build_expected(self.dynamizer, expected)
keywords['ReturnConsumedCapacity'] = \
self._default_capacity(return_capacity)
item = self.dynamizer.encode_keys(item)
ret = self.call('put_item', TableName=tablename, Item=item,
ReturnValues=returns, **keywords)
if ret:
return Result(self.dynamizer, ret, 'Attributes')
<|endoftext|> | [
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atl/py-smartdc | smartdc/datacenter.py | DataCenter.delete_key | def delete_key(self, key_id):
"""
::
DELETE /:login/keys/:key
:param key_id: identifier for an individual key record for the account
:type key_id: :py:class:`basestring`
Deletes an SSH key from the server identified by `key_id`.
"""
j, r = self.request('DELETE', '/keys/' + str(key_id))
r.raise_for_status()
return j | python | def delete_key(self, key_id):
"""
::
DELETE /:login/keys/:key
:param key_id: identifier for an individual key record for the account
:type key_id: :py:class:`basestring`
Deletes an SSH key from the server identified by `key_id`.
"""
j, r = self.request('DELETE', '/keys/' + str(key_id))
r.raise_for_status()
return j | [
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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 /:login/keys/:key
:param key_id: identifier for an individual key record for the account
:type key_id: :py:class:`basestring`
Deletes an SSH key from the server identified by `key_id`.
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::
DELETE /:login/keys/:key
:param key_id: identifier for an individual key record for the account
:type key_id: :py:class:`basestring`
Deletes an SSH key from the server identified by `key_id`.
"""
j, r = self.request('DELETE', '/keys/' + str(key_id))
r.raise_for_status()
return j
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saltstack/salt | salt/modules/file.py | restorecon | def restorecon(path, recursive=False):
'''
Reset the SELinux context on a given path
CLI Example:
.. code-block:: bash
salt '*' file.restorecon /home/user/.ssh/authorized_keys
'''
if recursive:
cmd = ['restorecon', '-FR', path]
else:
cmd = ['restorecon', '-F', path]
return not __salt__['cmd.retcode'](cmd, python_shell=False) | python | def restorecon(path, recursive=False):
'''
Reset the SELinux context on a given path
CLI Example:
.. code-block:: bash
salt '*' file.restorecon /home/user/.ssh/authorized_keys
'''
if recursive:
cmd = ['restorecon', '-FR', path]
else:
cmd = ['restorecon', '-F', path]
return not __salt__['cmd.retcode'](cmd, python_shell=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:
Reset the SELinux context on a given path
CLI Example:
.. code-block:: bash
salt '*' file.restorecon /home/user/.ssh/authorized_keys
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Reset the SELinux context on a given path
CLI Example:
.. code-block:: bash
salt '*' file.restorecon /home/user/.ssh/authorized_keys
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pyviz/holoviews | holoviews/core/spaces.py | DynamicMap.relabel | def relabel(self, label=None, group=None, depth=1):
"""Clone object and apply new group and/or label.
Applies relabeling to children up to the supplied depth.
Args:
label (str, optional): New label to apply to returned object
group (str, optional): New group to apply to returned object
depth (int, optional): Depth to which relabel will be applied
If applied to container allows applying relabeling to
contained objects up to the specified depth
Returns:
Returns relabelled object
"""
relabelled = super(DynamicMap, self).relabel(label, group, depth)
if depth > 0:
from ..util import Dynamic
def dynamic_relabel(obj, **dynkwargs):
return obj.relabel(group=group, label=label, depth=depth-1)
dmap = Dynamic(self, streams=self.streams, operation=dynamic_relabel)
dmap.data = relabelled.data
with util.disable_constant(dmap):
dmap.group = relabelled.group
dmap.label = relabelled.label
return dmap
return relabelled | python | def relabel(self, label=None, group=None, depth=1):
"""Clone object and apply new group and/or label.
Applies relabeling to children up to the supplied depth.
Args:
label (str, optional): New label to apply to returned object
group (str, optional): New group to apply to returned object
depth (int, optional): Depth to which relabel will be applied
If applied to container allows applying relabeling to
contained objects up to the specified depth
Returns:
Returns relabelled object
"""
relabelled = super(DynamicMap, self).relabel(label, group, depth)
if depth > 0:
from ..util import Dynamic
def dynamic_relabel(obj, **dynkwargs):
return obj.relabel(group=group, label=label, depth=depth-1)
dmap = Dynamic(self, streams=self.streams, operation=dynamic_relabel)
dmap.data = relabelled.data
with util.disable_constant(dmap):
dmap.group = relabelled.group
dmap.label = relabelled.label
return dmap
return relabelled | [
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group (str, optional): New group to apply to returned object
depth (int, optional): Depth to which relabel will be applied
If applied to container allows applying relabeling to
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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:
Clone object and apply new group and/or label.
Applies relabeling to children up to the supplied depth.
Args:
label (str, optional): New label to apply to returned object
group (str, optional): New group to apply to returned object
depth (int, optional): Depth to which relabel will be applied
If applied to container allows applying relabeling to
contained objects up to the specified depth
Returns:
Returns relabelled object
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def relabel(self, label=None, group=None, depth=1):
"""Clone object and apply new group and/or label.
Applies relabeling to children up to the supplied depth.
Args:
label (str, optional): New label to apply to returned object
group (str, optional): New group to apply to returned object
depth (int, optional): Depth to which relabel will be applied
If applied to container allows applying relabeling to
contained objects up to the specified depth
Returns:
Returns relabelled object
"""
relabelled = super(DynamicMap, self).relabel(label, group, depth)
if depth > 0:
from ..util import Dynamic
def dynamic_relabel(obj, **dynkwargs):
return obj.relabel(group=group, label=label, depth=depth-1)
dmap = Dynamic(self, streams=self.streams, operation=dynamic_relabel)
dmap.data = relabelled.data
with util.disable_constant(dmap):
dmap.group = relabelled.group
dmap.label = relabelled.label
return dmap
return relabelled
<|endoftext|> | [
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openstack/hacking | hacking/core.py | is_import_exception | def is_import_exception(mod):
"""Check module name to see if import has been whitelisted.
Import based rules should not run on any whitelisted module
"""
return (mod in IMPORT_EXCEPTIONS or
any(mod.startswith(m + '.') for m in IMPORT_EXCEPTIONS)) | python | def is_import_exception(mod):
"""Check module name to see if import has been whitelisted.
Import based rules should not run on any whitelisted module
"""
return (mod in IMPORT_EXCEPTIONS or
any(mod.startswith(m + '.') for m in IMPORT_EXCEPTIONS)) | [
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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 module name to see if import has been whitelisted.
Import based rules should not run on any whitelisted module
### Response:
def is_import_exception(mod):
"""Check module name to see if import has been whitelisted.
Import based rules should not run on any whitelisted module
"""
return (mod in IMPORT_EXCEPTIONS or
any(mod.startswith(m + '.') for m in IMPORT_EXCEPTIONS))
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mkoura/dump2polarion | dump2polarion/csv2sqlite_cli.py | dump2sqlite | def dump2sqlite(records, output_file):
"""Dumps tests results to database."""
results_keys = list(records.results[0].keys())
pad_data = []
for key in REQUIRED_KEYS:
if key not in results_keys:
results_keys.append(key)
pad_data.append("")
conn = sqlite3.connect(os.path.expanduser(output_file), detect_types=sqlite3.PARSE_DECLTYPES)
# in each row there needs to be data for every column
# last column is current time
pad_data.append(datetime.datetime.utcnow())
to_db = [list(row.values()) + pad_data for row in records.results]
cur = conn.cursor()
cur.execute(
"CREATE TABLE testcases ({},sqltime TIMESTAMP)".format(
",".join("{} TEXT".format(key) for key in results_keys)
)
)
cur.executemany(
"INSERT INTO testcases VALUES ({},?)".format(",".join(["?"] * len(results_keys))), to_db
)
if records.testrun:
cur.execute("CREATE TABLE testrun (testrun TEXT)")
cur.execute("INSERT INTO testrun VALUES (?)", (records.testrun,))
conn.commit()
conn.close()
logger.info("Data written to '%s'", output_file) | python | def dump2sqlite(records, output_file):
"""Dumps tests results to database."""
results_keys = list(records.results[0].keys())
pad_data = []
for key in REQUIRED_KEYS:
if key not in results_keys:
results_keys.append(key)
pad_data.append("")
conn = sqlite3.connect(os.path.expanduser(output_file), detect_types=sqlite3.PARSE_DECLTYPES)
# in each row there needs to be data for every column
# last column is current time
pad_data.append(datetime.datetime.utcnow())
to_db = [list(row.values()) + pad_data for row in records.results]
cur = conn.cursor()
cur.execute(
"CREATE TABLE testcases ({},sqltime TIMESTAMP)".format(
",".join("{} TEXT".format(key) for key in results_keys)
)
)
cur.executemany(
"INSERT INTO testcases VALUES ({},?)".format(",".join(["?"] * len(results_keys))), to_db
)
if records.testrun:
cur.execute("CREATE TABLE testrun (testrun TEXT)")
cur.execute("INSERT INTO testrun VALUES (?)", (records.testrun,))
conn.commit()
conn.close()
logger.info("Data written to '%s'", output_file) | [
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] | train | https://github.com/mkoura/dump2polarion/blob/f4bd24e9d5070e282aad15f1e8bb514c0525cd37/dump2polarion/csv2sqlite_cli.py#L42-L77 | <|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:
Dumps tests results to database.
### Response:
def dump2sqlite(records, output_file):
"""Dumps tests results to database."""
results_keys = list(records.results[0].keys())
pad_data = []
for key in REQUIRED_KEYS:
if key not in results_keys:
results_keys.append(key)
pad_data.append("")
conn = sqlite3.connect(os.path.expanduser(output_file), detect_types=sqlite3.PARSE_DECLTYPES)
# in each row there needs to be data for every column
# last column is current time
pad_data.append(datetime.datetime.utcnow())
to_db = [list(row.values()) + pad_data for row in records.results]
cur = conn.cursor()
cur.execute(
"CREATE TABLE testcases ({},sqltime TIMESTAMP)".format(
",".join("{} TEXT".format(key) for key in results_keys)
)
)
cur.executemany(
"INSERT INTO testcases VALUES ({},?)".format(",".join(["?"] * len(results_keys))), to_db
)
if records.testrun:
cur.execute("CREATE TABLE testrun (testrun TEXT)")
cur.execute("INSERT INTO testrun VALUES (?)", (records.testrun,))
conn.commit()
conn.close()
logger.info("Data written to '%s'", output_file)
<|endoftext|> | [
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nicolargo/glances | glances/plugins/glances_fs.py | Plugin.update | def update(self):
"""Update the FS stats using the input method."""
# Init new stats
stats = self.get_init_value()
if self.input_method == 'local':
# Update stats using the standard system lib
# Grab the stats using the psutil disk_partitions
# If 'all'=False return physical devices only (e.g. hard disks, cd-rom drives, USB keys)
# and ignore all others (e.g. memory partitions such as /dev/shm)
try:
fs_stat = psutil.disk_partitions(all=False)
except UnicodeDecodeError:
return self.stats
# Optionnal hack to allow logicals mounts points (issue #448)
# Ex: Had to put 'allow=zfs' in the [fs] section of the conf file
# to allow zfs monitoring
for fstype in self.get_conf_value('allow'):
try:
fs_stat += [f for f in psutil.disk_partitions(all=True) if f.fstype.find(fstype) >= 0]
except UnicodeDecodeError:
return self.stats
# Loop over fs
for fs in fs_stat:
# Do not take hidden file system into account
if self.is_hide(fs.mountpoint):
continue
# Grab the disk usage
try:
fs_usage = psutil.disk_usage(fs.mountpoint)
except OSError:
# Correct issue #346
# Disk is ejected during the command
continue
fs_current = {
'device_name': fs.device,
'fs_type': fs.fstype,
# Manage non breaking space (see issue #1065)
'mnt_point': u(fs.mountpoint).replace(u'\u00A0', ' '),
'size': fs_usage.total,
'used': fs_usage.used,
'free': fs_usage.free,
'percent': fs_usage.percent,
'key': self.get_key()}
stats.append(fs_current)
elif self.input_method == 'snmp':
# Update stats using SNMP
# SNMP bulk command to get all file system in one shot
try:
fs_stat = self.get_stats_snmp(snmp_oid=snmp_oid[self.short_system_name],
bulk=True)
except KeyError:
fs_stat = self.get_stats_snmp(snmp_oid=snmp_oid['default'],
bulk=True)
# Loop over fs
if self.short_system_name in ('windows', 'esxi'):
# Windows or ESXi tips
for fs in fs_stat:
# Memory stats are grabbed in the same OID table (ignore it)
if fs == 'Virtual Memory' or fs == 'Physical Memory' or fs == 'Real Memory':
continue
size = int(fs_stat[fs]['size']) * int(fs_stat[fs]['alloc_unit'])
used = int(fs_stat[fs]['used']) * int(fs_stat[fs]['alloc_unit'])
percent = float(used * 100 / size)
fs_current = {
'device_name': '',
'mnt_point': fs.partition(' ')[0],
'size': size,
'used': used,
'percent': percent,
'key': self.get_key()}
stats.append(fs_current)
else:
# Default behavior
for fs in fs_stat:
fs_current = {
'device_name': fs_stat[fs]['device_name'],
'mnt_point': fs,
'size': int(fs_stat[fs]['size']) * 1024,
'used': int(fs_stat[fs]['used']) * 1024,
'percent': float(fs_stat[fs]['percent']),
'key': self.get_key()}
stats.append(fs_current)
# Update the stats
self.stats = stats
return self.stats | python | def update(self):
"""Update the FS stats using the input method."""
# Init new stats
stats = self.get_init_value()
if self.input_method == 'local':
# Update stats using the standard system lib
# Grab the stats using the psutil disk_partitions
# If 'all'=False return physical devices only (e.g. hard disks, cd-rom drives, USB keys)
# and ignore all others (e.g. memory partitions such as /dev/shm)
try:
fs_stat = psutil.disk_partitions(all=False)
except UnicodeDecodeError:
return self.stats
# Optionnal hack to allow logicals mounts points (issue #448)
# Ex: Had to put 'allow=zfs' in the [fs] section of the conf file
# to allow zfs monitoring
for fstype in self.get_conf_value('allow'):
try:
fs_stat += [f for f in psutil.disk_partitions(all=True) if f.fstype.find(fstype) >= 0]
except UnicodeDecodeError:
return self.stats
# Loop over fs
for fs in fs_stat:
# Do not take hidden file system into account
if self.is_hide(fs.mountpoint):
continue
# Grab the disk usage
try:
fs_usage = psutil.disk_usage(fs.mountpoint)
except OSError:
# Correct issue #346
# Disk is ejected during the command
continue
fs_current = {
'device_name': fs.device,
'fs_type': fs.fstype,
# Manage non breaking space (see issue #1065)
'mnt_point': u(fs.mountpoint).replace(u'\u00A0', ' '),
'size': fs_usage.total,
'used': fs_usage.used,
'free': fs_usage.free,
'percent': fs_usage.percent,
'key': self.get_key()}
stats.append(fs_current)
elif self.input_method == 'snmp':
# Update stats using SNMP
# SNMP bulk command to get all file system in one shot
try:
fs_stat = self.get_stats_snmp(snmp_oid=snmp_oid[self.short_system_name],
bulk=True)
except KeyError:
fs_stat = self.get_stats_snmp(snmp_oid=snmp_oid['default'],
bulk=True)
# Loop over fs
if self.short_system_name in ('windows', 'esxi'):
# Windows or ESXi tips
for fs in fs_stat:
# Memory stats are grabbed in the same OID table (ignore it)
if fs == 'Virtual Memory' or fs == 'Physical Memory' or fs == 'Real Memory':
continue
size = int(fs_stat[fs]['size']) * int(fs_stat[fs]['alloc_unit'])
used = int(fs_stat[fs]['used']) * int(fs_stat[fs]['alloc_unit'])
percent = float(used * 100 / size)
fs_current = {
'device_name': '',
'mnt_point': fs.partition(' ')[0],
'size': size,
'used': used,
'percent': percent,
'key': self.get_key()}
stats.append(fs_current)
else:
# Default behavior
for fs in fs_stat:
fs_current = {
'device_name': fs_stat[fs]['device_name'],
'mnt_point': fs,
'size': int(fs_stat[fs]['size']) * 1024,
'used': int(fs_stat[fs]['used']) * 1024,
'percent': float(fs_stat[fs]['percent']),
'key': self.get_key()}
stats.append(fs_current)
# Update the stats
self.stats = stats
return self.stats | [
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"Update",
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] | train | https://github.com/nicolargo/glances/blob/5bd4d587a736e0d2b03170b56926841d2a3eb7ee/glances/plugins/glances_fs.py#L88-L181 | <|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 the FS stats using the input method.
### Response:
def update(self):
"""Update the FS stats using the input method."""
# Init new stats
stats = self.get_init_value()
if self.input_method == 'local':
# Update stats using the standard system lib
# Grab the stats using the psutil disk_partitions
# If 'all'=False return physical devices only (e.g. hard disks, cd-rom drives, USB keys)
# and ignore all others (e.g. memory partitions such as /dev/shm)
try:
fs_stat = psutil.disk_partitions(all=False)
except UnicodeDecodeError:
return self.stats
# Optionnal hack to allow logicals mounts points (issue #448)
# Ex: Had to put 'allow=zfs' in the [fs] section of the conf file
# to allow zfs monitoring
for fstype in self.get_conf_value('allow'):
try:
fs_stat += [f for f in psutil.disk_partitions(all=True) if f.fstype.find(fstype) >= 0]
except UnicodeDecodeError:
return self.stats
# Loop over fs
for fs in fs_stat:
# Do not take hidden file system into account
if self.is_hide(fs.mountpoint):
continue
# Grab the disk usage
try:
fs_usage = psutil.disk_usage(fs.mountpoint)
except OSError:
# Correct issue #346
# Disk is ejected during the command
continue
fs_current = {
'device_name': fs.device,
'fs_type': fs.fstype,
# Manage non breaking space (see issue #1065)
'mnt_point': u(fs.mountpoint).replace(u'\u00A0', ' '),
'size': fs_usage.total,
'used': fs_usage.used,
'free': fs_usage.free,
'percent': fs_usage.percent,
'key': self.get_key()}
stats.append(fs_current)
elif self.input_method == 'snmp':
# Update stats using SNMP
# SNMP bulk command to get all file system in one shot
try:
fs_stat = self.get_stats_snmp(snmp_oid=snmp_oid[self.short_system_name],
bulk=True)
except KeyError:
fs_stat = self.get_stats_snmp(snmp_oid=snmp_oid['default'],
bulk=True)
# Loop over fs
if self.short_system_name in ('windows', 'esxi'):
# Windows or ESXi tips
for fs in fs_stat:
# Memory stats are grabbed in the same OID table (ignore it)
if fs == 'Virtual Memory' or fs == 'Physical Memory' or fs == 'Real Memory':
continue
size = int(fs_stat[fs]['size']) * int(fs_stat[fs]['alloc_unit'])
used = int(fs_stat[fs]['used']) * int(fs_stat[fs]['alloc_unit'])
percent = float(used * 100 / size)
fs_current = {
'device_name': '',
'mnt_point': fs.partition(' ')[0],
'size': size,
'used': used,
'percent': percent,
'key': self.get_key()}
stats.append(fs_current)
else:
# Default behavior
for fs in fs_stat:
fs_current = {
'device_name': fs_stat[fs]['device_name'],
'mnt_point': fs,
'size': int(fs_stat[fs]['size']) * 1024,
'used': int(fs_stat[fs]['used']) * 1024,
'percent': float(fs_stat[fs]['percent']),
'key': self.get_key()}
stats.append(fs_current)
# Update the stats
self.stats = stats
return self.stats
<|endoftext|> | [
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bram85/topydo | topydo/ui/CLIApplicationBase.py | _retrieve_archive | def _retrieve_archive():
"""
Returns a tuple with archive content: the first element is a TodoListBase
and the second element is a TodoFile.
"""
archive_file = TodoFile.TodoFile(config().archive())
archive = TodoListBase.TodoListBase(archive_file.read())
return (archive, archive_file) | python | def _retrieve_archive():
"""
Returns a tuple with archive content: the first element is a TodoListBase
and the second element is a TodoFile.
"""
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archive = TodoListBase.TodoListBase(archive_file.read())
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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:
Returns a tuple with archive content: the first element is a TodoListBase
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### Response:
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Returns a tuple with archive content: the first element is a TodoListBase
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mikedh/trimesh | trimesh/proximity.py | closest_point | def closest_point(mesh, points):
"""
Given a mesh and a list of points, find the closest point on any triangle.
Parameters
----------
mesh : Trimesh object
points : (m,3) float, points in space
Returns
----------
closest : (m,3) float, closest point on triangles for each point
distance : (m,) float, distance
triangle_id : (m,) int, index of triangle containing closest point
"""
points = np.asanyarray(points, dtype=np.float64)
if not util.is_shape(points, (-1, 3)):
raise ValueError('points must be (n,3)!')
# do a tree- based query for faces near each point
candidates = nearby_faces(mesh, points)
# view triangles as an ndarray so we don't have to recompute
# the MD5 during all of the subsequent advanced indexing
triangles = mesh.triangles.view(np.ndarray)
# create the corresponding list of triangles
# and query points to send to the closest_point function
query_point = deque()
query_tri = deque()
for triangle_ids, point in zip(candidates, points):
query_point.append(np.tile(point, (len(triangle_ids), 1)))
query_tri.append(triangles[triangle_ids])
# stack points into an (n,3) array
query_point = np.vstack(query_point)
# stack triangles into an (n,3,3) array
query_tri = np.vstack(query_tri)
# do the computation for closest point
query_close = closest_point_corresponding(query_tri, query_point)
query_group = np.cumsum(np.array([len(i) for i in candidates]))[:-1]
distance_2 = ((query_close - query_point) ** 2).sum(axis=1)
# find the single closest point for each group of candidates
result_close = np.zeros((len(points), 3), dtype=np.float64)
result_tid = np.zeros(len(points), dtype=np.int64)
result_distance = np.zeros(len(points), dtype=np.float64)
# go through results to get minimum distance result
for i, close_points, distance, candidate in zip(
np.arange(len(points)),
np.array_split(query_close, query_group),
np.array_split(distance_2, query_group),
candidates):
# unless some other check is true use the smallest distance
idx = distance.argmin()
# if we have multiple candidates check them
if len(candidate) > 1:
# (2, ) int, list of 2 closest candidate indices
idxs = distance.argsort()[:2]
# make sure the two distances are identical
check_distance = distance[idxs].ptp() < tol.merge
# make sure the magnitude of both distances are nonzero
check_magnitude = (np.abs(distance[idxs]) > tol.merge).all()
# check if query-points are actually off-surface
if check_distance and check_magnitude:
# get face normals for two points
normals = mesh.face_normals[np.array(candidate)[idxs]]
# compute normalized surface-point to query-point vectors
vectors = ((points[i] - close_points[idxs]) /
distance[idxs, np.newaxis] ** 0.5)
# compare enclosed angle for both face normals
dots = util.diagonal_dot(normals, vectors)
# take the idx with the most positive angle
idx = idxs[dots.argmax()]
# take the single closest value from the group of values
result_close[i] = close_points[idx]
result_tid[i] = candidate[idx]
result_distance[i] = distance[idx]
# we were comparing the distance squared so
# now take the square root in one vectorized operation
result_distance **= .5
return result_close, result_distance, result_tid | python | def closest_point(mesh, points):
"""
Given a mesh and a list of points, find the closest point on any triangle.
Parameters
----------
mesh : Trimesh object
points : (m,3) float, points in space
Returns
----------
closest : (m,3) float, closest point on triangles for each point
distance : (m,) float, distance
triangle_id : (m,) int, index of triangle containing closest point
"""
points = np.asanyarray(points, dtype=np.float64)
if not util.is_shape(points, (-1, 3)):
raise ValueError('points must be (n,3)!')
# do a tree- based query for faces near each point
candidates = nearby_faces(mesh, points)
# view triangles as an ndarray so we don't have to recompute
# the MD5 during all of the subsequent advanced indexing
triangles = mesh.triangles.view(np.ndarray)
# create the corresponding list of triangles
# and query points to send to the closest_point function
query_point = deque()
query_tri = deque()
for triangle_ids, point in zip(candidates, points):
query_point.append(np.tile(point, (len(triangle_ids), 1)))
query_tri.append(triangles[triangle_ids])
# stack points into an (n,3) array
query_point = np.vstack(query_point)
# stack triangles into an (n,3,3) array
query_tri = np.vstack(query_tri)
# do the computation for closest point
query_close = closest_point_corresponding(query_tri, query_point)
query_group = np.cumsum(np.array([len(i) for i in candidates]))[:-1]
distance_2 = ((query_close - query_point) ** 2).sum(axis=1)
# find the single closest point for each group of candidates
result_close = np.zeros((len(points), 3), dtype=np.float64)
result_tid = np.zeros(len(points), dtype=np.int64)
result_distance = np.zeros(len(points), dtype=np.float64)
# go through results to get minimum distance result
for i, close_points, distance, candidate in zip(
np.arange(len(points)),
np.array_split(query_close, query_group),
np.array_split(distance_2, query_group),
candidates):
# unless some other check is true use the smallest distance
idx = distance.argmin()
# if we have multiple candidates check them
if len(candidate) > 1:
# (2, ) int, list of 2 closest candidate indices
idxs = distance.argsort()[:2]
# make sure the two distances are identical
check_distance = distance[idxs].ptp() < tol.merge
# make sure the magnitude of both distances are nonzero
check_magnitude = (np.abs(distance[idxs]) > tol.merge).all()
# check if query-points are actually off-surface
if check_distance and check_magnitude:
# get face normals for two points
normals = mesh.face_normals[np.array(candidate)[idxs]]
# compute normalized surface-point to query-point vectors
vectors = ((points[i] - close_points[idxs]) /
distance[idxs, np.newaxis] ** 0.5)
# compare enclosed angle for both face normals
dots = util.diagonal_dot(normals, vectors)
# take the idx with the most positive angle
idx = idxs[dots.argmax()]
# take the single closest value from the group of values
result_close[i] = close_points[idx]
result_tid[i] = candidate[idx]
result_distance[i] = distance[idx]
# we were comparing the distance squared so
# now take the square root in one vectorized operation
result_distance **= .5
return result_close, result_distance, result_tid | [
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] | Given a mesh and a list of points, find the closest point on any triangle.
Parameters
----------
mesh : Trimesh object
points : (m,3) float, points in space
Returns
----------
closest : (m,3) float, closest point on triangles for each point
distance : (m,) float, distance
triangle_id : (m,) int, index of triangle containing closest point | [
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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 a mesh and a list of points, find the closest point on any triangle.
Parameters
----------
mesh : Trimesh object
points : (m,3) float, points in space
Returns
----------
closest : (m,3) float, closest point on triangles for each point
distance : (m,) float, distance
triangle_id : (m,) int, index of triangle containing closest point
### Response:
def closest_point(mesh, points):
"""
Given a mesh and a list of points, find the closest point on any triangle.
Parameters
----------
mesh : Trimesh object
points : (m,3) float, points in space
Returns
----------
closest : (m,3) float, closest point on triangles for each point
distance : (m,) float, distance
triangle_id : (m,) int, index of triangle containing closest point
"""
points = np.asanyarray(points, dtype=np.float64)
if not util.is_shape(points, (-1, 3)):
raise ValueError('points must be (n,3)!')
# do a tree- based query for faces near each point
candidates = nearby_faces(mesh, points)
# view triangles as an ndarray so we don't have to recompute
# the MD5 during all of the subsequent advanced indexing
triangles = mesh.triangles.view(np.ndarray)
# create the corresponding list of triangles
# and query points to send to the closest_point function
query_point = deque()
query_tri = deque()
for triangle_ids, point in zip(candidates, points):
query_point.append(np.tile(point, (len(triangle_ids), 1)))
query_tri.append(triangles[triangle_ids])
# stack points into an (n,3) array
query_point = np.vstack(query_point)
# stack triangles into an (n,3,3) array
query_tri = np.vstack(query_tri)
# do the computation for closest point
query_close = closest_point_corresponding(query_tri, query_point)
query_group = np.cumsum(np.array([len(i) for i in candidates]))[:-1]
distance_2 = ((query_close - query_point) ** 2).sum(axis=1)
# find the single closest point for each group of candidates
result_close = np.zeros((len(points), 3), dtype=np.float64)
result_tid = np.zeros(len(points), dtype=np.int64)
result_distance = np.zeros(len(points), dtype=np.float64)
# go through results to get minimum distance result
for i, close_points, distance, candidate in zip(
np.arange(len(points)),
np.array_split(query_close, query_group),
np.array_split(distance_2, query_group),
candidates):
# unless some other check is true use the smallest distance
idx = distance.argmin()
# if we have multiple candidates check them
if len(candidate) > 1:
# (2, ) int, list of 2 closest candidate indices
idxs = distance.argsort()[:2]
# make sure the two distances are identical
check_distance = distance[idxs].ptp() < tol.merge
# make sure the magnitude of both distances are nonzero
check_magnitude = (np.abs(distance[idxs]) > tol.merge).all()
# check if query-points are actually off-surface
if check_distance and check_magnitude:
# get face normals for two points
normals = mesh.face_normals[np.array(candidate)[idxs]]
# compute normalized surface-point to query-point vectors
vectors = ((points[i] - close_points[idxs]) /
distance[idxs, np.newaxis] ** 0.5)
# compare enclosed angle for both face normals
dots = util.diagonal_dot(normals, vectors)
# take the idx with the most positive angle
idx = idxs[dots.argmax()]
# take the single closest value from the group of values
result_close[i] = close_points[idx]
result_tid[i] = candidate[idx]
result_distance[i] = distance[idx]
# we were comparing the distance squared so
# now take the square root in one vectorized operation
result_distance **= .5
return result_close, result_distance, result_tid
<|endoftext|> | [
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FNNDSC/pftree | pftree/pftree.py | pftree.tree_construct | def tree_construct(self, *args, **kwargs):
"""
Processes the <l_files> list of files from the tree_probe()
and builds the input/output dictionary structures.
Optionally execute a constructCallback function, and return
results
"""
l_files = []
d_constructCallback = {}
fn_constructCallback = None
for k, v in kwargs.items():
if k == 'l_files': l_files = v
if k == 'constructCallback': fn_constructCallback = v
index = 1
total = len(l_files)
for l_series in l_files:
str_path = os.path.dirname(l_series[0])
l_series = [ os.path.basename(i) for i in l_series]
self.simpleProgress_show(index, total)
self.d_inputTree[str_path] = l_series
if fn_constructCallback:
kwargs['path'] = str_path
d_constructCallback = fn_constructCallback(l_series, **kwargs)
self.d_inputTreeCallback[str_path] = d_constructCallback
self.d_outputTree[str_path] = ""
index += 1
return {
'status': True,
'd_constructCalback': d_constructCallback,
'totalNumberOfAllSeries': index
} | python | def tree_construct(self, *args, **kwargs):
"""
Processes the <l_files> list of files from the tree_probe()
and builds the input/output dictionary structures.
Optionally execute a constructCallback function, and return
results
"""
l_files = []
d_constructCallback = {}
fn_constructCallback = None
for k, v in kwargs.items():
if k == 'l_files': l_files = v
if k == 'constructCallback': fn_constructCallback = v
index = 1
total = len(l_files)
for l_series in l_files:
str_path = os.path.dirname(l_series[0])
l_series = [ os.path.basename(i) for i in l_series]
self.simpleProgress_show(index, total)
self.d_inputTree[str_path] = l_series
if fn_constructCallback:
kwargs['path'] = str_path
d_constructCallback = fn_constructCallback(l_series, **kwargs)
self.d_inputTreeCallback[str_path] = d_constructCallback
self.d_outputTree[str_path] = ""
index += 1
return {
'status': True,
'd_constructCalback': d_constructCallback,
'totalNumberOfAllSeries': index
} | [
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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:
Processes the <l_files> list of files from the tree_probe()
and builds the input/output dictionary structures.
Optionally execute a constructCallback function, and return
results
### Response:
def tree_construct(self, *args, **kwargs):
"""
Processes the <l_files> list of files from the tree_probe()
and builds the input/output dictionary structures.
Optionally execute a constructCallback function, and return
results
"""
l_files = []
d_constructCallback = {}
fn_constructCallback = None
for k, v in kwargs.items():
if k == 'l_files': l_files = v
if k == 'constructCallback': fn_constructCallback = v
index = 1
total = len(l_files)
for l_series in l_files:
str_path = os.path.dirname(l_series[0])
l_series = [ os.path.basename(i) for i in l_series]
self.simpleProgress_show(index, total)
self.d_inputTree[str_path] = l_series
if fn_constructCallback:
kwargs['path'] = str_path
d_constructCallback = fn_constructCallback(l_series, **kwargs)
self.d_inputTreeCallback[str_path] = d_constructCallback
self.d_outputTree[str_path] = ""
index += 1
return {
'status': True,
'd_constructCalback': d_constructCallback,
'totalNumberOfAllSeries': index
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<|endoftext|> | [
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etal/biofrills | biofrills/pairutils.py | identity_abs | def identity_abs(aseq, bseq):
"""Compute absolute identity (# matching sites) between sequence strings."""
assert len(aseq) == len(bseq)
return sum(a == b
for a, b in zip(aseq, bseq)
if not (a in '-.' and b in '-.')) | python | def identity_abs(aseq, bseq):
"""Compute absolute identity (# matching sites) between sequence strings."""
assert len(aseq) == len(bseq)
return sum(a == b
for a, b in zip(aseq, bseq)
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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:
Compute absolute identity (# matching sites) between sequence strings.
### Response:
def identity_abs(aseq, bseq):
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assert len(aseq) == len(bseq)
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quantmind/pulsar | examples/philosophers/manage.py | DiningPhilosophers.release_forks | async def release_forks(self, philosopher):
'''The ``philosopher`` has just eaten and is ready to release both
forks.
This method releases them, one by one, by sending the ``put_down``
action to the monitor.
'''
forks = self.forks
self.forks = []
self.started_waiting = 0
for fork in forks:
philosopher.logger.debug('Putting down fork %s', fork)
await philosopher.send('monitor', 'putdown_fork', fork)
await sleep(self.cfg.waiting_period) | python | async def release_forks(self, philosopher):
'''The ``philosopher`` has just eaten and is ready to release both
forks.
This method releases them, one by one, by sending the ``put_down``
action to the monitor.
'''
forks = self.forks
self.forks = []
self.started_waiting = 0
for fork in forks:
philosopher.logger.debug('Putting down fork %s', fork)
await philosopher.send('monitor', 'putdown_fork', fork)
await sleep(self.cfg.waiting_period) | [
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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:
The ``philosopher`` has just eaten and is ready to release both
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This method releases them, one by one, by sending the ``put_down``
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### Response:
async def release_forks(self, philosopher):
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Microsoft/LightGBM | python-package/lightgbm/engine.py | train | def train(params, train_set, num_boost_round=100,
valid_sets=None, valid_names=None,
fobj=None, feval=None, init_model=None,
feature_name='auto', categorical_feature='auto',
early_stopping_rounds=None, evals_result=None,
verbose_eval=True, learning_rates=None,
keep_training_booster=False, callbacks=None):
"""Perform the training with given parameters.
Parameters
----------
params : dict
Parameters for training.
train_set : Dataset
Data to be trained on.
num_boost_round : int, optional (default=100)
Number of boosting iterations.
valid_sets : list of Datasets or None, optional (default=None)
List of data to be evaluated on during training.
valid_names : list of strings or None, optional (default=None)
Names of ``valid_sets``.
fobj : callable or None, optional (default=None)
Customized objective function.
feval : callable or None, optional (default=None)
Customized evaluation function.
Should accept two parameters: preds, train_data,
and return (eval_name, eval_result, is_higher_better) or list of such tuples.
For multi-class task, the preds is group by class_id first, then group by row_id.
If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
To ignore the default metric corresponding to the used objective,
set the ``metric`` parameter to the string ``"None"`` in ``params``.
init_model : string, Booster or None, optional (default=None)
Filename of LightGBM model or Booster instance used for continue training.
feature_name : list of strings or 'auto', optional (default="auto")
Feature names.
If 'auto' and data is pandas DataFrame, data columns names are used.
categorical_feature : list of strings or int, or 'auto', optional (default="auto")
Categorical features.
If list of int, interpreted as indices.
If list of strings, interpreted as feature names (need to specify ``feature_name`` as well).
If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
All values in categorical features should be less than int32 max value (2147483647).
Large values could be memory consuming. Consider using consecutive integers starting from zero.
All negative values in categorical features will be treated as missing values.
early_stopping_rounds : int or None, optional (default=None)
Activates early stopping. The model will train until the validation score stops improving.
Validation score needs to improve at least every ``early_stopping_rounds`` round(s)
to continue training.
Requires at least one validation data and one metric.
If there's more than one, will check all of them. But the training data is ignored anyway.
To check only the first metric you can pass in ``callbacks``
``early_stopping`` callback with ``first_metric_only=True``.
The index of iteration that has the best performance will be saved in the ``best_iteration`` field
if early stopping logic is enabled by setting ``early_stopping_rounds``.
evals_result: dict or None, optional (default=None)
This dictionary used to store all evaluation results of all the items in ``valid_sets``.
Example
-------
With a ``valid_sets`` = [valid_set, train_set],
``valid_names`` = ['eval', 'train']
and a ``params`` = {'metric': 'logloss'}
returns {'train': {'logloss': ['0.48253', '0.35953', ...]},
'eval': {'logloss': ['0.480385', '0.357756', ...]}}.
verbose_eval : bool or int, optional (default=True)
Requires at least one validation data.
If True, the eval metric on the valid set is printed at each boosting stage.
If int, the eval metric on the valid set is printed at every ``verbose_eval`` boosting stage.
The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed.
Example
-------
With ``verbose_eval`` = 4 and at least one item in ``valid_sets``,
an evaluation metric is printed every 4 (instead of 1) boosting stages.
learning_rates : list, callable or None, optional (default=None)
List of learning rates for each boosting round
or a customized function that calculates ``learning_rate``
in terms of current number of round (e.g. yields learning rate decay).
keep_training_booster : bool, optional (default=False)
Whether the returned Booster will be used to keep training.
If False, the returned value will be converted into _InnerPredictor before returning.
You can still use _InnerPredictor as ``init_model`` for future continue training.
callbacks : list of callables or None, optional (default=None)
List of callback functions that are applied at each iteration.
See Callbacks in Python API for more information.
Returns
-------
booster : Booster
The trained Booster model.
"""
# create predictor first
params = copy.deepcopy(params)
if fobj is not None:
params['objective'] = 'none'
for alias in ["num_iterations", "num_iteration", "n_iter", "num_tree", "num_trees",
"num_round", "num_rounds", "num_boost_round", "n_estimators"]:
if alias in params:
num_boost_round = int(params.pop(alias))
warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
break
for alias in ["early_stopping_round", "early_stopping_rounds", "early_stopping"]:
if alias in params and params[alias] is not None:
early_stopping_rounds = int(params.pop(alias))
warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
break
if num_boost_round <= 0:
raise ValueError("num_boost_round should be greater than zero.")
if isinstance(init_model, string_type):
predictor = _InnerPredictor(model_file=init_model, pred_parameter=params)
elif isinstance(init_model, Booster):
predictor = init_model._to_predictor(dict(init_model.params, **params))
else:
predictor = None
init_iteration = predictor.num_total_iteration if predictor is not None else 0
# check dataset
if not isinstance(train_set, Dataset):
raise TypeError("Training only accepts Dataset object")
train_set._update_params(params) \
._set_predictor(predictor) \
.set_feature_name(feature_name) \
.set_categorical_feature(categorical_feature)
is_valid_contain_train = False
train_data_name = "training"
reduced_valid_sets = []
name_valid_sets = []
if valid_sets is not None:
if isinstance(valid_sets, Dataset):
valid_sets = [valid_sets]
if isinstance(valid_names, string_type):
valid_names = [valid_names]
for i, valid_data in enumerate(valid_sets):
# reduce cost for prediction training data
if valid_data is train_set:
is_valid_contain_train = True
if valid_names is not None:
train_data_name = valid_names[i]
continue
if not isinstance(valid_data, Dataset):
raise TypeError("Traninig only accepts Dataset object")
reduced_valid_sets.append(valid_data._update_params(params).set_reference(train_set))
if valid_names is not None and len(valid_names) > i:
name_valid_sets.append(valid_names[i])
else:
name_valid_sets.append('valid_' + str(i))
# process callbacks
if callbacks is None:
callbacks = set()
else:
for i, cb in enumerate(callbacks):
cb.__dict__.setdefault('order', i - len(callbacks))
callbacks = set(callbacks)
# Most of legacy advanced options becomes callbacks
if verbose_eval is True:
callbacks.add(callback.print_evaluation())
elif isinstance(verbose_eval, integer_types):
callbacks.add(callback.print_evaluation(verbose_eval))
if early_stopping_rounds is not None:
callbacks.add(callback.early_stopping(early_stopping_rounds, verbose=bool(verbose_eval)))
if learning_rates is not None:
callbacks.add(callback.reset_parameter(learning_rate=learning_rates))
if evals_result is not None:
callbacks.add(callback.record_evaluation(evals_result))
callbacks_before_iter = {cb for cb in callbacks if getattr(cb, 'before_iteration', False)}
callbacks_after_iter = callbacks - callbacks_before_iter
callbacks_before_iter = sorted(callbacks_before_iter, key=attrgetter('order'))
callbacks_after_iter = sorted(callbacks_after_iter, key=attrgetter('order'))
# construct booster
try:
booster = Booster(params=params, train_set=train_set)
if is_valid_contain_train:
booster.set_train_data_name(train_data_name)
for valid_set, name_valid_set in zip_(reduced_valid_sets, name_valid_sets):
booster.add_valid(valid_set, name_valid_set)
finally:
train_set._reverse_update_params()
for valid_set in reduced_valid_sets:
valid_set._reverse_update_params()
booster.best_iteration = 0
# start training
for i in range_(init_iteration, init_iteration + num_boost_round):
for cb in callbacks_before_iter:
cb(callback.CallbackEnv(model=booster,
params=params,
iteration=i,
begin_iteration=init_iteration,
end_iteration=init_iteration + num_boost_round,
evaluation_result_list=None))
booster.update(fobj=fobj)
evaluation_result_list = []
# check evaluation result.
if valid_sets is not None:
if is_valid_contain_train:
evaluation_result_list.extend(booster.eval_train(feval))
evaluation_result_list.extend(booster.eval_valid(feval))
try:
for cb in callbacks_after_iter:
cb(callback.CallbackEnv(model=booster,
params=params,
iteration=i,
begin_iteration=init_iteration,
end_iteration=init_iteration + num_boost_round,
evaluation_result_list=evaluation_result_list))
except callback.EarlyStopException as earlyStopException:
booster.best_iteration = earlyStopException.best_iteration + 1
evaluation_result_list = earlyStopException.best_score
break
booster.best_score = collections.defaultdict(dict)
for dataset_name, eval_name, score, _ in evaluation_result_list:
booster.best_score[dataset_name][eval_name] = score
if not keep_training_booster:
booster.model_from_string(booster.model_to_string(), False).free_dataset()
return booster | python | def train(params, train_set, num_boost_round=100,
valid_sets=None, valid_names=None,
fobj=None, feval=None, init_model=None,
feature_name='auto', categorical_feature='auto',
early_stopping_rounds=None, evals_result=None,
verbose_eval=True, learning_rates=None,
keep_training_booster=False, callbacks=None):
"""Perform the training with given parameters.
Parameters
----------
params : dict
Parameters for training.
train_set : Dataset
Data to be trained on.
num_boost_round : int, optional (default=100)
Number of boosting iterations.
valid_sets : list of Datasets or None, optional (default=None)
List of data to be evaluated on during training.
valid_names : list of strings or None, optional (default=None)
Names of ``valid_sets``.
fobj : callable or None, optional (default=None)
Customized objective function.
feval : callable or None, optional (default=None)
Customized evaluation function.
Should accept two parameters: preds, train_data,
and return (eval_name, eval_result, is_higher_better) or list of such tuples.
For multi-class task, the preds is group by class_id first, then group by row_id.
If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
To ignore the default metric corresponding to the used objective,
set the ``metric`` parameter to the string ``"None"`` in ``params``.
init_model : string, Booster or None, optional (default=None)
Filename of LightGBM model or Booster instance used for continue training.
feature_name : list of strings or 'auto', optional (default="auto")
Feature names.
If 'auto' and data is pandas DataFrame, data columns names are used.
categorical_feature : list of strings or int, or 'auto', optional (default="auto")
Categorical features.
If list of int, interpreted as indices.
If list of strings, interpreted as feature names (need to specify ``feature_name`` as well).
If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
All values in categorical features should be less than int32 max value (2147483647).
Large values could be memory consuming. Consider using consecutive integers starting from zero.
All negative values in categorical features will be treated as missing values.
early_stopping_rounds : int or None, optional (default=None)
Activates early stopping. The model will train until the validation score stops improving.
Validation score needs to improve at least every ``early_stopping_rounds`` round(s)
to continue training.
Requires at least one validation data and one metric.
If there's more than one, will check all of them. But the training data is ignored anyway.
To check only the first metric you can pass in ``callbacks``
``early_stopping`` callback with ``first_metric_only=True``.
The index of iteration that has the best performance will be saved in the ``best_iteration`` field
if early stopping logic is enabled by setting ``early_stopping_rounds``.
evals_result: dict or None, optional (default=None)
This dictionary used to store all evaluation results of all the items in ``valid_sets``.
Example
-------
With a ``valid_sets`` = [valid_set, train_set],
``valid_names`` = ['eval', 'train']
and a ``params`` = {'metric': 'logloss'}
returns {'train': {'logloss': ['0.48253', '0.35953', ...]},
'eval': {'logloss': ['0.480385', '0.357756', ...]}}.
verbose_eval : bool or int, optional (default=True)
Requires at least one validation data.
If True, the eval metric on the valid set is printed at each boosting stage.
If int, the eval metric on the valid set is printed at every ``verbose_eval`` boosting stage.
The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed.
Example
-------
With ``verbose_eval`` = 4 and at least one item in ``valid_sets``,
an evaluation metric is printed every 4 (instead of 1) boosting stages.
learning_rates : list, callable or None, optional (default=None)
List of learning rates for each boosting round
or a customized function that calculates ``learning_rate``
in terms of current number of round (e.g. yields learning rate decay).
keep_training_booster : bool, optional (default=False)
Whether the returned Booster will be used to keep training.
If False, the returned value will be converted into _InnerPredictor before returning.
You can still use _InnerPredictor as ``init_model`` for future continue training.
callbacks : list of callables or None, optional (default=None)
List of callback functions that are applied at each iteration.
See Callbacks in Python API for more information.
Returns
-------
booster : Booster
The trained Booster model.
"""
# create predictor first
params = copy.deepcopy(params)
if fobj is not None:
params['objective'] = 'none'
for alias in ["num_iterations", "num_iteration", "n_iter", "num_tree", "num_trees",
"num_round", "num_rounds", "num_boost_round", "n_estimators"]:
if alias in params:
num_boost_round = int(params.pop(alias))
warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
break
for alias in ["early_stopping_round", "early_stopping_rounds", "early_stopping"]:
if alias in params and params[alias] is not None:
early_stopping_rounds = int(params.pop(alias))
warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
break
if num_boost_round <= 0:
raise ValueError("num_boost_round should be greater than zero.")
if isinstance(init_model, string_type):
predictor = _InnerPredictor(model_file=init_model, pred_parameter=params)
elif isinstance(init_model, Booster):
predictor = init_model._to_predictor(dict(init_model.params, **params))
else:
predictor = None
init_iteration = predictor.num_total_iteration if predictor is not None else 0
# check dataset
if not isinstance(train_set, Dataset):
raise TypeError("Training only accepts Dataset object")
train_set._update_params(params) \
._set_predictor(predictor) \
.set_feature_name(feature_name) \
.set_categorical_feature(categorical_feature)
is_valid_contain_train = False
train_data_name = "training"
reduced_valid_sets = []
name_valid_sets = []
if valid_sets is not None:
if isinstance(valid_sets, Dataset):
valid_sets = [valid_sets]
if isinstance(valid_names, string_type):
valid_names = [valid_names]
for i, valid_data in enumerate(valid_sets):
# reduce cost for prediction training data
if valid_data is train_set:
is_valid_contain_train = True
if valid_names is not None:
train_data_name = valid_names[i]
continue
if not isinstance(valid_data, Dataset):
raise TypeError("Traninig only accepts Dataset object")
reduced_valid_sets.append(valid_data._update_params(params).set_reference(train_set))
if valid_names is not None and len(valid_names) > i:
name_valid_sets.append(valid_names[i])
else:
name_valid_sets.append('valid_' + str(i))
# process callbacks
if callbacks is None:
callbacks = set()
else:
for i, cb in enumerate(callbacks):
cb.__dict__.setdefault('order', i - len(callbacks))
callbacks = set(callbacks)
# Most of legacy advanced options becomes callbacks
if verbose_eval is True:
callbacks.add(callback.print_evaluation())
elif isinstance(verbose_eval, integer_types):
callbacks.add(callback.print_evaluation(verbose_eval))
if early_stopping_rounds is not None:
callbacks.add(callback.early_stopping(early_stopping_rounds, verbose=bool(verbose_eval)))
if learning_rates is not None:
callbacks.add(callback.reset_parameter(learning_rate=learning_rates))
if evals_result is not None:
callbacks.add(callback.record_evaluation(evals_result))
callbacks_before_iter = {cb for cb in callbacks if getattr(cb, 'before_iteration', False)}
callbacks_after_iter = callbacks - callbacks_before_iter
callbacks_before_iter = sorted(callbacks_before_iter, key=attrgetter('order'))
callbacks_after_iter = sorted(callbacks_after_iter, key=attrgetter('order'))
# construct booster
try:
booster = Booster(params=params, train_set=train_set)
if is_valid_contain_train:
booster.set_train_data_name(train_data_name)
for valid_set, name_valid_set in zip_(reduced_valid_sets, name_valid_sets):
booster.add_valid(valid_set, name_valid_set)
finally:
train_set._reverse_update_params()
for valid_set in reduced_valid_sets:
valid_set._reverse_update_params()
booster.best_iteration = 0
# start training
for i in range_(init_iteration, init_iteration + num_boost_round):
for cb in callbacks_before_iter:
cb(callback.CallbackEnv(model=booster,
params=params,
iteration=i,
begin_iteration=init_iteration,
end_iteration=init_iteration + num_boost_round,
evaluation_result_list=None))
booster.update(fobj=fobj)
evaluation_result_list = []
# check evaluation result.
if valid_sets is not None:
if is_valid_contain_train:
evaluation_result_list.extend(booster.eval_train(feval))
evaluation_result_list.extend(booster.eval_valid(feval))
try:
for cb in callbacks_after_iter:
cb(callback.CallbackEnv(model=booster,
params=params,
iteration=i,
begin_iteration=init_iteration,
end_iteration=init_iteration + num_boost_round,
evaluation_result_list=evaluation_result_list))
except callback.EarlyStopException as earlyStopException:
booster.best_iteration = earlyStopException.best_iteration + 1
evaluation_result_list = earlyStopException.best_score
break
booster.best_score = collections.defaultdict(dict)
for dataset_name, eval_name, score, _ in evaluation_result_list:
booster.best_score[dataset_name][eval_name] = score
if not keep_training_booster:
booster.model_from_string(booster.model_to_string(), False).free_dataset()
return booster | [
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] | Perform the training with given parameters.
Parameters
----------
params : dict
Parameters for training.
train_set : Dataset
Data to be trained on.
num_boost_round : int, optional (default=100)
Number of boosting iterations.
valid_sets : list of Datasets or None, optional (default=None)
List of data to be evaluated on during training.
valid_names : list of strings or None, optional (default=None)
Names of ``valid_sets``.
fobj : callable or None, optional (default=None)
Customized objective function.
feval : callable or None, optional (default=None)
Customized evaluation function.
Should accept two parameters: preds, train_data,
and return (eval_name, eval_result, is_higher_better) or list of such tuples.
For multi-class task, the preds is group by class_id first, then group by row_id.
If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
To ignore the default metric corresponding to the used objective,
set the ``metric`` parameter to the string ``"None"`` in ``params``.
init_model : string, Booster or None, optional (default=None)
Filename of LightGBM model or Booster instance used for continue training.
feature_name : list of strings or 'auto', optional (default="auto")
Feature names.
If 'auto' and data is pandas DataFrame, data columns names are used.
categorical_feature : list of strings or int, or 'auto', optional (default="auto")
Categorical features.
If list of int, interpreted as indices.
If list of strings, interpreted as feature names (need to specify ``feature_name`` as well).
If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
All values in categorical features should be less than int32 max value (2147483647).
Large values could be memory consuming. Consider using consecutive integers starting from zero.
All negative values in categorical features will be treated as missing values.
early_stopping_rounds : int or None, optional (default=None)
Activates early stopping. The model will train until the validation score stops improving.
Validation score needs to improve at least every ``early_stopping_rounds`` round(s)
to continue training.
Requires at least one validation data and one metric.
If there's more than one, will check all of them. But the training data is ignored anyway.
To check only the first metric you can pass in ``callbacks``
``early_stopping`` callback with ``first_metric_only=True``.
The index of iteration that has the best performance will be saved in the ``best_iteration`` field
if early stopping logic is enabled by setting ``early_stopping_rounds``.
evals_result: dict or None, optional (default=None)
This dictionary used to store all evaluation results of all the items in ``valid_sets``.
Example
-------
With a ``valid_sets`` = [valid_set, train_set],
``valid_names`` = ['eval', 'train']
and a ``params`` = {'metric': 'logloss'}
returns {'train': {'logloss': ['0.48253', '0.35953', ...]},
'eval': {'logloss': ['0.480385', '0.357756', ...]}}.
verbose_eval : bool or int, optional (default=True)
Requires at least one validation data.
If True, the eval metric on the valid set is printed at each boosting stage.
If int, the eval metric on the valid set is printed at every ``verbose_eval`` boosting stage.
The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed.
Example
-------
With ``verbose_eval`` = 4 and at least one item in ``valid_sets``,
an evaluation metric is printed every 4 (instead of 1) boosting stages.
learning_rates : list, callable or None, optional (default=None)
List of learning rates for each boosting round
or a customized function that calculates ``learning_rate``
in terms of current number of round (e.g. yields learning rate decay).
keep_training_booster : bool, optional (default=False)
Whether the returned Booster will be used to keep training.
If False, the returned value will be converted into _InnerPredictor before returning.
You can still use _InnerPredictor as ``init_model`` for future continue training.
callbacks : list of callables or None, optional (default=None)
List of callback functions that are applied at each iteration.
See Callbacks in Python API for more information.
Returns
-------
booster : Booster
The trained Booster model. | [
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"."
] | train | https://github.com/Microsoft/LightGBM/blob/8d2ec69f4f685b0ab1c4624d59ee2d3287bb3147/python-package/lightgbm/engine.py#L19-L245 | <|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 the training with given parameters.
Parameters
----------
params : dict
Parameters for training.
train_set : Dataset
Data to be trained on.
num_boost_round : int, optional (default=100)
Number of boosting iterations.
valid_sets : list of Datasets or None, optional (default=None)
List of data to be evaluated on during training.
valid_names : list of strings or None, optional (default=None)
Names of ``valid_sets``.
fobj : callable or None, optional (default=None)
Customized objective function.
feval : callable or None, optional (default=None)
Customized evaluation function.
Should accept two parameters: preds, train_data,
and return (eval_name, eval_result, is_higher_better) or list of such tuples.
For multi-class task, the preds is group by class_id first, then group by row_id.
If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
To ignore the default metric corresponding to the used objective,
set the ``metric`` parameter to the string ``"None"`` in ``params``.
init_model : string, Booster or None, optional (default=None)
Filename of LightGBM model or Booster instance used for continue training.
feature_name : list of strings or 'auto', optional (default="auto")
Feature names.
If 'auto' and data is pandas DataFrame, data columns names are used.
categorical_feature : list of strings or int, or 'auto', optional (default="auto")
Categorical features.
If list of int, interpreted as indices.
If list of strings, interpreted as feature names (need to specify ``feature_name`` as well).
If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
All values in categorical features should be less than int32 max value (2147483647).
Large values could be memory consuming. Consider using consecutive integers starting from zero.
All negative values in categorical features will be treated as missing values.
early_stopping_rounds : int or None, optional (default=None)
Activates early stopping. The model will train until the validation score stops improving.
Validation score needs to improve at least every ``early_stopping_rounds`` round(s)
to continue training.
Requires at least one validation data and one metric.
If there's more than one, will check all of them. But the training data is ignored anyway.
To check only the first metric you can pass in ``callbacks``
``early_stopping`` callback with ``first_metric_only=True``.
The index of iteration that has the best performance will be saved in the ``best_iteration`` field
if early stopping logic is enabled by setting ``early_stopping_rounds``.
evals_result: dict or None, optional (default=None)
This dictionary used to store all evaluation results of all the items in ``valid_sets``.
Example
-------
With a ``valid_sets`` = [valid_set, train_set],
``valid_names`` = ['eval', 'train']
and a ``params`` = {'metric': 'logloss'}
returns {'train': {'logloss': ['0.48253', '0.35953', ...]},
'eval': {'logloss': ['0.480385', '0.357756', ...]}}.
verbose_eval : bool or int, optional (default=True)
Requires at least one validation data.
If True, the eval metric on the valid set is printed at each boosting stage.
If int, the eval metric on the valid set is printed at every ``verbose_eval`` boosting stage.
The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed.
Example
-------
With ``verbose_eval`` = 4 and at least one item in ``valid_sets``,
an evaluation metric is printed every 4 (instead of 1) boosting stages.
learning_rates : list, callable or None, optional (default=None)
List of learning rates for each boosting round
or a customized function that calculates ``learning_rate``
in terms of current number of round (e.g. yields learning rate decay).
keep_training_booster : bool, optional (default=False)
Whether the returned Booster will be used to keep training.
If False, the returned value will be converted into _InnerPredictor before returning.
You can still use _InnerPredictor as ``init_model`` for future continue training.
callbacks : list of callables or None, optional (default=None)
List of callback functions that are applied at each iteration.
See Callbacks in Python API for more information.
Returns
-------
booster : Booster
The trained Booster model.
### Response:
def train(params, train_set, num_boost_round=100,
valid_sets=None, valid_names=None,
fobj=None, feval=None, init_model=None,
feature_name='auto', categorical_feature='auto',
early_stopping_rounds=None, evals_result=None,
verbose_eval=True, learning_rates=None,
keep_training_booster=False, callbacks=None):
"""Perform the training with given parameters.
Parameters
----------
params : dict
Parameters for training.
train_set : Dataset
Data to be trained on.
num_boost_round : int, optional (default=100)
Number of boosting iterations.
valid_sets : list of Datasets or None, optional (default=None)
List of data to be evaluated on during training.
valid_names : list of strings or None, optional (default=None)
Names of ``valid_sets``.
fobj : callable or None, optional (default=None)
Customized objective function.
feval : callable or None, optional (default=None)
Customized evaluation function.
Should accept two parameters: preds, train_data,
and return (eval_name, eval_result, is_higher_better) or list of such tuples.
For multi-class task, the preds is group by class_id first, then group by row_id.
If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i].
To ignore the default metric corresponding to the used objective,
set the ``metric`` parameter to the string ``"None"`` in ``params``.
init_model : string, Booster or None, optional (default=None)
Filename of LightGBM model or Booster instance used for continue training.
feature_name : list of strings or 'auto', optional (default="auto")
Feature names.
If 'auto' and data is pandas DataFrame, data columns names are used.
categorical_feature : list of strings or int, or 'auto', optional (default="auto")
Categorical features.
If list of int, interpreted as indices.
If list of strings, interpreted as feature names (need to specify ``feature_name`` as well).
If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
All values in categorical features should be less than int32 max value (2147483647).
Large values could be memory consuming. Consider using consecutive integers starting from zero.
All negative values in categorical features will be treated as missing values.
early_stopping_rounds : int or None, optional (default=None)
Activates early stopping. The model will train until the validation score stops improving.
Validation score needs to improve at least every ``early_stopping_rounds`` round(s)
to continue training.
Requires at least one validation data and one metric.
If there's more than one, will check all of them. But the training data is ignored anyway.
To check only the first metric you can pass in ``callbacks``
``early_stopping`` callback with ``first_metric_only=True``.
The index of iteration that has the best performance will be saved in the ``best_iteration`` field
if early stopping logic is enabled by setting ``early_stopping_rounds``.
evals_result: dict or None, optional (default=None)
This dictionary used to store all evaluation results of all the items in ``valid_sets``.
Example
-------
With a ``valid_sets`` = [valid_set, train_set],
``valid_names`` = ['eval', 'train']
and a ``params`` = {'metric': 'logloss'}
returns {'train': {'logloss': ['0.48253', '0.35953', ...]},
'eval': {'logloss': ['0.480385', '0.357756', ...]}}.
verbose_eval : bool or int, optional (default=True)
Requires at least one validation data.
If True, the eval metric on the valid set is printed at each boosting stage.
If int, the eval metric on the valid set is printed at every ``verbose_eval`` boosting stage.
The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed.
Example
-------
With ``verbose_eval`` = 4 and at least one item in ``valid_sets``,
an evaluation metric is printed every 4 (instead of 1) boosting stages.
learning_rates : list, callable or None, optional (default=None)
List of learning rates for each boosting round
or a customized function that calculates ``learning_rate``
in terms of current number of round (e.g. yields learning rate decay).
keep_training_booster : bool, optional (default=False)
Whether the returned Booster will be used to keep training.
If False, the returned value will be converted into _InnerPredictor before returning.
You can still use _InnerPredictor as ``init_model`` for future continue training.
callbacks : list of callables or None, optional (default=None)
List of callback functions that are applied at each iteration.
See Callbacks in Python API for more information.
Returns
-------
booster : Booster
The trained Booster model.
"""
# create predictor first
params = copy.deepcopy(params)
if fobj is not None:
params['objective'] = 'none'
for alias in ["num_iterations", "num_iteration", "n_iter", "num_tree", "num_trees",
"num_round", "num_rounds", "num_boost_round", "n_estimators"]:
if alias in params:
num_boost_round = int(params.pop(alias))
warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
break
for alias in ["early_stopping_round", "early_stopping_rounds", "early_stopping"]:
if alias in params and params[alias] is not None:
early_stopping_rounds = int(params.pop(alias))
warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
break
if num_boost_round <= 0:
raise ValueError("num_boost_round should be greater than zero.")
if isinstance(init_model, string_type):
predictor = _InnerPredictor(model_file=init_model, pred_parameter=params)
elif isinstance(init_model, Booster):
predictor = init_model._to_predictor(dict(init_model.params, **params))
else:
predictor = None
init_iteration = predictor.num_total_iteration if predictor is not None else 0
# check dataset
if not isinstance(train_set, Dataset):
raise TypeError("Training only accepts Dataset object")
train_set._update_params(params) \
._set_predictor(predictor) \
.set_feature_name(feature_name) \
.set_categorical_feature(categorical_feature)
is_valid_contain_train = False
train_data_name = "training"
reduced_valid_sets = []
name_valid_sets = []
if valid_sets is not None:
if isinstance(valid_sets, Dataset):
valid_sets = [valid_sets]
if isinstance(valid_names, string_type):
valid_names = [valid_names]
for i, valid_data in enumerate(valid_sets):
# reduce cost for prediction training data
if valid_data is train_set:
is_valid_contain_train = True
if valid_names is not None:
train_data_name = valid_names[i]
continue
if not isinstance(valid_data, Dataset):
raise TypeError("Traninig only accepts Dataset object")
reduced_valid_sets.append(valid_data._update_params(params).set_reference(train_set))
if valid_names is not None and len(valid_names) > i:
name_valid_sets.append(valid_names[i])
else:
name_valid_sets.append('valid_' + str(i))
# process callbacks
if callbacks is None:
callbacks = set()
else:
for i, cb in enumerate(callbacks):
cb.__dict__.setdefault('order', i - len(callbacks))
callbacks = set(callbacks)
# Most of legacy advanced options becomes callbacks
if verbose_eval is True:
callbacks.add(callback.print_evaluation())
elif isinstance(verbose_eval, integer_types):
callbacks.add(callback.print_evaluation(verbose_eval))
if early_stopping_rounds is not None:
callbacks.add(callback.early_stopping(early_stopping_rounds, verbose=bool(verbose_eval)))
if learning_rates is not None:
callbacks.add(callback.reset_parameter(learning_rate=learning_rates))
if evals_result is not None:
callbacks.add(callback.record_evaluation(evals_result))
callbacks_before_iter = {cb for cb in callbacks if getattr(cb, 'before_iteration', False)}
callbacks_after_iter = callbacks - callbacks_before_iter
callbacks_before_iter = sorted(callbacks_before_iter, key=attrgetter('order'))
callbacks_after_iter = sorted(callbacks_after_iter, key=attrgetter('order'))
# construct booster
try:
booster = Booster(params=params, train_set=train_set)
if is_valid_contain_train:
booster.set_train_data_name(train_data_name)
for valid_set, name_valid_set in zip_(reduced_valid_sets, name_valid_sets):
booster.add_valid(valid_set, name_valid_set)
finally:
train_set._reverse_update_params()
for valid_set in reduced_valid_sets:
valid_set._reverse_update_params()
booster.best_iteration = 0
# start training
for i in range_(init_iteration, init_iteration + num_boost_round):
for cb in callbacks_before_iter:
cb(callback.CallbackEnv(model=booster,
params=params,
iteration=i,
begin_iteration=init_iteration,
end_iteration=init_iteration + num_boost_round,
evaluation_result_list=None))
booster.update(fobj=fobj)
evaluation_result_list = []
# check evaluation result.
if valid_sets is not None:
if is_valid_contain_train:
evaluation_result_list.extend(booster.eval_train(feval))
evaluation_result_list.extend(booster.eval_valid(feval))
try:
for cb in callbacks_after_iter:
cb(callback.CallbackEnv(model=booster,
params=params,
iteration=i,
begin_iteration=init_iteration,
end_iteration=init_iteration + num_boost_round,
evaluation_result_list=evaluation_result_list))
except callback.EarlyStopException as earlyStopException:
booster.best_iteration = earlyStopException.best_iteration + 1
evaluation_result_list = earlyStopException.best_score
break
booster.best_score = collections.defaultdict(dict)
for dataset_name, eval_name, score, _ in evaluation_result_list:
booster.best_score[dataset_name][eval_name] = score
if not keep_training_booster:
booster.model_from_string(booster.model_to_string(), False).free_dataset()
return booster
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adaptive-learning/proso-apps | proso_models/models.py | ItemManager.get_graph | def get_graph(self, item_ids, language=None):
"""
Get a subgraph of items reachable from the given set of items through
any relation.
Args:
item_ids (list): items which are taken as roots for the reachability
language (str): if specified, filter out items which are not
available in the given language
Returns:
dict: item id -> list of items (parent items), root items are
referenced by None key
"""
def _related(item_ids):
if item_ids is None:
items = Item.objects.filter(active=True).prefetch_related('parents', 'children')
else:
item_ids = [ii for iis in item_ids.values() for ii in iis]
items = Item.objects.filter(id__in=item_ids, active=True).prefetch_related('parents', 'children')
return {item.id: sorted([_item.id for rel in [item.parents.all(), item.children.all()] for _item in rel]) for item in items}
if item_ids is None:
return self._reachable_graph(None, _related, language=language)
else:
graph = self.get_graph(None, language)
return self._subset_graph(graph, item_ids) | python | def get_graph(self, item_ids, language=None):
"""
Get a subgraph of items reachable from the given set of items through
any relation.
Args:
item_ids (list): items which are taken as roots for the reachability
language (str): if specified, filter out items which are not
available in the given language
Returns:
dict: item id -> list of items (parent items), root items are
referenced by None key
"""
def _related(item_ids):
if item_ids is None:
items = Item.objects.filter(active=True).prefetch_related('parents', 'children')
else:
item_ids = [ii for iis in item_ids.values() for ii in iis]
items = Item.objects.filter(id__in=item_ids, active=True).prefetch_related('parents', 'children')
return {item.id: sorted([_item.id for rel in [item.parents.all(), item.children.all()] for _item in rel]) for item in items}
if item_ids is None:
return self._reachable_graph(None, _related, language=language)
else:
graph = self.get_graph(None, language)
return self._subset_graph(graph, item_ids) | [
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any relation.
Args:
item_ids (list): items which are taken as roots for the reachability
language (str): if specified, filter out items which are not
available in the given language
Returns:
dict: item id -> list of items (parent items), root items are
referenced by None 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:
Get a subgraph of items reachable from the given set of items through
any relation.
Args:
item_ids (list): items which are taken as roots for the reachability
language (str): if specified, filter out items which are not
available in the given language
Returns:
dict: item id -> list of items (parent items), root items are
referenced by None key
### Response:
def get_graph(self, item_ids, language=None):
"""
Get a subgraph of items reachable from the given set of items through
any relation.
Args:
item_ids (list): items which are taken as roots for the reachability
language (str): if specified, filter out items which are not
available in the given language
Returns:
dict: item id -> list of items (parent items), root items are
referenced by None key
"""
def _related(item_ids):
if item_ids is None:
items = Item.objects.filter(active=True).prefetch_related('parents', 'children')
else:
item_ids = [ii for iis in item_ids.values() for ii in iis]
items = Item.objects.filter(id__in=item_ids, active=True).prefetch_related('parents', 'children')
return {item.id: sorted([_item.id for rel in [item.parents.all(), item.children.all()] for _item in rel]) for item in items}
if item_ids is None:
return self._reachable_graph(None, _related, language=language)
else:
graph = self.get_graph(None, language)
return self._subset_graph(graph, item_ids)
<|endoftext|> | [
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ipfs/py-ipfs-api | ipfsapi/client.py | Client.pin_rm | def pin_rm(self, path, *paths, **kwargs):
"""Removes a pinned object from local storage.
Removes the pin from the given object allowing it to be garbage
collected if needed.
.. code-block:: python
>>> c.pin_rm('QmfZY61ukoQuCX8e5Pt7v8pRfhkyxwZKZMTodAtmvyGZ5d')
{'Pins': ['QmfZY61ukoQuCX8e5Pt7v8pRfhkyxwZKZMTodAtmvyGZ5d']}
Parameters
----------
path : str
Path to object(s) to be unpinned
recursive : bool
Recursively unpin the object linked to by the specified object(s)
Returns
-------
dict : List of IPFS objects that have been unpinned
"""
#PY2: No support for kw-only parameters after glob parameters
if "recursive" in kwargs:
kwargs.setdefault("opts", {"recursive": kwargs["recursive"]})
del kwargs["recursive"]
args = (path,) + paths
return self._client.request('/pin/rm', args, decoder='json', **kwargs) | python | def pin_rm(self, path, *paths, **kwargs):
"""Removes a pinned object from local storage.
Removes the pin from the given object allowing it to be garbage
collected if needed.
.. code-block:: python
>>> c.pin_rm('QmfZY61ukoQuCX8e5Pt7v8pRfhkyxwZKZMTodAtmvyGZ5d')
{'Pins': ['QmfZY61ukoQuCX8e5Pt7v8pRfhkyxwZKZMTodAtmvyGZ5d']}
Parameters
----------
path : str
Path to object(s) to be unpinned
recursive : bool
Recursively unpin the object linked to by the specified object(s)
Returns
-------
dict : List of IPFS objects that have been unpinned
"""
#PY2: No support for kw-only parameters after glob parameters
if "recursive" in kwargs:
kwargs.setdefault("opts", {"recursive": kwargs["recursive"]})
del kwargs["recursive"]
args = (path,) + paths
return self._client.request('/pin/rm', args, decoder='json', **kwargs) | [
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Removes the pin from the given object allowing it to be garbage
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.. code-block:: python
>>> c.pin_rm('QmfZY61ukoQuCX8e5Pt7v8pRfhkyxwZKZMTodAtmvyGZ5d')
{'Pins': ['QmfZY61ukoQuCX8e5Pt7v8pRfhkyxwZKZMTodAtmvyGZ5d']}
Parameters
----------
path : str
Path to object(s) to be unpinned
recursive : bool
Recursively unpin the object linked to by the specified object(s)
Returns
-------
dict : List of IPFS objects that have been unpinned | [
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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 a pinned object from local storage.
Removes the pin from the given object allowing it to be garbage
collected if needed.
.. code-block:: python
>>> c.pin_rm('QmfZY61ukoQuCX8e5Pt7v8pRfhkyxwZKZMTodAtmvyGZ5d')
{'Pins': ['QmfZY61ukoQuCX8e5Pt7v8pRfhkyxwZKZMTodAtmvyGZ5d']}
Parameters
----------
path : str
Path to object(s) to be unpinned
recursive : bool
Recursively unpin the object linked to by the specified object(s)
Returns
-------
dict : List of IPFS objects that have been unpinned
### Response:
def pin_rm(self, path, *paths, **kwargs):
"""Removes a pinned object from local storage.
Removes the pin from the given object allowing it to be garbage
collected if needed.
.. code-block:: python
>>> c.pin_rm('QmfZY61ukoQuCX8e5Pt7v8pRfhkyxwZKZMTodAtmvyGZ5d')
{'Pins': ['QmfZY61ukoQuCX8e5Pt7v8pRfhkyxwZKZMTodAtmvyGZ5d']}
Parameters
----------
path : str
Path to object(s) to be unpinned
recursive : bool
Recursively unpin the object linked to by the specified object(s)
Returns
-------
dict : List of IPFS objects that have been unpinned
"""
#PY2: No support for kw-only parameters after glob parameters
if "recursive" in kwargs:
kwargs.setdefault("opts", {"recursive": kwargs["recursive"]})
del kwargs["recursive"]
args = (path,) + paths
return self._client.request('/pin/rm', args, decoder='json', **kwargs)
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davidfokkema/artist | artist/utils.py | create_graph_name | def create_graph_name(suffix='', dirname=None):
"""Create a graph name using the name of the caller.
:param suffix: optional suffix to add to name
:param dirname: optional directory name
:return: path for the named graph
:rtype: string
"""
if suffix:
suffix = '-%s' % suffix
caller = get_callers_name(level=3)
name = '%s%s%s%s' % (__prefix, caller, suffix, __suffix)
if dirname:
name = os.path.join(dirname, name)
return name | python | def create_graph_name(suffix='', dirname=None):
"""Create a graph name using the name of the caller.
:param suffix: optional suffix to add to name
:param dirname: optional directory name
:return: path for the named graph
:rtype: string
"""
if suffix:
suffix = '-%s' % suffix
caller = get_callers_name(level=3)
name = '%s%s%s%s' % (__prefix, caller, suffix, __suffix)
if dirname:
name = os.path.join(dirname, name)
return 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:
Create a graph name using the name of the caller.
:param suffix: optional suffix to add to name
:param dirname: optional directory name
:return: path for the named graph
:rtype: string
### Response:
def create_graph_name(suffix='', dirname=None):
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:param suffix: optional suffix to add to name
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name = '%s%s%s%s' % (__prefix, caller, suffix, __suffix)
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elifesciences/elife-tools | elifetools/parseJATS.py | version_history | def version_history(soup, html_flag=True):
"extract the article version history details"
convert = lambda xml_string: xml_to_html(html_flag, xml_string)
version_history = []
related_object_tags = raw_parser.related_object(raw_parser.article_meta(soup))
for tag in related_object_tags:
article_version = OrderedDict()
date_tag = first(raw_parser.date(tag))
if date_tag:
copy_attribute(date_tag.attrs, 'date-type', article_version, 'version')
(day, month, year) = ymd(date_tag)
article_version['day'] = day
article_version['month'] = month
article_version['year'] = year
article_version['date'] = date_struct_nn(year, month, day)
copy_attribute(tag.attrs, 'xlink:href', article_version, 'xlink_href')
set_if_value(article_version, "comment",
convert(node_contents_str(first(raw_parser.comment(tag)))))
version_history.append(article_version)
return version_history | python | def version_history(soup, html_flag=True):
"extract the article version history details"
convert = lambda xml_string: xml_to_html(html_flag, xml_string)
version_history = []
related_object_tags = raw_parser.related_object(raw_parser.article_meta(soup))
for tag in related_object_tags:
article_version = OrderedDict()
date_tag = first(raw_parser.date(tag))
if date_tag:
copy_attribute(date_tag.attrs, 'date-type', article_version, 'version')
(day, month, year) = ymd(date_tag)
article_version['day'] = day
article_version['month'] = month
article_version['year'] = year
article_version['date'] = date_struct_nn(year, month, day)
copy_attribute(tag.attrs, 'xlink:href', article_version, 'xlink_href')
set_if_value(article_version, "comment",
convert(node_contents_str(first(raw_parser.comment(tag)))))
version_history.append(article_version)
return version_history | [
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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:
extract the article version history details
### Response:
def version_history(soup, html_flag=True):
"extract the article version history details"
convert = lambda xml_string: xml_to_html(html_flag, xml_string)
version_history = []
related_object_tags = raw_parser.related_object(raw_parser.article_meta(soup))
for tag in related_object_tags:
article_version = OrderedDict()
date_tag = first(raw_parser.date(tag))
if date_tag:
copy_attribute(date_tag.attrs, 'date-type', article_version, 'version')
(day, month, year) = ymd(date_tag)
article_version['day'] = day
article_version['month'] = month
article_version['year'] = year
article_version['date'] = date_struct_nn(year, month, day)
copy_attribute(tag.attrs, 'xlink:href', article_version, 'xlink_href')
set_if_value(article_version, "comment",
convert(node_contents_str(first(raw_parser.comment(tag)))))
version_history.append(article_version)
return version_history
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SBRG/ssbio | ssbio/protein/sequence/properties/scratch.py | SCRATCH.sspro_summary | def sspro_summary(self):
"""Parse the SSpro output file and return a summary of secondary structure composition.
The output file is just a FASTA formatted file, so you can get residue level
information by parsing it like a normal sequence file.
Returns:
dict: Percentage of:
H: helix
E: strand
C: the rest
"""
summary = {}
records = ssbio.protein.sequence.utils.fasta.load_fasta_file(self.out_sspro)
for r in records:
seq_summary = {}
seq_summary['percent_H-sspro'] = r.seq.count('H')/float(len(r))
seq_summary['percent_E-sspro'] = r.seq.count('E')/float(len(r))
seq_summary['percent_C-sspro'] = r.seq.count('C')/float(len(r))
summary[r.id] = seq_summary
return summary | python | def sspro_summary(self):
"""Parse the SSpro output file and return a summary of secondary structure composition.
The output file is just a FASTA formatted file, so you can get residue level
information by parsing it like a normal sequence file.
Returns:
dict: Percentage of:
H: helix
E: strand
C: the rest
"""
summary = {}
records = ssbio.protein.sequence.utils.fasta.load_fasta_file(self.out_sspro)
for r in records:
seq_summary = {}
seq_summary['percent_H-sspro'] = r.seq.count('H')/float(len(r))
seq_summary['percent_E-sspro'] = r.seq.count('E')/float(len(r))
seq_summary['percent_C-sspro'] = r.seq.count('C')/float(len(r))
summary[r.id] = seq_summary
return summary | [
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The output file is just a FASTA formatted file, so you can get residue level
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E: strand
C: the rest | [
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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:
Parse the SSpro output file and return a summary of secondary structure composition.
The output file is just a FASTA formatted file, so you can get residue level
information by parsing it like a normal sequence file.
Returns:
dict: Percentage of:
H: helix
E: strand
C: the rest
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def sspro_summary(self):
"""Parse the SSpro output file and return a summary of secondary structure composition.
The output file is just a FASTA formatted file, so you can get residue level
information by parsing it like a normal sequence file.
Returns:
dict: Percentage of:
H: helix
E: strand
C: the rest
"""
summary = {}
records = ssbio.protein.sequence.utils.fasta.load_fasta_file(self.out_sspro)
for r in records:
seq_summary = {}
seq_summary['percent_H-sspro'] = r.seq.count('H')/float(len(r))
seq_summary['percent_E-sspro'] = r.seq.count('E')/float(len(r))
seq_summary['percent_C-sspro'] = r.seq.count('C')/float(len(r))
summary[r.id] = seq_summary
return summary
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trezor/python-trezor | trezorlib/cosi.py | sign_with_privkey | def sign_with_privkey(
digest: bytes,
privkey: Ed25519PrivateKey,
global_pubkey: Ed25519PublicPoint,
nonce: int,
global_commit: Ed25519PublicPoint,
) -> Ed25519Signature:
"""Create a CoSi signature of `digest` with the supplied private key.
This function needs to know the global public key and global commitment.
"""
h = _ed25519.H(privkey)
a = _ed25519.decodecoord(h)
S = (nonce + _ed25519.Hint(global_commit + global_pubkey + digest) * a) % _ed25519.l
return Ed25519Signature(_ed25519.encodeint(S)) | python | def sign_with_privkey(
digest: bytes,
privkey: Ed25519PrivateKey,
global_pubkey: Ed25519PublicPoint,
nonce: int,
global_commit: Ed25519PublicPoint,
) -> Ed25519Signature:
"""Create a CoSi signature of `digest` with the supplied private key.
This function needs to know the global public key and global commitment.
"""
h = _ed25519.H(privkey)
a = _ed25519.decodecoord(h)
S = (nonce + _ed25519.Hint(global_commit + global_pubkey + digest) * a) % _ed25519.l
return Ed25519Signature(_ed25519.encodeint(S)) | [
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This function needs to know the global public key and global commitment. | [
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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 CoSi signature of `digest` with the supplied private key.
This function needs to know the global public key and global commitment.
### Response:
def sign_with_privkey(
digest: bytes,
privkey: Ed25519PrivateKey,
global_pubkey: Ed25519PublicPoint,
nonce: int,
global_commit: Ed25519PublicPoint,
) -> Ed25519Signature:
"""Create a CoSi signature of `digest` with the supplied private key.
This function needs to know the global public key and global commitment.
"""
h = _ed25519.H(privkey)
a = _ed25519.decodecoord(h)
S = (nonce + _ed25519.Hint(global_commit + global_pubkey + digest) * a) % _ed25519.l
return Ed25519Signature(_ed25519.encodeint(S))
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botswana-harvard/edc-registration | edc_registration/signals.py | update_registered_subject_from_model_on_post_save | def update_registered_subject_from_model_on_post_save(sender, instance, raw, created, using, **kwargs):
"""Updates RegisteredSubject from models using UpdatesOrCreatesRegistrationModelMixin."""
if not raw and not kwargs.get('update_fields'):
try:
instance.registration_update_or_create()
except AttributeError as e:
if 'registration_update_or_create' not in str(e):
raise AttributeError(str(e)) | python | def update_registered_subject_from_model_on_post_save(sender, instance, raw, created, using, **kwargs):
"""Updates RegisteredSubject from models using UpdatesOrCreatesRegistrationModelMixin."""
if not raw and not kwargs.get('update_fields'):
try:
instance.registration_update_or_create()
except AttributeError as e:
if 'registration_update_or_create' not in str(e):
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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:
Updates RegisteredSubject from models using UpdatesOrCreatesRegistrationModelMixin.
### Response:
def update_registered_subject_from_model_on_post_save(sender, instance, raw, created, using, **kwargs):
"""Updates RegisteredSubject from models using UpdatesOrCreatesRegistrationModelMixin."""
if not raw and not kwargs.get('update_fields'):
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instance.registration_update_or_create()
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sigsep/sigsep-mus-eval | museval/metrics.py | _zeropad | def _zeropad(sig, N, axis=0):
"""pads with N zeros at the end of the signal, along given axis"""
# ensures concatenation dimension is the first
sig = np.moveaxis(sig, axis, 0)
# zero pad
out = np.zeros((sig.shape[0] + N,) + sig.shape[1:])
out[:sig.shape[0], ...] = sig
# put back axis in place
out = np.moveaxis(out, 0, axis)
return out | python | def _zeropad(sig, N, axis=0):
"""pads with N zeros at the end of the signal, along given axis"""
# ensures concatenation dimension is the first
sig = np.moveaxis(sig, axis, 0)
# zero pad
out = np.zeros((sig.shape[0] + N,) + sig.shape[1:])
out[:sig.shape[0], ...] = sig
# put back axis in place
out = np.moveaxis(out, 0, axis)
return out | [
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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:
pads with N zeros at the end of the signal, along given axis
### Response:
def _zeropad(sig, N, axis=0):
"""pads with N zeros at the end of the signal, along given axis"""
# ensures concatenation dimension is the first
sig = np.moveaxis(sig, axis, 0)
# zero pad
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# put back axis in place
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return out
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JasonKessler/scattertext | scattertext/TermDocMatrixFactory.py | TermDocMatrixFactory.censor_entity_types | def censor_entity_types(self, entity_types):
# type: (set) -> TermDocMatrixFactory
'''
Entity types to exclude from feature construction. Terms matching
specificed entities, instead of labeled by their lower case orthographic
form or lemma, will be labeled by their entity type.
Parameters
----------
entity_types : set of entity types outputted by spaCy
'TIME', 'WORK_OF_ART', 'PERSON', 'MONEY', 'ORG', 'ORDINAL', 'DATE',
'CARDINAL', 'LAW', 'QUANTITY', 'GPE', 'PERCENT'
Returns
---------
self
'''
assert type(entity_types) == set
self._entity_types_to_censor = entity_types
self._feats_from_spacy_doc = FeatsFromSpacyDoc(
use_lemmas=self._use_lemmas,
entity_types_to_censor=self._entity_types_to_censor
)
return self | python | def censor_entity_types(self, entity_types):
# type: (set) -> TermDocMatrixFactory
'''
Entity types to exclude from feature construction. Terms matching
specificed entities, instead of labeled by their lower case orthographic
form or lemma, will be labeled by their entity type.
Parameters
----------
entity_types : set of entity types outputted by spaCy
'TIME', 'WORK_OF_ART', 'PERSON', 'MONEY', 'ORG', 'ORDINAL', 'DATE',
'CARDINAL', 'LAW', 'QUANTITY', 'GPE', 'PERCENT'
Returns
---------
self
'''
assert type(entity_types) == set
self._entity_types_to_censor = entity_types
self._feats_from_spacy_doc = FeatsFromSpacyDoc(
use_lemmas=self._use_lemmas,
entity_types_to_censor=self._entity_types_to_censor
)
return self | [
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'TIME', 'WORK_OF_ART', 'PERSON', 'MONEY', 'ORG', 'ORDINAL', 'DATE',
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Returns
---------
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:
Entity types to exclude from feature construction. Terms matching
specificed entities, instead of labeled by their lower case orthographic
form or lemma, will be labeled by their entity type.
Parameters
----------
entity_types : set of entity types outputted by spaCy
'TIME', 'WORK_OF_ART', 'PERSON', 'MONEY', 'ORG', 'ORDINAL', 'DATE',
'CARDINAL', 'LAW', 'QUANTITY', 'GPE', 'PERCENT'
Returns
---------
self
### Response:
def censor_entity_types(self, entity_types):
# type: (set) -> TermDocMatrixFactory
'''
Entity types to exclude from feature construction. Terms matching
specificed entities, instead of labeled by their lower case orthographic
form or lemma, will be labeled by their entity type.
Parameters
----------
entity_types : set of entity types outputted by spaCy
'TIME', 'WORK_OF_ART', 'PERSON', 'MONEY', 'ORG', 'ORDINAL', 'DATE',
'CARDINAL', 'LAW', 'QUANTITY', 'GPE', 'PERCENT'
Returns
---------
self
'''
assert type(entity_types) == set
self._entity_types_to_censor = entity_types
self._feats_from_spacy_doc = FeatsFromSpacyDoc(
use_lemmas=self._use_lemmas,
entity_types_to_censor=self._entity_types_to_censor
)
return self
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erikrose/more-itertools | more_itertools/more.py | map_reduce | def map_reduce(iterable, keyfunc, valuefunc=None, reducefunc=None):
"""Return a dictionary that maps the items in *iterable* to categories
defined by *keyfunc*, transforms them with *valuefunc*, and
then summarizes them by category with *reducefunc*.
*valuefunc* defaults to the identity function if it is unspecified.
If *reducefunc* is unspecified, no summarization takes place:
>>> keyfunc = lambda x: x.upper()
>>> result = map_reduce('abbccc', keyfunc)
>>> sorted(result.items())
[('A', ['a']), ('B', ['b', 'b']), ('C', ['c', 'c', 'c'])]
Specifying *valuefunc* transforms the categorized items:
>>> keyfunc = lambda x: x.upper()
>>> valuefunc = lambda x: 1
>>> result = map_reduce('abbccc', keyfunc, valuefunc)
>>> sorted(result.items())
[('A', [1]), ('B', [1, 1]), ('C', [1, 1, 1])]
Specifying *reducefunc* summarizes the categorized items:
>>> keyfunc = lambda x: x.upper()
>>> valuefunc = lambda x: 1
>>> reducefunc = sum
>>> result = map_reduce('abbccc', keyfunc, valuefunc, reducefunc)
>>> sorted(result.items())
[('A', 1), ('B', 2), ('C', 3)]
You may want to filter the input iterable before applying the map/reduce
procedure:
>>> all_items = range(30)
>>> items = [x for x in all_items if 10 <= x <= 20] # Filter
>>> keyfunc = lambda x: x % 2 # Evens map to 0; odds to 1
>>> categories = map_reduce(items, keyfunc=keyfunc)
>>> sorted(categories.items())
[(0, [10, 12, 14, 16, 18, 20]), (1, [11, 13, 15, 17, 19])]
>>> summaries = map_reduce(items, keyfunc=keyfunc, reducefunc=sum)
>>> sorted(summaries.items())
[(0, 90), (1, 75)]
Note that all items in the iterable are gathered into a list before the
summarization step, which may require significant storage.
The returned object is a :obj:`collections.defaultdict` with the
``default_factory`` set to ``None``, such that it behaves like a normal
dictionary.
"""
valuefunc = (lambda x: x) if (valuefunc is None) else valuefunc
ret = defaultdict(list)
for item in iterable:
key = keyfunc(item)
value = valuefunc(item)
ret[key].append(value)
if reducefunc is not None:
for key, value_list in ret.items():
ret[key] = reducefunc(value_list)
ret.default_factory = None
return ret | python | def map_reduce(iterable, keyfunc, valuefunc=None, reducefunc=None):
"""Return a dictionary that maps the items in *iterable* to categories
defined by *keyfunc*, transforms them with *valuefunc*, and
then summarizes them by category with *reducefunc*.
*valuefunc* defaults to the identity function if it is unspecified.
If *reducefunc* is unspecified, no summarization takes place:
>>> keyfunc = lambda x: x.upper()
>>> result = map_reduce('abbccc', keyfunc)
>>> sorted(result.items())
[('A', ['a']), ('B', ['b', 'b']), ('C', ['c', 'c', 'c'])]
Specifying *valuefunc* transforms the categorized items:
>>> keyfunc = lambda x: x.upper()
>>> valuefunc = lambda x: 1
>>> result = map_reduce('abbccc', keyfunc, valuefunc)
>>> sorted(result.items())
[('A', [1]), ('B', [1, 1]), ('C', [1, 1, 1])]
Specifying *reducefunc* summarizes the categorized items:
>>> keyfunc = lambda x: x.upper()
>>> valuefunc = lambda x: 1
>>> reducefunc = sum
>>> result = map_reduce('abbccc', keyfunc, valuefunc, reducefunc)
>>> sorted(result.items())
[('A', 1), ('B', 2), ('C', 3)]
You may want to filter the input iterable before applying the map/reduce
procedure:
>>> all_items = range(30)
>>> items = [x for x in all_items if 10 <= x <= 20] # Filter
>>> keyfunc = lambda x: x % 2 # Evens map to 0; odds to 1
>>> categories = map_reduce(items, keyfunc=keyfunc)
>>> sorted(categories.items())
[(0, [10, 12, 14, 16, 18, 20]), (1, [11, 13, 15, 17, 19])]
>>> summaries = map_reduce(items, keyfunc=keyfunc, reducefunc=sum)
>>> sorted(summaries.items())
[(0, 90), (1, 75)]
Note that all items in the iterable are gathered into a list before the
summarization step, which may require significant storage.
The returned object is a :obj:`collections.defaultdict` with the
``default_factory`` set to ``None``, such that it behaves like a normal
dictionary.
"""
valuefunc = (lambda x: x) if (valuefunc is None) else valuefunc
ret = defaultdict(list)
for item in iterable:
key = keyfunc(item)
value = valuefunc(item)
ret[key].append(value)
if reducefunc is not None:
for key, value_list in ret.items():
ret[key] = reducefunc(value_list)
ret.default_factory = None
return ret | [
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*valuefunc* defaults to the identity function if it is unspecified.
If *reducefunc* is unspecified, no summarization takes place:
>>> keyfunc = lambda x: x.upper()
>>> result = map_reduce('abbccc', keyfunc)
>>> sorted(result.items())
[('A', ['a']), ('B', ['b', 'b']), ('C', ['c', 'c', 'c'])]
Specifying *valuefunc* transforms the categorized items:
>>> keyfunc = lambda x: x.upper()
>>> valuefunc = lambda x: 1
>>> result = map_reduce('abbccc', keyfunc, valuefunc)
>>> sorted(result.items())
[('A', [1]), ('B', [1, 1]), ('C', [1, 1, 1])]
Specifying *reducefunc* summarizes the categorized items:
>>> keyfunc = lambda x: x.upper()
>>> valuefunc = lambda x: 1
>>> reducefunc = sum
>>> result = map_reduce('abbccc', keyfunc, valuefunc, reducefunc)
>>> sorted(result.items())
[('A', 1), ('B', 2), ('C', 3)]
You may want to filter the input iterable before applying the map/reduce
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>>> all_items = range(30)
>>> items = [x for x in all_items if 10 <= x <= 20] # Filter
>>> keyfunc = lambda x: x % 2 # Evens map to 0; odds to 1
>>> categories = map_reduce(items, keyfunc=keyfunc)
>>> sorted(categories.items())
[(0, [10, 12, 14, 16, 18, 20]), (1, [11, 13, 15, 17, 19])]
>>> summaries = map_reduce(items, keyfunc=keyfunc, reducefunc=sum)
>>> sorted(summaries.items())
[(0, 90), (1, 75)]
Note that all items in the iterable are gathered into a list before the
summarization step, which may require significant storage.
The returned object is a :obj:`collections.defaultdict` with the
``default_factory`` set to ``None``, such that it behaves like a normal
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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 dictionary that maps the items in *iterable* to categories
defined by *keyfunc*, transforms them with *valuefunc*, and
then summarizes them by category with *reducefunc*.
*valuefunc* defaults to the identity function if it is unspecified.
If *reducefunc* is unspecified, no summarization takes place:
>>> keyfunc = lambda x: x.upper()
>>> result = map_reduce('abbccc', keyfunc)
>>> sorted(result.items())
[('A', ['a']), ('B', ['b', 'b']), ('C', ['c', 'c', 'c'])]
Specifying *valuefunc* transforms the categorized items:
>>> keyfunc = lambda x: x.upper()
>>> valuefunc = lambda x: 1
>>> result = map_reduce('abbccc', keyfunc, valuefunc)
>>> sorted(result.items())
[('A', [1]), ('B', [1, 1]), ('C', [1, 1, 1])]
Specifying *reducefunc* summarizes the categorized items:
>>> keyfunc = lambda x: x.upper()
>>> valuefunc = lambda x: 1
>>> reducefunc = sum
>>> result = map_reduce('abbccc', keyfunc, valuefunc, reducefunc)
>>> sorted(result.items())
[('A', 1), ('B', 2), ('C', 3)]
You may want to filter the input iterable before applying the map/reduce
procedure:
>>> all_items = range(30)
>>> items = [x for x in all_items if 10 <= x <= 20] # Filter
>>> keyfunc = lambda x: x % 2 # Evens map to 0; odds to 1
>>> categories = map_reduce(items, keyfunc=keyfunc)
>>> sorted(categories.items())
[(0, [10, 12, 14, 16, 18, 20]), (1, [11, 13, 15, 17, 19])]
>>> summaries = map_reduce(items, keyfunc=keyfunc, reducefunc=sum)
>>> sorted(summaries.items())
[(0, 90), (1, 75)]
Note that all items in the iterable are gathered into a list before the
summarization step, which may require significant storage.
The returned object is a :obj:`collections.defaultdict` with the
``default_factory`` set to ``None``, such that it behaves like a normal
dictionary.
### Response:
def map_reduce(iterable, keyfunc, valuefunc=None, reducefunc=None):
"""Return a dictionary that maps the items in *iterable* to categories
defined by *keyfunc*, transforms them with *valuefunc*, and
then summarizes them by category with *reducefunc*.
*valuefunc* defaults to the identity function if it is unspecified.
If *reducefunc* is unspecified, no summarization takes place:
>>> keyfunc = lambda x: x.upper()
>>> result = map_reduce('abbccc', keyfunc)
>>> sorted(result.items())
[('A', ['a']), ('B', ['b', 'b']), ('C', ['c', 'c', 'c'])]
Specifying *valuefunc* transforms the categorized items:
>>> keyfunc = lambda x: x.upper()
>>> valuefunc = lambda x: 1
>>> result = map_reduce('abbccc', keyfunc, valuefunc)
>>> sorted(result.items())
[('A', [1]), ('B', [1, 1]), ('C', [1, 1, 1])]
Specifying *reducefunc* summarizes the categorized items:
>>> keyfunc = lambda x: x.upper()
>>> valuefunc = lambda x: 1
>>> reducefunc = sum
>>> result = map_reduce('abbccc', keyfunc, valuefunc, reducefunc)
>>> sorted(result.items())
[('A', 1), ('B', 2), ('C', 3)]
You may want to filter the input iterable before applying the map/reduce
procedure:
>>> all_items = range(30)
>>> items = [x for x in all_items if 10 <= x <= 20] # Filter
>>> keyfunc = lambda x: x % 2 # Evens map to 0; odds to 1
>>> categories = map_reduce(items, keyfunc=keyfunc)
>>> sorted(categories.items())
[(0, [10, 12, 14, 16, 18, 20]), (1, [11, 13, 15, 17, 19])]
>>> summaries = map_reduce(items, keyfunc=keyfunc, reducefunc=sum)
>>> sorted(summaries.items())
[(0, 90), (1, 75)]
Note that all items in the iterable are gathered into a list before the
summarization step, which may require significant storage.
The returned object is a :obj:`collections.defaultdict` with the
``default_factory`` set to ``None``, such that it behaves like a normal
dictionary.
"""
valuefunc = (lambda x: x) if (valuefunc is None) else valuefunc
ret = defaultdict(list)
for item in iterable:
key = keyfunc(item)
value = valuefunc(item)
ret[key].append(value)
if reducefunc is not None:
for key, value_list in ret.items():
ret[key] = reducefunc(value_list)
ret.default_factory = None
return ret
<|endoftext|> | [
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ikegami-yukino/jaconv | jaconv/jaconv.py | normalize | def normalize(text, mode='NFKC', ignore=''):
"""Convert Half-width (Hankaku) Katakana to Full-width (Zenkaku) Katakana,
Full-width (Zenkaku) ASCII and DIGIT to Half-width (Hankaku) ASCII
and DIGIT.
Additionally, Full-width wave dash (〜) etc. are normalized
Parameters
----------
text : str
Source string.
mode : str
Unicode normalization mode.
ignore : str
Characters to be ignored in converting.
Return
------
str
Normalized string.
Examples
--------
>>> print(jaconv.normalize('ﾃｨﾛ･フィナ〜レ', 'NFKC'))
ティロ・フィナーレ
"""
text = text.replace('〜', 'ー').replace('～', 'ー')
text = text.replace("’", "'").replace('”', '"').replace('“', '``')
text = text.replace('―', '-').replace('‐', '-').replace('˗', '-').replace('֊', '-')
text = text.replace('‐', '-').replace('‑', '-').replace('‒', '-').replace('–', '-')
text = text.replace('⁃', '-').replace('⁻', '-').replace('₋', '-').replace('−', '-')
text = text.replace('﹣', 'ー').replace('－', 'ー').replace('—', 'ー').replace('―', 'ー')
text = text.replace('━', 'ー').replace('─', 'ー')
return unicodedata.normalize(mode, text) | python | def normalize(text, mode='NFKC', ignore=''):
"""Convert Half-width (Hankaku) Katakana to Full-width (Zenkaku) Katakana,
Full-width (Zenkaku) ASCII and DIGIT to Half-width (Hankaku) ASCII
and DIGIT.
Additionally, Full-width wave dash (〜) etc. are normalized
Parameters
----------
text : str
Source string.
mode : str
Unicode normalization mode.
ignore : str
Characters to be ignored in converting.
Return
------
str
Normalized string.
Examples
--------
>>> print(jaconv.normalize('ﾃｨﾛ･フィナ〜レ', 'NFKC'))
ティロ・フィナーレ
"""
text = text.replace('〜', 'ー').replace('～', 'ー')
text = text.replace("’", "'").replace('”', '"').replace('“', '``')
text = text.replace('―', '-').replace('‐', '-').replace('˗', '-').replace('֊', '-')
text = text.replace('‐', '-').replace('‑', '-').replace('‒', '-').replace('–', '-')
text = text.replace('⁃', '-').replace('⁻', '-').replace('₋', '-').replace('−', '-')
text = text.replace('﹣', 'ー').replace('－', 'ー').replace('—', 'ー').replace('―', 'ー')
text = text.replace('━', 'ー').replace('─', 'ー')
return unicodedata.normalize(mode, text) | [
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Full-width (Zenkaku) ASCII and DIGIT to Half-width (Hankaku) ASCII
and DIGIT.
Additionally, Full-width wave dash (〜) etc. are normalized
Parameters
----------
text : str
Source string.
mode : str
Unicode normalization mode.
ignore : str
Characters to be ignored in converting.
Return
------
str
Normalized string.
Examples
--------
>>> print(jaconv.normalize('ﾃｨﾛ･フィナ〜レ', 'NFKC'))
ティロ・フィナーレ | [
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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:
Convert Half-width (Hankaku) Katakana to Full-width (Zenkaku) Katakana,
Full-width (Zenkaku) ASCII and DIGIT to Half-width (Hankaku) ASCII
and DIGIT.
Additionally, Full-width wave dash (〜) etc. are normalized
Parameters
----------
text : str
Source string.
mode : str
Unicode normalization mode.
ignore : str
Characters to be ignored in converting.
Return
------
str
Normalized string.
Examples
--------
>>> print(jaconv.normalize('ﾃｨﾛ･フィナ〜レ', 'NFKC'))
ティロ・フィナーレ
### Response:
def normalize(text, mode='NFKC', ignore=''):
"""Convert Half-width (Hankaku) Katakana to Full-width (Zenkaku) Katakana,
Full-width (Zenkaku) ASCII and DIGIT to Half-width (Hankaku) ASCII
and DIGIT.
Additionally, Full-width wave dash (〜) etc. are normalized
Parameters
----------
text : str
Source string.
mode : str
Unicode normalization mode.
ignore : str
Characters to be ignored in converting.
Return
------
str
Normalized string.
Examples
--------
>>> print(jaconv.normalize('ﾃｨﾛ･フィナ〜レ', 'NFKC'))
ティロ・フィナーレ
"""
text = text.replace('〜', 'ー').replace('～', 'ー')
text = text.replace("’", "'").replace('”', '"').replace('“', '``')
text = text.replace('―', '-').replace('‐', '-').replace('˗', '-').replace('֊', '-')
text = text.replace('‐', '-').replace('‑', '-').replace('‒', '-').replace('–', '-')
text = text.replace('⁃', '-').replace('⁻', '-').replace('₋', '-').replace('−', '-')
text = text.replace('﹣', 'ー').replace('－', 'ー').replace('—', 'ー').replace('―', 'ー')
text = text.replace('━', 'ー').replace('─', 'ー')
return unicodedata.normalize(mode, text)
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pytroll/satpy | satpy/readers/goes_imager_nc.py | GOESNCFileHandler.calibrate | def calibrate(self, counts, calibration, channel):
"""Perform calibration"""
# Convert 16bit counts from netCDF4 file to the original 10bit
# GVAR counts by dividing by 32. See [FAQ].
counts = counts / 32.
coefs = CALIB_COEFS[self.platform_name][channel]
if calibration == 'counts':
return counts
elif calibration in ['radiance', 'reflectance',
'brightness_temperature']:
radiance = self._counts2radiance(counts=counts, coefs=coefs,
channel=channel)
if calibration == 'radiance':
return radiance
return self._calibrate(radiance=radiance, coefs=coefs,
channel=channel, calibration=calibration)
else:
raise ValueError('Unsupported calibration for channel {}: {}'
.format(channel, calibration)) | python | def calibrate(self, counts, calibration, channel):
"""Perform calibration"""
# Convert 16bit counts from netCDF4 file to the original 10bit
# GVAR counts by dividing by 32. See [FAQ].
counts = counts / 32.
coefs = CALIB_COEFS[self.platform_name][channel]
if calibration == 'counts':
return counts
elif calibration in ['radiance', 'reflectance',
'brightness_temperature']:
radiance = self._counts2radiance(counts=counts, coefs=coefs,
channel=channel)
if calibration == 'radiance':
return radiance
return self._calibrate(radiance=radiance, coefs=coefs,
channel=channel, calibration=calibration)
else:
raise ValueError('Unsupported calibration for channel {}: {}'
.format(channel, calibration)) | [
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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:
Perform calibration
### Response:
def calibrate(self, counts, calibration, channel):
"""Perform calibration"""
# Convert 16bit counts from netCDF4 file to the original 10bit
# GVAR counts by dividing by 32. See [FAQ].
counts = counts / 32.
coefs = CALIB_COEFS[self.platform_name][channel]
if calibration == 'counts':
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return radiance
return self._calibrate(radiance=radiance, coefs=coefs,
channel=channel, calibration=calibration)
else:
raise ValueError('Unsupported calibration for channel {}: {}'
.format(channel, calibration))
<|endoftext|> | [
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"""
Load the prefix key binding.
"""
pymux = self.pymux
# Remove previous binding.
if self._prefix_binding:
self.custom_key_bindings.remove_binding(self._prefix_binding)
# Create new Python binding.
@self.custom_key_bindings.add(*self._prefix, filter=
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def enter_prefix_handler(event):
" Enter prefix mode. "
pymux.get_client_state().has_prefix = True
self._prefix_binding = enter_prefix_handler | python | def _load_prefix_binding(self):
"""
Load the prefix key binding.
"""
pymux = self.pymux
# Remove previous binding.
if self._prefix_binding:
self.custom_key_bindings.remove_binding(self._prefix_binding)
# Create new Python binding.
@self.custom_key_bindings.add(*self._prefix, filter=
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WaitsForConfirmation(pymux)))
def enter_prefix_handler(event):
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pymux.get_client_state().has_prefix = True
self._prefix_binding = enter_prefix_handler | [
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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:
Load the prefix key binding.
### Response:
def _load_prefix_binding(self):
"""
Load the prefix key binding.
"""
pymux = self.pymux
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if self._prefix_binding:
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happyleavesaoc/aoc-mgz | mgz/enums.py | GameActionModeEnum | def GameActionModeEnum(ctx):
"""Game Action Modes."""
return Enum(
ctx,
diplomacy=0,
speed=1,
instant_build=2,
quick_build=4,
allied_victory=5,
cheat=6,
unk0=9,
spy=10,
unk1=11,
farm_queue=13,
farm_unqueue=14,
default=Pass
) | python | def GameActionModeEnum(ctx):
"""Game Action Modes."""
return Enum(
ctx,
diplomacy=0,
speed=1,
instant_build=2,
quick_build=4,
allied_victory=5,
cheat=6,
unk0=9,
spy=10,
unk1=11,
farm_queue=13,
farm_unqueue=14,
default=Pass
) | [
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] | train | https://github.com/happyleavesaoc/aoc-mgz/blob/13fc379cc062d7640bfa028eed9c0d45d37a7b2b/mgz/enums.py#L250-L266 | <|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:
Game Action Modes.
### Response:
def GameActionModeEnum(ctx):
"""Game Action Modes."""
return Enum(
ctx,
diplomacy=0,
speed=1,
instant_build=2,
quick_build=4,
allied_victory=5,
cheat=6,
unk0=9,
spy=10,
unk1=11,
farm_queue=13,
farm_unqueue=14,
default=Pass
)
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DarkEnergySurvey/ugali | ugali/utils/config.py | Config._createFilenames | def _createFilenames(self):
"""
Create a masked records array of all filenames for the given set of
pixels and store the existence of those files in the mask values.
Parameters:
-----------
None
Returns:
--------
recarray : pixels and mask value
"""
nside_catalog = self['coords']['nside_catalog']
npix = hp.nside2npix(nside_catalog)
pixels = np.arange(npix)
catalog_dir = self['catalog']['dirname']
catalog_base = self['catalog']['basename']
catalog_path = os.path.join(catalog_dir,catalog_base)
mask_dir = self['mask']['dirname']
mask_base_1 = self['mask']['basename_1']
mask_base_2 = self['mask']['basename_2']
mask_path_1 = os.path.join(mask_dir,mask_base_1)
mask_path_2 = os.path.join(mask_dir,mask_base_2)
data = np.ma.empty(npix,dtype=[('pix',int), ('catalog',object),
('mask_1',object), ('mask_2',object)])
mask = np.ma.empty(npix,dtype=[('pix',bool), ('catalog',bool),
('mask_1',bool), ('mask_2',bool)])
# Build the filenames
data['pix'] = pixels
data['catalog'] = np.char.mod(catalog_path,pixels)
data['mask_1'] = np.char.mod(mask_path_1,pixels)
data['mask_2'] = np.char.mod(mask_path_2,pixels)
# Build the mask of existing files using glob
mask['catalog'] = ~np.in1d(data['catalog'],glob.glob(catalog_dir+'/*'))
mask['mask_1'] = ~np.in1d(data['mask_1'],glob.glob(mask_dir+'/*'))
mask['mask_2'] = ~np.in1d(data['mask_2'],glob.glob(mask_dir+'/*'))
for name in ['catalog','mask_1','mask_2']:
if np.all(mask[name]): logger.warn("All '%s' files masked"%name)
# mask 'pix' if all files not present
mask['pix'] = mask['catalog'] | mask['mask_1'] | mask['mask_2']
if np.all(mask['pix']): logger.warn("All pixels masked")
return np.ma.MaskedArray(data, mask, fill_value=[-1,'','','']) | python | def _createFilenames(self):
"""
Create a masked records array of all filenames for the given set of
pixels and store the existence of those files in the mask values.
Parameters:
-----------
None
Returns:
--------
recarray : pixels and mask value
"""
nside_catalog = self['coords']['nside_catalog']
npix = hp.nside2npix(nside_catalog)
pixels = np.arange(npix)
catalog_dir = self['catalog']['dirname']
catalog_base = self['catalog']['basename']
catalog_path = os.path.join(catalog_dir,catalog_base)
mask_dir = self['mask']['dirname']
mask_base_1 = self['mask']['basename_1']
mask_base_2 = self['mask']['basename_2']
mask_path_1 = os.path.join(mask_dir,mask_base_1)
mask_path_2 = os.path.join(mask_dir,mask_base_2)
data = np.ma.empty(npix,dtype=[('pix',int), ('catalog',object),
('mask_1',object), ('mask_2',object)])
mask = np.ma.empty(npix,dtype=[('pix',bool), ('catalog',bool),
('mask_1',bool), ('mask_2',bool)])
# Build the filenames
data['pix'] = pixels
data['catalog'] = np.char.mod(catalog_path,pixels)
data['mask_1'] = np.char.mod(mask_path_1,pixels)
data['mask_2'] = np.char.mod(mask_path_2,pixels)
# Build the mask of existing files using glob
mask['catalog'] = ~np.in1d(data['catalog'],glob.glob(catalog_dir+'/*'))
mask['mask_1'] = ~np.in1d(data['mask_1'],glob.glob(mask_dir+'/*'))
mask['mask_2'] = ~np.in1d(data['mask_2'],glob.glob(mask_dir+'/*'))
for name in ['catalog','mask_1','mask_2']:
if np.all(mask[name]): logger.warn("All '%s' files masked"%name)
# mask 'pix' if all files not present
mask['pix'] = mask['catalog'] | mask['mask_1'] | mask['mask_2']
if np.all(mask['pix']): logger.warn("All pixels masked")
return np.ma.MaskedArray(data, mask, fill_value=[-1,'','','']) | [
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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 masked records array of all filenames for the given set of
pixels and store the existence of those files in the mask values.
Parameters:
-----------
None
Returns:
--------
recarray : pixels and mask value
### Response:
def _createFilenames(self):
"""
Create a masked records array of all filenames for the given set of
pixels and store the existence of those files in the mask values.
Parameters:
-----------
None
Returns:
--------
recarray : pixels and mask value
"""
nside_catalog = self['coords']['nside_catalog']
npix = hp.nside2npix(nside_catalog)
pixels = np.arange(npix)
catalog_dir = self['catalog']['dirname']
catalog_base = self['catalog']['basename']
catalog_path = os.path.join(catalog_dir,catalog_base)
mask_dir = self['mask']['dirname']
mask_base_1 = self['mask']['basename_1']
mask_base_2 = self['mask']['basename_2']
mask_path_1 = os.path.join(mask_dir,mask_base_1)
mask_path_2 = os.path.join(mask_dir,mask_base_2)
data = np.ma.empty(npix,dtype=[('pix',int), ('catalog',object),
('mask_1',object), ('mask_2',object)])
mask = np.ma.empty(npix,dtype=[('pix',bool), ('catalog',bool),
('mask_1',bool), ('mask_2',bool)])
# Build the filenames
data['pix'] = pixels
data['catalog'] = np.char.mod(catalog_path,pixels)
data['mask_1'] = np.char.mod(mask_path_1,pixels)
data['mask_2'] = np.char.mod(mask_path_2,pixels)
# Build the mask of existing files using glob
mask['catalog'] = ~np.in1d(data['catalog'],glob.glob(catalog_dir+'/*'))
mask['mask_1'] = ~np.in1d(data['mask_1'],glob.glob(mask_dir+'/*'))
mask['mask_2'] = ~np.in1d(data['mask_2'],glob.glob(mask_dir+'/*'))
for name in ['catalog','mask_1','mask_2']:
if np.all(mask[name]): logger.warn("All '%s' files masked"%name)
# mask 'pix' if all files not present
mask['pix'] = mask['catalog'] | mask['mask_1'] | mask['mask_2']
if np.all(mask['pix']): logger.warn("All pixels masked")
return np.ma.MaskedArray(data, mask, fill_value=[-1,'','',''])
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uber/tchannel-python | tchannel/tornado/message_factory.py | MessageFactory.build | def build(self, message):
"""buffer all the streaming messages based on the
message id. Reconstruct all fragments together.
:param message:
incoming message
:return: next complete message or None if streaming
is not done
"""
context = None
if message.message_type in [Types.CALL_REQ,
Types.CALL_RES]:
self.verify_message(message)
context = self.build_context(message)
# streaming message
if message.flags == common.FlagsType.fragment:
self.message_buffer[message.id] = context
# find the incompleted stream
num = 0
for i, arg in enumerate(context.argstreams):
if arg.state != StreamState.completed:
num = i
break
self.close_argstream(context, num)
return context
elif message.message_type in [Types.CALL_REQ_CONTINUE,
Types.CALL_RES_CONTINUE]:
context = self.message_buffer.get(message.id)
if context is None:
# missing call msg before continue msg
raise FatalProtocolError(
"missing call message after receiving continue message",
message.id,
)
# find the incompleted stream
dst = 0
for i, arg in enumerate(context.argstreams):
if arg.state != StreamState.completed:
dst = i
break
try:
self.verify_message(message)
except InvalidChecksumError as e:
context.argstreams[dst].set_exception(e)
raise
src = 0
while src < len(message.args):
context.argstreams[dst].write(message.args[src])
dst += 1
src += 1
if message.flags != FlagsType.fragment:
# get last fragment. mark it as completed
assert (len(context.argstreams) ==
CallContinueMessage.max_args_num)
self.message_buffer.pop(message.id, None)
context.flags = FlagsType.none
self.close_argstream(context, dst - 1)
return None
elif message.message_type == Types.ERROR:
context = self.message_buffer.pop(message.id, None)
if context is None:
log.info('Unconsumed error %s', message)
return None
else:
error = TChannelError.from_code(
message.code,
description=message.description,
tracing=context.tracing,
)
context.set_exception(error)
return error
else:
return message | python | def build(self, message):
"""buffer all the streaming messages based on the
message id. Reconstruct all fragments together.
:param message:
incoming message
:return: next complete message or None if streaming
is not done
"""
context = None
if message.message_type in [Types.CALL_REQ,
Types.CALL_RES]:
self.verify_message(message)
context = self.build_context(message)
# streaming message
if message.flags == common.FlagsType.fragment:
self.message_buffer[message.id] = context
# find the incompleted stream
num = 0
for i, arg in enumerate(context.argstreams):
if arg.state != StreamState.completed:
num = i
break
self.close_argstream(context, num)
return context
elif message.message_type in [Types.CALL_REQ_CONTINUE,
Types.CALL_RES_CONTINUE]:
context = self.message_buffer.get(message.id)
if context is None:
# missing call msg before continue msg
raise FatalProtocolError(
"missing call message after receiving continue message",
message.id,
)
# find the incompleted stream
dst = 0
for i, arg in enumerate(context.argstreams):
if arg.state != StreamState.completed:
dst = i
break
try:
self.verify_message(message)
except InvalidChecksumError as e:
context.argstreams[dst].set_exception(e)
raise
src = 0
while src < len(message.args):
context.argstreams[dst].write(message.args[src])
dst += 1
src += 1
if message.flags != FlagsType.fragment:
# get last fragment. mark it as completed
assert (len(context.argstreams) ==
CallContinueMessage.max_args_num)
self.message_buffer.pop(message.id, None)
context.flags = FlagsType.none
self.close_argstream(context, dst - 1)
return None
elif message.message_type == Types.ERROR:
context = self.message_buffer.pop(message.id, None)
if context is None:
log.info('Unconsumed error %s', message)
return None
else:
error = TChannelError.from_code(
message.code,
description=message.description,
tracing=context.tracing,
)
context.set_exception(error)
return error
else:
return message | [
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message id. Reconstruct all fragments together.
:param message:
incoming message
:return: next complete message or None if streaming
is not done | [
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] | train | https://github.com/uber/tchannel-python/blob/ee08cce6234f24fd2373774988186dd374306c43/tchannel/tornado/message_factory.py#L226-L309 | <|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:
buffer all the streaming messages based on the
message id. Reconstruct all fragments together.
:param message:
incoming message
:return: next complete message or None if streaming
is not done
### Response:
def build(self, message):
"""buffer all the streaming messages based on the
message id. Reconstruct all fragments together.
:param message:
incoming message
:return: next complete message or None if streaming
is not done
"""
context = None
if message.message_type in [Types.CALL_REQ,
Types.CALL_RES]:
self.verify_message(message)
context = self.build_context(message)
# streaming message
if message.flags == common.FlagsType.fragment:
self.message_buffer[message.id] = context
# find the incompleted stream
num = 0
for i, arg in enumerate(context.argstreams):
if arg.state != StreamState.completed:
num = i
break
self.close_argstream(context, num)
return context
elif message.message_type in [Types.CALL_REQ_CONTINUE,
Types.CALL_RES_CONTINUE]:
context = self.message_buffer.get(message.id)
if context is None:
# missing call msg before continue msg
raise FatalProtocolError(
"missing call message after receiving continue message",
message.id,
)
# find the incompleted stream
dst = 0
for i, arg in enumerate(context.argstreams):
if arg.state != StreamState.completed:
dst = i
break
try:
self.verify_message(message)
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raise
src = 0
while src < len(message.args):
context.argstreams[dst].write(message.args[src])
dst += 1
src += 1
if message.flags != FlagsType.fragment:
# get last fragment. mark it as completed
assert (len(context.argstreams) ==
CallContinueMessage.max_args_num)
self.message_buffer.pop(message.id, None)
context.flags = FlagsType.none
self.close_argstream(context, dst - 1)
return None
elif message.message_type == Types.ERROR:
context = self.message_buffer.pop(message.id, None)
if context is None:
log.info('Unconsumed error %s', message)
return None
else:
error = TChannelError.from_code(
message.code,
description=message.description,
tracing=context.tracing,
)
context.set_exception(error)
return error
else:
return message
<|endoftext|> | [
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geertj/gruvi | lib/gruvi/http.py | HttpRequest.end_request | def end_request(self):
"""End the request body."""
if not self._chunked:
return
trailers = [(n, get_header(self._headers, n)) for n in self._trailer] \
if self._trailer else None
ending = create_chunked_body_end(trailers)
self._protocol.writer.write(ending) | python | def end_request(self):
"""End the request body."""
if not self._chunked:
return
trailers = [(n, get_header(self._headers, n)) for n in self._trailer] \
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ending = create_chunked_body_end(trailers)
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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:
End the request body.
### Response:
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trailers = [(n, get_header(self._headers, n)) for n in self._trailer] \
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