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zeromake/aiko | aiko/request.py | Request.fresh | def fresh(self) -> bool:
"""
检查请求缓存是否“新鲜”，也就是内容没有改变。
此方法用于 If-None-Match / ETag, 和 If-Modified-Since 和 Last-Modified 之间的缓存协商。
在设置一个或多个这些响应头后应该引用它。
"""
method_str = self.method
if method_str != 'GET' and method_str != 'HEAD':
return False
s = self.ctx.status
if (s >= 200 and s < 300) or s == 304:
return fresh(
self.headers,
(self.response and self.response.headers) or {},
)
return False | python | def fresh(self) -> bool:
"""
检查请求缓存是否“新鲜”，也就是内容没有改变。
此方法用于 If-None-Match / ETag, 和 If-Modified-Since 和 Last-Modified 之间的缓存协商。
在设置一个或多个这些响应头后应该引用它。
"""
method_str = self.method
if method_str != 'GET' and method_str != 'HEAD':
return False
s = self.ctx.status
if (s >= 200 and s < 300) or s == 304:
return fresh(
self.headers,
(self.response and self.response.headers) or {},
)
return False | [
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在设置一个或多个这些响应头后应该引用它。 | [
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] | train | https://github.com/zeromake/aiko/blob/53b246fa88652466a9e38ac3d1a99a6198195b0f/aiko/request.py#L567-L582 | <|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:
检查请求缓存是否“新鲜”，也就是内容没有改变。
此方法用于 If-None-Match / ETag, 和 If-Modified-Since 和 Last-Modified 之间的缓存协商。
在设置一个或多个这些响应头后应该引用它。
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def fresh(self) -> bool:
"""
检查请求缓存是否“新鲜”，也就是内容没有改变。
此方法用于 If-None-Match / ETag, 和 If-Modified-Since 和 Last-Modified 之间的缓存协商。
在设置一个或多个这些响应头后应该引用它。
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method_str = self.method
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"""
General XML element attributes for a seismic source, as a dict.
"""
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('name', str(self.name)),
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"""
General XML element attributes for a seismic source, as a dict.
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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:
General XML element attributes for a seismic source, as a dict.
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"""Update stats views."""
# Call the father's method
super(Plugin, self).update_views()
# Add specifics informations
# Alert and log
self.views['used']['decoration'] = self.get_alert_log(self.stats['used'], maximum=self.stats['total']) | python | def update_views(self):
"""Update stats views."""
# Call the father's method
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Update stats views.
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SuryaSankar/flask-sqlalchemy-booster | flask_sqlalchemy_booster/model_booster/queryable_mixin.py | QueryableMixin.add | def add(cls, model, commit=True):
"""Adds a model instance to session and commits the
transaction.
Args:
model: The instance to add.
Examples:
>>> customer = Customer.new(name="hari", email="[email protected]")
>>> Customer.add(customer)
[email protected]
"""
if not isinstance(model, cls):
raise ValueError('%s is not of type %s' % (model, cls))
cls.session.add(model)
try:
if commit:
cls.session.commit()
return model
except:
cls.session.rollback()
raise | python | def add(cls, model, commit=True):
"""Adds a model instance to session and commits the
transaction.
Args:
model: The instance to add.
Examples:
>>> customer = Customer.new(name="hari", email="[email protected]")
>>> Customer.add(customer)
[email protected]
"""
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Adds a model instance to session and commits the
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jbasko/pytest-random-order | random_order/bucket_types.py | bucket_type_key | def bucket_type_key(bucket_type):
"""
Registers a function that calculates test item key for the specified bucket type.
"""
def decorator(f):
@functools.wraps(f)
def wrapped(item, session):
key = f(item)
if session is not None:
for handler in session.random_order_bucket_type_key_handlers:
key = handler(item, key)
return key
bucket_type_keys[bucket_type] = wrapped
return wrapped
return decorator | python | def bucket_type_key(bucket_type):
"""
Registers a function that calculates test item key for the specified bucket type.
"""
def decorator(f):
@functools.wraps(f)
def wrapped(item, session):
key = f(item)
if session is not None:
for handler in session.random_order_bucket_type_key_handlers:
key = handler(item, key)
return key
bucket_type_keys[bucket_type] = wrapped
return wrapped
return decorator | [
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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:
Registers a function that calculates test item key for the specified bucket type.
### Response:
def bucket_type_key(bucket_type):
"""
Registers a function that calculates test item key for the specified bucket type.
"""
def decorator(f):
@functools.wraps(f)
def wrapped(item, session):
key = f(item)
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bucket_type_keys[bucket_type] = wrapped
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return decorator
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python/core-workflow | cherry_picker/cherry_picker/cherry_picker.py | normalize_commit_message | def normalize_commit_message(commit_message):
"""
Return a tuple of title and body from the commit message
"""
split_commit_message = commit_message.split("\n")
title = split_commit_message[0]
body = "\n".join(split_commit_message[1:])
return title, body.lstrip("\n") | python | def normalize_commit_message(commit_message):
"""
Return a tuple of title and body from the commit message
"""
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title = split_commit_message[0]
body = "\n".join(split_commit_message[1:])
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"and",
"body",
"from",
"the",
"commit",
"message"
] | train | https://github.com/python/core-workflow/blob/b93c76195f6db382cfcefee334380fb4c68d4e21/cherry_picker/cherry_picker/cherry_picker.py#L713-L720 | <|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:
Return a tuple of title and body from the commit message
### Response:
def normalize_commit_message(commit_message):
"""
Return a tuple of title and body from the commit message
"""
split_commit_message = commit_message.split("\n")
title = split_commit_message[0]
body = "\n".join(split_commit_message[1:])
return title, body.lstrip("\n")
<|endoftext|> | [
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clalancette/pycdlib | pycdlib/pycdlib.py | _yield_children | def _yield_children(rec):
# type: (dr.DirectoryRecord) -> Generator
'''
An internal function to gather and yield all of the children of a Directory
Record.
Parameters:
rec - The Directory Record to get all of the children from (must be a
directory)
Yields:
Children of this Directory Record.
Returns:
Nothing.
'''
if not rec.is_dir():
raise pycdlibexception.PyCdlibInvalidInput('Record is not a directory!')
last = b''
for child in rec.children:
# Check to see if the filename of this child is the same as the
# last one, and if so, skip the child. This can happen if we
# have very large files with more than one directory entry.
fi = child.file_identifier()
if fi == last:
continue
last = fi
if child.rock_ridge is not None and child.rock_ridge.child_link_record_exists() and child.rock_ridge.cl_to_moved_dr is not None and child.rock_ridge.cl_to_moved_dr.parent is not None:
# If this is the case, this is a relocated entry. We actually
# want to go find the entry this was relocated to; we do that
# by following the child_link, then going up to the parent and
# finding the entry that links to the same one as this one.
cl_parent = child.rock_ridge.cl_to_moved_dr.parent
for cl_child in cl_parent.children:
if cl_child.rock_ridge is not None and cl_child.rock_ridge.name() == child.rock_ridge.name():
child = cl_child
break
# If we ended up not finding the right one in the parent of the
# moved entry, weird, but just return the one we would have
# anyway.
yield child | python | def _yield_children(rec):
# type: (dr.DirectoryRecord) -> Generator
'''
An internal function to gather and yield all of the children of a Directory
Record.
Parameters:
rec - The Directory Record to get all of the children from (must be a
directory)
Yields:
Children of this Directory Record.
Returns:
Nothing.
'''
if not rec.is_dir():
raise pycdlibexception.PyCdlibInvalidInput('Record is not a directory!')
last = b''
for child in rec.children:
# Check to see if the filename of this child is the same as the
# last one, and if so, skip the child. This can happen if we
# have very large files with more than one directory entry.
fi = child.file_identifier()
if fi == last:
continue
last = fi
if child.rock_ridge is not None and child.rock_ridge.child_link_record_exists() and child.rock_ridge.cl_to_moved_dr is not None and child.rock_ridge.cl_to_moved_dr.parent is not None:
# If this is the case, this is a relocated entry. We actually
# want to go find the entry this was relocated to; we do that
# by following the child_link, then going up to the parent and
# finding the entry that links to the same one as this one.
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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:
An internal function to gather and yield all of the children of a Directory
Record.
Parameters:
rec - The Directory Record to get all of the children from (must be a
directory)
Yields:
Children of this Directory Record.
Returns:
Nothing.
### Response:
def _yield_children(rec):
# type: (dr.DirectoryRecord) -> Generator
'''
An internal function to gather and yield all of the children of a Directory
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Parameters:
rec - The Directory Record to get all of the children from (must be a
directory)
Yields:
Children of this Directory Record.
Returns:
Nothing.
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return property_set | python | def get_properties_from_graph(graph):
"""
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NICTA/revrand | revrand/basis_functions.py | FastFoodRBF.grad | def grad(self, X, lenscale=None):
r"""
Get the gradients of this basis w.r.t.\ the length scale.
Parameters
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lenscale: scalar or ndarray
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dPhi.append(np.hstack((dVX * sinVX, dVX * cosVX)) /
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return np.dstack(dPhi) if len(lenscale) != 1 else dPhi[0] | python | def grad(self, X, lenscale=None):
r"""
Get the gradients of this basis w.r.t.\ the length scale.
Parameters
----------
x: ndarray
(n, d) array of observations where n is the number of samples, and
d is the dimensionality of x.
lenscale: scalar or ndarray
scalar or array of shape (d,) length scales (one for each dimension
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"""
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sinVX = - np.sin(VX)
cosVX = np.cos(VX)
dPhi = []
for i, l in enumerate(lenscale):
indlen = np.zeros(d)
indlen[i] = 1. / l**2
dVX = - self._makeVX(X * indlen) # FIXME make this more efficient?
dPhi.append(np.hstack((dVX * sinVX, dVX * cosVX)) /
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Get the gradients of this basis w.r.t.\ the length scale.
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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"""
Get the gradients of this basis w.r.t.\ the length scale.
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x: ndarray
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### Response:
def grad(self, X, lenscale=None):
r"""
Get the gradients of this basis w.r.t.\ the length scale.
Parameters
----------
x: ndarray
(n, d) array of observations where n is the number of samples, and
d is the dimensionality of x.
lenscale: scalar or ndarray
scalar or array of shape (d,) length scales (one for each dimension
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scale.
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-------
ndarray:
shape (n, 2*nbases) where nbases is number of random rbf bases,
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"""
d = X.shape[1]
lenscale = self._check_dim(d, lenscale)
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sinVX = - np.sin(VX)
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dPhi = []
for i, l in enumerate(lenscale):
indlen = np.zeros(d)
indlen[i] = 1. / l**2
dVX = - self._makeVX(X * indlen) # FIXME make this more efficient?
dPhi.append(np.hstack((dVX * sinVX, dVX * cosVX)) /
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return np.dstack(dPhi) if len(lenscale) != 1 else dPhi[0]
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spyder-ide/spyder | spyder/plugins/outlineexplorer/widgets.py | OutlineExplorerTreeWidget.root_item_selected | def root_item_selected(self, item):
"""Root item has been selected: expanding it and collapsing others"""
if self.show_all_files:
return
for root_item in self.get_top_level_items():
if root_item is item:
self.expandItem(root_item)
else:
self.collapseItem(root_item) | python | def root_item_selected(self, item):
"""Root item has been selected: expanding it and collapsing others"""
if self.show_all_files:
return
for root_item in self.get_top_level_items():
if root_item is item:
self.expandItem(root_item)
else:
self.collapseItem(root_item) | [
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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:
Root item has been selected: expanding it and collapsing others
### Response:
def root_item_selected(self, item):
"""Root item has been selected: expanding it and collapsing others"""
if self.show_all_files:
return
for root_item in self.get_top_level_items():
if root_item is item:
self.expandItem(root_item)
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self.collapseItem(root_item)
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facelessuser/wcmatch | wcmatch/glob.py | Glob._match_excluded | def _match_excluded(self, filename, patterns):
"""Call match real directly to skip unnecessary `exists` check."""
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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:
Call match real directly to skip unnecessary `exists` check.
### Response:
def _match_excluded(self, filename, patterns):
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return _wcparse._match_real(
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atztogo/phonopy | phonopy/structure/spglib.py | get_pointgroup | def get_pointgroup(rotations):
"""Return point group in international table symbol and number.
The symbols are mapped to the numbers as follows:
1 "1 "
2 "-1 "
3 "2 "
4 "m "
5 "2/m "
6 "222 "
7 "mm2 "
8 "mmm "
9 "4 "
10 "-4 "
11 "4/m "
12 "422 "
13 "4mm "
14 "-42m "
15 "4/mmm"
16 "3 "
17 "-3 "
18 "32 "
19 "3m "
20 "-3m "
21 "6 "
22 "-6 "
23 "6/m "
24 "622 "
25 "6mm "
26 "-62m "
27 "6/mmm"
28 "23 "
29 "m-3 "
30 "432 "
31 "-43m "
32 "m-3m "
"""
_set_no_error()
# (symbol, pointgroup_number, transformation_matrix)
pointgroup = spg.pointgroup(np.array(rotations, dtype='intc', order='C'))
_set_error_message()
return pointgroup | python | def get_pointgroup(rotations):
"""Return point group in international table symbol and number.
The symbols are mapped to the numbers as follows:
1 "1 "
2 "-1 "
3 "2 "
4 "m "
5 "2/m "
6 "222 "
7 "mm2 "
8 "mmm "
9 "4 "
10 "-4 "
11 "4/m "
12 "422 "
13 "4mm "
14 "-42m "
15 "4/mmm"
16 "3 "
17 "-3 "
18 "32 "
19 "3m "
20 "-3m "
21 "6 "
22 "-6 "
23 "6/m "
24 "622 "
25 "6mm "
26 "-62m "
27 "6/mmm"
28 "23 "
29 "m-3 "
30 "432 "
31 "-43m "
32 "m-3m "
"""
_set_no_error()
# (symbol, pointgroup_number, transformation_matrix)
pointgroup = spg.pointgroup(np.array(rotations, dtype='intc', order='C'))
_set_error_message()
return pointgroup | [
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The symbols are mapped to the numbers as follows:
1 "1 "
2 "-1 "
3 "2 "
4 "m "
5 "2/m "
6 "222 "
7 "mm2 "
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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 point group in international table symbol and number.
The symbols are mapped to the numbers as follows:
1 "1 "
2 "-1 "
3 "2 "
4 "m "
5 "2/m "
6 "222 "
7 "mm2 "
8 "mmm "
9 "4 "
10 "-4 "
11 "4/m "
12 "422 "
13 "4mm "
14 "-42m "
15 "4/mmm"
16 "3 "
17 "-3 "
18 "32 "
19 "3m "
20 "-3m "
21 "6 "
22 "-6 "
23 "6/m "
24 "622 "
25 "6mm "
26 "-62m "
27 "6/mmm"
28 "23 "
29 "m-3 "
30 "432 "
31 "-43m "
32 "m-3m "
### Response:
def get_pointgroup(rotations):
"""Return point group in international table symbol and number.
The symbols are mapped to the numbers as follows:
1 "1 "
2 "-1 "
3 "2 "
4 "m "
5 "2/m "
6 "222 "
7 "mm2 "
8 "mmm "
9 "4 "
10 "-4 "
11 "4/m "
12 "422 "
13 "4mm "
14 "-42m "
15 "4/mmm"
16 "3 "
17 "-3 "
18 "32 "
19 "3m "
20 "-3m "
21 "6 "
22 "-6 "
23 "6/m "
24 "622 "
25 "6mm "
26 "-62m "
27 "6/mmm"
28 "23 "
29 "m-3 "
30 "432 "
31 "-43m "
32 "m-3m "
"""
_set_no_error()
# (symbol, pointgroup_number, transformation_matrix)
pointgroup = spg.pointgroup(np.array(rotations, dtype='intc', order='C'))
_set_error_message()
return pointgroup
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automl/HpBandSter | hpbandster/core/nameserver.py | NameServer.start | def start(self):
"""
starts a Pyro4 nameserver in a separate thread
Returns
-------
tuple (str, int):
the host name and the used port
"""
if self.host is None:
if self.nic_name is None:
self.host = 'localhost'
else:
self.host = nic_name_to_host(self.nic_name)
uri, self.pyro_ns, _ = Pyro4.naming.startNS(host=self.host, port=self.port)
self.host, self.port = self.pyro_ns.locationStr.split(':')
self.port = int(self.port)
thread = threading.Thread(target=self.pyro_ns.requestLoop, name='Pyro4 nameserver started by HpBandSter')
thread.start()
if not self.dir is None:
os.makedirs(self.dir, exist_ok=True)
self.conf_fn = os.path.join(self.dir, 'HPB_run_%s_pyro.pkl'%self.run_id)
with open(self.conf_fn, 'wb') as fh:
pickle.dump((self.host, self.port), fh)
return(self.host, self.port) | python | def start(self):
"""
starts a Pyro4 nameserver in a separate thread
Returns
-------
tuple (str, int):
the host name and the used port
"""
if self.host is None:
if self.nic_name is None:
self.host = 'localhost'
else:
self.host = nic_name_to_host(self.nic_name)
uri, self.pyro_ns, _ = Pyro4.naming.startNS(host=self.host, port=self.port)
self.host, self.port = self.pyro_ns.locationStr.split(':')
self.port = int(self.port)
thread = threading.Thread(target=self.pyro_ns.requestLoop, name='Pyro4 nameserver started by HpBandSter')
thread.start()
if not self.dir is None:
os.makedirs(self.dir, exist_ok=True)
self.conf_fn = os.path.join(self.dir, 'HPB_run_%s_pyro.pkl'%self.run_id)
with open(self.conf_fn, 'wb') as fh:
pickle.dump((self.host, self.port), fh)
return(self.host, self.port) | [
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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:
starts a Pyro4 nameserver in a separate thread
Returns
-------
tuple (str, int):
the host name and the used port
### Response:
def start(self):
"""
starts a Pyro4 nameserver in a separate thread
Returns
-------
tuple (str, int):
the host name and the used port
"""
if self.host is None:
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self.port = int(self.port)
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thread.start()
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os.makedirs(self.dir, exist_ok=True)
self.conf_fn = os.path.join(self.dir, 'HPB_run_%s_pyro.pkl'%self.run_id)
with open(self.conf_fn, 'wb') as fh:
pickle.dump((self.host, self.port), fh)
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ploneintranet/ploneintranet.workspace | src/ploneintranet/workspace/utils.py | get_storage | def get_storage(clear=False):
"""helper function to get annotation storage on the portal
:param clear: If true is passed in, annotations will be cleared
:returns: portal annotations
:rtype: IAnnotations
"""
portal = getUtility(ISiteRoot)
annotations = IAnnotations(portal)
if ANNOTATION_KEY not in annotations or clear:
annotations[ANNOTATION_KEY] = OOBTree()
return annotations[ANNOTATION_KEY] | python | def get_storage(clear=False):
"""helper function to get annotation storage on the portal
:param clear: If true is passed in, annotations will be cleared
:returns: portal annotations
:rtype: IAnnotations
"""
portal = getUtility(ISiteRoot)
annotations = IAnnotations(portal)
if ANNOTATION_KEY not in annotations or clear:
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return annotations[ANNOTATION_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:
helper function to get annotation storage on the portal
:param clear: If true is passed in, annotations will be cleared
:returns: portal annotations
:rtype: IAnnotations
### Response:
def get_storage(clear=False):
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saltstack/salt | salt/modules/osquery.py | kernel_integrity | def kernel_integrity(attrs=None, where=None):
'''
Return kernel_integrity information from osquery
CLI Example:
.. code-block:: bash
salt '*' osquery.kernel_integrity
'''
if __grains__['os_family'] in ['RedHat', 'Debian']:
return _osquery_cmd(table='kernel_integrity', attrs=attrs, where=where)
return {'result': False, 'comment': 'Only available on Red Hat or Debian based systems.'} | python | def kernel_integrity(attrs=None, where=None):
'''
Return kernel_integrity information from osquery
CLI Example:
.. code-block:: bash
salt '*' osquery.kernel_integrity
'''
if __grains__['os_family'] in ['RedHat', 'Debian']:
return _osquery_cmd(table='kernel_integrity', attrs=attrs, where=where)
return {'result': False, 'comment': 'Only available on Red Hat or Debian based systems.'} | [
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### Task:
Return kernel_integrity information from osquery
CLI Example:
.. code-block:: bash
salt '*' osquery.kernel_integrity
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'''
Return kernel_integrity information from osquery
CLI Example:
.. code-block:: bash
salt '*' osquery.kernel_integrity
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"""Returns True if a step input parameter is optional.
Args:
inp (dict): a dictionary representation of an input.
Raises:
ValueError: The inp provided is not valid.
"""
if 'default' in inp.keys():
return True
typ = inp.get('type')
if isinstance(typ, six.string_types):
return typ.endswith('?')
elif isinstance(typ, dict):
# TODO: handle case where iput type is dict
return False
elif isinstance(typ, list):
# The cwltool validation expands optional arguments to
# [u'null', <type>]
return bool(u'null' in typ)
else:
raise ValueError('Invalid input "{}"'.format(inp.get['id'])) | python | def _input_optional(inp):
"""Returns True if a step input parameter is optional.
Args:
inp (dict): a dictionary representation of an input.
Raises:
ValueError: The inp provided is not valid.
"""
if 'default' in inp.keys():
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# TODO: handle case where iput type is dict
return False
elif isinstance(typ, 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:
Returns True if a step input parameter is optional.
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inp (dict): a dictionary representation of an input.
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saltstack/salt | salt/utils/yast.py | mksls | def mksls(src, dst=None):
'''
Convert an AutoYAST file to an SLS file
'''
with salt.utils.files.fopen(src, 'r') as fh_:
ps_opts = xml.to_dict(ET.fromstring(fh_.read()))
if dst is not None:
with salt.utils.files.fopen(dst, 'w') as fh_:
salt.utils.yaml.safe_dump(ps_opts, fh_, default_flow_style=False)
else:
return salt.utils.yaml.safe_dump(ps_opts, default_flow_style=False) | python | def mksls(src, dst=None):
'''
Convert an AutoYAST file to an SLS file
'''
with salt.utils.files.fopen(src, 'r') as fh_:
ps_opts = xml.to_dict(ET.fromstring(fh_.read()))
if dst is not None:
with salt.utils.files.fopen(dst, 'w') as fh_:
salt.utils.yaml.safe_dump(ps_opts, fh_, default_flow_style=False)
else:
return salt.utils.yaml.safe_dump(ps_opts, default_flow_style=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:
Convert an AutoYAST file to an SLS file
### Response:
def mksls(src, dst=None):
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Convert an AutoYAST file to an SLS file
'''
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jwodder/doapi | doapi/doapi.py | doapi.wait_actions | def wait_actions(self, actions, wait_interval=None, wait_time=None):
r"""
Poll the server periodically until all actions in ``actions`` have
either completed or errored out, yielding each `Action`'s final value
as it ends.
If ``wait_time`` is exceeded, a `WaitTimeoutError` (containing any
remaining in-progress actions) is raised.
If a `KeyboardInterrupt` is caught, any remaining actions are returned
immediately without waiting for completion.
.. versionchanged:: 0.2.0
Raises `WaitTimeoutError` on timeout
:param iterable actions: an iterable of `Action`\ s and/or other values
that are acceptable arguments to :meth:`fetch_action`
:param number wait_interval: how many seconds to sleep between
requests; defaults to :attr:`wait_interval` if not specified or
`None`
:param number wait_time: the total number of seconds after which the
method will raise an error if any actions have not yet completed,
or a negative number to wait indefinitely; defaults to
:attr:`wait_time` if not specified or `None`
:rtype: generator of `Action`\ s
:raises DOAPIError: if the API endpoint replies with an error
:raises WaitTimeoutError: if ``wait_time`` is exceeded
"""
return self._wait(map(self._action, actions), "done", True,
wait_interval, wait_time) | python | def wait_actions(self, actions, wait_interval=None, wait_time=None):
r"""
Poll the server periodically until all actions in ``actions`` have
either completed or errored out, yielding each `Action`'s final value
as it ends.
If ``wait_time`` is exceeded, a `WaitTimeoutError` (containing any
remaining in-progress actions) is raised.
If a `KeyboardInterrupt` is caught, any remaining actions are returned
immediately without waiting for completion.
.. versionchanged:: 0.2.0
Raises `WaitTimeoutError` on timeout
:param iterable actions: an iterable of `Action`\ s and/or other values
that are acceptable arguments to :meth:`fetch_action`
:param number wait_interval: how many seconds to sleep between
requests; defaults to :attr:`wait_interval` if not specified or
`None`
:param number wait_time: the total number of seconds after which the
method will raise an error if any actions have not yet completed,
or a negative number to wait indefinitely; defaults to
:attr:`wait_time` if not specified or `None`
:rtype: generator of `Action`\ s
:raises DOAPIError: if the API endpoint replies with an error
:raises WaitTimeoutError: if ``wait_time`` is exceeded
"""
return self._wait(map(self._action, actions), "done", True,
wait_interval, wait_time) | [
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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"""
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def wait_actions(self, actions, wait_interval=None, wait_time=None):
r"""
Poll the server periodically until all actions in ``actions`` have
either completed or errored out, yielding each `Action`'s final value
as it ends.
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remaining in-progress actions) is raised.
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angr/angr | angr/utils/graph.py | compute_dominance_frontier | def compute_dominance_frontier(graph, domtree):
"""
Compute a dominance frontier based on the given post-dominator tree.
This implementation is based on figure 2 of paper An Efficient Method of Computing Static Single Assignment
Form by Ron Cytron, etc.
:param graph: The graph where we want to compute the dominance frontier.
:param domtree: The dominator tree
:returns: A dict of dominance frontier
"""
df = {}
# Perform a post-order search on the dominator tree
for x in networkx.dfs_postorder_nodes(domtree):
if x not in graph:
# Skip nodes that are not in the graph
continue
df[x] = set()
# local set
for y in graph.successors(x):
if x not in domtree.predecessors(y):
df[x].add(y)
# up set
if x is None:
continue
for z in domtree.successors(x):
if z is x:
continue
if z not in df:
continue
for y in df[z]:
if x not in list(domtree.predecessors(y)):
df[x].add(y)
return df | python | def compute_dominance_frontier(graph, domtree):
"""
Compute a dominance frontier based on the given post-dominator tree.
This implementation is based on figure 2 of paper An Efficient Method of Computing Static Single Assignment
Form by Ron Cytron, etc.
:param graph: The graph where we want to compute the dominance frontier.
:param domtree: The dominator tree
:returns: A dict of dominance frontier
"""
df = {}
# Perform a post-order search on the dominator tree
for x in networkx.dfs_postorder_nodes(domtree):
if x not in graph:
# Skip nodes that are not in the graph
continue
df[x] = set()
# local set
for y in graph.successors(x):
if x not in domtree.predecessors(y):
df[x].add(y)
# up set
if x is None:
continue
for z in domtree.successors(x):
if z is x:
continue
if z not in df:
continue
for y in df[z]:
if x not in list(domtree.predecessors(y)):
df[x].add(y)
return df | [
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This implementation is based on figure 2 of paper An Efficient Method of Computing Static Single Assignment
Form by Ron Cytron, etc.
:param graph: The graph where we want to compute the dominance frontier.
:param domtree: The dominator tree
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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 a dominance frontier based on the given post-dominator tree.
This implementation is based on figure 2 of paper An Efficient Method of Computing Static Single Assignment
Form by Ron Cytron, etc.
:param graph: The graph where we want to compute the dominance frontier.
:param domtree: The dominator tree
:returns: A dict of dominance frontier
### Response:
def compute_dominance_frontier(graph, domtree):
"""
Compute a dominance frontier based on the given post-dominator tree.
This implementation is based on figure 2 of paper An Efficient Method of Computing Static Single Assignment
Form by Ron Cytron, etc.
:param graph: The graph where we want to compute the dominance frontier.
:param domtree: The dominator tree
:returns: A dict of dominance frontier
"""
df = {}
# Perform a post-order search on the dominator tree
for x in networkx.dfs_postorder_nodes(domtree):
if x not in graph:
# Skip nodes that are not in the graph
continue
df[x] = set()
# local set
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df[x].add(y)
# up set
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continue
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if z is x:
continue
if z not in df:
continue
for y in df[z]:
if x not in list(domtree.predecessors(y)):
df[x].add(y)
return df
<|endoftext|> | [
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coin-or/GiMPy | src/gimpy/graph.py | Graph.simplex_determine_leaving_arc | def simplex_determine_leaving_arc(self, t, k, l):
'''
API:
simplex_determine_leaving_arc(self, t, k, l)
Description:
Determines and returns the leaving arc.
Input:
t: current spanning tree solution.
k: tail of the entering arc.
l: head of the entering arc.
Return:
Returns the tuple that represents leaving arc, capacity of the
cycle and cycle.
'''
# k,l are the first two elements of the cycle
cycle = self.simplex_identify_cycle(t, k, l)
flow_kl = self.get_edge_attr(k, l, 'flow')
capacity_kl = self.get_edge_attr(k, l, 'capacity')
min_capacity = capacity_kl
# check if k,l is in U or L
if flow_kl==capacity_kl:
# l,k will be the last two elements
cycle.reverse()
n = len(cycle)
index = 0
# determine last blocking arc
t.add_edge(k, l)
tel = t.get_edge_list()
while index < (n-1):
if (cycle[index], cycle[index+1]) in tel:
flow = self.edge_attr[(cycle[index], cycle[index+1])]['flow']
capacity = \
self.edge_attr[(cycle[index],cycle[index+1])]['capacity']
if min_capacity >= (capacity-flow):
candidate = (cycle[index], cycle[index+1])
min_capacity = capacity-flow
else:
flow = self.edge_attr[(cycle[index+1], cycle[index])]['flow']
if min_capacity >= flow:
candidate = (cycle[index+1], cycle[index])
min_capacity = flow
index += 1
# check arc (cycle[n-1], cycle[0])
if (cycle[n-1], cycle[0]) in tel:
flow = self.edge_attr[(cycle[n-1], cycle[0])]['flow']
capacity = self.edge_attr[(cycle[n-1], cycle[0])]['capacity']
if min_capacity >= (capacity-flow):
candidate = (cycle[n-1], cycle[0])
min_capacity = capacity-flow
else:
flow = self.edge_attr[(cycle[0], cycle[n-1])]['flow']
if min_capacity >= flow:
candidate = (cycle[0], cycle[n-1])
min_capacity = flow
return (candidate, min_capacity, cycle) | python | def simplex_determine_leaving_arc(self, t, k, l):
'''
API:
simplex_determine_leaving_arc(self, t, k, l)
Description:
Determines and returns the leaving arc.
Input:
t: current spanning tree solution.
k: tail of the entering arc.
l: head of the entering arc.
Return:
Returns the tuple that represents leaving arc, capacity of the
cycle and cycle.
'''
# k,l are the first two elements of the cycle
cycle = self.simplex_identify_cycle(t, k, l)
flow_kl = self.get_edge_attr(k, l, 'flow')
capacity_kl = self.get_edge_attr(k, l, 'capacity')
min_capacity = capacity_kl
# check if k,l is in U or L
if flow_kl==capacity_kl:
# l,k will be the last two elements
cycle.reverse()
n = len(cycle)
index = 0
# determine last blocking arc
t.add_edge(k, l)
tel = t.get_edge_list()
while index < (n-1):
if (cycle[index], cycle[index+1]) in tel:
flow = self.edge_attr[(cycle[index], cycle[index+1])]['flow']
capacity = \
self.edge_attr[(cycle[index],cycle[index+1])]['capacity']
if min_capacity >= (capacity-flow):
candidate = (cycle[index], cycle[index+1])
min_capacity = capacity-flow
else:
flow = self.edge_attr[(cycle[index+1], cycle[index])]['flow']
if min_capacity >= flow:
candidate = (cycle[index+1], cycle[index])
min_capacity = flow
index += 1
# check arc (cycle[n-1], cycle[0])
if (cycle[n-1], cycle[0]) in tel:
flow = self.edge_attr[(cycle[n-1], cycle[0])]['flow']
capacity = self.edge_attr[(cycle[n-1], cycle[0])]['capacity']
if min_capacity >= (capacity-flow):
candidate = (cycle[n-1], cycle[0])
min_capacity = capacity-flow
else:
flow = self.edge_attr[(cycle[0], cycle[n-1])]['flow']
if min_capacity >= flow:
candidate = (cycle[0], cycle[n-1])
min_capacity = flow
return (candidate, min_capacity, cycle) | [
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simplex_determine_leaving_arc(self, t, k, l)
Description:
Determines and returns the leaving arc.
Input:
t: current spanning tree solution.
k: tail of the entering arc.
l: head of the entering arc.
Return:
Returns the tuple that represents leaving arc, capacity of the
cycle and cycle. | [
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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:
API:
simplex_determine_leaving_arc(self, t, k, l)
Description:
Determines and returns the leaving arc.
Input:
t: current spanning tree solution.
k: tail of the entering arc.
l: head of the entering arc.
Return:
Returns the tuple that represents leaving arc, capacity of the
cycle and cycle.
### Response:
def simplex_determine_leaving_arc(self, t, k, l):
'''
API:
simplex_determine_leaving_arc(self, t, k, l)
Description:
Determines and returns the leaving arc.
Input:
t: current spanning tree solution.
k: tail of the entering arc.
l: head of the entering arc.
Return:
Returns the tuple that represents leaving arc, capacity of the
cycle and cycle.
'''
# k,l are the first two elements of the cycle
cycle = self.simplex_identify_cycle(t, k, l)
flow_kl = self.get_edge_attr(k, l, 'flow')
capacity_kl = self.get_edge_attr(k, l, 'capacity')
min_capacity = capacity_kl
# check if k,l is in U or L
if flow_kl==capacity_kl:
# l,k will be the last two elements
cycle.reverse()
n = len(cycle)
index = 0
# determine last blocking arc
t.add_edge(k, l)
tel = t.get_edge_list()
while index < (n-1):
if (cycle[index], cycle[index+1]) in tel:
flow = self.edge_attr[(cycle[index], cycle[index+1])]['flow']
capacity = \
self.edge_attr[(cycle[index],cycle[index+1])]['capacity']
if min_capacity >= (capacity-flow):
candidate = (cycle[index], cycle[index+1])
min_capacity = capacity-flow
else:
flow = self.edge_attr[(cycle[index+1], cycle[index])]['flow']
if min_capacity >= flow:
candidate = (cycle[index+1], cycle[index])
min_capacity = flow
index += 1
# check arc (cycle[n-1], cycle[0])
if (cycle[n-1], cycle[0]) in tel:
flow = self.edge_attr[(cycle[n-1], cycle[0])]['flow']
capacity = self.edge_attr[(cycle[n-1], cycle[0])]['capacity']
if min_capacity >= (capacity-flow):
candidate = (cycle[n-1], cycle[0])
min_capacity = capacity-flow
else:
flow = self.edge_attr[(cycle[0], cycle[n-1])]['flow']
if min_capacity >= flow:
candidate = (cycle[0], cycle[n-1])
min_capacity = flow
return (candidate, min_capacity, cycle)
<|endoftext|> | [
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CamDavidsonPilon/lifelines | lifelines/utils/__init__.py | survival_table_from_events | def survival_table_from_events(
death_times,
event_observed,
birth_times=None,
columns=["removed", "observed", "censored", "entrance", "at_risk"],
weights=None,
collapse=False,
intervals=None,
): # pylint: disable=dangerous-default-value,too-many-locals
"""
Parameters
----------
death_times: (n,) array
represent the event times
event_observed: (n,) array
1 if observed event, 0 is censored event.
birth_times: a (n,) array, optional
representing when the subject was first observed. A subject's death event is then at [birth times + duration observed].
If None (default), birth_times are set to be the first observation or 0, which ever is smaller.
columns: iterable, optional
a 3-length array to call the, in order, removed individuals, observed deaths
and censorships.
weights: (n,1) array, optional
Optional argument to use weights for individuals. Assumes weights of 1 if not provided.
collapse: boolean, optional (default=False)
If True, collapses survival table into lifetable to show events in interval bins
intervals: iterable, optional
Default None, otherwise a list/(n,1) array of interval edge measures. If left as None
while collapse=True, then Freedman-Diaconis rule for histogram bins will be used to determine intervals.
Returns
-------
DataFrame
Pandas DataFrame with index as the unique times or intervals in event_times. The columns named
'removed' refers to the number of individuals who were removed from the population
by the end of the period. The column 'observed' refers to the number of removed
individuals who were observed to have died (i.e. not censored.) The column
'censored' is defined as 'removed' - 'observed' (the number of individuals who
left the population due to event_observed)
Example
-------
>>> #Uncollapsed output
>>> removed observed censored entrance at_risk
>>> event_at
>>> 0 0 0 0 11 11
>>> 6 1 1 0 0 11
>>> 7 2 2 0 0 10
>>> 9 3 3 0 0 8
>>> 13 3 3 0 0 5
>>> 15 2 2 0 0 2
>>> #Collapsed output
>>> removed observed censored at_risk
>>> sum sum sum max
>>> event_at
>>> (0, 2] 34 33 1 312
>>> (2, 4] 84 42 42 278
>>> (4, 6] 64 17 47 194
>>> (6, 8] 63 16 47 130
>>> (8, 10] 35 12 23 67
>>> (10, 12] 24 5 19 32
See Also
--------
group_survival_table_from_events
"""
removed, observed, censored, entrance, at_risk = columns
death_times = np.asarray(death_times)
if birth_times is None:
birth_times = min(0, death_times.min()) * np.ones(death_times.shape[0])
else:
birth_times = np.asarray(birth_times)
if np.any(birth_times > death_times):
raise ValueError("birth time must be less than time of death.")
if weights is None:
weights = 1
# deal with deaths and censorships
df = pd.DataFrame(death_times, columns=["event_at"])
df[removed] = np.asarray(weights)
df[observed] = np.asarray(weights) * (np.asarray(event_observed).astype(bool))
death_table = df.groupby("event_at").sum()
death_table[censored] = (death_table[removed] - death_table[observed]).astype(int)
# deal with late births
births = pd.DataFrame(birth_times, columns=["event_at"])
births[entrance] = np.asarray(weights)
births_table = births.groupby("event_at").sum()
event_table = death_table.join(births_table, how="outer", sort=True).fillna(0) # http://wesmckinney.com/blog/?p=414
event_table[at_risk] = event_table[entrance].cumsum() - event_table[removed].cumsum().shift(1).fillna(0)
# group by intervals
if (collapse) or (intervals is not None):
event_table = _group_event_table_by_intervals(event_table, intervals)
if (np.asarray(weights).astype(int) != weights).any():
return event_table.astype(float)
return event_table.astype(int) | python | def survival_table_from_events(
death_times,
event_observed,
birth_times=None,
columns=["removed", "observed", "censored", "entrance", "at_risk"],
weights=None,
collapse=False,
intervals=None,
): # pylint: disable=dangerous-default-value,too-many-locals
"""
Parameters
----------
death_times: (n,) array
represent the event times
event_observed: (n,) array
1 if observed event, 0 is censored event.
birth_times: a (n,) array, optional
representing when the subject was first observed. A subject's death event is then at [birth times + duration observed].
If None (default), birth_times are set to be the first observation or 0, which ever is smaller.
columns: iterable, optional
a 3-length array to call the, in order, removed individuals, observed deaths
and censorships.
weights: (n,1) array, optional
Optional argument to use weights for individuals. Assumes weights of 1 if not provided.
collapse: boolean, optional (default=False)
If True, collapses survival table into lifetable to show events in interval bins
intervals: iterable, optional
Default None, otherwise a list/(n,1) array of interval edge measures. If left as None
while collapse=True, then Freedman-Diaconis rule for histogram bins will be used to determine intervals.
Returns
-------
DataFrame
Pandas DataFrame with index as the unique times or intervals in event_times. The columns named
'removed' refers to the number of individuals who were removed from the population
by the end of the period. The column 'observed' refers to the number of removed
individuals who were observed to have died (i.e. not censored.) The column
'censored' is defined as 'removed' - 'observed' (the number of individuals who
left the population due to event_observed)
Example
-------
>>> #Uncollapsed output
>>> removed observed censored entrance at_risk
>>> event_at
>>> 0 0 0 0 11 11
>>> 6 1 1 0 0 11
>>> 7 2 2 0 0 10
>>> 9 3 3 0 0 8
>>> 13 3 3 0 0 5
>>> 15 2 2 0 0 2
>>> #Collapsed output
>>> removed observed censored at_risk
>>> sum sum sum max
>>> event_at
>>> (0, 2] 34 33 1 312
>>> (2, 4] 84 42 42 278
>>> (4, 6] 64 17 47 194
>>> (6, 8] 63 16 47 130
>>> (8, 10] 35 12 23 67
>>> (10, 12] 24 5 19 32
See Also
--------
group_survival_table_from_events
"""
removed, observed, censored, entrance, at_risk = columns
death_times = np.asarray(death_times)
if birth_times is None:
birth_times = min(0, death_times.min()) * np.ones(death_times.shape[0])
else:
birth_times = np.asarray(birth_times)
if np.any(birth_times > death_times):
raise ValueError("birth time must be less than time of death.")
if weights is None:
weights = 1
# deal with deaths and censorships
df = pd.DataFrame(death_times, columns=["event_at"])
df[removed] = np.asarray(weights)
df[observed] = np.asarray(weights) * (np.asarray(event_observed).astype(bool))
death_table = df.groupby("event_at").sum()
death_table[censored] = (death_table[removed] - death_table[observed]).astype(int)
# deal with late births
births = pd.DataFrame(birth_times, columns=["event_at"])
births[entrance] = np.asarray(weights)
births_table = births.groupby("event_at").sum()
event_table = death_table.join(births_table, how="outer", sort=True).fillna(0) # http://wesmckinney.com/blog/?p=414
event_table[at_risk] = event_table[entrance].cumsum() - event_table[removed].cumsum().shift(1).fillna(0)
# group by intervals
if (collapse) or (intervals is not None):
event_table = _group_event_table_by_intervals(event_table, intervals)
if (np.asarray(weights).astype(int) != weights).any():
return event_table.astype(float)
return event_table.astype(int) | [
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----------
death_times: (n,) array
represent the event times
event_observed: (n,) array
1 if observed event, 0 is censored event.
birth_times: a (n,) array, optional
representing when the subject was first observed. A subject's death event is then at [birth times + duration observed].
If None (default), birth_times are set to be the first observation or 0, which ever is smaller.
columns: iterable, optional
a 3-length array to call the, in order, removed individuals, observed deaths
and censorships.
weights: (n,1) array, optional
Optional argument to use weights for individuals. Assumes weights of 1 if not provided.
collapse: boolean, optional (default=False)
If True, collapses survival table into lifetable to show events in interval bins
intervals: iterable, optional
Default None, otherwise a list/(n,1) array of interval edge measures. If left as None
while collapse=True, then Freedman-Diaconis rule for histogram bins will be used to determine intervals.
Returns
-------
DataFrame
Pandas DataFrame with index as the unique times or intervals in event_times. The columns named
'removed' refers to the number of individuals who were removed from the population
by the end of the period. The column 'observed' refers to the number of removed
individuals who were observed to have died (i.e. not censored.) The column
'censored' is defined as 'removed' - 'observed' (the number of individuals who
left the population due to event_observed)
Example
-------
>>> #Uncollapsed output
>>> removed observed censored entrance at_risk
>>> event_at
>>> 0 0 0 0 11 11
>>> 6 1 1 0 0 11
>>> 7 2 2 0 0 10
>>> 9 3 3 0 0 8
>>> 13 3 3 0 0 5
>>> 15 2 2 0 0 2
>>> #Collapsed output
>>> removed observed censored at_risk
>>> sum sum sum max
>>> event_at
>>> (0, 2] 34 33 1 312
>>> (2, 4] 84 42 42 278
>>> (4, 6] 64 17 47 194
>>> (6, 8] 63 16 47 130
>>> (8, 10] 35 12 23 67
>>> (10, 12] 24 5 19 32
See Also
--------
group_survival_table_from_events | [
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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:
Parameters
----------
death_times: (n,) array
represent the event times
event_observed: (n,) array
1 if observed event, 0 is censored event.
birth_times: a (n,) array, optional
representing when the subject was first observed. A subject's death event is then at [birth times + duration observed].
If None (default), birth_times are set to be the first observation or 0, which ever is smaller.
columns: iterable, optional
a 3-length array to call the, in order, removed individuals, observed deaths
and censorships.
weights: (n,1) array, optional
Optional argument to use weights for individuals. Assumes weights of 1 if not provided.
collapse: boolean, optional (default=False)
If True, collapses survival table into lifetable to show events in interval bins
intervals: iterable, optional
Default None, otherwise a list/(n,1) array of interval edge measures. If left as None
while collapse=True, then Freedman-Diaconis rule for histogram bins will be used to determine intervals.
Returns
-------
DataFrame
Pandas DataFrame with index as the unique times or intervals in event_times. The columns named
'removed' refers to the number of individuals who were removed from the population
by the end of the period. The column 'observed' refers to the number of removed
individuals who were observed to have died (i.e. not censored.) The column
'censored' is defined as 'removed' - 'observed' (the number of individuals who
left the population due to event_observed)
Example
-------
>>> #Uncollapsed output
>>> removed observed censored entrance at_risk
>>> event_at
>>> 0 0 0 0 11 11
>>> 6 1 1 0 0 11
>>> 7 2 2 0 0 10
>>> 9 3 3 0 0 8
>>> 13 3 3 0 0 5
>>> 15 2 2 0 0 2
>>> #Collapsed output
>>> removed observed censored at_risk
>>> sum sum sum max
>>> event_at
>>> (0, 2] 34 33 1 312
>>> (2, 4] 84 42 42 278
>>> (4, 6] 64 17 47 194
>>> (6, 8] 63 16 47 130
>>> (8, 10] 35 12 23 67
>>> (10, 12] 24 5 19 32
See Also
--------
group_survival_table_from_events
### Response:
def survival_table_from_events(
death_times,
event_observed,
birth_times=None,
columns=["removed", "observed", "censored", "entrance", "at_risk"],
weights=None,
collapse=False,
intervals=None,
): # pylint: disable=dangerous-default-value,too-many-locals
"""
Parameters
----------
death_times: (n,) array
represent the event times
event_observed: (n,) array
1 if observed event, 0 is censored event.
birth_times: a (n,) array, optional
representing when the subject was first observed. A subject's death event is then at [birth times + duration observed].
If None (default), birth_times are set to be the first observation or 0, which ever is smaller.
columns: iterable, optional
a 3-length array to call the, in order, removed individuals, observed deaths
and censorships.
weights: (n,1) array, optional
Optional argument to use weights for individuals. Assumes weights of 1 if not provided.
collapse: boolean, optional (default=False)
If True, collapses survival table into lifetable to show events in interval bins
intervals: iterable, optional
Default None, otherwise a list/(n,1) array of interval edge measures. If left as None
while collapse=True, then Freedman-Diaconis rule for histogram bins will be used to determine intervals.
Returns
-------
DataFrame
Pandas DataFrame with index as the unique times or intervals in event_times. The columns named
'removed' refers to the number of individuals who were removed from the population
by the end of the period. The column 'observed' refers to the number of removed
individuals who were observed to have died (i.e. not censored.) The column
'censored' is defined as 'removed' - 'observed' (the number of individuals who
left the population due to event_observed)
Example
-------
>>> #Uncollapsed output
>>> removed observed censored entrance at_risk
>>> event_at
>>> 0 0 0 0 11 11
>>> 6 1 1 0 0 11
>>> 7 2 2 0 0 10
>>> 9 3 3 0 0 8
>>> 13 3 3 0 0 5
>>> 15 2 2 0 0 2
>>> #Collapsed output
>>> removed observed censored at_risk
>>> sum sum sum max
>>> event_at
>>> (0, 2] 34 33 1 312
>>> (2, 4] 84 42 42 278
>>> (4, 6] 64 17 47 194
>>> (6, 8] 63 16 47 130
>>> (8, 10] 35 12 23 67
>>> (10, 12] 24 5 19 32
See Also
--------
group_survival_table_from_events
"""
removed, observed, censored, entrance, at_risk = columns
death_times = np.asarray(death_times)
if birth_times is None:
birth_times = min(0, death_times.min()) * np.ones(death_times.shape[0])
else:
birth_times = np.asarray(birth_times)
if np.any(birth_times > death_times):
raise ValueError("birth time must be less than time of death.")
if weights is None:
weights = 1
# deal with deaths and censorships
df = pd.DataFrame(death_times, columns=["event_at"])
df[removed] = np.asarray(weights)
df[observed] = np.asarray(weights) * (np.asarray(event_observed).astype(bool))
death_table = df.groupby("event_at").sum()
death_table[censored] = (death_table[removed] - death_table[observed]).astype(int)
# deal with late births
births = pd.DataFrame(birth_times, columns=["event_at"])
births[entrance] = np.asarray(weights)
births_table = births.groupby("event_at").sum()
event_table = death_table.join(births_table, how="outer", sort=True).fillna(0) # http://wesmckinney.com/blog/?p=414
event_table[at_risk] = event_table[entrance].cumsum() - event_table[removed].cumsum().shift(1).fillna(0)
# group by intervals
if (collapse) or (intervals is not None):
event_table = _group_event_table_by_intervals(event_table, intervals)
if (np.asarray(weights).astype(int) != weights).any():
return event_table.astype(float)
return event_table.astype(int)
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sptonkin/fuzzyhashlib | fuzzyhashlib/__init__.py | tlsh.update | def update(self, buf):
"""Update this hash object's state with the provided string."""
if self._final:
raise InvalidOperation("Cannot update finalised tlsh")
else:
self._buf_len += len(buf)
return self._tlsh.update(buf) | python | def update(self, buf):
"""Update this hash object's state with the provided string."""
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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:
Update this hash object's state with the provided string.
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apetrynet/pyfilemail | pyfilemail/transfer.py | Transfer.is_complete | def is_complete(self):
""":rtype: ``bool`` ``True`` if transfer is complete"""
if 'status' in self.transfer_info:
self._complete = self.transfer_info['status'] == 'STATUS_COMPLETE'
return self._complete | python | def is_complete(self):
""":rtype: ``bool`` ``True`` if transfer is complete"""
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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:
:rtype: ``bool`` ``True`` if transfer is complete
### Response:
def is_complete(self):
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sorgerlab/indra | indra/tools/assemble_corpus.py | standardize_names_groundings | def standardize_names_groundings(stmts):
"""Standardize the names of Concepts with respect to an ontology.
NOTE: this function is currently optimized for Influence Statements
obtained from Eidos, Hume, Sofia and CWMS. It will possibly yield
unexpected results for biology-specific Statements.
"""
print('Standardize names to groundings')
for stmt in stmts:
for concept in stmt.agent_list():
db_ns, db_id = concept.get_grounding()
if db_id is not None:
if isinstance(db_id, list):
db_id = db_id[0][0].split('/')[-1]
else:
db_id = db_id.split('/')[-1]
db_id = db_id.replace('|', ' ')
db_id = db_id.replace('_', ' ')
db_id = db_id.replace('ONT::', '')
db_id = db_id.capitalize()
concept.name = db_id
return stmts | python | def standardize_names_groundings(stmts):
"""Standardize the names of Concepts with respect to an ontology.
NOTE: this function is currently optimized for Influence Statements
obtained from Eidos, Hume, Sofia and CWMS. It will possibly yield
unexpected results for biology-specific Statements.
"""
print('Standardize names to groundings')
for stmt in stmts:
for concept in stmt.agent_list():
db_ns, db_id = concept.get_grounding()
if db_id is not None:
if isinstance(db_id, list):
db_id = db_id[0][0].split('/')[-1]
else:
db_id = db_id.split('/')[-1]
db_id = db_id.replace('|', ' ')
db_id = db_id.replace('_', ' ')
db_id = db_id.replace('ONT::', '')
db_id = db_id.capitalize()
concept.name = db_id
return stmts | [
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NOTE: this function is currently optimized for Influence Statements
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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:
Standardize the names of Concepts with respect to an ontology.
NOTE: this function is currently optimized for Influence Statements
obtained from Eidos, Hume, Sofia and CWMS. It will possibly yield
unexpected results for biology-specific Statements.
### Response:
def standardize_names_groundings(stmts):
"""Standardize the names of Concepts with respect to an ontology.
NOTE: this function is currently optimized for Influence Statements
obtained from Eidos, Hume, Sofia and CWMS. It will possibly yield
unexpected results for biology-specific Statements.
"""
print('Standardize names to groundings')
for stmt in stmts:
for concept in stmt.agent_list():
db_ns, db_id = concept.get_grounding()
if db_id is not None:
if isinstance(db_id, list):
db_id = db_id[0][0].split('/')[-1]
else:
db_id = db_id.split('/')[-1]
db_id = db_id.replace('|', ' ')
db_id = db_id.replace('_', ' ')
db_id = db_id.replace('ONT::', '')
db_id = db_id.capitalize()
concept.name = db_id
return stmts
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ARMmbed/icetea | icetea_lib/Plugin/plugins/LocalAllocator/DutTcp.py | DutTcp.open_connection | def open_connection(self):
"""
Open connection over TCP socket.
:return: Nothing
"""
self.logger.debug("Open COM %s", self.comport)
self.port = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
(ip_addr, port) = self.comport.split(':')
self.port.connect(ip_addr, num(port)) | python | def open_connection(self):
"""
Open connection over TCP socket.
:return: Nothing
"""
self.logger.debug("Open COM %s", self.comport)
self.port = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
(ip_addr, port) = self.comport.split(':')
self.port.connect(ip_addr, num(port)) | [
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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:
Open connection over TCP socket.
:return: Nothing
### Response:
def open_connection(self):
"""
Open connection over TCP socket.
:return: Nothing
"""
self.logger.debug("Open COM %s", self.comport)
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anteater/anteater | anteater/src/patch_scan.py | scan_patch | def scan_patch(project, patch_file, binaries, ips, urls, file_audit_list,
file_audit_project_list, flag_list, ignore_list, file_ignore,
ignore_directories, url_ignore, ip_ignore, apikey):
"""
Scan actions for each commited file in patch set
"""
global failure
split_path = patch_file.split(project + '/', 1)[-1]
if not any(x in split_path for x in ignore_directories):
if is_binary(patch_file) and binaries:
hashlist = get_lists.GetLists()
binary_hash = hashlist.binary_hash(project, split_path)
with open(patch_file, 'rb') as afile:
hasher = hashlib.sha256()
buf = afile.read()
hasher.update(buf)
sha256hash = hasher.hexdigest()
if sha256hash in binary_hash:
logger.info('Found matching file hash for: %s',
patch_file)
else:
logger.info('sha256hash: %s', sha256hash)
logger.error('Non Whitelisted Binary file: %s',
patch_file)
scan_binary(patch_file, project, sha256hash, apikey)
failure = True
with open(reports_dir + "binaries-" + project + ".log", "a") \
as gate_report:
gate_report.write('Non Whitelisted Binary file: {0}\n'.
format(patch_file))
else:
# Check file names / extensions
if file_audit_list.search(patch_file) and not \
file_audit_project_list.search(patch_file):
match = file_audit_list.search(patch_file)
logger.error('Blacklisted file: %s', patch_file)
logger.error('Matched String: %s', match.group())
failure = True
with open(reports_dir + "file-names_" + project + ".log", "a") \
as gate_report:
gate_report.write('Blacklisted file: {0}\n'.
format(patch_file))
gate_report.write('Matched String: {0}'.
format(match.group()))
# Open file to check for blacklisted content
if not is_binary(patch_file):
try:
fo = open(patch_file, 'r')
lines = fo.readlines()
file_exists = True
except IOError:
file_exists = False
if file_exists and not patch_file.endswith(tuple(file_ignore)):
for line in lines:
# Find IP Addresses and send for report to Virus Total
if ips:
ipaddr = re.findall(r'(?:\d{1,3}\.)+(?:\d{1,3})', line)
if ipaddr:
ipaddr = ipaddr[0]
if re.search(ip_ignore, ipaddr):
logger.info('%s is in IP ignore list.', ipaddr)
else:
try:
ipaddress.ip_address(ipaddr).is_global
scan_ipaddr(ipaddr, apikey)
except:
pass # Ok to pass here, as this captures the odd string which is not an IP Address
# Check for URLs and send for report to Virus Total
if urls:
url = re.search("(?P<url>https?://[^\s]+)", line) or re.search("(?P<url>www[^\s]+)", line)
if url:
url = url.group("url")
if re.search(url_ignore, url):
logger.info('%s is in URL ignore list.', url)
else:
scan_url(url, apikey)
# Perform search within text files
for key, value in flag_list.items():
regex = value['regex']
desc = value['desc']
if re.search(regex, line) and not re.search(
ignore_list, line):
logger.error('File contains violation: %s', patch_file)
logger.error('Flagged Content: %s', line.rstrip())
logger.error('Rationale: %s', desc.rstrip())
failure = True
with open(reports_dir + "contents_" + project + ".log", "a") as gate_report:
gate_report.write('File contains violation: {0}\n'.format(patch_file))
gate_report.write('Flagged Content: {0}'.format(line))
gate_report.write('Matched Regular Exp: {0}\n'.format(regex))
gate_report.write('Rationale: {0}\n'.format(desc.rstrip())) | python | def scan_patch(project, patch_file, binaries, ips, urls, file_audit_list,
file_audit_project_list, flag_list, ignore_list, file_ignore,
ignore_directories, url_ignore, ip_ignore, apikey):
"""
Scan actions for each commited file in patch set
"""
global failure
split_path = patch_file.split(project + '/', 1)[-1]
if not any(x in split_path for x in ignore_directories):
if is_binary(patch_file) and binaries:
hashlist = get_lists.GetLists()
binary_hash = hashlist.binary_hash(project, split_path)
with open(patch_file, 'rb') as afile:
hasher = hashlib.sha256()
buf = afile.read()
hasher.update(buf)
sha256hash = hasher.hexdigest()
if sha256hash in binary_hash:
logger.info('Found matching file hash for: %s',
patch_file)
else:
logger.info('sha256hash: %s', sha256hash)
logger.error('Non Whitelisted Binary file: %s',
patch_file)
scan_binary(patch_file, project, sha256hash, apikey)
failure = True
with open(reports_dir + "binaries-" + project + ".log", "a") \
as gate_report:
gate_report.write('Non Whitelisted Binary file: {0}\n'.
format(patch_file))
else:
# Check file names / extensions
if file_audit_list.search(patch_file) and not \
file_audit_project_list.search(patch_file):
match = file_audit_list.search(patch_file)
logger.error('Blacklisted file: %s', patch_file)
logger.error('Matched String: %s', match.group())
failure = True
with open(reports_dir + "file-names_" + project + ".log", "a") \
as gate_report:
gate_report.write('Blacklisted file: {0}\n'.
format(patch_file))
gate_report.write('Matched String: {0}'.
format(match.group()))
# Open file to check for blacklisted content
if not is_binary(patch_file):
try:
fo = open(patch_file, 'r')
lines = fo.readlines()
file_exists = True
except IOError:
file_exists = False
if file_exists and not patch_file.endswith(tuple(file_ignore)):
for line in lines:
# Find IP Addresses and send for report to Virus Total
if ips:
ipaddr = re.findall(r'(?:\d{1,3}\.)+(?:\d{1,3})', line)
if ipaddr:
ipaddr = ipaddr[0]
if re.search(ip_ignore, ipaddr):
logger.info('%s is in IP ignore list.', ipaddr)
else:
try:
ipaddress.ip_address(ipaddr).is_global
scan_ipaddr(ipaddr, apikey)
except:
pass # Ok to pass here, as this captures the odd string which is not an IP Address
# Check for URLs and send for report to Virus Total
if urls:
url = re.search("(?P<url>https?://[^\s]+)", line) or re.search("(?P<url>www[^\s]+)", line)
if url:
url = url.group("url")
if re.search(url_ignore, url):
logger.info('%s is in URL ignore list.', url)
else:
scan_url(url, apikey)
# Perform search within text files
for key, value in flag_list.items():
regex = value['regex']
desc = value['desc']
if re.search(regex, line) and not re.search(
ignore_list, line):
logger.error('File contains violation: %s', patch_file)
logger.error('Flagged Content: %s', line.rstrip())
logger.error('Rationale: %s', desc.rstrip())
failure = True
with open(reports_dir + "contents_" + project + ".log", "a") as gate_report:
gate_report.write('File contains violation: {0}\n'.format(patch_file))
gate_report.write('Flagged Content: {0}'.format(line))
gate_report.write('Matched Regular Exp: {0}\n'.format(regex))
gate_report.write('Rationale: {0}\n'.format(desc.rstrip())) | [
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] | Scan actions for each commited file in patch set | [
"Scan",
"actions",
"for",
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"commited",
"file",
"in",
"patch",
"set"
] | train | https://github.com/anteater/anteater/blob/a980adbed8563ef92494f565acd371e91f50f155/anteater/src/patch_scan.py#L104-L206 | <|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:
Scan actions for each commited file in patch set
### Response:
def scan_patch(project, patch_file, binaries, ips, urls, file_audit_list,
file_audit_project_list, flag_list, ignore_list, file_ignore,
ignore_directories, url_ignore, ip_ignore, apikey):
"""
Scan actions for each commited file in patch set
"""
global failure
split_path = patch_file.split(project + '/', 1)[-1]
if not any(x in split_path for x in ignore_directories):
if is_binary(patch_file) and binaries:
hashlist = get_lists.GetLists()
binary_hash = hashlist.binary_hash(project, split_path)
with open(patch_file, 'rb') as afile:
hasher = hashlib.sha256()
buf = afile.read()
hasher.update(buf)
sha256hash = hasher.hexdigest()
if sha256hash in binary_hash:
logger.info('Found matching file hash for: %s',
patch_file)
else:
logger.info('sha256hash: %s', sha256hash)
logger.error('Non Whitelisted Binary file: %s',
patch_file)
scan_binary(patch_file, project, sha256hash, apikey)
failure = True
with open(reports_dir + "binaries-" + project + ".log", "a") \
as gate_report:
gate_report.write('Non Whitelisted Binary file: {0}\n'.
format(patch_file))
else:
# Check file names / extensions
if file_audit_list.search(patch_file) and not \
file_audit_project_list.search(patch_file):
match = file_audit_list.search(patch_file)
logger.error('Blacklisted file: %s', patch_file)
logger.error('Matched String: %s', match.group())
failure = True
with open(reports_dir + "file-names_" + project + ".log", "a") \
as gate_report:
gate_report.write('Blacklisted file: {0}\n'.
format(patch_file))
gate_report.write('Matched String: {0}'.
format(match.group()))
# Open file to check for blacklisted content
if not is_binary(patch_file):
try:
fo = open(patch_file, 'r')
lines = fo.readlines()
file_exists = True
except IOError:
file_exists = False
if file_exists and not patch_file.endswith(tuple(file_ignore)):
for line in lines:
# Find IP Addresses and send for report to Virus Total
if ips:
ipaddr = re.findall(r'(?:\d{1,3}\.)+(?:\d{1,3})', line)
if ipaddr:
ipaddr = ipaddr[0]
if re.search(ip_ignore, ipaddr):
logger.info('%s is in IP ignore list.', ipaddr)
else:
try:
ipaddress.ip_address(ipaddr).is_global
scan_ipaddr(ipaddr, apikey)
except:
pass # Ok to pass here, as this captures the odd string which is not an IP Address
# Check for URLs and send for report to Virus Total
if urls:
url = re.search("(?P<url>https?://[^\s]+)", line) or re.search("(?P<url>www[^\s]+)", line)
if url:
url = url.group("url")
if re.search(url_ignore, url):
logger.info('%s is in URL ignore list.', url)
else:
scan_url(url, apikey)
# Perform search within text files
for key, value in flag_list.items():
regex = value['regex']
desc = value['desc']
if re.search(regex, line) and not re.search(
ignore_list, line):
logger.error('File contains violation: %s', patch_file)
logger.error('Flagged Content: %s', line.rstrip())
logger.error('Rationale: %s', desc.rstrip())
failure = True
with open(reports_dir + "contents_" + project + ".log", "a") as gate_report:
gate_report.write('File contains violation: {0}\n'.format(patch_file))
gate_report.write('Flagged Content: {0}'.format(line))
gate_report.write('Matched Regular Exp: {0}\n'.format(regex))
gate_report.write('Rationale: {0}\n'.format(desc.rstrip()))
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google/grr | grr/server/grr_response_server/databases/mysql_flows.py | MySQLDBFlowMixin.CountFlowResultsByType | def CountFlowResultsByType(self, client_id, flow_id, cursor=None):
"""Returns counts of flow results grouped by result type."""
query = ("SELECT type, COUNT(*) FROM flow_results "
"FORCE INDEX (flow_results_by_client_id_flow_id_timestamp) "
"WHERE client_id = %s AND flow_id = %s "
"GROUP BY type")
args = [db_utils.ClientIDToInt(client_id), db_utils.FlowIDToInt(flow_id)]
cursor.execute(query, args)
return dict(cursor.fetchall()) | python | def CountFlowResultsByType(self, client_id, flow_id, cursor=None):
"""Returns counts of flow results grouped by result type."""
query = ("SELECT type, COUNT(*) FROM flow_results "
"FORCE INDEX (flow_results_by_client_id_flow_id_timestamp) "
"WHERE client_id = %s AND flow_id = %s "
"GROUP BY type")
args = [db_utils.ClientIDToInt(client_id), db_utils.FlowIDToInt(flow_id)]
cursor.execute(query, args)
return dict(cursor.fetchall()) | [
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] | Returns counts of flow results grouped by result type. | [
"Returns",
"counts",
"of",
"flow",
"results",
"grouped",
"by",
"result",
"type",
"."
] | train | https://github.com/google/grr/blob/5cef4e8e2f0d5df43ea4877e9c798e0bf60bfe74/grr/server/grr_response_server/databases/mysql_flows.py#L1350-L1360 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Returns counts of flow results grouped by result type.
### Response:
def CountFlowResultsByType(self, client_id, flow_id, cursor=None):
"""Returns counts of flow results grouped by result type."""
query = ("SELECT type, COUNT(*) FROM flow_results "
"FORCE INDEX (flow_results_by_client_id_flow_id_timestamp) "
"WHERE client_id = %s AND flow_id = %s "
"GROUP BY type")
args = [db_utils.ClientIDToInt(client_id), db_utils.FlowIDToInt(flow_id)]
cursor.execute(query, args)
return dict(cursor.fetchall())
<|endoftext|> | [
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google/grr | grr/client/grr_response_client/client_actions/artifact_collector.py | ArtifactCollector._ProcessGrepSource | def _ProcessGrepSource(self, source):
"""Find files fulfilling regex conditions."""
attributes = source.base_source.attributes
paths = artifact_utils.InterpolateListKbAttributes(
attributes["paths"], self.knowledge_base,
self.ignore_interpolation_errors)
regex = utils.RegexListDisjunction(attributes["content_regex_list"])
condition = rdf_file_finder.FileFinderCondition.ContentsRegexMatch(
regex=regex, mode="ALL_HITS")
file_finder_action = rdf_file_finder.FileFinderAction.Stat()
request = rdf_file_finder.FileFinderArgs(
paths=paths,
action=file_finder_action,
conditions=[condition],
follow_links=True)
action = file_finder.FileFinderOSFromClient
yield action, request | python | def _ProcessGrepSource(self, source):
"""Find files fulfilling regex conditions."""
attributes = source.base_source.attributes
paths = artifact_utils.InterpolateListKbAttributes(
attributes["paths"], self.knowledge_base,
self.ignore_interpolation_errors)
regex = utils.RegexListDisjunction(attributes["content_regex_list"])
condition = rdf_file_finder.FileFinderCondition.ContentsRegexMatch(
regex=regex, mode="ALL_HITS")
file_finder_action = rdf_file_finder.FileFinderAction.Stat()
request = rdf_file_finder.FileFinderArgs(
paths=paths,
action=file_finder_action,
conditions=[condition],
follow_links=True)
action = file_finder.FileFinderOSFromClient
yield action, request | [
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"files",
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"conditions",
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] | train | https://github.com/google/grr/blob/5cef4e8e2f0d5df43ea4877e9c798e0bf60bfe74/grr/client/grr_response_client/client_actions/artifact_collector.py#L208-L225 | <|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:
Find files fulfilling regex conditions.
### Response:
def _ProcessGrepSource(self, source):
"""Find files fulfilling regex conditions."""
attributes = source.base_source.attributes
paths = artifact_utils.InterpolateListKbAttributes(
attributes["paths"], self.knowledge_base,
self.ignore_interpolation_errors)
regex = utils.RegexListDisjunction(attributes["content_regex_list"])
condition = rdf_file_finder.FileFinderCondition.ContentsRegexMatch(
regex=regex, mode="ALL_HITS")
file_finder_action = rdf_file_finder.FileFinderAction.Stat()
request = rdf_file_finder.FileFinderArgs(
paths=paths,
action=file_finder_action,
conditions=[condition],
follow_links=True)
action = file_finder.FileFinderOSFromClient
yield action, request
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F5Networks/f5-common-python | f5/multi_device/cluster/__init__.py | ClusterManager.manage_extant | def manage_extant(self, **kwargs):
'''Manage an existing cluster
:param kwargs: dict -- keyword args in dict
'''
self._check_device_number(kwargs['devices'])
self.trust_domain = TrustDomain(
devices=kwargs['devices'],
partition=kwargs['device_group_partition']
)
self.device_group = DeviceGroup(**kwargs)
self.cluster = Cluster(**kwargs) | python | def manage_extant(self, **kwargs):
'''Manage an existing cluster
:param kwargs: dict -- keyword args in dict
'''
self._check_device_number(kwargs['devices'])
self.trust_domain = TrustDomain(
devices=kwargs['devices'],
partition=kwargs['device_group_partition']
)
self.device_group = DeviceGroup(**kwargs)
self.cluster = Cluster(**kwargs) | [
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"cluster",
"=",
"Cluster",
"(",
"*",
"*",
"kwargs",
")"
] | Manage an existing cluster
:param kwargs: dict -- keyword args in dict | [
"Manage",
"an",
"existing",
"cluster"
] | train | https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/multi_device/cluster/__init__.py#L136-L148 | <|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:
Manage an existing cluster
:param kwargs: dict -- keyword args in dict
### Response:
def manage_extant(self, **kwargs):
'''Manage an existing cluster
:param kwargs: dict -- keyword args in dict
'''
self._check_device_number(kwargs['devices'])
self.trust_domain = TrustDomain(
devices=kwargs['devices'],
partition=kwargs['device_group_partition']
)
self.device_group = DeviceGroup(**kwargs)
self.cluster = Cluster(**kwargs)
<|endoftext|> | [
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woolfson-group/isambard | isambard/ampal/interactions.py | find_covalent_bonds | def find_covalent_bonds(ampal, max_range=2.2, threshold=1.1, tag=True):
"""Finds all covalent bonds in the AMPAL object.
Parameters
----------
ampal : AMPAL Object
Any AMPAL object with a `get_atoms` method.
max_range : float, optional
Used to define the sector size, so interactions at longer ranges
will not be found.
threshold : float, optional
Allows deviation from ideal covalent bond distance to be included.
For example, a value of 1.1 would allow interactions up to 10% further
from the ideal distance to be included.
tag : bool, optional
If `True`, will add the covalent bond to the tags dictionary of
each `Atom` involved in the interaction under the `covalent_bonds`
key.
"""
sectors=gen_sectors(ampal.get_atoms(), max_range * 1.1)
bonds=[]
for sector in sectors.values():
atoms=itertools.combinations(sector, 2)
bonds.extend(covalent_bonds(atoms, threshold=threshold))
bond_set=list(set(bonds))
if tag:
for bond in bond_set:
a, b=bond.a, bond.b
if 'covalent_bonds' not in a.tags:
a.tags['covalent_bonds']=[b]
else:
a.tags['covalent_bonds'].append(b)
if 'covalent_bonds' not in b.tags:
b.tags['covalent_bonds']=[a]
else:
b.tags['covalent_bonds'].append(a)
return bond_set | python | def find_covalent_bonds(ampal, max_range=2.2, threshold=1.1, tag=True):
"""Finds all covalent bonds in the AMPAL object.
Parameters
----------
ampal : AMPAL Object
Any AMPAL object with a `get_atoms` method.
max_range : float, optional
Used to define the sector size, so interactions at longer ranges
will not be found.
threshold : float, optional
Allows deviation from ideal covalent bond distance to be included.
For example, a value of 1.1 would allow interactions up to 10% further
from the ideal distance to be included.
tag : bool, optional
If `True`, will add the covalent bond to the tags dictionary of
each `Atom` involved in the interaction under the `covalent_bonds`
key.
"""
sectors=gen_sectors(ampal.get_atoms(), max_range * 1.1)
bonds=[]
for sector in sectors.values():
atoms=itertools.combinations(sector, 2)
bonds.extend(covalent_bonds(atoms, threshold=threshold))
bond_set=list(set(bonds))
if tag:
for bond in bond_set:
a, b=bond.a, bond.b
if 'covalent_bonds' not in a.tags:
a.tags['covalent_bonds']=[b]
else:
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if 'covalent_bonds' not in b.tags:
b.tags['covalent_bonds']=[a]
else:
b.tags['covalent_bonds'].append(a)
return bond_set | [
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] | Finds all covalent bonds in the AMPAL object.
Parameters
----------
ampal : AMPAL Object
Any AMPAL object with a `get_atoms` method.
max_range : float, optional
Used to define the sector size, so interactions at longer ranges
will not be found.
threshold : float, optional
Allows deviation from ideal covalent bond distance to be included.
For example, a value of 1.1 would allow interactions up to 10% further
from the ideal distance to be included.
tag : bool, optional
If `True`, will add the covalent bond to the tags dictionary of
each `Atom` involved in the interaction under the `covalent_bonds`
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:
Finds all covalent bonds in the AMPAL object.
Parameters
----------
ampal : AMPAL Object
Any AMPAL object with a `get_atoms` method.
max_range : float, optional
Used to define the sector size, so interactions at longer ranges
will not be found.
threshold : float, optional
Allows deviation from ideal covalent bond distance to be included.
For example, a value of 1.1 would allow interactions up to 10% further
from the ideal distance to be included.
tag : bool, optional
If `True`, will add the covalent bond to the tags dictionary of
each `Atom` involved in the interaction under the `covalent_bonds`
key.
### Response:
def find_covalent_bonds(ampal, max_range=2.2, threshold=1.1, tag=True):
"""Finds all covalent bonds in the AMPAL object.
Parameters
----------
ampal : AMPAL Object
Any AMPAL object with a `get_atoms` method.
max_range : float, optional
Used to define the sector size, so interactions at longer ranges
will not be found.
threshold : float, optional
Allows deviation from ideal covalent bond distance to be included.
For example, a value of 1.1 would allow interactions up to 10% further
from the ideal distance to be included.
tag : bool, optional
If `True`, will add the covalent bond to the tags dictionary of
each `Atom` involved in the interaction under the `covalent_bonds`
key.
"""
sectors=gen_sectors(ampal.get_atoms(), max_range * 1.1)
bonds=[]
for sector in sectors.values():
atoms=itertools.combinations(sector, 2)
bonds.extend(covalent_bonds(atoms, threshold=threshold))
bond_set=list(set(bonds))
if tag:
for bond in bond_set:
a, b=bond.a, bond.b
if 'covalent_bonds' not in a.tags:
a.tags['covalent_bonds']=[b]
else:
a.tags['covalent_bonds'].append(b)
if 'covalent_bonds' not in b.tags:
b.tags['covalent_bonds']=[a]
else:
b.tags['covalent_bonds'].append(a)
return bond_set
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all-umass/graphs | graphs/mixins/label.py | LabelMixin.classify_nearest | def classify_nearest(self, partial_labels):
'''Simple semi-supervised classification, by assigning unlabeled vertices
the label of nearest labeled vertex.
partial_labels: (n,) array of integer labels, -1 for unlabeled.
'''
labels = np.array(partial_labels, copy=True)
unlabeled = labels == -1
# compute geodesic distances from unlabeled vertices
D_unlabeled = self.shortest_path(weighted=True)[unlabeled]
# set distances to other unlabeled vertices to infinity
D_unlabeled[:,unlabeled] = np.inf
# find shortest distances to labeled vertices
idx = D_unlabeled.argmin(axis=1)
# apply the label of the closest vertex
labels[unlabeled] = labels[idx]
return labels | python | def classify_nearest(self, partial_labels):
'''Simple semi-supervised classification, by assigning unlabeled vertices
the label of nearest labeled vertex.
partial_labels: (n,) array of integer labels, -1 for unlabeled.
'''
labels = np.array(partial_labels, copy=True)
unlabeled = labels == -1
# compute geodesic distances from unlabeled vertices
D_unlabeled = self.shortest_path(weighted=True)[unlabeled]
# set distances to other unlabeled vertices to infinity
D_unlabeled[:,unlabeled] = np.inf
# find shortest distances to labeled vertices
idx = D_unlabeled.argmin(axis=1)
# apply the label of the closest vertex
labels[unlabeled] = labels[idx]
return labels | [
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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:
Simple semi-supervised classification, by assigning unlabeled vertices
the label of nearest labeled vertex.
partial_labels: (n,) array of integer labels, -1 for unlabeled.
### Response:
def classify_nearest(self, partial_labels):
'''Simple semi-supervised classification, by assigning unlabeled vertices
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partial_labels: (n,) array of integer labels, -1 for unlabeled.
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labels = np.array(partial_labels, copy=True)
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# find shortest distances to labeled vertices
idx = D_unlabeled.argmin(axis=1)
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labels[unlabeled] = labels[idx]
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llazzaro/django-scheduler | schedule/templatetags/scheduletags.py | _cook_slots | def _cook_slots(period, increment):
"""
Prepare slots to be displayed on the left hand side
calculate dimensions (in px) for each slot.
Arguments:
period - time period for the whole series
increment - slot size in minutes
"""
tdiff = datetime.timedelta(minutes=increment)
num = int((period.end - period.start).total_seconds()) // int(tdiff.total_seconds())
s = period.start
slots = []
for i in range(num):
sl = period.get_time_slot(s, s + tdiff)
slots.append(sl)
s = s + tdiff
return slots | python | def _cook_slots(period, increment):
"""
Prepare slots to be displayed on the left hand side
calculate dimensions (in px) for each slot.
Arguments:
period - time period for the whole series
increment - slot size in minutes
"""
tdiff = datetime.timedelta(minutes=increment)
num = int((period.end - period.start).total_seconds()) // int(tdiff.total_seconds())
s = period.start
slots = []
for i in range(num):
sl = period.get_time_slot(s, s + tdiff)
slots.append(sl)
s = s + tdiff
return slots | [
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calculate dimensions (in px) for each slot.
Arguments:
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increment - slot size in minutes | [
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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:
Prepare slots to be displayed on the left hand side
calculate dimensions (in px) for each slot.
Arguments:
period - time period for the whole series
increment - slot size in minutes
### Response:
def _cook_slots(period, increment):
"""
Prepare slots to be displayed on the left hand side
calculate dimensions (in px) for each slot.
Arguments:
period - time period for the whole series
increment - slot size in minutes
"""
tdiff = datetime.timedelta(minutes=increment)
num = int((period.end - period.start).total_seconds()) // int(tdiff.total_seconds())
s = period.start
slots = []
for i in range(num):
sl = period.get_time_slot(s, s + tdiff)
slots.append(sl)
s = s + tdiff
return slots
<|endoftext|> | [
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pyviz/param | param/parameterized.py | Parameters.get_value_generator | def get_value_generator(self_,name): # pylint: disable-msg=E0213
"""
Return the value or value-generating object of the named
attribute.
For most parameters, this is simply the parameter's value
(i.e. the same as getattr()), but Dynamic parameters have
their value-generating object returned.
"""
cls_or_slf = self_.self_or_cls
param_obj = cls_or_slf.param.objects('existing').get(name)
if not param_obj:
value = getattr(cls_or_slf,name)
# CompositeParameter detected by being a Parameter and having 'attribs'
elif hasattr(param_obj,'attribs'):
value = [cls_or_slf.param.get_value_generator(a) for a in param_obj.attribs]
# not a Dynamic Parameter
elif not hasattr(param_obj,'_value_is_dynamic'):
value = getattr(cls_or_slf,name)
# Dynamic Parameter...
else:
internal_name = "_%s_param_value"%name
if hasattr(cls_or_slf,internal_name):
# dealing with object and it's been set on this object
value = getattr(cls_or_slf,internal_name)
else:
# dealing with class or isn't set on the object
value = param_obj.default
return value | python | def get_value_generator(self_,name): # pylint: disable-msg=E0213
"""
Return the value or value-generating object of the named
attribute.
For most parameters, this is simply the parameter's value
(i.e. the same as getattr()), but Dynamic parameters have
their value-generating object returned.
"""
cls_or_slf = self_.self_or_cls
param_obj = cls_or_slf.param.objects('existing').get(name)
if not param_obj:
value = getattr(cls_or_slf,name)
# CompositeParameter detected by being a Parameter and having 'attribs'
elif hasattr(param_obj,'attribs'):
value = [cls_or_slf.param.get_value_generator(a) for a in param_obj.attribs]
# not a Dynamic Parameter
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value = getattr(cls_or_slf,name)
# Dynamic Parameter...
else:
internal_name = "_%s_param_value"%name
if hasattr(cls_or_slf,internal_name):
# dealing with object and it's been set on this object
value = getattr(cls_or_slf,internal_name)
else:
# dealing with class or isn't set on the object
value = param_obj.default
return 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:
Return the value or value-generating object of the named
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For most parameters, this is simply the parameter's value
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### Response:
def get_value_generator(self_,name): # pylint: disable-msg=E0213
"""
Return the value or value-generating object of the named
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For most parameters, this is simply the parameter's value
(i.e. the same as getattr()), but Dynamic parameters have
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"""
cls_or_slf = self_.self_or_cls
param_obj = cls_or_slf.param.objects('existing').get(name)
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# CompositeParameter detected by being a Parameter and having 'attribs'
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value = [cls_or_slf.param.get_value_generator(a) for a in param_obj.attribs]
# not a Dynamic Parameter
elif not hasattr(param_obj,'_value_is_dynamic'):
value = getattr(cls_or_slf,name)
# Dynamic Parameter...
else:
internal_name = "_%s_param_value"%name
if hasattr(cls_or_slf,internal_name):
# dealing with object and it's been set on this object
value = getattr(cls_or_slf,internal_name)
else:
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return value
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mitsei/dlkit | dlkit/json_/proxy/sessions.py | ProxySession.get_proxy | def get_proxy(self, input_):
"""Gets a proxy.
arg: input (osid.proxy.ProxyCondition): a proxy condition
return: (osid.proxy.Proxy) - a proxy
raise: NullArgument - ``input`` is ``null``
raise: OperationFailed - unable to complete request
raise: PermissionDenied - authorization failure
raise: Unsupported - ``input`` is not of this service
*compliance: mandatory -- This method is must be implemented.*
"""
if input_._http_request is not None:
authentication = Authentication()
authentication.set_django_user(input_._http_request.user)
else:
authentication = None
effective_agent_id = input_._effective_agent_id
# Also need to deal with effective dates and Local
return rules.Proxy(authentication=authentication,
effective_agent_id=effective_agent_id) | python | def get_proxy(self, input_):
"""Gets a proxy.
arg: input (osid.proxy.ProxyCondition): a proxy condition
return: (osid.proxy.Proxy) - a proxy
raise: NullArgument - ``input`` is ``null``
raise: OperationFailed - unable to complete request
raise: PermissionDenied - authorization failure
raise: Unsupported - ``input`` is not of this service
*compliance: mandatory -- This method is must be implemented.*
"""
if input_._http_request is not None:
authentication = Authentication()
authentication.set_django_user(input_._http_request.user)
else:
authentication = None
effective_agent_id = input_._effective_agent_id
# Also need to deal with effective dates and Local
return rules.Proxy(authentication=authentication,
effective_agent_id=effective_agent_id) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Gets a proxy.
arg: input (osid.proxy.ProxyCondition): a proxy condition
return: (osid.proxy.Proxy) - a proxy
raise: NullArgument - ``input`` is ``null``
raise: OperationFailed - unable to complete request
raise: PermissionDenied - authorization failure
raise: Unsupported - ``input`` is not of this service
*compliance: mandatory -- This method is must be implemented.*
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def get_proxy(self, input_):
"""Gets a proxy.
arg: input (osid.proxy.ProxyCondition): a proxy condition
return: (osid.proxy.Proxy) - a proxy
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authentication.set_django_user(input_._http_request.user)
else:
authentication = None
effective_agent_id = input_._effective_agent_id
# Also need to deal with effective dates and Local
return rules.Proxy(authentication=authentication,
effective_agent_id=effective_agent_id)
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DataDog/integrations-core | openstack/datadog_checks/openstack/openstack.py | KeystoneCatalog.get_neutron_endpoint | def get_neutron_endpoint(cls, json_resp):
"""
Parse the service catalog returned by the Identity API for an endpoint matching the Neutron service
Sends a CRITICAL service check when none are found registered in the Catalog
"""
catalog = json_resp.get('token', {}).get('catalog', [])
match = 'neutron'
neutron_endpoint = None
for entry in catalog:
if entry['name'] == match or 'Networking' in entry['name']:
valid_endpoints = {}
for ep in entry['endpoints']:
interface = ep.get('interface', '')
if interface in ['public', 'internal']:
valid_endpoints[interface] = ep['url']
if valid_endpoints:
# Favor public endpoints over internal
neutron_endpoint = valid_endpoints.get("public", valid_endpoints.get("internal"))
break
else:
raise MissingNeutronEndpoint()
return neutron_endpoint | python | def get_neutron_endpoint(cls, json_resp):
"""
Parse the service catalog returned by the Identity API for an endpoint matching the Neutron service
Sends a CRITICAL service check when none are found registered in the Catalog
"""
catalog = json_resp.get('token', {}).get('catalog', [])
match = 'neutron'
neutron_endpoint = None
for entry in catalog:
if entry['name'] == match or 'Networking' in entry['name']:
valid_endpoints = {}
for ep in entry['endpoints']:
interface = ep.get('interface', '')
if interface in ['public', 'internal']:
valid_endpoints[interface] = ep['url']
if valid_endpoints:
# Favor public endpoints over internal
neutron_endpoint = valid_endpoints.get("public", valid_endpoints.get("internal"))
break
else:
raise MissingNeutronEndpoint()
return neutron_endpoint | [
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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 service catalog returned by the Identity API for an endpoint matching the Neutron service
Sends a CRITICAL service check when none are found registered in the Catalog
### Response:
def get_neutron_endpoint(cls, json_resp):
"""
Parse the service catalog returned by the Identity API for an endpoint matching the Neutron service
Sends a CRITICAL service check when none are found registered in the Catalog
"""
catalog = json_resp.get('token', {}).get('catalog', [])
match = 'neutron'
neutron_endpoint = None
for entry in catalog:
if entry['name'] == match or 'Networking' in entry['name']:
valid_endpoints = {}
for ep in entry['endpoints']:
interface = ep.get('interface', '')
if interface in ['public', 'internal']:
valid_endpoints[interface] = ep['url']
if valid_endpoints:
# Favor public endpoints over internal
neutron_endpoint = valid_endpoints.get("public", valid_endpoints.get("internal"))
break
else:
raise MissingNeutronEndpoint()
return neutron_endpoint
<|endoftext|> | [
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materialsproject/pymatgen | pymatgen/io/abinit/flows.py | Flow.validate_json_schema | def validate_json_schema(self):
"""Validate the JSON schema. Return list of errors."""
errors = []
for work in self:
for task in work:
if not task.get_results().validate_json_schema():
errors.append(task)
if not work.get_results().validate_json_schema():
errors.append(work)
if not self.get_results().validate_json_schema():
errors.append(self)
return errors | python | def validate_json_schema(self):
"""Validate the JSON schema. Return list of errors."""
errors = []
for work in self:
for task in work:
if not task.get_results().validate_json_schema():
errors.append(task)
if not work.get_results().validate_json_schema():
errors.append(work)
if not self.get_results().validate_json_schema():
errors.append(self)
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Validate the JSON schema. Return list of errors.
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juju/python-libjuju | juju/client/overrides.py | ResourcesFacade.AddPendingResources | async def AddPendingResources(self, application_tag, charm_url, resources):
"""Fix the calling signature of AddPendingResources.
The ResourcesFacade doesn't conform to the standard facade pattern in
the Juju source, which leads to the schemagened code not matching up
properly with the actual calling convention in the API. There is work
planned to fix this in Juju, but we have to work around it for now.
application_tag : str
charm_url : str
resources : typing.Sequence<+T_co>[~CharmResource]<~CharmResource>
Returns -> typing.Union[_ForwardRef('ErrorResult'),
typing.Sequence<+T_co>[str]]
"""
# map input types to rpc msg
_params = dict()
msg = dict(type='Resources',
request='AddPendingResources',
version=1,
params=_params)
_params['tag'] = application_tag
_params['url'] = charm_url
_params['resources'] = resources
reply = await self.rpc(msg)
return reply | python | async def AddPendingResources(self, application_tag, charm_url, resources):
"""Fix the calling signature of AddPendingResources.
The ResourcesFacade doesn't conform to the standard facade pattern in
the Juju source, which leads to the schemagened code not matching up
properly with the actual calling convention in the API. There is work
planned to fix this in Juju, but we have to work around it for now.
application_tag : str
charm_url : str
resources : typing.Sequence<+T_co>[~CharmResource]<~CharmResource>
Returns -> typing.Union[_ForwardRef('ErrorResult'),
typing.Sequence<+T_co>[str]]
"""
# map input types to rpc msg
_params = dict()
msg = dict(type='Resources',
request='AddPendingResources',
version=1,
params=_params)
_params['tag'] = application_tag
_params['url'] = charm_url
_params['resources'] = resources
reply = await self.rpc(msg)
return reply | [
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The ResourcesFacade doesn't conform to the standard facade pattern in
the Juju source, which leads to the schemagened code not matching up
properly with the actual calling convention in the API. There is work
planned to fix this in Juju, but we have to work around it for now.
application_tag : str
charm_url : str
resources : typing.Sequence<+T_co>[~CharmResource]<~CharmResource>
Returns -> typing.Union[_ForwardRef('ErrorResult'),
typing.Sequence<+T_co>[str]] | [
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] | train | https://github.com/juju/python-libjuju/blob/58f0011f4c57cd68830258952fa952eaadca6b38/juju/client/overrides.py#L65-L89 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Fix the calling signature of AddPendingResources.
The ResourcesFacade doesn't conform to the standard facade pattern in
the Juju source, which leads to the schemagened code not matching up
properly with the actual calling convention in the API. There is work
planned to fix this in Juju, but we have to work around it for now.
application_tag : str
charm_url : str
resources : typing.Sequence<+T_co>[~CharmResource]<~CharmResource>
Returns -> typing.Union[_ForwardRef('ErrorResult'),
typing.Sequence<+T_co>[str]]
### Response:
async def AddPendingResources(self, application_tag, charm_url, resources):
"""Fix the calling signature of AddPendingResources.
The ResourcesFacade doesn't conform to the standard facade pattern in
the Juju source, which leads to the schemagened code not matching up
properly with the actual calling convention in the API. There is work
planned to fix this in Juju, but we have to work around it for now.
application_tag : str
charm_url : str
resources : typing.Sequence<+T_co>[~CharmResource]<~CharmResource>
Returns -> typing.Union[_ForwardRef('ErrorResult'),
typing.Sequence<+T_co>[str]]
"""
# map input types to rpc msg
_params = dict()
msg = dict(type='Resources',
request='AddPendingResources',
version=1,
params=_params)
_params['tag'] = application_tag
_params['url'] = charm_url
_params['resources'] = resources
reply = await self.rpc(msg)
return reply
<|endoftext|> | [
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inveniosoftware-contrib/invenio-classifier | invenio_classifier/find.py | get_reference_section_beginning | def get_reference_section_beginning(fulltext):
"""Get start of reference section."""
sect_start = {
'start_line': None,
'end_line': None,
'title_string': None,
'marker_pattern': None,
'marker': None,
'how_found_start': None,
}
# Find start of refs section:
sect_start = find_reference_section(fulltext)
if sect_start is not None:
sect_start['how_found_start'] = 1
else:
# No references found - try with no title option
sect_start = find_reference_section_no_title_via_brackets(fulltext)
if sect_start is not None:
sect_start['how_found_start'] = 2
# Try weaker set of patterns if needed
if sect_start is None:
# No references found - try with no title option (with weaker
# patterns..)
sect_start = find_reference_section_no_title_via_dots(fulltext)
if sect_start is not None:
sect_start['how_found_start'] = 3
if sect_start is None:
# No references found - try with no title option (with even
# weaker patterns..)
sect_start = find_reference_section_no_title_via_numbers(
fulltext)
if sect_start is not None:
sect_start['how_found_start'] = 4
if sect_start:
current_app.logger.debug('* title %r' % sect_start['title_string'])
current_app.logger.debug('* marker %r' % sect_start['marker'])
current_app.logger.debug('* title_marker_same_line %s'
% sect_start['title_marker_same_line'])
else:
current_app.logger.debug('* could not find references section')
return sect_start | python | def get_reference_section_beginning(fulltext):
"""Get start of reference section."""
sect_start = {
'start_line': None,
'end_line': None,
'title_string': None,
'marker_pattern': None,
'marker': None,
'how_found_start': None,
}
# Find start of refs section:
sect_start = find_reference_section(fulltext)
if sect_start is not None:
sect_start['how_found_start'] = 1
else:
# No references found - try with no title option
sect_start = find_reference_section_no_title_via_brackets(fulltext)
if sect_start is not None:
sect_start['how_found_start'] = 2
# Try weaker set of patterns if needed
if sect_start is None:
# No references found - try with no title option (with weaker
# patterns..)
sect_start = find_reference_section_no_title_via_dots(fulltext)
if sect_start is not None:
sect_start['how_found_start'] = 3
if sect_start is None:
# No references found - try with no title option (with even
# weaker patterns..)
sect_start = find_reference_section_no_title_via_numbers(
fulltext)
if sect_start is not None:
sect_start['how_found_start'] = 4
if sect_start:
current_app.logger.debug('* title %r' % sect_start['title_string'])
current_app.logger.debug('* marker %r' % sect_start['marker'])
current_app.logger.debug('* title_marker_same_line %s'
% sect_start['title_marker_same_line'])
else:
current_app.logger.debug('* could not find references section')
return sect_start | [
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"Get",
"start",
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] | train | https://github.com/inveniosoftware-contrib/invenio-classifier/blob/3c758cf34dca6bf0548e7da5de34e5f72e3b255e/invenio_classifier/find.py#L476-L518 | <|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:
Get start of reference section.
### Response:
def get_reference_section_beginning(fulltext):
"""Get start of reference section."""
sect_start = {
'start_line': None,
'end_line': None,
'title_string': None,
'marker_pattern': None,
'marker': None,
'how_found_start': None,
}
# Find start of refs section:
sect_start = find_reference_section(fulltext)
if sect_start is not None:
sect_start['how_found_start'] = 1
else:
# No references found - try with no title option
sect_start = find_reference_section_no_title_via_brackets(fulltext)
if sect_start is not None:
sect_start['how_found_start'] = 2
# Try weaker set of patterns if needed
if sect_start is None:
# No references found - try with no title option (with weaker
# patterns..)
sect_start = find_reference_section_no_title_via_dots(fulltext)
if sect_start is not None:
sect_start['how_found_start'] = 3
if sect_start is None:
# No references found - try with no title option (with even
# weaker patterns..)
sect_start = find_reference_section_no_title_via_numbers(
fulltext)
if sect_start is not None:
sect_start['how_found_start'] = 4
if sect_start:
current_app.logger.debug('* title %r' % sect_start['title_string'])
current_app.logger.debug('* marker %r' % sect_start['marker'])
current_app.logger.debug('* title_marker_same_line %s'
% sect_start['title_marker_same_line'])
else:
current_app.logger.debug('* could not find references section')
return sect_start
<|endoftext|> | [
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ixc/python-edtf | edtf/convert.py | struct_time_to_jd | def struct_time_to_jd(st):
"""
Return a float number representing the Julian Date for the given
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NOTE: extra fields `tm_wday`, `tm_yday`, and `tm_isdst` are ignored.
"""
year, month, day = st[:3]
hours, minutes, seconds = st[3:6]
# Convert time of day to fraction of day
day += jdutil.hmsm_to_days(hours, minutes, seconds)
return jdutil.date_to_jd(year, month, day) | python | def struct_time_to_jd(st):
"""
Return a float number representing the Julian Date for the given
`struct_time`.
NOTE: extra fields `tm_wday`, `tm_yday`, and `tm_isdst` are ignored.
"""
year, month, day = st[:3]
hours, minutes, seconds = st[3:6]
# Convert time of day to fraction of day
day += jdutil.hmsm_to_days(hours, minutes, seconds)
return jdutil.date_to_jd(year, month, day) | [
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Return a float number representing the Julian Date for the given
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saltstack/salt | salt/modules/debian_ip.py | _parse_current_network_settings | def _parse_current_network_settings():
'''
Parse /etc/default/networking and return current configuration
'''
opts = salt.utils.odict.OrderedDict()
opts['networking'] = ''
if os.path.isfile(_DEB_NETWORKING_FILE):
with salt.utils.files.fopen(_DEB_NETWORKING_FILE) as contents:
for line in contents:
salt.utils.stringutils.to_unicode(line)
if line.startswith('#'):
continue
elif line.startswith('CONFIGURE_INTERFACES'):
opts['networking'] = line.split('=', 1)[1].strip()
hostname = _parse_hostname()
domainname = _parse_domainname()
searchdomain = _parse_searchdomain()
opts['hostname'] = hostname
opts['domainname'] = domainname
opts['searchdomain'] = searchdomain
return opts | python | def _parse_current_network_settings():
'''
Parse /etc/default/networking and return current configuration
'''
opts = salt.utils.odict.OrderedDict()
opts['networking'] = ''
if os.path.isfile(_DEB_NETWORKING_FILE):
with salt.utils.files.fopen(_DEB_NETWORKING_FILE) as contents:
for line in contents:
salt.utils.stringutils.to_unicode(line)
if line.startswith('#'):
continue
elif line.startswith('CONFIGURE_INTERFACES'):
opts['networking'] = line.split('=', 1)[1].strip()
hostname = _parse_hostname()
domainname = _parse_domainname()
searchdomain = _parse_searchdomain()
opts['hostname'] = hostname
opts['domainname'] = domainname
opts['searchdomain'] = searchdomain
return opts | [
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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 /etc/default/networking and return current configuration
### Response:
def _parse_current_network_settings():
'''
Parse /etc/default/networking and return current configuration
'''
opts = salt.utils.odict.OrderedDict()
opts['networking'] = ''
if os.path.isfile(_DEB_NETWORKING_FILE):
with salt.utils.files.fopen(_DEB_NETWORKING_FILE) as contents:
for line in contents:
salt.utils.stringutils.to_unicode(line)
if line.startswith('#'):
continue
elif line.startswith('CONFIGURE_INTERFACES'):
opts['networking'] = line.split('=', 1)[1].strip()
hostname = _parse_hostname()
domainname = _parse_domainname()
searchdomain = _parse_searchdomain()
opts['hostname'] = hostname
opts['domainname'] = domainname
opts['searchdomain'] = searchdomain
return opts
<|endoftext|> | [
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wandb/client | wandb/__init__.py | log | def log(row=None, commit=True, *args, **kargs):
"""Log a dict to the global run's history. If commit is false, enables multiple calls before commiting.
Eg.
wandb.log({'train-loss': 0.5, 'accuracy': 0.9})
"""
if run is None:
raise ValueError(
"You must call `wandb.init` in the same process before calling log")
if row is None:
row = {}
if commit:
run.history.add(row, *args, **kargs)
else:
run.history.update(row, *args, **kargs) | python | def log(row=None, commit=True, *args, **kargs):
"""Log a dict to the global run's history. If commit is false, enables multiple calls before commiting.
Eg.
wandb.log({'train-loss': 0.5, 'accuracy': 0.9})
"""
if run is None:
raise ValueError(
"You must call `wandb.init` in the same process before calling log")
if row is None:
row = {}
if commit:
run.history.add(row, *args, **kargs)
else:
run.history.update(row, *args, **kargs) | [
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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:
Log a dict to the global run's history. If commit is false, enables multiple calls before commiting.
Eg.
wandb.log({'train-loss': 0.5, 'accuracy': 0.9})
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def log(row=None, commit=True, *args, **kargs):
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Eg.
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omederos/pyspinner | spinning/spinning.py | override_params | def override_params(opening_char='{', closing_char='}', separator_char='|'):
"""
Override some character settings
@type opening_char: str
@param opening_char: Opening character. Default: '{'
@type closing_char: str
@param closing_char: Closing character. Default: '}'
@type separator_char: str
@param separator_char: Separator char. Default: '|'
"""
global char_separator, char_opening, char_closing
char_separator = separator_char
char_opening = opening_char
char_closing = closing_char | python | def override_params(opening_char='{', closing_char='}', separator_char='|'):
"""
Override some character settings
@type opening_char: str
@param opening_char: Opening character. Default: '{'
@type closing_char: str
@param closing_char: Closing character. Default: '}'
@type separator_char: str
@param separator_char: Separator char. Default: '|'
"""
global char_separator, char_opening, char_closing
char_separator = separator_char
char_opening = opening_char
char_closing = closing_char | [
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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:
Override some character settings
@type opening_char: str
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@type closing_char: str
@param closing_char: Closing character. Default: '}'
@type separator_char: str
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def override_params(opening_char='{', closing_char='}', separator_char='|'):
"""
Override some character settings
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@type closing_char: str
@param closing_char: Closing character. Default: '}'
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saltstack/salt | salt/states/zone.py | detached | def detached(name):
'''
Ensure zone is detached
name : string
name of the zone
'''
ret = {'name': name,
'changes': {},
'result': None,
'comment': ''}
zones = __salt__['zoneadm.list'](installed=True, configured=True)
if name in zones:
if zones[name]['state'] != 'configured':
if __opts__['test']:
res_detach = {'status': True}
else:
res_detach = __salt__['zoneadm.detach'](name)
ret['result'] = res_detach['status']
if ret['result']:
ret['changes'][name] = 'detached'
ret['comment'] = 'The zone {0} was detached.'.format(name)
else:
ret['comment'] = []
ret['comment'].append('Failed to detach zone {0}!'.format(name))
if 'message' in res_detach:
ret['comment'].append(res_detach['message'])
ret['comment'] = "\n".join(ret['comment'])
else:
ret['result'] = True
ret['comment'] = 'zone {0} already detached.'.format(name)
else:
## note: a non existing zone is not attached, we do not consider this a failure
ret['result'] = True
ret['comment'] = 'zone {0} is not configured!'.format(name)
return ret | python | def detached(name):
'''
Ensure zone is detached
name : string
name of the zone
'''
ret = {'name': name,
'changes': {},
'result': None,
'comment': ''}
zones = __salt__['zoneadm.list'](installed=True, configured=True)
if name in zones:
if zones[name]['state'] != 'configured':
if __opts__['test']:
res_detach = {'status': True}
else:
res_detach = __salt__['zoneadm.detach'](name)
ret['result'] = res_detach['status']
if ret['result']:
ret['changes'][name] = 'detached'
ret['comment'] = 'The zone {0} was detached.'.format(name)
else:
ret['comment'] = []
ret['comment'].append('Failed to detach zone {0}!'.format(name))
if 'message' in res_detach:
ret['comment'].append(res_detach['message'])
ret['comment'] = "\n".join(ret['comment'])
else:
ret['result'] = True
ret['comment'] = 'zone {0} already detached.'.format(name)
else:
## note: a non existing zone is not attached, we do not consider this a failure
ret['result'] = True
ret['comment'] = 'zone {0} is not configured!'.format(name)
return ret | [
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name : string
name of the zone | [
"Ensure",
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] | train | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/states/zone.py#L1100-L1138 | <|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:
Ensure zone is detached
name : string
name of the zone
### Response:
def detached(name):
'''
Ensure zone is detached
name : string
name of the zone
'''
ret = {'name': name,
'changes': {},
'result': None,
'comment': ''}
zones = __salt__['zoneadm.list'](installed=True, configured=True)
if name in zones:
if zones[name]['state'] != 'configured':
if __opts__['test']:
res_detach = {'status': True}
else:
res_detach = __salt__['zoneadm.detach'](name)
ret['result'] = res_detach['status']
if ret['result']:
ret['changes'][name] = 'detached'
ret['comment'] = 'The zone {0} was detached.'.format(name)
else:
ret['comment'] = []
ret['comment'].append('Failed to detach zone {0}!'.format(name))
if 'message' in res_detach:
ret['comment'].append(res_detach['message'])
ret['comment'] = "\n".join(ret['comment'])
else:
ret['result'] = True
ret['comment'] = 'zone {0} already detached.'.format(name)
else:
## note: a non existing zone is not attached, we do not consider this a failure
ret['result'] = True
ret['comment'] = 'zone {0} is not configured!'.format(name)
return ret
<|endoftext|> | [
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onelogin/python3-saml | src/onelogin/saml2/response.py | OneLogin_Saml2_Response.__query | def __query(self, query, tagid=None):
"""
Extracts nodes that match the query from the Response
:param query: Xpath Expresion
:type query: String
:param tagid: Tag ID
:type query: String
:returns: The queried nodes
:rtype: list
"""
if self.encrypted:
document = self.decrypted_document
else:
document = self.document
return OneLogin_Saml2_XML.query(document, query, None, tagid) | python | def __query(self, query, tagid=None):
"""
Extracts nodes that match the query from the Response
:param query: Xpath Expresion
:type query: String
:param tagid: Tag ID
:type query: String
:returns: The queried nodes
:rtype: list
"""
if self.encrypted:
document = self.decrypted_document
else:
document = self.document
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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:
Extracts nodes that match the query from the Response
:param query: Xpath Expresion
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HiPERCAM/hcam_widgets | hcam_widgets/widgets.py | Windows.enable | def enable(self):
"""
Enables all settings
"""
nwin = self.nwin.value()
for label, xs, ys, nx, ny in \
zip(self.label[:nwin], self.xs[:nwin], self.ys[:nwin],
self.nx[:nwin], self.ny[:nwin]):
label.config(state='normal')
xs.enable()
ys.enable()
nx.enable()
ny.enable()
for label, xs, ys, nx, ny in \
zip(self.label[nwin:], self.xs[nwin:], self.ys[nwin:],
self.nx[nwin:], self.ny[nwin:]):
label.config(state='disable')
xs.disable()
ys.disable()
nx.disable()
ny.disable()
self.nwin.enable()
self.xbin.enable()
self.ybin.enable()
self.sbutt.enable() | python | def enable(self):
"""
Enables all settings
"""
nwin = self.nwin.value()
for label, xs, ys, nx, ny in \
zip(self.label[:nwin], self.xs[:nwin], self.ys[:nwin],
self.nx[:nwin], self.ny[:nwin]):
label.config(state='normal')
xs.enable()
ys.enable()
nx.enable()
ny.enable()
for label, xs, ys, nx, ny in \
zip(self.label[nwin:], self.xs[nwin:], self.ys[nwin:],
self.nx[nwin:], self.ny[nwin:]):
label.config(state='disable')
xs.disable()
ys.disable()
nx.disable()
ny.disable()
self.nwin.enable()
self.xbin.enable()
self.ybin.enable()
self.sbutt.enable() | [
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"Enables",
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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:
Enables all settings
### Response:
def enable(self):
"""
Enables all settings
"""
nwin = self.nwin.value()
for label, xs, ys, nx, ny in \
zip(self.label[:nwin], self.xs[:nwin], self.ys[:nwin],
self.nx[:nwin], self.ny[:nwin]):
label.config(state='normal')
xs.enable()
ys.enable()
nx.enable()
ny.enable()
for label, xs, ys, nx, ny in \
zip(self.label[nwin:], self.xs[nwin:], self.ys[nwin:],
self.nx[nwin:], self.ny[nwin:]):
label.config(state='disable')
xs.disable()
ys.disable()
nx.disable()
ny.disable()
self.nwin.enable()
self.xbin.enable()
self.ybin.enable()
self.sbutt.enable()
<|endoftext|> | [
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klen/peewee_migrate | peewee_migrate/migrator.py | Migrator.add_default | def add_default(self, model, name, default):
"""Add default."""
field = model._meta.fields[name]
model._meta.defaults[field] = field.default = default
self.ops.append(self.migrator.apply_default(model._meta.table_name, name, field))
return model | python | def add_default(self, model, name, default):
"""Add default."""
field = model._meta.fields[name]
model._meta.defaults[field] = field.default = default
self.ops.append(self.migrator.apply_default(model._meta.table_name, name, field))
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Add default.
### Response:
def add_default(self, model, name, default):
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MartinThoma/mpu | mpu/pd.py | describe | def describe(df, dtype=None):
"""
Print a description of a Pandas dataframe.
Parameters
----------
df : Pandas.DataFrame
dtype : dict
Maps column names to types
"""
if dtype is None:
dtype = {}
print('Number of datapoints: {datapoints}'.format(datapoints=len(df)))
column_info, column_info_meta = _get_column_info(df, dtype)
if len(column_info['int']) > 0:
_describe_int(df, column_info)
if len(column_info['float']) > 0:
_describe_float(df, column_info)
if len(column_info['category']) > 0:
_describe_category(df, column_info, column_info_meta)
if len(column_info['time']) > 0:
_describe_time(df, column_info, column_info_meta)
if len(column_info['other']) > 0:
_describe_other(df, column_info, column_info_meta)
column_types = {}
for column_type, columns in column_info.items():
for column_name in columns:
if column_type == 'other':
column_type = 'str'
column_types[column_name] = column_type
return column_types | python | def describe(df, dtype=None):
"""
Print a description of a Pandas dataframe.
Parameters
----------
df : Pandas.DataFrame
dtype : dict
Maps column names to types
"""
if dtype is None:
dtype = {}
print('Number of datapoints: {datapoints}'.format(datapoints=len(df)))
column_info, column_info_meta = _get_column_info(df, dtype)
if len(column_info['int']) > 0:
_describe_int(df, column_info)
if len(column_info['float']) > 0:
_describe_float(df, column_info)
if len(column_info['category']) > 0:
_describe_category(df, column_info, column_info_meta)
if len(column_info['time']) > 0:
_describe_time(df, column_info, column_info_meta)
if len(column_info['other']) > 0:
_describe_other(df, column_info, column_info_meta)
column_types = {}
for column_type, columns in column_info.items():
for column_name in columns:
if column_type == 'other':
column_type = 'str'
column_types[column_name] = column_type
return column_types | [
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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:
Print a description of a Pandas dataframe.
Parameters
----------
df : Pandas.DataFrame
dtype : dict
Maps column names to types
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def describe(df, dtype=None):
"""
Print a description of a Pandas dataframe.
Parameters
----------
df : Pandas.DataFrame
dtype : dict
Maps column names to types
"""
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wummel/linkchecker | linkcheck/checker/__init__.py | get_url_from | def get_url_from (base_url, recursion_level, aggregate,
parent_url=None, base_ref=None, line=0, column=0, page=0,
name=u"", parent_content_type=None, extern=None):
"""
Get url data from given base data.
@param base_url: base url from a link tag
@type base_url: string or None
@param recursion_level: current recursion level
@type recursion_level: number
@param aggregate: aggregate object
@type aggregate: aggregate.Consumer
@param parent_url: parent url
@type parent_url: string or None
@param base_ref: base url from <base> tag
@type base_ref string or None
@param line: line number
@type line: number
@param column: column number
@type column: number
@param page: page number
@type page: number
@param name: link name
@type name: string
@param extern: (is_extern, is_strict) or None
@type extern: tuple(int, int) or None
"""
if base_url is not None:
base_url = strformat.unicode_safe(base_url)
# left strip for detection of URL scheme
base_url_stripped = base_url.lstrip()
else:
base_url_stripped = base_url
if parent_url is not None:
parent_url = strformat.unicode_safe(parent_url)
if base_ref is not None:
base_ref = strformat.unicode_safe(base_ref)
name = strformat.unicode_safe(name)
url = absolute_url(base_url_stripped, base_ref, parent_url).lower()
if ":" in url:
scheme = url.split(":", 1)[0].lower()
else:
scheme = None
if not (url or name):
# use filename as base url, with slash as path seperator
name = base_url.replace("\\", "/")
allowed_schemes = aggregate.config["allowedschemes"]
# ignore local PHP files with execution directives
local_php = (parent_content_type == 'application/x-httpd-php' and
'<?' in base_url and '?>' in base_url and scheme == 'file')
if local_php or (allowed_schemes and scheme not in allowed_schemes):
klass = ignoreurl.IgnoreUrl
else:
assume_local_file = (recursion_level == 0)
klass = get_urlclass_from(scheme, assume_local_file=assume_local_file)
log.debug(LOG_CHECK, "%s handles url %s", klass.__name__, base_url)
return klass(base_url, recursion_level, aggregate,
parent_url=parent_url, base_ref=base_ref,
line=line, column=column, page=page, name=name, extern=extern) | python | def get_url_from (base_url, recursion_level, aggregate,
parent_url=None, base_ref=None, line=0, column=0, page=0,
name=u"", parent_content_type=None, extern=None):
"""
Get url data from given base data.
@param base_url: base url from a link tag
@type base_url: string or None
@param recursion_level: current recursion level
@type recursion_level: number
@param aggregate: aggregate object
@type aggregate: aggregate.Consumer
@param parent_url: parent url
@type parent_url: string or None
@param base_ref: base url from <base> tag
@type base_ref string or None
@param line: line number
@type line: number
@param column: column number
@type column: number
@param page: page number
@type page: number
@param name: link name
@type name: string
@param extern: (is_extern, is_strict) or None
@type extern: tuple(int, int) or None
"""
if base_url is not None:
base_url = strformat.unicode_safe(base_url)
# left strip for detection of URL scheme
base_url_stripped = base_url.lstrip()
else:
base_url_stripped = base_url
if parent_url is not None:
parent_url = strformat.unicode_safe(parent_url)
if base_ref is not None:
base_ref = strformat.unicode_safe(base_ref)
name = strformat.unicode_safe(name)
url = absolute_url(base_url_stripped, base_ref, parent_url).lower()
if ":" in url:
scheme = url.split(":", 1)[0].lower()
else:
scheme = None
if not (url or name):
# use filename as base url, with slash as path seperator
name = base_url.replace("\\", "/")
allowed_schemes = aggregate.config["allowedschemes"]
# ignore local PHP files with execution directives
local_php = (parent_content_type == 'application/x-httpd-php' and
'<?' in base_url and '?>' in base_url and scheme == 'file')
if local_php or (allowed_schemes and scheme not in allowed_schemes):
klass = ignoreurl.IgnoreUrl
else:
assume_local_file = (recursion_level == 0)
klass = get_urlclass_from(scheme, assume_local_file=assume_local_file)
log.debug(LOG_CHECK, "%s handles url %s", klass.__name__, base_url)
return klass(base_url, recursion_level, aggregate,
parent_url=parent_url, base_ref=base_ref,
line=line, column=column, page=page, name=name, extern=extern) | [
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@type column: number
@param page: page number
@type page: number
@param name: link name
@type name: string
@param extern: (is_extern, is_strict) or None
@type extern: tuple(int, int) or None | [
"Get",
"url",
"data",
"from",
"given",
"base",
"data",
"."
] | train | https://github.com/wummel/linkchecker/blob/c2ce810c3fb00b895a841a7be6b2e78c64e7b042/linkcheck/checker/__init__.py#L67-L125 | <|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:
Get url data from given base data.
@param base_url: base url from a link tag
@type base_url: string or None
@param recursion_level: current recursion level
@type recursion_level: number
@param aggregate: aggregate object
@type aggregate: aggregate.Consumer
@param parent_url: parent url
@type parent_url: string or None
@param base_ref: base url from <base> tag
@type base_ref string or None
@param line: line number
@type line: number
@param column: column number
@type column: number
@param page: page number
@type page: number
@param name: link name
@type name: string
@param extern: (is_extern, is_strict) or None
@type extern: tuple(int, int) or None
### Response:
def get_url_from (base_url, recursion_level, aggregate,
parent_url=None, base_ref=None, line=0, column=0, page=0,
name=u"", parent_content_type=None, extern=None):
"""
Get url data from given base data.
@param base_url: base url from a link tag
@type base_url: string or None
@param recursion_level: current recursion level
@type recursion_level: number
@param aggregate: aggregate object
@type aggregate: aggregate.Consumer
@param parent_url: parent url
@type parent_url: string or None
@param base_ref: base url from <base> tag
@type base_ref string or None
@param line: line number
@type line: number
@param column: column number
@type column: number
@param page: page number
@type page: number
@param name: link name
@type name: string
@param extern: (is_extern, is_strict) or None
@type extern: tuple(int, int) or None
"""
if base_url is not None:
base_url = strformat.unicode_safe(base_url)
# left strip for detection of URL scheme
base_url_stripped = base_url.lstrip()
else:
base_url_stripped = base_url
if parent_url is not None:
parent_url = strformat.unicode_safe(parent_url)
if base_ref is not None:
base_ref = strformat.unicode_safe(base_ref)
name = strformat.unicode_safe(name)
url = absolute_url(base_url_stripped, base_ref, parent_url).lower()
if ":" in url:
scheme = url.split(":", 1)[0].lower()
else:
scheme = None
if not (url or name):
# use filename as base url, with slash as path seperator
name = base_url.replace("\\", "/")
allowed_schemes = aggregate.config["allowedschemes"]
# ignore local PHP files with execution directives
local_php = (parent_content_type == 'application/x-httpd-php' and
'<?' in base_url and '?>' in base_url and scheme == 'file')
if local_php or (allowed_schemes and scheme not in allowed_schemes):
klass = ignoreurl.IgnoreUrl
else:
assume_local_file = (recursion_level == 0)
klass = get_urlclass_from(scheme, assume_local_file=assume_local_file)
log.debug(LOG_CHECK, "%s handles url %s", klass.__name__, base_url)
return klass(base_url, recursion_level, aggregate,
parent_url=parent_url, base_ref=base_ref,
line=line, column=column, page=page, name=name, extern=extern)
<|endoftext|> | [
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yyuu/botornado | boto/gs/bucket.py | Bucket.set_def_acl | def set_def_acl(self, acl_or_str, key_name='', headers=None):
"""sets or changes a bucket's default object acl"""
if isinstance(acl_or_str, Policy):
raise InvalidAclError('Attempt to set S3 Policy on GS ACL')
elif isinstance(acl_or_str, ACL):
self.set_def_xml_acl(acl_or_str.to_xml(), key_name, headers=headers)
else:
self.set_def_canned_acl(acl_or_str, key_name, headers=headers) | python | def set_def_acl(self, acl_or_str, key_name='', headers=None):
"""sets or changes a bucket's default object acl"""
if isinstance(acl_or_str, Policy):
raise InvalidAclError('Attempt to set S3 Policy on GS ACL')
elif isinstance(acl_or_str, ACL):
self.set_def_xml_acl(acl_or_str.to_xml(), key_name, headers=headers)
else:
self.set_def_canned_acl(acl_or_str, key_name, headers=headers) | [
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] | train | https://github.com/yyuu/botornado/blob/fffb056f5ff2324d1d5c1304014cfb1d899f602e/boto/gs/bucket.py#L53-L60 | <|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:
sets or changes a bucket's default object acl
### Response:
def set_def_acl(self, acl_or_str, key_name='', headers=None):
"""sets or changes a bucket's default object acl"""
if isinstance(acl_or_str, Policy):
raise InvalidAclError('Attempt to set S3 Policy on GS ACL')
elif isinstance(acl_or_str, ACL):
self.set_def_xml_acl(acl_or_str.to_xml(), key_name, headers=headers)
else:
self.set_def_canned_acl(acl_or_str, key_name, headers=headers)
<|endoftext|> | [
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BD2KGenomics/toil-scripts | src/toil_scripts/spladder_pipeline/spladder_pipeline.py | star | def star(job, inputs, r1_cutadapt, r2_cutadapt):
"""
Performs alignment of fastqs to BAM via STAR
:param JobFunctionWrappingJob job: passed by Toil automatically
:param Namespace inputs: Stores input arguments (see main)
:param str r1_cutadapt: FileStore ID of read 1 fastq
:param str r2_cutadapt: FileStore ID of read 2 fastq
"""
job.fileStore.logToMaster('Aligning with STAR: {}'.format(inputs.uuid))
work_dir = job.fileStore.getLocalTempDir()
cores = min(inputs.cores, 16)
# Retrieve files
job.fileStore.readGlobalFile(r1_cutadapt, os.path.join(work_dir, 'R1_cutadapt.fastq'))
job.fileStore.readGlobalFile(r2_cutadapt, os.path.join(work_dir, 'R2_cutadapt.fastq'))
# Get starIndex
download_url(job=job, url=inputs.star_index, work_dir=work_dir, name='starIndex.tar.gz')
subprocess.check_call(['tar', '-xvf', os.path.join(work_dir, 'starIndex.tar.gz'), '-C', work_dir])
# Parameters
parameters = ['--runThreadN', str(cores),
'--genomeDir', '/data/starIndex',
'--outFileNamePrefix', 'rna',
'--outSAMtype', 'BAM', 'SortedByCoordinate',
'--outSAMunmapped', 'Within',
'--quantMode', 'TranscriptomeSAM',
'--outSAMattributes', 'NH', 'HI', 'AS', 'NM', 'MD',
'--outFilterType', 'BySJout',
'--outFilterMultimapNmax', '20',
'--outFilterMismatchNmax', '999',
'--outFilterMismatchNoverReadLmax', '0.04',
'--alignIntronMin', '20',
'--alignIntronMax', '1000000',
'--alignMatesGapMax', '1000000',
'--alignSJoverhangMin', '8',
'--alignSJDBoverhangMin', '1',
'--sjdbScore', '1',
'--readFilesIn', '/data/R1_cutadapt.fastq', '/data/R2_cutadapt.fastq']
# Call: STAR Map
docker_call(job=job, tool='quay.io/ucsc_cgl/star:2.4.2a--bcbd5122b69ff6ac4ef61958e47bde94001cfe80',
work_dir=work_dir, parameters=parameters)
# Call Samtools Index
index_command = ['index', '/data/rnaAligned.sortedByCoord.out.bam']
docker_call(job=job, work_dir=work_dir, parameters=index_command,
tool='quay.io/ucsc_cgl/samtools:1.3--256539928ea162949d8a65ca5c79a72ef557ce7c')
# fileStore
bam_id = job.fileStore.writeGlobalFile(os.path.join(work_dir, 'rnaAligned.sortedByCoord.out.bam'))
bai_id = job.fileStore.writeGlobalFile(os.path.join(work_dir, 'rnaAligned.sortedByCoord.out.bam.bai'))
job.fileStore.deleteGlobalFile(r1_cutadapt)
job.fileStore.deleteGlobalFile(r2_cutadapt)
# Launch children and follow-on
vcqc_id = job.addChildJobFn(variant_calling_and_qc, inputs, bam_id, bai_id, cores=2, disk='30G').rv()
spladder_id = job.addChildJobFn(spladder, inputs, bam_id, bai_id, disk='30G').rv()
job.addFollowOnJobFn(consolidate_output_tarballs, inputs, vcqc_id, spladder_id, disk='30G') | python | def star(job, inputs, r1_cutadapt, r2_cutadapt):
"""
Performs alignment of fastqs to BAM via STAR
:param JobFunctionWrappingJob job: passed by Toil automatically
:param Namespace inputs: Stores input arguments (see main)
:param str r1_cutadapt: FileStore ID of read 1 fastq
:param str r2_cutadapt: FileStore ID of read 2 fastq
"""
job.fileStore.logToMaster('Aligning with STAR: {}'.format(inputs.uuid))
work_dir = job.fileStore.getLocalTempDir()
cores = min(inputs.cores, 16)
# Retrieve files
job.fileStore.readGlobalFile(r1_cutadapt, os.path.join(work_dir, 'R1_cutadapt.fastq'))
job.fileStore.readGlobalFile(r2_cutadapt, os.path.join(work_dir, 'R2_cutadapt.fastq'))
# Get starIndex
download_url(job=job, url=inputs.star_index, work_dir=work_dir, name='starIndex.tar.gz')
subprocess.check_call(['tar', '-xvf', os.path.join(work_dir, 'starIndex.tar.gz'), '-C', work_dir])
# Parameters
parameters = ['--runThreadN', str(cores),
'--genomeDir', '/data/starIndex',
'--outFileNamePrefix', 'rna',
'--outSAMtype', 'BAM', 'SortedByCoordinate',
'--outSAMunmapped', 'Within',
'--quantMode', 'TranscriptomeSAM',
'--outSAMattributes', 'NH', 'HI', 'AS', 'NM', 'MD',
'--outFilterType', 'BySJout',
'--outFilterMultimapNmax', '20',
'--outFilterMismatchNmax', '999',
'--outFilterMismatchNoverReadLmax', '0.04',
'--alignIntronMin', '20',
'--alignIntronMax', '1000000',
'--alignMatesGapMax', '1000000',
'--alignSJoverhangMin', '8',
'--alignSJDBoverhangMin', '1',
'--sjdbScore', '1',
'--readFilesIn', '/data/R1_cutadapt.fastq', '/data/R2_cutadapt.fastq']
# Call: STAR Map
docker_call(job=job, tool='quay.io/ucsc_cgl/star:2.4.2a--bcbd5122b69ff6ac4ef61958e47bde94001cfe80',
work_dir=work_dir, parameters=parameters)
# Call Samtools Index
index_command = ['index', '/data/rnaAligned.sortedByCoord.out.bam']
docker_call(job=job, work_dir=work_dir, parameters=index_command,
tool='quay.io/ucsc_cgl/samtools:1.3--256539928ea162949d8a65ca5c79a72ef557ce7c')
# fileStore
bam_id = job.fileStore.writeGlobalFile(os.path.join(work_dir, 'rnaAligned.sortedByCoord.out.bam'))
bai_id = job.fileStore.writeGlobalFile(os.path.join(work_dir, 'rnaAligned.sortedByCoord.out.bam.bai'))
job.fileStore.deleteGlobalFile(r1_cutadapt)
job.fileStore.deleteGlobalFile(r2_cutadapt)
# Launch children and follow-on
vcqc_id = job.addChildJobFn(variant_calling_and_qc, inputs, bam_id, bai_id, cores=2, disk='30G').rv()
spladder_id = job.addChildJobFn(spladder, inputs, bam_id, bai_id, disk='30G').rv()
job.addFollowOnJobFn(consolidate_output_tarballs, inputs, vcqc_id, spladder_id, disk='30G') | [
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] | Performs alignment of fastqs to BAM via STAR
:param JobFunctionWrappingJob job: passed by Toil automatically
:param Namespace inputs: Stores input arguments (see main)
:param str r1_cutadapt: FileStore ID of read 1 fastq
:param str r2_cutadapt: FileStore ID of read 2 fastq | [
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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:
Performs alignment of fastqs to BAM via STAR
:param JobFunctionWrappingJob job: passed by Toil automatically
:param Namespace inputs: Stores input arguments (see main)
:param str r1_cutadapt: FileStore ID of read 1 fastq
:param str r2_cutadapt: FileStore ID of read 2 fastq
### Response:
def star(job, inputs, r1_cutadapt, r2_cutadapt):
"""
Performs alignment of fastqs to BAM via STAR
:param JobFunctionWrappingJob job: passed by Toil automatically
:param Namespace inputs: Stores input arguments (see main)
:param str r1_cutadapt: FileStore ID of read 1 fastq
:param str r2_cutadapt: FileStore ID of read 2 fastq
"""
job.fileStore.logToMaster('Aligning with STAR: {}'.format(inputs.uuid))
work_dir = job.fileStore.getLocalTempDir()
cores = min(inputs.cores, 16)
# Retrieve files
job.fileStore.readGlobalFile(r1_cutadapt, os.path.join(work_dir, 'R1_cutadapt.fastq'))
job.fileStore.readGlobalFile(r2_cutadapt, os.path.join(work_dir, 'R2_cutadapt.fastq'))
# Get starIndex
download_url(job=job, url=inputs.star_index, work_dir=work_dir, name='starIndex.tar.gz')
subprocess.check_call(['tar', '-xvf', os.path.join(work_dir, 'starIndex.tar.gz'), '-C', work_dir])
# Parameters
parameters = ['--runThreadN', str(cores),
'--genomeDir', '/data/starIndex',
'--outFileNamePrefix', 'rna',
'--outSAMtype', 'BAM', 'SortedByCoordinate',
'--outSAMunmapped', 'Within',
'--quantMode', 'TranscriptomeSAM',
'--outSAMattributes', 'NH', 'HI', 'AS', 'NM', 'MD',
'--outFilterType', 'BySJout',
'--outFilterMultimapNmax', '20',
'--outFilterMismatchNmax', '999',
'--outFilterMismatchNoverReadLmax', '0.04',
'--alignIntronMin', '20',
'--alignIntronMax', '1000000',
'--alignMatesGapMax', '1000000',
'--alignSJoverhangMin', '8',
'--alignSJDBoverhangMin', '1',
'--sjdbScore', '1',
'--readFilesIn', '/data/R1_cutadapt.fastq', '/data/R2_cutadapt.fastq']
# Call: STAR Map
docker_call(job=job, tool='quay.io/ucsc_cgl/star:2.4.2a--bcbd5122b69ff6ac4ef61958e47bde94001cfe80',
work_dir=work_dir, parameters=parameters)
# Call Samtools Index
index_command = ['index', '/data/rnaAligned.sortedByCoord.out.bam']
docker_call(job=job, work_dir=work_dir, parameters=index_command,
tool='quay.io/ucsc_cgl/samtools:1.3--256539928ea162949d8a65ca5c79a72ef557ce7c')
# fileStore
bam_id = job.fileStore.writeGlobalFile(os.path.join(work_dir, 'rnaAligned.sortedByCoord.out.bam'))
bai_id = job.fileStore.writeGlobalFile(os.path.join(work_dir, 'rnaAligned.sortedByCoord.out.bam.bai'))
job.fileStore.deleteGlobalFile(r1_cutadapt)
job.fileStore.deleteGlobalFile(r2_cutadapt)
# Launch children and follow-on
vcqc_id = job.addChildJobFn(variant_calling_and_qc, inputs, bam_id, bai_id, cores=2, disk='30G').rv()
spladder_id = job.addChildJobFn(spladder, inputs, bam_id, bai_id, disk='30G').rv()
job.addFollowOnJobFn(consolidate_output_tarballs, inputs, vcqc_id, spladder_id, disk='30G')
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f3at/feat | src/feat/common/manhole.py | Parser.split | def split(self, text):
'''
Splits the text with function arguments into the array with first
class citizens separated. See the unit tests for clarificatin.
'''
# nesting character -> count
counters = {"'": False, '(': 0}
def reverse(char):
def wrapped():
counters[char] = not counters[char]
return wrapped
def increase(char):
def wrapped():
counters[char] += 1
return wrapped
def decrease(char):
def wrapped():
counters[char] -= 1
return wrapped
def is_top_level():
return all([not x for x in counters.values()])
def fail():
raise BadSyntax('Syntax error processing line: %s' %\
self._last_line)
temp = ""
# end of field flag indicates that we expect next character to be
# either whitespace of split
eof_flag = False
def append_char(temp, char):
if eof_flag:
fail()
temp += char
return temp
# dictionary char -> handler
nesters = {"'": reverse("'"), "(": increase('('), ")": decrease('(')}
# chars to split on
split = (',', )
# chars to swallow
consume = (' ', '\n')
result = list()
self.log("spliting: %s", text)
for char in text:
if char in nesters:
nesters[char]()
temp = append_char(temp, char)
elif not is_top_level():
temp = append_char(temp, char)
elif char in consume:
if len(temp) > 0:
eof_flag = True
continue
elif char in split:
result.append(temp)
temp = ""
eof_flag = False
else:
temp = append_char(temp, char)
if len(temp) > 0:
result.append(temp)
if not is_top_level():
fail()
self.log('Split returns %r', result)
return result | python | def split(self, text):
'''
Splits the text with function arguments into the array with first
class citizens separated. See the unit tests for clarificatin.
'''
# nesting character -> count
counters = {"'": False, '(': 0}
def reverse(char):
def wrapped():
counters[char] = not counters[char]
return wrapped
def increase(char):
def wrapped():
counters[char] += 1
return wrapped
def decrease(char):
def wrapped():
counters[char] -= 1
return wrapped
def is_top_level():
return all([not x for x in counters.values()])
def fail():
raise BadSyntax('Syntax error processing line: %s' %\
self._last_line)
temp = ""
# end of field flag indicates that we expect next character to be
# either whitespace of split
eof_flag = False
def append_char(temp, char):
if eof_flag:
fail()
temp += char
return temp
# dictionary char -> handler
nesters = {"'": reverse("'"), "(": increase('('), ")": decrease('(')}
# chars to split on
split = (',', )
# chars to swallow
consume = (' ', '\n')
result = list()
self.log("spliting: %s", text)
for char in text:
if char in nesters:
nesters[char]()
temp = append_char(temp, char)
elif not is_top_level():
temp = append_char(temp, char)
elif char in consume:
if len(temp) > 0:
eof_flag = True
continue
elif char in split:
result.append(temp)
temp = ""
eof_flag = False
else:
temp = append_char(temp, char)
if len(temp) > 0:
result.append(temp)
if not is_top_level():
fail()
self.log('Split returns %r', result)
return result | [
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] | train | https://github.com/f3at/feat/blob/15da93fc9d6ec8154f52a9172824e25821195ef8/src/feat/common/manhole.py#L155-L233 | <|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:
Splits the text with function arguments into the array with first
class citizens separated. See the unit tests for clarificatin.
### Response:
def split(self, text):
'''
Splits the text with function arguments into the array with first
class citizens separated. See the unit tests for clarificatin.
'''
# nesting character -> count
counters = {"'": False, '(': 0}
def reverse(char):
def wrapped():
counters[char] = not counters[char]
return wrapped
def increase(char):
def wrapped():
counters[char] += 1
return wrapped
def decrease(char):
def wrapped():
counters[char] -= 1
return wrapped
def is_top_level():
return all([not x for x in counters.values()])
def fail():
raise BadSyntax('Syntax error processing line: %s' %\
self._last_line)
temp = ""
# end of field flag indicates that we expect next character to be
# either whitespace of split
eof_flag = False
def append_char(temp, char):
if eof_flag:
fail()
temp += char
return temp
# dictionary char -> handler
nesters = {"'": reverse("'"), "(": increase('('), ")": decrease('(')}
# chars to split on
split = (',', )
# chars to swallow
consume = (' ', '\n')
result = list()
self.log("spliting: %s", text)
for char in text:
if char in nesters:
nesters[char]()
temp = append_char(temp, char)
elif not is_top_level():
temp = append_char(temp, char)
elif char in consume:
if len(temp) > 0:
eof_flag = True
continue
elif char in split:
result.append(temp)
temp = ""
eof_flag = False
else:
temp = append_char(temp, char)
if len(temp) > 0:
result.append(temp)
if not is_top_level():
fail()
self.log('Split returns %r', result)
return result
<|endoftext|> | [
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globocom/GloboNetworkAPI-client-python | networkapiclient/Ip.py | Ip.get_available_ip6 | def get_available_ip6(self, id_network6):
"""
Get a available IP in Network ipv6
:param id_network6: Network ipv6 identifier. Integer value and greater than zero.
:return: Dictionary with the following structure:
::
{'ip6': {'ip6': < available_ip6 >}}
:raise IpNotAvailableError: Network dont have available IP.
:raise NetworkIPv4NotFoundError: Network was not found.
:raise UserNotAuthorizedError: User dont have permission to get a available IP.
:raise InvalidParameterError: Network ipv6 identifier is none or invalid.
:raise XMLError: Networkapi failed to generate the XML response.
:raise DataBaseError: Networkapi failed to access the database.
"""
if not is_valid_int_param(id_network6):
raise InvalidParameterError(
u'Network ipv6 identifier is invalid or was not informed.')
url = 'ip/availableip6/' + str(id_network6) + "/"
code, xml = self.submit(None, 'GET', url)
return self.response(code, xml) | python | def get_available_ip6(self, id_network6):
"""
Get a available IP in Network ipv6
:param id_network6: Network ipv6 identifier. Integer value and greater than zero.
:return: Dictionary with the following structure:
::
{'ip6': {'ip6': < available_ip6 >}}
:raise IpNotAvailableError: Network dont have available IP.
:raise NetworkIPv4NotFoundError: Network was not found.
:raise UserNotAuthorizedError: User dont have permission to get a available IP.
:raise InvalidParameterError: Network ipv6 identifier is none or invalid.
:raise XMLError: Networkapi failed to generate the XML response.
:raise DataBaseError: Networkapi failed to access the database.
"""
if not is_valid_int_param(id_network6):
raise InvalidParameterError(
u'Network ipv6 identifier is invalid or was not informed.')
url = 'ip/availableip6/' + str(id_network6) + "/"
code, xml = self.submit(None, 'GET', url)
return self.response(code, xml) | [
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:param id_network6: Network ipv6 identifier. Integer value and greater than zero.
:return: Dictionary with the following structure:
::
{'ip6': {'ip6': < available_ip6 >}}
:raise IpNotAvailableError: Network dont have available IP.
:raise NetworkIPv4NotFoundError: Network was not found.
:raise UserNotAuthorizedError: User dont have permission to get a available IP.
:raise InvalidParameterError: Network ipv6 identifier is none or invalid.
:raise XMLError: Networkapi failed to generate the XML response.
:raise DataBaseError: Networkapi failed to access the database. | [
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] | train | https://github.com/globocom/GloboNetworkAPI-client-python/blob/cf34f913da48d9abbf750114f5d2ac4b2dde137d/networkapiclient/Ip.py#L275-L304 | <|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:
Get a available IP in Network ipv6
:param id_network6: Network ipv6 identifier. Integer value and greater than zero.
:return: Dictionary with the following structure:
::
{'ip6': {'ip6': < available_ip6 >}}
:raise IpNotAvailableError: Network dont have available IP.
:raise NetworkIPv4NotFoundError: Network was not found.
:raise UserNotAuthorizedError: User dont have permission to get a available IP.
:raise InvalidParameterError: Network ipv6 identifier is none or invalid.
:raise XMLError: Networkapi failed to generate the XML response.
:raise DataBaseError: Networkapi failed to access the database.
### Response:
def get_available_ip6(self, id_network6):
"""
Get a available IP in Network ipv6
:param id_network6: Network ipv6 identifier. Integer value and greater than zero.
:return: Dictionary with the following structure:
::
{'ip6': {'ip6': < available_ip6 >}}
:raise IpNotAvailableError: Network dont have available IP.
:raise NetworkIPv4NotFoundError: Network was not found.
:raise UserNotAuthorizedError: User dont have permission to get a available IP.
:raise InvalidParameterError: Network ipv6 identifier is none or invalid.
:raise XMLError: Networkapi failed to generate the XML response.
:raise DataBaseError: Networkapi failed to access the database.
"""
if not is_valid_int_param(id_network6):
raise InvalidParameterError(
u'Network ipv6 identifier is invalid or was not informed.')
url = 'ip/availableip6/' + str(id_network6) + "/"
code, xml = self.submit(None, 'GET', url)
return self.response(code, xml)
<|endoftext|> | [
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jrigden/pyPodcastParser | pyPodcastParser/Podcast.py | Podcast.set_extended_elements | def set_extended_elements(self):
"""Parses and sets non required elements"""
self.set_creative_commons()
self.set_owner()
self.set_subtitle()
self.set_summary() | python | def set_extended_elements(self):
"""Parses and sets non required elements"""
self.set_creative_commons()
self.set_owner()
self.set_subtitle()
self.set_summary() | [
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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:
Parses and sets non required elements
### Response:
def set_extended_elements(self):
"""Parses and sets non required elements"""
self.set_creative_commons()
self.set_owner()
self.set_subtitle()
self.set_summary()
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nameko/nameko | nameko/cli/shell.py | make_nameko_helper | def make_nameko_helper(config):
"""Create a fake module that provides some convenient access to nameko
standalone functionality for interactive shell usage.
"""
module = ModuleType('nameko')
module.__doc__ = """Nameko shell helper for making rpc calls and dispatching
events.
Usage:
>>> n.rpc.service.method()
"reply"
>>> n.dispatch_event('service', 'event_type', 'event_data')
"""
proxy = ClusterRpcProxy(config)
module.rpc = proxy.start()
module.dispatch_event = event_dispatcher(config)
module.config = config
module.disconnect = proxy.stop
return module | python | def make_nameko_helper(config):
"""Create a fake module that provides some convenient access to nameko
standalone functionality for interactive shell usage.
"""
module = ModuleType('nameko')
module.__doc__ = """Nameko shell helper for making rpc calls and dispatching
events.
Usage:
>>> n.rpc.service.method()
"reply"
>>> n.dispatch_event('service', 'event_type', 'event_data')
"""
proxy = ClusterRpcProxy(config)
module.rpc = proxy.start()
module.dispatch_event = event_dispatcher(config)
module.config = config
module.disconnect = proxy.stop
return module | [
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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 fake module that provides some convenient access to nameko
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### Response:
def make_nameko_helper(config):
"""Create a fake module that provides some convenient access to nameko
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"""
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module.dispatch_event = event_dispatcher(config)
module.config = config
module.disconnect = proxy.stop
return module
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"""All contained data array are in the same projection."""
all_areas = [x.attrs.get('area', None) for x in self.values()]
all_areas = [x for x in all_areas if x is not None]
return all(all_areas[0].proj_str == x.proj_str for x in all_areas[1:]) | python | def all_same_proj(self):
"""All contained data array are in the same projection."""
all_areas = [x.attrs.get('area', None) for x in self.values()]
all_areas = [x for x in all_areas if x is not None]
return all(all_areas[0].proj_str == x.proj_str for x in all_areas[1:]) | [
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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:
All contained data array are in the same projection.
### Response:
def all_same_proj(self):
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"""Function takes a subject and an existing graph, returns a new graph with
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subject as the new subject
Args:
subject(rdflib.URIRef): A URIRef subject
existing_graph(rdflib.Graph): A rdflib.Graph
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for predicate, object_ in existing_graph.predicate_objects():
new_graph.add((subject, predicate, object_))
return new_graph | python | def copy_graph(subject, existing_graph):
"""Function takes a subject and an existing graph, returns a new graph with
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subject as the new subject
Args:
subject(rdflib.URIRef): A URIRef subject
existing_graph(rdflib.Graph): A rdflib.Graph
Returns:
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"""
new_graph = rdflib.Graph()
for predicate, object_ in existing_graph.predicate_objects():
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rduplain/jeni-python | jeni.py | Injector.eager_partial_regardless | def eager_partial_regardless(self, fn, *a, **kw):
"""Like `eager_partial`, but applies if callable is not annotated."""
if self.has_annotations(fn):
return self.eager_partial(fn, *a, **kw)
return functools.partial(fn, *a, **kw) | python | def eager_partial_regardless(self, fn, *a, **kw):
"""Like `eager_partial`, but applies if callable is not annotated."""
if self.has_annotations(fn):
return self.eager_partial(fn, *a, **kw)
return functools.partial(fn, *a, **kw) | [
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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:
Like `eager_partial`, but applies if callable is not annotated.
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def eager_partial_regardless(self, fn, *a, **kw):
"""Like `eager_partial`, but applies if callable is not annotated."""
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krukas/Trionyx | trionyx/navigation.py | TabRegister.get_tabs | def get_tabs(self, model_alias, object):
"""
Get all active tabs for given model
:param model_alias:
:param object: Object used to filter tabs
:return:
"""
model_alias = self.get_model_alias(model_alias)
for item in self.tabs[model_alias]:
if item.display_filter(object):
yield item | python | def get_tabs(self, model_alias, object):
"""
Get all active tabs for given model
:param model_alias:
:param object: Object used to filter tabs
:return:
"""
model_alias = self.get_model_alias(model_alias)
for item in self.tabs[model_alias]:
if item.display_filter(object):
yield item | [
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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 all active tabs for given model
:param model_alias:
:param object: Object used to filter tabs
:return:
### Response:
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"""
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:param object: Object used to filter tabs
:return:
"""
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pantsbuild/pants | pants-plugins/src/python/internal_backend/sitegen/tasks/sitegen.py | load_soups | def load_soups(config):
"""Generate BeautifulSoup AST for each page listed in config."""
soups = {}
for page, path in config['sources'].items():
with open(path, 'r') as orig_file:
soups[page] = beautiful_soup(orig_file.read(), features='html.parser')
return soups | python | def load_soups(config):
"""Generate BeautifulSoup AST for each page listed in config."""
soups = {}
for page, path in config['sources'].items():
with open(path, 'r') as orig_file:
soups[page] = beautiful_soup(orig_file.read(), features='html.parser')
return soups | [
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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 BeautifulSoup AST for each page listed in config.
### Response:
def load_soups(config):
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soups = {}
for page, path in config['sources'].items():
with open(path, 'r') as orig_file:
soups[page] = beautiful_soup(orig_file.read(), features='html.parser')
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aws/sagemaker-python-sdk | src/sagemaker/predictor.py | RealTimePredictor.delete_model | def delete_model(self):
"""Deletes the Amazon SageMaker models backing this predictor.
"""
request_failed = False
failed_models = []
for model_name in self._model_names:
try:
self.sagemaker_session.delete_model(model_name)
except Exception: # pylint: disable=broad-except
request_failed = True
failed_models.append(model_name)
if request_failed:
raise Exception('One or more models cannot be deleted, please retry. \n'
'Failed models: {}'.format(', '.join(failed_models))) | python | def delete_model(self):
"""Deletes the Amazon SageMaker models backing this predictor.
"""
request_failed = False
failed_models = []
for model_name in self._model_names:
try:
self.sagemaker_session.delete_model(model_name)
except Exception: # pylint: disable=broad-except
request_failed = True
failed_models.append(model_name)
if request_failed:
raise Exception('One or more models cannot be deleted, please retry. \n'
'Failed models: {}'.format(', '.join(failed_models))) | [
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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:
Deletes the Amazon SageMaker models backing this predictor.
### Response:
def delete_model(self):
"""Deletes the Amazon SageMaker models backing this predictor.
"""
request_failed = False
failed_models = []
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<|endoftext|> | [
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google/mobly | mobly/asserts.py | assert_raises | def assert_raises(expected_exception, extras=None, *args, **kwargs):
"""Assert that an exception is raised when a function is called.
If no exception is raised, test fail. If an exception is raised but not
of the expected type, the exception is let through.
This should only be used as a context manager:
with assert_raises(Exception):
func()
Args:
expected_exception: An exception class that is expected to be
raised.
extras: An optional field for extra information to be included in
test result.
"""
context = _AssertRaisesContext(expected_exception, extras=extras)
return context | python | def assert_raises(expected_exception, extras=None, *args, **kwargs):
"""Assert that an exception is raised when a function is called.
If no exception is raised, test fail. If an exception is raised but not
of the expected type, the exception is let through.
This should only be used as a context manager:
with assert_raises(Exception):
func()
Args:
expected_exception: An exception class that is expected to be
raised.
extras: An optional field for extra information to be included in
test result.
"""
context = _AssertRaisesContext(expected_exception, extras=extras)
return context | [
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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:
Assert that an exception is raised when a function is called.
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with assert_raises(Exception):
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def assert_raises(expected_exception, extras=None, *args, **kwargs):
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titilambert/pyhydroquebec | pyhydroquebec/client.py | HydroQuebecClient._get_monthly_data | def _get_monthly_data(self, p_p_id):
"""Get monthly data."""
params = {"p_p_id": p_p_id,
"p_p_lifecycle": 2,
"p_p_resource_id": ("resourceObtenirDonnees"
"PeriodesConsommation")}
try:
raw_res = yield from self._session.get(PROFILE_URL,
params=params,
timeout=self._timeout)
except OSError:
raise PyHydroQuebecError("Can not get monthly data")
try:
json_output = yield from raw_res.json(content_type='text/json')
except (OSError, json.decoder.JSONDecodeError):
raise PyHydroQuebecError("Could not get monthly data")
if not json_output.get('success'):
raise PyHydroQuebecError("Could not get monthly data")
return json_output.get('results') | python | def _get_monthly_data(self, p_p_id):
"""Get monthly data."""
params = {"p_p_id": p_p_id,
"p_p_lifecycle": 2,
"p_p_resource_id": ("resourceObtenirDonnees"
"PeriodesConsommation")}
try:
raw_res = yield from self._session.get(PROFILE_URL,
params=params,
timeout=self._timeout)
except OSError:
raise PyHydroQuebecError("Can not get monthly data")
try:
json_output = yield from raw_res.json(content_type='text/json')
except (OSError, json.decoder.JSONDecodeError):
raise PyHydroQuebecError("Could not get monthly data")
if not json_output.get('success'):
raise PyHydroQuebecError("Could not get monthly data")
return json_output.get('results') | [
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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 monthly data.
### Response:
def _get_monthly_data(self, p_p_id):
"""Get monthly data."""
params = {"p_p_id": p_p_id,
"p_p_lifecycle": 2,
"p_p_resource_id": ("resourceObtenirDonnees"
"PeriodesConsommation")}
try:
raw_res = yield from self._session.get(PROFILE_URL,
params=params,
timeout=self._timeout)
except OSError:
raise PyHydroQuebecError("Can not get monthly data")
try:
json_output = yield from raw_res.json(content_type='text/json')
except (OSError, json.decoder.JSONDecodeError):
raise PyHydroQuebecError("Could not get monthly data")
if not json_output.get('success'):
raise PyHydroQuebecError("Could not get monthly data")
return json_output.get('results')
<|endoftext|> | [
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smarie/python-valid8 | valid8/composition.py | failure_raiser | def failure_raiser(*validation_func, # type: ValidationFuncs
**kwargs
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# type: (...) -> Callable
"""
This function is automatically used if you provide a tuple `(<function>, <msg>_or_<Failure_type>)`, to any of the
methods in this page or to one of the `valid8` decorators. It transforms the provided `<function>` into a failure
raiser, raising a subclass of `Failure` in case of failure (either not returning `True` or raising an exception)
:param validation_func: the base validation function or list of base validation functions to use. A callable, a
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# the rest of keyword arguments is used as context.
kw_context_args = kwargs
main_func = _process_validation_function_s(list(validation_func))
return _failure_raiser(main_func, failure_type=failure_type, help_msg=help_msg, **kw_context_args) | python | def failure_raiser(*validation_func, # type: ValidationFuncs
**kwargs
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# type: (...) -> Callable
"""
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kw_context_args = kwargs
main_func = _process_validation_function_s(list(validation_func))
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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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This function is automatically used if you provide a tuple `(<function>, <msg>_or_<Failure_type>)`, to any of the
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def failure_raiser(*validation_func, # type: ValidationFuncs
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# type: (...) -> Callable
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failure_type, help_msg = pop_kwargs(kwargs, [('failure_type', None), ('help_msg', None)], allow_others=True)
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kw_context_args = kwargs
main_func = _process_validation_function_s(list(validation_func))
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jciskey/pygraph | pygraph/functions/planarity/kocay_algorithm.py | S_star | def S_star(u, dfs_data):
"""The set of all descendants of u, with u added."""
s_u = S(u, dfs_data)
if u not in s_u:
s_u.append(u)
return s_u | python | def S_star(u, dfs_data):
"""The set of all descendants of u, with u added."""
s_u = S(u, dfs_data)
if u not in s_u:
s_u.append(u)
return s_u | [
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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 set of all descendants of u, with u added.
### Response:
def S_star(u, dfs_data):
"""The set of all descendants of u, with u added."""
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psss/did | did/utils.py | listed | def listed(items, singular=None, plural=None, max=None, quote=""):
"""
Convert an iterable into a nice, human readable list or description::
listed(range(1)) .................... 0
listed(range(2)) .................... 0 and 1
listed(range(3), quote='"') ......... "0", "1" and "2"
listed(range(4), max=3) ............. 0, 1, 2 and 1 more
listed(range(5), 'number', max=3) ... 0, 1, 2 and 2 more numbers
listed(range(6), 'category') ........ 6 categories
listed(7, "leaf", "leaves") ......... 7 leaves
If singular form is provided but max not set the description-only
mode is activated as shown in the last two examples. Also, an int
can be used in this case to get a simple inflection functionality.
"""
# Convert items to list if necessary
items = range(items) if isinstance(items, int) else list(items)
more = " more"
# Description mode expected when singular provided but no maximum set
if singular is not None and max is None:
max = 0
more = ""
# Set the default plural form
if singular is not None and plural is None:
plural = pluralize(singular)
# Convert to strings and optionally quote each item
items = ["{0}{1}{0}".format(quote, item) for item in items]
# Select the maximum of items and describe the rest if max provided
if max is not None:
# Special case when the list is empty (0 items)
if max == 0 and len(items) == 0:
return "0 {0}".format(plural)
# Cut the list if maximum exceeded
if len(items) > max:
rest = len(items[max:])
items = items[:max]
if singular is not None:
more += " {0}".format(singular if rest == 1 else plural)
items.append("{0}{1}".format(rest, more))
# For two and more items use 'and' instead of the last comma
if len(items) < 2:
return "".join(items)
else:
return ", ".join(items[0:-2] + [" and ".join(items[-2:])]) | python | def listed(items, singular=None, plural=None, max=None, quote=""):
"""
Convert an iterable into a nice, human readable list or description::
listed(range(1)) .................... 0
listed(range(2)) .................... 0 and 1
listed(range(3), quote='"') ......... "0", "1" and "2"
listed(range(4), max=3) ............. 0, 1, 2 and 1 more
listed(range(5), 'number', max=3) ... 0, 1, 2 and 2 more numbers
listed(range(6), 'category') ........ 6 categories
listed(7, "leaf", "leaves") ......... 7 leaves
If singular form is provided but max not set the description-only
mode is activated as shown in the last two examples. Also, an int
can be used in this case to get a simple inflection functionality.
"""
# Convert items to list if necessary
items = range(items) if isinstance(items, int) else list(items)
more = " more"
# Description mode expected when singular provided but no maximum set
if singular is not None and max is None:
max = 0
more = ""
# Set the default plural form
if singular is not None and plural is None:
plural = pluralize(singular)
# Convert to strings and optionally quote each item
items = ["{0}{1}{0}".format(quote, item) for item in items]
# Select the maximum of items and describe the rest if max provided
if max is not None:
# Special case when the list is empty (0 items)
if max == 0 and len(items) == 0:
return "0 {0}".format(plural)
# Cut the list if maximum exceeded
if len(items) > max:
rest = len(items[max:])
items = items[:max]
if singular is not None:
more += " {0}".format(singular if rest == 1 else plural)
items.append("{0}{1}".format(rest, more))
# For two and more items use 'and' instead of the last comma
if len(items) < 2:
return "".join(items)
else:
return ", ".join(items[0:-2] + [" and ".join(items[-2:])]) | [
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listed(range(1)) .................... 0
listed(range(2)) .................... 0 and 1
listed(range(3), quote='"') ......... "0", "1" and "2"
listed(range(4), max=3) ............. 0, 1, 2 and 1 more
listed(range(5), 'number', max=3) ... 0, 1, 2 and 2 more numbers
listed(range(6), 'category') ........ 6 categories
listed(7, "leaf", "leaves") ......... 7 leaves
If singular form is provided but max not set the description-only
mode is activated as shown in the last two examples. Also, an int
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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 an iterable into a nice, human readable list or description::
listed(range(1)) .................... 0
listed(range(2)) .................... 0 and 1
listed(range(3), quote='"') ......... "0", "1" and "2"
listed(range(4), max=3) ............. 0, 1, 2 and 1 more
listed(range(5), 'number', max=3) ... 0, 1, 2 and 2 more numbers
listed(range(6), 'category') ........ 6 categories
listed(7, "leaf", "leaves") ......... 7 leaves
If singular form is provided but max not set the description-only
mode is activated as shown in the last two examples. Also, an int
can be used in this case to get a simple inflection functionality.
### Response:
def listed(items, singular=None, plural=None, max=None, quote=""):
"""
Convert an iterable into a nice, human readable list or description::
listed(range(1)) .................... 0
listed(range(2)) .................... 0 and 1
listed(range(3), quote='"') ......... "0", "1" and "2"
listed(range(4), max=3) ............. 0, 1, 2 and 1 more
listed(range(5), 'number', max=3) ... 0, 1, 2 and 2 more numbers
listed(range(6), 'category') ........ 6 categories
listed(7, "leaf", "leaves") ......... 7 leaves
If singular form is provided but max not set the description-only
mode is activated as shown in the last two examples. Also, an int
can be used in this case to get a simple inflection functionality.
"""
# Convert items to list if necessary
items = range(items) if isinstance(items, int) else list(items)
more = " more"
# Description mode expected when singular provided but no maximum set
if singular is not None and max is None:
max = 0
more = ""
# Set the default plural form
if singular is not None and plural is None:
plural = pluralize(singular)
# Convert to strings and optionally quote each item
items = ["{0}{1}{0}".format(quote, item) for item in items]
# Select the maximum of items and describe the rest if max provided
if max is not None:
# Special case when the list is empty (0 items)
if max == 0 and len(items) == 0:
return "0 {0}".format(plural)
# Cut the list if maximum exceeded
if len(items) > max:
rest = len(items[max:])
items = items[:max]
if singular is not None:
more += " {0}".format(singular if rest == 1 else plural)
items.append("{0}{1}".format(rest, more))
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SpriteLink/NIPAP | nipap-cli/nipap_cli/nipap_cli.py | get_vrf | def get_vrf(arg = None, default_var = 'default_vrf_rt', abort = False):
""" Returns VRF to work in
Returns a pynipap.VRF object representing the VRF we are working
in. If there is a VRF set globally, return this. If not, fetch the
VRF named 'arg'. If 'arg' is None, fetch the default_vrf
attribute from the config file and return this VRF.
"""
# yep, global variables are evil
global vrf
# if there is a VRF set, return it
if vrf is not None:
return vrf
if arg is None:
# fetch default vrf
try:
vrf_rt = cfg.get('global', default_var)
except configparser.NoOptionError:
# default to all VRFs
vrf_rt = 'all'
else:
vrf_rt = arg
if vrf_rt.lower() == 'all':
vrf = VRF()
vrf.rt = 'all'
else:
if vrf_rt.lower() in ('-', 'none'):
vrf_rt = None
try:
vrf = VRF.search({ 'val1': 'rt',
'operator': 'equals',
'val2': vrf_rt
})['result'][0]
except (KeyError, IndexError):
if abort:
print("VRF with [RT: %s] not found." % str(vrf_rt), file=sys.stderr)
sys.exit(1)
else:
vrf = False
return vrf | python | def get_vrf(arg = None, default_var = 'default_vrf_rt', abort = False):
""" Returns VRF to work in
Returns a pynipap.VRF object representing the VRF we are working
in. If there is a VRF set globally, return this. If not, fetch the
VRF named 'arg'. If 'arg' is None, fetch the default_vrf
attribute from the config file and return this VRF.
"""
# yep, global variables are evil
global vrf
# if there is a VRF set, return it
if vrf is not None:
return vrf
if arg is None:
# fetch default vrf
try:
vrf_rt = cfg.get('global', default_var)
except configparser.NoOptionError:
# default to all VRFs
vrf_rt = 'all'
else:
vrf_rt = arg
if vrf_rt.lower() == 'all':
vrf = VRF()
vrf.rt = 'all'
else:
if vrf_rt.lower() in ('-', 'none'):
vrf_rt = None
try:
vrf = VRF.search({ 'val1': 'rt',
'operator': 'equals',
'val2': vrf_rt
})['result'][0]
except (KeyError, IndexError):
if abort:
print("VRF with [RT: %s] not found." % str(vrf_rt), file=sys.stderr)
sys.exit(1)
else:
vrf = False
return vrf | [
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":",
"print",
"(",
"\"VRF with [RT: %s] not found.\"",
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] | Returns VRF to work in
Returns a pynipap.VRF object representing the VRF we are working
in. If there is a VRF set globally, return this. If not, fetch the
VRF named 'arg'. If 'arg' is None, fetch the default_vrf
attribute from the config file and return this VRF. | [
"Returns",
"VRF",
"to",
"work",
"in"
] | train | https://github.com/SpriteLink/NIPAP/blob/f96069f11ab952d80b13cab06e0528f2d24b3de9/nipap-cli/nipap_cli/nipap_cli.py#L109-L154 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Returns VRF to work in
Returns a pynipap.VRF object representing the VRF we are working
in. If there is a VRF set globally, return this. If not, fetch the
VRF named 'arg'. If 'arg' is None, fetch the default_vrf
attribute from the config file and return this VRF.
### Response:
def get_vrf(arg = None, default_var = 'default_vrf_rt', abort = False):
""" Returns VRF to work in
Returns a pynipap.VRF object representing the VRF we are working
in. If there is a VRF set globally, return this. If not, fetch the
VRF named 'arg'. If 'arg' is None, fetch the default_vrf
attribute from the config file and return this VRF.
"""
# yep, global variables are evil
global vrf
# if there is a VRF set, return it
if vrf is not None:
return vrf
if arg is None:
# fetch default vrf
try:
vrf_rt = cfg.get('global', default_var)
except configparser.NoOptionError:
# default to all VRFs
vrf_rt = 'all'
else:
vrf_rt = arg
if vrf_rt.lower() == 'all':
vrf = VRF()
vrf.rt = 'all'
else:
if vrf_rt.lower() in ('-', 'none'):
vrf_rt = None
try:
vrf = VRF.search({ 'val1': 'rt',
'operator': 'equals',
'val2': vrf_rt
})['result'][0]
except (KeyError, IndexError):
if abort:
print("VRF with [RT: %s] not found." % str(vrf_rt), file=sys.stderr)
sys.exit(1)
else:
vrf = False
return vrf
<|endoftext|> | [
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BD2KGenomics/toil-scripts | src/toil_scripts/adam_kmers/count_kmers.py | download_count_upload | def download_count_upload(job,
master_ip,
input_file,
output_file,
kmer_length,
spark_conf,
memory,
sudo):
'''
Runs k-mer counting.
1. If the input file is located in S3, the file is copied into HDFS.
2. If the input file is not in Parquet format, the file is converted into Parquet.
3. The k-mers are counted and saved as text.
4. If the output path is an S3 URL, the file is copied back to S3.
:param job: Toil job
:param input_file: URL/path to input file to count k-mers on
:param output_file: URL/path to save k-mer counts at
:param kmer_length: The length of k-mer substrings to count.
:param spark_conf: Optional Spark configuration. If set, memory should \
not be set.
:param memory: Amount of memory to provided to Spark workers. Must be set \
if spark_conf is not set.
:param sudo: Whether or not to run Spark containers with sudo.
:type job: toil.Job
:type input_file: string
:type output_file: string
:type kmer_length: int or string
:type spark_conf: list of string or None
:type memory: int or None
:type sudo: boolean
'''
if master_ip is not None:
hdfs_dir = "hdfs://{0}:{1}/".format(master_ip, HDFS_MASTER_PORT)
else:
_log.warn('Master IP is not set. If default filesystem is not set, jobs may fail.')
hdfs_dir = ""
# if the file isn't already in hdfs, copy it in
hdfs_input_file = hdfs_dir
if input_file.startswith("s3://"):
# append the s3 file name to our hdfs path
hdfs_input_file += input_file.split("/")[-1]
# run the download
_log.info("Downloading input file %s to %s.", input_file, hdfs_input_file)
call_conductor(job, master_ip, input_file, hdfs_input_file,
memory=memory, override_parameters=spark_conf)
else:
if not input_file.startswith("hdfs://"):
_log.warn("If not in S3, input file (%s) expected to be in HDFS (%s).",
input_file, hdfs_dir)
# where are we writing the output to? is it going to a location in hdfs or not?
run_upload = True
hdfs_output_file = hdfs_dir + "kmer_output.txt"
if output_file.startswith(hdfs_dir):
run_upload = False
hdfs_output_file = output_file
# do we need to convert to adam?
if (hdfs_input_file.endswith('.bam') or
hdfs_input_file.endswith('.sam') or
hdfs_input_file.endswith('.fq') or
hdfs_input_file.endswith('.fastq')):
hdfs_tmp_file = hdfs_input_file
# change the file extension to adam
hdfs_input_file = '.'.join(hdfs_input_file.split('.')[:-1].append('adam'))
# convert the file
_log.info('Converting %s into ADAM format at %s.', hdfs_tmp_file, hdfs_input_file)
call_adam(job, master_ip,
['transform',
hdfs_tmp_file, hdfs_input_file],
memory=memory, override_parameters=spark_conf)
# run k-mer counting
_log.info('Counting %d-mers in %s, and saving to %s.',
kmer_length, hdfs_input_file, hdfs_output_file)
call_adam(job, master_ip,
['count_kmers',
hdfs_input_file, hdfs_output_file,
str(kmer_length)],
memory=memory, override_parameters=spark_conf)
# do we need to upload the file back? if so, run upload
if run_upload:
_log.info("Uploading output file %s to %s.", hdfs_output_file, output_file)
call_conductor(job, master_ip, hdfs_output_file, output_file,
memory=memory, override_parameters=spark_conf) | python | def download_count_upload(job,
master_ip,
input_file,
output_file,
kmer_length,
spark_conf,
memory,
sudo):
'''
Runs k-mer counting.
1. If the input file is located in S3, the file is copied into HDFS.
2. If the input file is not in Parquet format, the file is converted into Parquet.
3. The k-mers are counted and saved as text.
4. If the output path is an S3 URL, the file is copied back to S3.
:param job: Toil job
:param input_file: URL/path to input file to count k-mers on
:param output_file: URL/path to save k-mer counts at
:param kmer_length: The length of k-mer substrings to count.
:param spark_conf: Optional Spark configuration. If set, memory should \
not be set.
:param memory: Amount of memory to provided to Spark workers. Must be set \
if spark_conf is not set.
:param sudo: Whether or not to run Spark containers with sudo.
:type job: toil.Job
:type input_file: string
:type output_file: string
:type kmer_length: int or string
:type spark_conf: list of string or None
:type memory: int or None
:type sudo: boolean
'''
if master_ip is not None:
hdfs_dir = "hdfs://{0}:{1}/".format(master_ip, HDFS_MASTER_PORT)
else:
_log.warn('Master IP is not set. If default filesystem is not set, jobs may fail.')
hdfs_dir = ""
# if the file isn't already in hdfs, copy it in
hdfs_input_file = hdfs_dir
if input_file.startswith("s3://"):
# append the s3 file name to our hdfs path
hdfs_input_file += input_file.split("/")[-1]
# run the download
_log.info("Downloading input file %s to %s.", input_file, hdfs_input_file)
call_conductor(job, master_ip, input_file, hdfs_input_file,
memory=memory, override_parameters=spark_conf)
else:
if not input_file.startswith("hdfs://"):
_log.warn("If not in S3, input file (%s) expected to be in HDFS (%s).",
input_file, hdfs_dir)
# where are we writing the output to? is it going to a location in hdfs or not?
run_upload = True
hdfs_output_file = hdfs_dir + "kmer_output.txt"
if output_file.startswith(hdfs_dir):
run_upload = False
hdfs_output_file = output_file
# do we need to convert to adam?
if (hdfs_input_file.endswith('.bam') or
hdfs_input_file.endswith('.sam') or
hdfs_input_file.endswith('.fq') or
hdfs_input_file.endswith('.fastq')):
hdfs_tmp_file = hdfs_input_file
# change the file extension to adam
hdfs_input_file = '.'.join(hdfs_input_file.split('.')[:-1].append('adam'))
# convert the file
_log.info('Converting %s into ADAM format at %s.', hdfs_tmp_file, hdfs_input_file)
call_adam(job, master_ip,
['transform',
hdfs_tmp_file, hdfs_input_file],
memory=memory, override_parameters=spark_conf)
# run k-mer counting
_log.info('Counting %d-mers in %s, and saving to %s.',
kmer_length, hdfs_input_file, hdfs_output_file)
call_adam(job, master_ip,
['count_kmers',
hdfs_input_file, hdfs_output_file,
str(kmer_length)],
memory=memory, override_parameters=spark_conf)
# do we need to upload the file back? if so, run upload
if run_upload:
_log.info("Uploading output file %s to %s.", hdfs_output_file, output_file)
call_conductor(job, master_ip, hdfs_output_file, output_file,
memory=memory, override_parameters=spark_conf) | [
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1. If the input file is located in S3, the file is copied into HDFS.
2. If the input file is not in Parquet format, the file is converted into Parquet.
3. The k-mers are counted and saved as text.
4. If the output path is an S3 URL, the file is copied back to S3.
:param job: Toil job
:param input_file: URL/path to input file to count k-mers on
:param output_file: URL/path to save k-mer counts at
:param kmer_length: The length of k-mer substrings to count.
:param spark_conf: Optional Spark configuration. If set, memory should \
not be set.
:param memory: Amount of memory to provided to Spark workers. Must be set \
if spark_conf is not set.
:param sudo: Whether or not to run Spark containers with sudo.
:type job: toil.Job
:type input_file: string
:type output_file: string
:type kmer_length: int or string
:type spark_conf: list of string or None
:type memory: int or None
:type sudo: boolean | [
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] | train | https://github.com/BD2KGenomics/toil-scripts/blob/f878d863defcdccaabb7fe06f991451b7a198fb7/src/toil_scripts/adam_kmers/count_kmers.py#L76-L172 | <|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:
Runs k-mer counting.
1. If the input file is located in S3, the file is copied into HDFS.
2. If the input file is not in Parquet format, the file is converted into Parquet.
3. The k-mers are counted and saved as text.
4. If the output path is an S3 URL, the file is copied back to S3.
:param job: Toil job
:param input_file: URL/path to input file to count k-mers on
:param output_file: URL/path to save k-mer counts at
:param kmer_length: The length of k-mer substrings to count.
:param spark_conf: Optional Spark configuration. If set, memory should \
not be set.
:param memory: Amount of memory to provided to Spark workers. Must be set \
if spark_conf is not set.
:param sudo: Whether or not to run Spark containers with sudo.
:type job: toil.Job
:type input_file: string
:type output_file: string
:type kmer_length: int or string
:type spark_conf: list of string or None
:type memory: int or None
:type sudo: boolean
### Response:
def download_count_upload(job,
master_ip,
input_file,
output_file,
kmer_length,
spark_conf,
memory,
sudo):
'''
Runs k-mer counting.
1. If the input file is located in S3, the file is copied into HDFS.
2. If the input file is not in Parquet format, the file is converted into Parquet.
3. The k-mers are counted and saved as text.
4. If the output path is an S3 URL, the file is copied back to S3.
:param job: Toil job
:param input_file: URL/path to input file to count k-mers on
:param output_file: URL/path to save k-mer counts at
:param kmer_length: The length of k-mer substrings to count.
:param spark_conf: Optional Spark configuration. If set, memory should \
not be set.
:param memory: Amount of memory to provided to Spark workers. Must be set \
if spark_conf is not set.
:param sudo: Whether or not to run Spark containers with sudo.
:type job: toil.Job
:type input_file: string
:type output_file: string
:type kmer_length: int or string
:type spark_conf: list of string or None
:type memory: int or None
:type sudo: boolean
'''
if master_ip is not None:
hdfs_dir = "hdfs://{0}:{1}/".format(master_ip, HDFS_MASTER_PORT)
else:
_log.warn('Master IP is not set. If default filesystem is not set, jobs may fail.')
hdfs_dir = ""
# if the file isn't already in hdfs, copy it in
hdfs_input_file = hdfs_dir
if input_file.startswith("s3://"):
# append the s3 file name to our hdfs path
hdfs_input_file += input_file.split("/")[-1]
# run the download
_log.info("Downloading input file %s to %s.", input_file, hdfs_input_file)
call_conductor(job, master_ip, input_file, hdfs_input_file,
memory=memory, override_parameters=spark_conf)
else:
if not input_file.startswith("hdfs://"):
_log.warn("If not in S3, input file (%s) expected to be in HDFS (%s).",
input_file, hdfs_dir)
# where are we writing the output to? is it going to a location in hdfs or not?
run_upload = True
hdfs_output_file = hdfs_dir + "kmer_output.txt"
if output_file.startswith(hdfs_dir):
run_upload = False
hdfs_output_file = output_file
# do we need to convert to adam?
if (hdfs_input_file.endswith('.bam') or
hdfs_input_file.endswith('.sam') or
hdfs_input_file.endswith('.fq') or
hdfs_input_file.endswith('.fastq')):
hdfs_tmp_file = hdfs_input_file
# change the file extension to adam
hdfs_input_file = '.'.join(hdfs_input_file.split('.')[:-1].append('adam'))
# convert the file
_log.info('Converting %s into ADAM format at %s.', hdfs_tmp_file, hdfs_input_file)
call_adam(job, master_ip,
['transform',
hdfs_tmp_file, hdfs_input_file],
memory=memory, override_parameters=spark_conf)
# run k-mer counting
_log.info('Counting %d-mers in %s, and saving to %s.',
kmer_length, hdfs_input_file, hdfs_output_file)
call_adam(job, master_ip,
['count_kmers',
hdfs_input_file, hdfs_output_file,
str(kmer_length)],
memory=memory, override_parameters=spark_conf)
# do we need to upload the file back? if so, run upload
if run_upload:
_log.info("Uploading output file %s to %s.", hdfs_output_file, output_file)
call_conductor(job, master_ip, hdfs_output_file, output_file,
memory=memory, override_parameters=spark_conf)
<|endoftext|> | [
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peterldowns/python-mustache | mustache/utils.py | html_escape | def html_escape(s, encoding='utf-8', encoding_errors='strict'):
""" Return the HTML-escaped version of an input. """
return escape(make_unicode(s, encoding, encoding_errors), quote=True) | python | def html_escape(s, encoding='utf-8', encoding_errors='strict'):
""" Return the HTML-escaped version of an input. """
return escape(make_unicode(s, encoding, encoding_errors), quote=True) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Return the HTML-escaped version of an input.
### Response:
def html_escape(s, encoding='utf-8', encoding_errors='strict'):
""" Return the HTML-escaped version of an input. """
return escape(make_unicode(s, encoding, encoding_errors), quote=True)
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LeKono/pyhgnc | src/pyhgnc/manager/query.py | QueryManager._limit_and_df | def _limit_and_df(self, query, limit, as_df=False):
"""adds a limit (limit==None := no limit) to any query and allow a return as pandas.DataFrame
:param bool as_df: if is set to True results return as pandas.DataFrame
:param `sqlalchemy.orm.query.Query` query: SQL Alchemy query
:param int,tuple limit: maximum number of results
:return: query result of pyhgnc.manager.models.XY objects
"""
if limit:
if isinstance(limit, int):
query = query.limit(limit)
if isinstance(limit, Iterable) and len(limit) == 2 and [int, int] == [type(x) for x in limit]:
page, page_size = limit
query = query.limit(page_size)
query = query.offset(page * page_size)
if as_df:
results = read_sql(query.statement, self.engine)
else:
try:
results = query.all()
except:
query.session.rollback()
results = query.all()
return results | python | def _limit_and_df(self, query, limit, as_df=False):
"""adds a limit (limit==None := no limit) to any query and allow a return as pandas.DataFrame
:param bool as_df: if is set to True results return as pandas.DataFrame
:param `sqlalchemy.orm.query.Query` query: SQL Alchemy query
:param int,tuple limit: maximum number of results
:return: query result of pyhgnc.manager.models.XY objects
"""
if limit:
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query = query.limit(limit)
if isinstance(limit, Iterable) and len(limit) == 2 and [int, int] == [type(x) for x in limit]:
page, page_size = limit
query = query.limit(page_size)
query = query.offset(page * page_size)
if as_df:
results = read_sql(query.statement, self.engine)
else:
try:
results = query.all()
except:
query.session.rollback()
results = query.all()
return results | [
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:param bool as_df: if is set to True results return as pandas.DataFrame
:param `sqlalchemy.orm.query.Query` query: SQL Alchemy query
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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:
adds a limit (limit==None := no limit) to any query and allow a return as pandas.DataFrame
:param bool as_df: if is set to True results return as pandas.DataFrame
:param `sqlalchemy.orm.query.Query` query: SQL Alchemy query
:param int,tuple limit: maximum number of results
:return: query result of pyhgnc.manager.models.XY objects
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def _limit_and_df(self, query, limit, as_df=False):
"""adds a limit (limit==None := no limit) to any query and allow a return as pandas.DataFrame
:param bool as_df: if is set to True results return as pandas.DataFrame
:param `sqlalchemy.orm.query.Query` query: SQL Alchemy query
:param int,tuple limit: maximum number of results
:return: query result of pyhgnc.manager.models.XY objects
"""
if limit:
if isinstance(limit, int):
query = query.limit(limit)
if isinstance(limit, Iterable) and len(limit) == 2 and [int, int] == [type(x) for x in limit]:
page, page_size = limit
query = query.limit(page_size)
query = query.offset(page * page_size)
if as_df:
results = read_sql(query.statement, self.engine)
else:
try:
results = query.all()
except:
query.session.rollback()
results = query.all()
return results
<|endoftext|> | [
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cltk/cltk | cltk/prosody/latin/pentameter_scanner.py | PentameterScanner.correct_penultimate_dactyl_chain | def correct_penultimate_dactyl_chain(self, scansion: str) -> str:
"""
For pentameter the last two feet of the verse are predictable dactyls,
and do not regularly allow substitutions.
:param scansion: scansion line thus far
:return: corrected line of scansion
>>> print(PentameterScanner().correct_penultimate_dactyl_chain(
... "U U U U U U U U U U U U U U"))
U U U U U U U - U U - U U U
"""
mark_list = string_utils.mark_list(scansion)
vals = list(scansion.replace(" ", ""))
n_vals = vals[:-7] + [self.constants.DACTYL + self.constants.DACTYL] + [vals[-1]]
corrected = "".join(n_vals)
new_line = list(" " * len(scansion))
for idx, car in enumerate(corrected):
new_line[mark_list[idx]] = car
return "".join(new_line) | python | def correct_penultimate_dactyl_chain(self, scansion: str) -> str:
"""
For pentameter the last two feet of the verse are predictable dactyls,
and do not regularly allow substitutions.
:param scansion: scansion line thus far
:return: corrected line of scansion
>>> print(PentameterScanner().correct_penultimate_dactyl_chain(
... "U U U U U U U U U U U U U U"))
U U U U U U U - U U - U U U
"""
mark_list = string_utils.mark_list(scansion)
vals = list(scansion.replace(" ", ""))
n_vals = vals[:-7] + [self.constants.DACTYL + self.constants.DACTYL] + [vals[-1]]
corrected = "".join(n_vals)
new_line = list(" " * len(scansion))
for idx, car in enumerate(corrected):
new_line[mark_list[idx]] = car
return "".join(new_line) | [
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:param scansion: scansion line thus far
:return: corrected line of scansion
>>> print(PentameterScanner().correct_penultimate_dactyl_chain(
... "U U U U U U U U U U U U U U"))
U U U U U U U - U U - U U U | [
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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:
For pentameter the last two feet of the verse are predictable dactyls,
and do not regularly allow substitutions.
:param scansion: scansion line thus far
:return: corrected line of scansion
>>> print(PentameterScanner().correct_penultimate_dactyl_chain(
... "U U U U U U U U U U U U U U"))
U U U U U U U - U U - U U U
### Response:
def correct_penultimate_dactyl_chain(self, scansion: str) -> str:
"""
For pentameter the last two feet of the verse are predictable dactyls,
and do not regularly allow substitutions.
:param scansion: scansion line thus far
:return: corrected line of scansion
>>> print(PentameterScanner().correct_penultimate_dactyl_chain(
... "U U U U U U U U U U U U U U"))
U U U U U U U - U U - U U U
"""
mark_list = string_utils.mark_list(scansion)
vals = list(scansion.replace(" ", ""))
n_vals = vals[:-7] + [self.constants.DACTYL + self.constants.DACTYL] + [vals[-1]]
corrected = "".join(n_vals)
new_line = list(" " * len(scansion))
for idx, car in enumerate(corrected):
new_line[mark_list[idx]] = car
return "".join(new_line)
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networks-lab/metaknowledge | metaknowledge/proquest/recordProQuest.py | proQuestRecordParser | def proQuestRecordParser(enRecordFile, recNum):
"""The parser [ProQuestRecords](../classes/ProQuestRecord.html#metaknowledge.proquest.ProQuestRecord) use. This takes an entry from [proQuestParser()](#metaknowledge.proquest.proQuestHandlers.proQuestParser) and parses it a part of the creation of a `ProQuestRecord`.
# Parameters
_enRecordFile_ : `enumerate object`
> a file wrapped by `enumerate()`
_recNum_ : `int`
> The number given to the entry in the first section of the ProQuest file
# Returns
`collections.OrderedDict`
> An ordered dictionary of the key-vaue pairs in the entry
"""
tagDict = collections.OrderedDict()
currentEntry = 'Name'
while True:
lineNum, line = next(enRecordFile)
if line == '_' * 60 + '\n':
break
elif line == '\n':
pass
elif currentEntry is 'Name' or currentEntry is 'url':
tagDict[currentEntry] = [line.rstrip()]
currentEntry = None
elif ':' in line and not line.startswith('http://'):
splitLine = line.split(': ')
currentEntry = splitLine[0]
tagDict[currentEntry] = [': '.join(splitLine[1:]).rstrip()]
if currentEntry == 'Author':
currentEntry = 'url'
else:
tagDict[currentEntry].append(line.rstrip())
return tagDict | python | def proQuestRecordParser(enRecordFile, recNum):
"""The parser [ProQuestRecords](../classes/ProQuestRecord.html#metaknowledge.proquest.ProQuestRecord) use. This takes an entry from [proQuestParser()](#metaknowledge.proquest.proQuestHandlers.proQuestParser) and parses it a part of the creation of a `ProQuestRecord`.
# Parameters
_enRecordFile_ : `enumerate object`
> a file wrapped by `enumerate()`
_recNum_ : `int`
> The number given to the entry in the first section of the ProQuest file
# Returns
`collections.OrderedDict`
> An ordered dictionary of the key-vaue pairs in the entry
"""
tagDict = collections.OrderedDict()
currentEntry = 'Name'
while True:
lineNum, line = next(enRecordFile)
if line == '_' * 60 + '\n':
break
elif line == '\n':
pass
elif currentEntry is 'Name' or currentEntry is 'url':
tagDict[currentEntry] = [line.rstrip()]
currentEntry = None
elif ':' in line and not line.startswith('http://'):
splitLine = line.split(': ')
currentEntry = splitLine[0]
tagDict[currentEntry] = [': '.join(splitLine[1:]).rstrip()]
if currentEntry == 'Author':
currentEntry = 'url'
else:
tagDict[currentEntry].append(line.rstrip())
return tagDict | [
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# Parameters
_enRecordFile_ : `enumerate object`
> a file wrapped by `enumerate()`
_recNum_ : `int`
> The number given to the entry in the first section of the ProQuest file
# Returns
`collections.OrderedDict`
> An ordered dictionary of the key-vaue pairs in the entry | [
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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 parser [ProQuestRecords](../classes/ProQuestRecord.html#metaknowledge.proquest.ProQuestRecord) use. This takes an entry from [proQuestParser()](#metaknowledge.proquest.proQuestHandlers.proQuestParser) and parses it a part of the creation of a `ProQuestRecord`.
# Parameters
_enRecordFile_ : `enumerate object`
> a file wrapped by `enumerate()`
_recNum_ : `int`
> The number given to the entry in the first section of the ProQuest file
# Returns
`collections.OrderedDict`
> An ordered dictionary of the key-vaue pairs in the entry
### Response:
def proQuestRecordParser(enRecordFile, recNum):
"""The parser [ProQuestRecords](../classes/ProQuestRecord.html#metaknowledge.proquest.ProQuestRecord) use. This takes an entry from [proQuestParser()](#metaknowledge.proquest.proQuestHandlers.proQuestParser) and parses it a part of the creation of a `ProQuestRecord`.
# Parameters
_enRecordFile_ : `enumerate object`
> a file wrapped by `enumerate()`
_recNum_ : `int`
> The number given to the entry in the first section of the ProQuest file
# Returns
`collections.OrderedDict`
> An ordered dictionary of the key-vaue pairs in the entry
"""
tagDict = collections.OrderedDict()
currentEntry = 'Name'
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lineNum, line = next(enRecordFile)
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break
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pass
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tagDict[currentEntry] = [line.rstrip()]
currentEntry = None
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splitLine = line.split(': ')
currentEntry = splitLine[0]
tagDict[currentEntry] = [': '.join(splitLine[1:]).rstrip()]
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tagDict[currentEntry].append(line.rstrip())
return tagDict
<|endoftext|> | [
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halcy/Mastodon.py | mastodon/Mastodon.py | Mastodon.retrieve_mastodon_version | def retrieve_mastodon_version(self):
"""
Determine installed mastodon version and set major, minor and patch (not including RC info) accordingly.
Returns the version string, possibly including rc info.
"""
try:
version_str = self.__instance()["version"]
except:
# instance() was added in 1.1.0, so our best guess is 1.0.0.
version_str = "1.0.0"
self.mastodon_major, self.mastodon_minor, self.mastodon_patch = parse_version_string(version_str)
return version_str | python | def retrieve_mastodon_version(self):
"""
Determine installed mastodon version and set major, minor and patch (not including RC info) accordingly.
Returns the version string, possibly including rc info.
"""
try:
version_str = self.__instance()["version"]
except:
# instance() was added in 1.1.0, so our best guess is 1.0.0.
version_str = "1.0.0"
self.mastodon_major, self.mastodon_minor, self.mastodon_patch = parse_version_string(version_str)
return version_str | [
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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:
Determine installed mastodon version and set major, minor and patch (not including RC info) accordingly.
Returns the version string, possibly including rc info.
### Response:
def retrieve_mastodon_version(self):
"""
Determine installed mastodon version and set major, minor and patch (not including RC info) accordingly.
Returns the version string, possibly including rc info.
"""
try:
version_str = self.__instance()["version"]
except:
# instance() was added in 1.1.0, so our best guess is 1.0.0.
version_str = "1.0.0"
self.mastodon_major, self.mastodon_minor, self.mastodon_patch = parse_version_string(version_str)
return version_str
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evhub/coconut | coconut/compiler/compiler.py | Compiler.destructuring_stmt_handle | def destructuring_stmt_handle(self, original, loc, tokens):
"""Process match assign blocks."""
internal_assert(len(tokens) == 2, "invalid destructuring assignment tokens", tokens)
matches, item = tokens
out = match_handle(loc, [matches, "in", item, None])
out += self.pattern_error(original, loc, match_to_var, match_check_var)
return out | python | def destructuring_stmt_handle(self, original, loc, tokens):
"""Process match assign blocks."""
internal_assert(len(tokens) == 2, "invalid destructuring assignment tokens", tokens)
matches, item = tokens
out = match_handle(loc, [matches, "in", item, None])
out += self.pattern_error(original, loc, match_to_var, match_check_var)
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:
Process match assign blocks.
### Response:
def destructuring_stmt_handle(self, original, loc, tokens):
"""Process match assign blocks."""
internal_assert(len(tokens) == 2, "invalid destructuring assignment tokens", tokens)
matches, item = tokens
out = match_handle(loc, [matches, "in", item, None])
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taskcluster/taskcluster-client.py | taskcluster/aio/awsprovisioner.py | AwsProvisioner.createWorkerType | async def createWorkerType(self, *args, **kwargs):
"""
Create new Worker Type
Create a worker type. A worker type contains all the configuration
needed for the provisioner to manage the instances. Each worker type
knows which regions and which instance types are allowed for that
worker type. Remember that Capacity is the number of concurrent tasks
that can be run on a given EC2 resource and that Utility is the relative
performance rate between different instance types. There is no way to
configure different regions to have different sets of instance types
so ensure that all instance types are available in all regions.
This function is idempotent.
Once a worker type is in the provisioner, a back ground process will
begin creating instances for it based on its capacity bounds and its
pending task count from the Queue. It is the worker's responsibility
to shut itself down. The provisioner has a limit (currently 96hours)
for all instances to prevent zombie instances from running indefinitely.
The provisioner will ensure that all instances created are tagged with
aws resource tags containing the provisioner id and the worker type.
If provided, the secrets in the global, region and instance type sections
are available using the secrets api. If specified, the scopes provided
will be used to generate a set of temporary credentials available with
the other secrets.
This method takes input: ``http://schemas.taskcluster.net/aws-provisioner/v1/create-worker-type-request.json#``
This method gives output: ``http://schemas.taskcluster.net/aws-provisioner/v1/get-worker-type-response.json#``
This method is ``stable``
"""
return await self._makeApiCall(self.funcinfo["createWorkerType"], *args, **kwargs) | python | async def createWorkerType(self, *args, **kwargs):
"""
Create new Worker Type
Create a worker type. A worker type contains all the configuration
needed for the provisioner to manage the instances. Each worker type
knows which regions and which instance types are allowed for that
worker type. Remember that Capacity is the number of concurrent tasks
that can be run on a given EC2 resource and that Utility is the relative
performance rate between different instance types. There is no way to
configure different regions to have different sets of instance types
so ensure that all instance types are available in all regions.
This function is idempotent.
Once a worker type is in the provisioner, a back ground process will
begin creating instances for it based on its capacity bounds and its
pending task count from the Queue. It is the worker's responsibility
to shut itself down. The provisioner has a limit (currently 96hours)
for all instances to prevent zombie instances from running indefinitely.
The provisioner will ensure that all instances created are tagged with
aws resource tags containing the provisioner id and the worker type.
If provided, the secrets in the global, region and instance type sections
are available using the secrets api. If specified, the scopes provided
will be used to generate a set of temporary credentials available with
the other secrets.
This method takes input: ``http://schemas.taskcluster.net/aws-provisioner/v1/create-worker-type-request.json#``
This method gives output: ``http://schemas.taskcluster.net/aws-provisioner/v1/get-worker-type-response.json#``
This method is ``stable``
"""
return await self._makeApiCall(self.funcinfo["createWorkerType"], *args, **kwargs) | [
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Once a worker type is in the provisioner, a back ground process will
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pending task count from the Queue. It is the worker's responsibility
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for all instances to prevent zombie instances from running indefinitely.
The provisioner will ensure that all instances created are tagged with
aws resource tags containing the provisioner id and the worker type.
If provided, the secrets in the global, region and instance type sections
are available using the secrets api. If specified, the scopes provided
will be used to generate a set of temporary credentials available with
the other secrets.
This method takes input: ``http://schemas.taskcluster.net/aws-provisioner/v1/create-worker-type-request.json#``
This method gives output: ``http://schemas.taskcluster.net/aws-provisioner/v1/get-worker-type-response.json#``
This method is ``stable`` | [
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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 new Worker Type
Create a worker type. A worker type contains all the configuration
needed for the provisioner to manage the instances. Each worker type
knows which regions and which instance types are allowed for that
worker type. Remember that Capacity is the number of concurrent tasks
that can be run on a given EC2 resource and that Utility is the relative
performance rate between different instance types. There is no way to
configure different regions to have different sets of instance types
so ensure that all instance types are available in all regions.
This function is idempotent.
Once a worker type is in the provisioner, a back ground process will
begin creating instances for it based on its capacity bounds and its
pending task count from the Queue. It is the worker's responsibility
to shut itself down. The provisioner has a limit (currently 96hours)
for all instances to prevent zombie instances from running indefinitely.
The provisioner will ensure that all instances created are tagged with
aws resource tags containing the provisioner id and the worker type.
If provided, the secrets in the global, region and instance type sections
are available using the secrets api. If specified, the scopes provided
will be used to generate a set of temporary credentials available with
the other secrets.
This method takes input: ``http://schemas.taskcluster.net/aws-provisioner/v1/create-worker-type-request.json#``
This method gives output: ``http://schemas.taskcluster.net/aws-provisioner/v1/get-worker-type-response.json#``
This method is ``stable``
### Response:
async def createWorkerType(self, *args, **kwargs):
"""
Create new Worker Type
Create a worker type. A worker type contains all the configuration
needed for the provisioner to manage the instances. Each worker type
knows which regions and which instance types are allowed for that
worker type. Remember that Capacity is the number of concurrent tasks
that can be run on a given EC2 resource and that Utility is the relative
performance rate between different instance types. There is no way to
configure different regions to have different sets of instance types
so ensure that all instance types are available in all regions.
This function is idempotent.
Once a worker type is in the provisioner, a back ground process will
begin creating instances for it based on its capacity bounds and its
pending task count from the Queue. It is the worker's responsibility
to shut itself down. The provisioner has a limit (currently 96hours)
for all instances to prevent zombie instances from running indefinitely.
The provisioner will ensure that all instances created are tagged with
aws resource tags containing the provisioner id and the worker type.
If provided, the secrets in the global, region and instance type sections
are available using the secrets api. If specified, the scopes provided
will be used to generate a set of temporary credentials available with
the other secrets.
This method takes input: ``http://schemas.taskcluster.net/aws-provisioner/v1/create-worker-type-request.json#``
This method gives output: ``http://schemas.taskcluster.net/aws-provisioner/v1/get-worker-type-response.json#``
This method is ``stable``
"""
return await self._makeApiCall(self.funcinfo["createWorkerType"], *args, **kwargs)
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jorgenschaefer/elpy | elpy/server.py | ElpyRPCServer.rpc_get_names | def rpc_get_names(self, filename, source, offset):
"""Get all possible names
"""
source = get_source(source)
if hasattr(self.backend, "rpc_get_names"):
return self.backend.rpc_get_names(filename, source, offset)
else:
raise Fault("get_names not implemented by current backend",
code=400) | python | def rpc_get_names(self, filename, source, offset):
"""Get all possible names
"""
source = get_source(source)
if hasattr(self.backend, "rpc_get_names"):
return self.backend.rpc_get_names(filename, source, offset)
else:
raise Fault("get_names not implemented by current backend",
code=400) | [
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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 all possible names
### Response:
def rpc_get_names(self, filename, source, offset):
"""Get all possible names
"""
source = get_source(source)
if hasattr(self.backend, "rpc_get_names"):
return self.backend.rpc_get_names(filename, source, offset)
else:
raise Fault("get_names not implemented by current backend",
code=400)
<|endoftext|> | [
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bartTC/django-attachments | attachments/templatetags/attachments_tags.py | attachment_form | def attachment_form(context, obj):
"""
Renders a "upload attachment" form.
The user must own ``attachments.add_attachment permission`` to add
attachments.
"""
if context['user'].has_perm('attachments.add_attachment'):
return {
'form': AttachmentForm(),
'form_url': add_url_for_obj(obj),
'next': context.request.build_absolute_uri(),
}
else:
return {'form': None} | python | def attachment_form(context, obj):
"""
Renders a "upload attachment" form.
The user must own ``attachments.add_attachment permission`` to add
attachments.
"""
if context['user'].has_perm('attachments.add_attachment'):
return {
'form': AttachmentForm(),
'form_url': add_url_for_obj(obj),
'next': context.request.build_absolute_uri(),
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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:
Renders a "upload attachment" form.
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Renders a "upload attachment" form.
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F5Networks/f5-common-python | f5/bigip/tm/ltm/pool.py | Members.update | def update(self, **kwargs):
"""Call this to change the configuration of the service on the device.
This method uses HTTP PUT to alter the service state on the device.
The attributes of the instance will be packaged as a dictionary. That
dictionary will be updated with kwargs. It is then submitted as JSON
to the device. Various edge cases are handled:
* read-only attributes that are unchangeable are removed
* If ``fqdn`` is in the kwargs or set as an attribute, removes the
``autopopulate`` and ``addressFamily`` keys from it.
:param kwargs: keys and associated values to alter on the device
"""
checked = self._check_member_parameters(**kwargs)
return super(Members, self)._update(**checked) | python | def update(self, **kwargs):
"""Call this to change the configuration of the service on the device.
This method uses HTTP PUT to alter the service state on the device.
The attributes of the instance will be packaged as a dictionary. That
dictionary will be updated with kwargs. It is then submitted as JSON
to the device. Various edge cases are handled:
* read-only attributes that are unchangeable are removed
* If ``fqdn`` is in the kwargs or set as an attribute, removes the
``autopopulate`` and ``addressFamily`` keys from it.
:param kwargs: keys and associated values to alter on the device
"""
checked = self._check_member_parameters(**kwargs)
return super(Members, self)._update(**checked) | [
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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:
Call this to change the configuration of the service on the device.
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def update(self, **kwargs):
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* read-only attributes that are unchangeable are removed
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ethereum/py-evm | eth/estimators/gas.py | binary_gas_search | def binary_gas_search(state: BaseState, transaction: BaseTransaction, tolerance: int=1) -> int:
"""
Run the transaction with various gas limits, progressively
approaching the minimum needed to succeed without an OutOfGas exception.
The starting range of possible estimates is:
[transaction.intrinsic_gas, state.gas_limit].
After the first OutOfGas exception, the range is: (largest_limit_out_of_gas, state.gas_limit].
After the first run not out of gas, the range is: (largest_limit_out_of_gas, smallest_success].
:param int tolerance: When the range of estimates is less than tolerance,
return the top of the range.
:returns int: The smallest confirmed gas to not throw an OutOfGas exception,
subject to tolerance. If OutOfGas is thrown at block limit, return block limit.
:raises VMError: if the computation fails even when given the block gas_limit to complete
"""
if not hasattr(transaction, 'sender'):
raise TypeError(
"Transaction is missing attribute sender.",
"If sending an unsigned transaction, use SpoofTransaction and provide the",
"sender using the 'from' parameter")
minimum_transaction = SpoofTransaction(
transaction,
gas=transaction.intrinsic_gas,
gas_price=0,
)
if _get_computation_error(state, minimum_transaction) is None:
return transaction.intrinsic_gas
maximum_transaction = SpoofTransaction(
transaction,
gas=state.gas_limit,
gas_price=0,
)
error = _get_computation_error(state, maximum_transaction)
if error is not None:
raise error
minimum_viable = state.gas_limit
maximum_out_of_gas = transaction.intrinsic_gas
while minimum_viable - maximum_out_of_gas > tolerance:
midpoint = (minimum_viable + maximum_out_of_gas) // 2
test_transaction = SpoofTransaction(transaction, gas=midpoint)
if _get_computation_error(state, test_transaction) is None:
minimum_viable = midpoint
else:
maximum_out_of_gas = midpoint
return minimum_viable | python | def binary_gas_search(state: BaseState, transaction: BaseTransaction, tolerance: int=1) -> int:
"""
Run the transaction with various gas limits, progressively
approaching the minimum needed to succeed without an OutOfGas exception.
The starting range of possible estimates is:
[transaction.intrinsic_gas, state.gas_limit].
After the first OutOfGas exception, the range is: (largest_limit_out_of_gas, state.gas_limit].
After the first run not out of gas, the range is: (largest_limit_out_of_gas, smallest_success].
:param int tolerance: When the range of estimates is less than tolerance,
return the top of the range.
:returns int: The smallest confirmed gas to not throw an OutOfGas exception,
subject to tolerance. If OutOfGas is thrown at block limit, return block limit.
:raises VMError: if the computation fails even when given the block gas_limit to complete
"""
if not hasattr(transaction, 'sender'):
raise TypeError(
"Transaction is missing attribute sender.",
"If sending an unsigned transaction, use SpoofTransaction and provide the",
"sender using the 'from' parameter")
minimum_transaction = SpoofTransaction(
transaction,
gas=transaction.intrinsic_gas,
gas_price=0,
)
if _get_computation_error(state, minimum_transaction) is None:
return transaction.intrinsic_gas
maximum_transaction = SpoofTransaction(
transaction,
gas=state.gas_limit,
gas_price=0,
)
error = _get_computation_error(state, maximum_transaction)
if error is not None:
raise error
minimum_viable = state.gas_limit
maximum_out_of_gas = transaction.intrinsic_gas
while minimum_viable - maximum_out_of_gas > tolerance:
midpoint = (minimum_viable + maximum_out_of_gas) // 2
test_transaction = SpoofTransaction(transaction, gas=midpoint)
if _get_computation_error(state, test_transaction) is None:
minimum_viable = midpoint
else:
maximum_out_of_gas = midpoint
return minimum_viable | [
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[transaction.intrinsic_gas, state.gas_limit].
After the first OutOfGas exception, the range is: (largest_limit_out_of_gas, state.gas_limit].
After the first run not out of gas, the range is: (largest_limit_out_of_gas, smallest_success].
:param int tolerance: When the range of estimates is less than tolerance,
return the top of the range.
:returns int: The smallest confirmed gas to not throw an OutOfGas exception,
subject to tolerance. If OutOfGas is thrown at block limit, return block limit.
:raises VMError: if the computation fails even when given the block gas_limit to complete | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Run the transaction with various gas limits, progressively
approaching the minimum needed to succeed without an OutOfGas exception.
The starting range of possible estimates is:
[transaction.intrinsic_gas, state.gas_limit].
After the first OutOfGas exception, the range is: (largest_limit_out_of_gas, state.gas_limit].
After the first run not out of gas, the range is: (largest_limit_out_of_gas, smallest_success].
:param int tolerance: When the range of estimates is less than tolerance,
return the top of the range.
:returns int: The smallest confirmed gas to not throw an OutOfGas exception,
subject to tolerance. If OutOfGas is thrown at block limit, return block limit.
:raises VMError: if the computation fails even when given the block gas_limit to complete
### Response:
def binary_gas_search(state: BaseState, transaction: BaseTransaction, tolerance: int=1) -> int:
"""
Run the transaction with various gas limits, progressively
approaching the minimum needed to succeed without an OutOfGas exception.
The starting range of possible estimates is:
[transaction.intrinsic_gas, state.gas_limit].
After the first OutOfGas exception, the range is: (largest_limit_out_of_gas, state.gas_limit].
After the first run not out of gas, the range is: (largest_limit_out_of_gas, smallest_success].
:param int tolerance: When the range of estimates is less than tolerance,
return the top of the range.
:returns int: The smallest confirmed gas to not throw an OutOfGas exception,
subject to tolerance. If OutOfGas is thrown at block limit, return block limit.
:raises VMError: if the computation fails even when given the block gas_limit to complete
"""
if not hasattr(transaction, 'sender'):
raise TypeError(
"Transaction is missing attribute sender.",
"If sending an unsigned transaction, use SpoofTransaction and provide the",
"sender using the 'from' parameter")
minimum_transaction = SpoofTransaction(
transaction,
gas=transaction.intrinsic_gas,
gas_price=0,
)
if _get_computation_error(state, minimum_transaction) is None:
return transaction.intrinsic_gas
maximum_transaction = SpoofTransaction(
transaction,
gas=state.gas_limit,
gas_price=0,
)
error = _get_computation_error(state, maximum_transaction)
if error is not None:
raise error
minimum_viable = state.gas_limit
maximum_out_of_gas = transaction.intrinsic_gas
while minimum_viable - maximum_out_of_gas > tolerance:
midpoint = (minimum_viable + maximum_out_of_gas) // 2
test_transaction = SpoofTransaction(transaction, gas=midpoint)
if _get_computation_error(state, test_transaction) is None:
minimum_viable = midpoint
else:
maximum_out_of_gas = midpoint
return minimum_viable
<|endoftext|> | [
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SatelliteQE/nailgun | nailgun/entities.py | ContentViewPuppetModule.read | def read(self, entity=None, attrs=None, ignore=None, params=None):
"""Provide a default value for ``entity``.
By default, ``nailgun.entity_mixins.EntityReadMixin.read provides a
default value for ``entity`` like so::
entity = type(self)()
However, :class:`ContentViewPuppetModule` requires that an
``content_view`` be provided, so this technique will not work. Do
this instead::
entity = type(self)(content_view=self.content_view.id)
"""
# read() should not change the state of the object it's called on, but
# super() alters the attributes of any entity passed in. Creating a new
# object and passing it to super() lets this one avoid changing state.
if entity is None:
entity = type(self)(
self._server_config,
content_view=self.content_view, # pylint:disable=no-member
)
if ignore is None:
ignore = set()
ignore.add('content_view')
return super(ContentViewPuppetModule, self).read(
entity, attrs, ignore, params) | python | def read(self, entity=None, attrs=None, ignore=None, params=None):
"""Provide a default value for ``entity``.
By default, ``nailgun.entity_mixins.EntityReadMixin.read provides a
default value for ``entity`` like so::
entity = type(self)()
However, :class:`ContentViewPuppetModule` requires that an
``content_view`` be provided, so this technique will not work. Do
this instead::
entity = type(self)(content_view=self.content_view.id)
"""
# read() should not change the state of the object it's called on, but
# super() alters the attributes of any entity passed in. Creating a new
# object and passing it to super() lets this one avoid changing state.
if entity is None:
entity = type(self)(
self._server_config,
content_view=self.content_view, # pylint:disable=no-member
)
if ignore is None:
ignore = set()
ignore.add('content_view')
return super(ContentViewPuppetModule, self).read(
entity, attrs, ignore, params) | [
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By default, ``nailgun.entity_mixins.EntityReadMixin.read provides a
default value for ``entity`` like so::
entity = type(self)()
However, :class:`ContentViewPuppetModule` requires that an
``content_view`` be provided, so this technique will not work. Do
this instead::
entity = type(self)(content_view=self.content_view.id) | [
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] | train | https://github.com/SatelliteQE/nailgun/blob/c36d8c20862e87bf6975bd48ac1ca40a9e634eaa/nailgun/entities.py#L2453-L2480 | <|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:
Provide a default value for ``entity``.
By default, ``nailgun.entity_mixins.EntityReadMixin.read provides a
default value for ``entity`` like so::
entity = type(self)()
However, :class:`ContentViewPuppetModule` requires that an
``content_view`` be provided, so this technique will not work. Do
this instead::
entity = type(self)(content_view=self.content_view.id)
### Response:
def read(self, entity=None, attrs=None, ignore=None, params=None):
"""Provide a default value for ``entity``.
By default, ``nailgun.entity_mixins.EntityReadMixin.read provides a
default value for ``entity`` like so::
entity = type(self)()
However, :class:`ContentViewPuppetModule` requires that an
``content_view`` be provided, so this technique will not work. Do
this instead::
entity = type(self)(content_view=self.content_view.id)
"""
# read() should not change the state of the object it's called on, but
# super() alters the attributes of any entity passed in. Creating a new
# object and passing it to super() lets this one avoid changing state.
if entity is None:
entity = type(self)(
self._server_config,
content_view=self.content_view, # pylint:disable=no-member
)
if ignore is None:
ignore = set()
ignore.add('content_view')
return super(ContentViewPuppetModule, self).read(
entity, attrs, ignore, params)
<|endoftext|> | [
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taskcluster/json-e | jsone/prattparser.py | ParseContext.attempt | def attempt(self, *kinds):
"""Try to get the next token if it matches one of the kinds given,
otherwise returning None. If no kinds are given, any kind is
accepted."""
if self._error:
raise self._error
token = self.next_token
if not token:
return None
if kinds and token.kind not in kinds:
return None
self._advance()
return token | python | def attempt(self, *kinds):
"""Try to get the next token if it matches one of the kinds given,
otherwise returning None. If no kinds are given, any kind is
accepted."""
if self._error:
raise self._error
token = self.next_token
if not token:
return None
if kinds and token.kind not in kinds:
return None
self._advance()
return token | [
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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 to get the next token if it matches one of the kinds given,
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### Response:
def attempt(self, *kinds):
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markovmodel/PyEMMA | pyemma/plots/plots2d.py | plot_contour | def plot_contour(
xall, yall, zall, ax=None, cmap=None,
ncontours=100, vmin=None, vmax=None, levels=None,
cbar=True, cax=None, cbar_label=None,
cbar_orientation='vertical', norm=None, nbins=100,
method='nearest', mask=False, **kwargs):
"""Plot a two-dimensional contour map by interpolating
scattered data on a grid.
Parameters
----------
xall : ndarray(T)
Sample x-coordinates.
yall : ndarray(T)
Sample y-coordinates.
zall : ndarray(T)
Sample z-coordinates.
ax : matplotlib.Axes object, optional, default=None
The ax to plot to; if ax=None, a new ax (and fig) is created.
cmap : matplotlib colormap, optional, default=None
The color map to use.
ncontours : int, optional, default=100
Number of contour levels.
vmin : float, optional, default=None
Lowest z-value to be plotted.
vmax : float, optional, default=None
Highest z-value to be plotted.
levels : iterable of float, optional, default=None
Contour levels to plot; use legacy style calculation
if 'legacy'.
cbar : boolean, optional, default=True
Plot a color bar.
cax : matplotlib.Axes object, optional, default=None
Plot the colorbar into a custom axes object instead of
stealing space from ax.
cbar_label : str, optional, default=None
Colorbar label string; use None to suppress it.
cbar_orientation : str, optional, default='vertical'
Colorbar orientation; choose 'vertical' or 'horizontal'.
norm : matplotlib norm, optional, default=None
Use a norm when coloring the contour plot.
nbins : int, optional, default=100
Number of grid points used in each dimension.
method : str, optional, default='nearest'
Assignment method; scipy.interpolate.griddata supports the
methods 'nearest', 'linear', and 'cubic'.
mask : boolean, optional, default=False
Hide unsampled areas is True.
Optional parameters for contourf (**kwargs)
-------------------------------------------
corner_mask : boolean, optional
Enable/disable corner masking, which only has an effect if
z is a masked array. If False, any quad touching a masked
point is masked out. If True, only the triangular corners
of quads nearest those points are always masked out, other
triangular corners comprising three unmasked points are
contoured as usual.
Defaults to rcParams['contour.corner_mask'], which
defaults to True.
alpha : float
The alpha blending value.
locator : [ None | ticker.Locator subclass ]
If locator is None, the default MaxNLocator is used. The
locator is used to determine the contour levels if they are
not given explicitly via the levels argument.
extend : [ ‘neither’ | ‘both’ | ‘min’ | ‘max’ ]
Unless this is ‘neither’, contour levels are automatically
added to one or both ends of the range so that all data are
included. These added ranges are then mapped to the special
colormap values which default to the ends of the
colormap range, but can be set via
matplotlib.colors.Colormap.set_under() and
matplotlib.colors.Colormap.set_over() methods.
xunits, yunits : [ None | registered units ]
Override axis units by specifying an instance of a
matplotlib.units.ConversionInterface.
antialiased : boolean, optional
Enable antialiasing, overriding the defaults. For filled
contours, the default is True. For line contours, it is
taken from rcParams[‘lines.antialiased’].
nchunk : [ 0 | integer ]
If 0, no subdivision of the domain. Specify a positive
integer to divide the domain into subdomains of nchunk by
nchunk quads. Chunking reduces the maximum length of polygons
generated by the contouring algorithm which reduces the
rendering workload passed on to the backend and also requires
slightly less RAM. It can however introduce rendering
artifacts at chunk boundaries depending on the backend, the
antialiased flag and value of alpha.
hatches :
A list of cross hatch patterns to use on the filled areas.
If None, no hatching will be added to the contour. Hatching
is supported in the PostScript, PDF, SVG and Agg backends
only.
zorder : float
Set the zorder for the artist. Artists with lower zorder
values are drawn first.
Returns
-------
fig : matplotlib.Figure object
The figure in which the used ax resides.
ax : matplotlib.Axes object
The ax in which the map was plotted.
misc : dict
Contains a matplotlib.contour.QuadContourSet 'mappable' and,
if requested, a matplotlib.Colorbar object 'cbar'.
"""
x, y, z = get_grid_data(
xall, yall, zall, nbins=nbins, method=method)
if vmin is None:
vmin = _np.min(zall[zall > -_np.inf])
if vmax is None:
vmax = _np.max(zall[zall < _np.inf])
if levels == 'legacy':
eps = (vmax - vmin) / float(ncontours)
levels = _np.linspace(vmin - eps, vmax + eps)
if mask:
_, _, counts = get_histogram(
xall, yall, nbins=nbins, weights=None,
avoid_zero_count=None)
z = _np.ma.masked_where(counts.T <= 0, z)
return plot_map(
x, y, z, ax=ax, cmap=cmap,
ncontours=ncontours, vmin=vmin, vmax=vmax, levels=levels,
cbar=cbar, cax=cax, cbar_label=cbar_label,
cbar_orientation=cbar_orientation, norm=norm,
**kwargs) | python | def plot_contour(
xall, yall, zall, ax=None, cmap=None,
ncontours=100, vmin=None, vmax=None, levels=None,
cbar=True, cax=None, cbar_label=None,
cbar_orientation='vertical', norm=None, nbins=100,
method='nearest', mask=False, **kwargs):
"""Plot a two-dimensional contour map by interpolating
scattered data on a grid.
Parameters
----------
xall : ndarray(T)
Sample x-coordinates.
yall : ndarray(T)
Sample y-coordinates.
zall : ndarray(T)
Sample z-coordinates.
ax : matplotlib.Axes object, optional, default=None
The ax to plot to; if ax=None, a new ax (and fig) is created.
cmap : matplotlib colormap, optional, default=None
The color map to use.
ncontours : int, optional, default=100
Number of contour levels.
vmin : float, optional, default=None
Lowest z-value to be plotted.
vmax : float, optional, default=None
Highest z-value to be plotted.
levels : iterable of float, optional, default=None
Contour levels to plot; use legacy style calculation
if 'legacy'.
cbar : boolean, optional, default=True
Plot a color bar.
cax : matplotlib.Axes object, optional, default=None
Plot the colorbar into a custom axes object instead of
stealing space from ax.
cbar_label : str, optional, default=None
Colorbar label string; use None to suppress it.
cbar_orientation : str, optional, default='vertical'
Colorbar orientation; choose 'vertical' or 'horizontal'.
norm : matplotlib norm, optional, default=None
Use a norm when coloring the contour plot.
nbins : int, optional, default=100
Number of grid points used in each dimension.
method : str, optional, default='nearest'
Assignment method; scipy.interpolate.griddata supports the
methods 'nearest', 'linear', and 'cubic'.
mask : boolean, optional, default=False
Hide unsampled areas is True.
Optional parameters for contourf (**kwargs)
-------------------------------------------
corner_mask : boolean, optional
Enable/disable corner masking, which only has an effect if
z is a masked array. If False, any quad touching a masked
point is masked out. If True, only the triangular corners
of quads nearest those points are always masked out, other
triangular corners comprising three unmasked points are
contoured as usual.
Defaults to rcParams['contour.corner_mask'], which
defaults to True.
alpha : float
The alpha blending value.
locator : [ None | ticker.Locator subclass ]
If locator is None, the default MaxNLocator is used. The
locator is used to determine the contour levels if they are
not given explicitly via the levels argument.
extend : [ ‘neither’ | ‘both’ | ‘min’ | ‘max’ ]
Unless this is ‘neither’, contour levels are automatically
added to one or both ends of the range so that all data are
included. These added ranges are then mapped to the special
colormap values which default to the ends of the
colormap range, but can be set via
matplotlib.colors.Colormap.set_under() and
matplotlib.colors.Colormap.set_over() methods.
xunits, yunits : [ None | registered units ]
Override axis units by specifying an instance of a
matplotlib.units.ConversionInterface.
antialiased : boolean, optional
Enable antialiasing, overriding the defaults. For filled
contours, the default is True. For line contours, it is
taken from rcParams[‘lines.antialiased’].
nchunk : [ 0 | integer ]
If 0, no subdivision of the domain. Specify a positive
integer to divide the domain into subdomains of nchunk by
nchunk quads. Chunking reduces the maximum length of polygons
generated by the contouring algorithm which reduces the
rendering workload passed on to the backend and also requires
slightly less RAM. It can however introduce rendering
artifacts at chunk boundaries depending on the backend, the
antialiased flag and value of alpha.
hatches :
A list of cross hatch patterns to use on the filled areas.
If None, no hatching will be added to the contour. Hatching
is supported in the PostScript, PDF, SVG and Agg backends
only.
zorder : float
Set the zorder for the artist. Artists with lower zorder
values are drawn first.
Returns
-------
fig : matplotlib.Figure object
The figure in which the used ax resides.
ax : matplotlib.Axes object
The ax in which the map was plotted.
misc : dict
Contains a matplotlib.contour.QuadContourSet 'mappable' and,
if requested, a matplotlib.Colorbar object 'cbar'.
"""
x, y, z = get_grid_data(
xall, yall, zall, nbins=nbins, method=method)
if vmin is None:
vmin = _np.min(zall[zall > -_np.inf])
if vmax is None:
vmax = _np.max(zall[zall < _np.inf])
if levels == 'legacy':
eps = (vmax - vmin) / float(ncontours)
levels = _np.linspace(vmin - eps, vmax + eps)
if mask:
_, _, counts = get_histogram(
xall, yall, nbins=nbins, weights=None,
avoid_zero_count=None)
z = _np.ma.masked_where(counts.T <= 0, z)
return plot_map(
x, y, z, ax=ax, cmap=cmap,
ncontours=ncontours, vmin=vmin, vmax=vmax, levels=levels,
cbar=cbar, cax=cax, cbar_label=cbar_label,
cbar_orientation=cbar_orientation, norm=norm,
**kwargs) | [
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scattered data on a grid.
Parameters
----------
xall : ndarray(T)
Sample x-coordinates.
yall : ndarray(T)
Sample y-coordinates.
zall : ndarray(T)
Sample z-coordinates.
ax : matplotlib.Axes object, optional, default=None
The ax to plot to; if ax=None, a new ax (and fig) is created.
cmap : matplotlib colormap, optional, default=None
The color map to use.
ncontours : int, optional, default=100
Number of contour levels.
vmin : float, optional, default=None
Lowest z-value to be plotted.
vmax : float, optional, default=None
Highest z-value to be plotted.
levels : iterable of float, optional, default=None
Contour levels to plot; use legacy style calculation
if 'legacy'.
cbar : boolean, optional, default=True
Plot a color bar.
cax : matplotlib.Axes object, optional, default=None
Plot the colorbar into a custom axes object instead of
stealing space from ax.
cbar_label : str, optional, default=None
Colorbar label string; use None to suppress it.
cbar_orientation : str, optional, default='vertical'
Colorbar orientation; choose 'vertical' or 'horizontal'.
norm : matplotlib norm, optional, default=None
Use a norm when coloring the contour plot.
nbins : int, optional, default=100
Number of grid points used in each dimension.
method : str, optional, default='nearest'
Assignment method; scipy.interpolate.griddata supports the
methods 'nearest', 'linear', and 'cubic'.
mask : boolean, optional, default=False
Hide unsampled areas is True.
Optional parameters for contourf (**kwargs)
-------------------------------------------
corner_mask : boolean, optional
Enable/disable corner masking, which only has an effect if
z is a masked array. If False, any quad touching a masked
point is masked out. If True, only the triangular corners
of quads nearest those points are always masked out, other
triangular corners comprising three unmasked points are
contoured as usual.
Defaults to rcParams['contour.corner_mask'], which
defaults to True.
alpha : float
The alpha blending value.
locator : [ None | ticker.Locator subclass ]
If locator is None, the default MaxNLocator is used. The
locator is used to determine the contour levels if they are
not given explicitly via the levels argument.
extend : [ ‘neither’ | ‘both’ | ‘min’ | ‘max’ ]
Unless this is ‘neither’, contour levels are automatically
added to one or both ends of the range so that all data are
included. These added ranges are then mapped to the special
colormap values which default to the ends of the
colormap range, but can be set via
matplotlib.colors.Colormap.set_under() and
matplotlib.colors.Colormap.set_over() methods.
xunits, yunits : [ None | registered units ]
Override axis units by specifying an instance of a
matplotlib.units.ConversionInterface.
antialiased : boolean, optional
Enable antialiasing, overriding the defaults. For filled
contours, the default is True. For line contours, it is
taken from rcParams[‘lines.antialiased’].
nchunk : [ 0 | integer ]
If 0, no subdivision of the domain. Specify a positive
integer to divide the domain into subdomains of nchunk by
nchunk quads. Chunking reduces the maximum length of polygons
generated by the contouring algorithm which reduces the
rendering workload passed on to the backend and also requires
slightly less RAM. It can however introduce rendering
artifacts at chunk boundaries depending on the backend, the
antialiased flag and value of alpha.
hatches :
A list of cross hatch patterns to use on the filled areas.
If None, no hatching will be added to the contour. Hatching
is supported in the PostScript, PDF, SVG and Agg backends
only.
zorder : float
Set the zorder for the artist. Artists with lower zorder
values are drawn first.
Returns
-------
fig : matplotlib.Figure object
The figure in which the used ax resides.
ax : matplotlib.Axes object
The ax in which the map was plotted.
misc : dict
Contains a matplotlib.contour.QuadContourSet 'mappable' and,
if requested, a matplotlib.Colorbar object 'cbar'. | [
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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:
Plot a two-dimensional contour map by interpolating
scattered data on a grid.
Parameters
----------
xall : ndarray(T)
Sample x-coordinates.
yall : ndarray(T)
Sample y-coordinates.
zall : ndarray(T)
Sample z-coordinates.
ax : matplotlib.Axes object, optional, default=None
The ax to plot to; if ax=None, a new ax (and fig) is created.
cmap : matplotlib colormap, optional, default=None
The color map to use.
ncontours : int, optional, default=100
Number of contour levels.
vmin : float, optional, default=None
Lowest z-value to be plotted.
vmax : float, optional, default=None
Highest z-value to be plotted.
levels : iterable of float, optional, default=None
Contour levels to plot; use legacy style calculation
if 'legacy'.
cbar : boolean, optional, default=True
Plot a color bar.
cax : matplotlib.Axes object, optional, default=None
Plot the colorbar into a custom axes object instead of
stealing space from ax.
cbar_label : str, optional, default=None
Colorbar label string; use None to suppress it.
cbar_orientation : str, optional, default='vertical'
Colorbar orientation; choose 'vertical' or 'horizontal'.
norm : matplotlib norm, optional, default=None
Use a norm when coloring the contour plot.
nbins : int, optional, default=100
Number of grid points used in each dimension.
method : str, optional, default='nearest'
Assignment method; scipy.interpolate.griddata supports the
methods 'nearest', 'linear', and 'cubic'.
mask : boolean, optional, default=False
Hide unsampled areas is True.
Optional parameters for contourf (**kwargs)
-------------------------------------------
corner_mask : boolean, optional
Enable/disable corner masking, which only has an effect if
z is a masked array. If False, any quad touching a masked
point is masked out. If True, only the triangular corners
of quads nearest those points are always masked out, other
triangular corners comprising three unmasked points are
contoured as usual.
Defaults to rcParams['contour.corner_mask'], which
defaults to True.
alpha : float
The alpha blending value.
locator : [ None | ticker.Locator subclass ]
If locator is None, the default MaxNLocator is used. The
locator is used to determine the contour levels if they are
not given explicitly via the levels argument.
extend : [ ‘neither’ | ‘both’ | ‘min’ | ‘max’ ]
Unless this is ‘neither’, contour levels are automatically
added to one or both ends of the range so that all data are
included. These added ranges are then mapped to the special
colormap values which default to the ends of the
colormap range, but can be set via
matplotlib.colors.Colormap.set_under() and
matplotlib.colors.Colormap.set_over() methods.
xunits, yunits : [ None | registered units ]
Override axis units by specifying an instance of a
matplotlib.units.ConversionInterface.
antialiased : boolean, optional
Enable antialiasing, overriding the defaults. For filled
contours, the default is True. For line contours, it is
taken from rcParams[‘lines.antialiased’].
nchunk : [ 0 | integer ]
If 0, no subdivision of the domain. Specify a positive
integer to divide the domain into subdomains of nchunk by
nchunk quads. Chunking reduces the maximum length of polygons
generated by the contouring algorithm which reduces the
rendering workload passed on to the backend and also requires
slightly less RAM. It can however introduce rendering
artifacts at chunk boundaries depending on the backend, the
antialiased flag and value of alpha.
hatches :
A list of cross hatch patterns to use on the filled areas.
If None, no hatching will be added to the contour. Hatching
is supported in the PostScript, PDF, SVG and Agg backends
only.
zorder : float
Set the zorder for the artist. Artists with lower zorder
values are drawn first.
Returns
-------
fig : matplotlib.Figure object
The figure in which the used ax resides.
ax : matplotlib.Axes object
The ax in which the map was plotted.
misc : dict
Contains a matplotlib.contour.QuadContourSet 'mappable' and,
if requested, a matplotlib.Colorbar object 'cbar'.
### Response:
def plot_contour(
xall, yall, zall, ax=None, cmap=None,
ncontours=100, vmin=None, vmax=None, levels=None,
cbar=True, cax=None, cbar_label=None,
cbar_orientation='vertical', norm=None, nbins=100,
method='nearest', mask=False, **kwargs):
"""Plot a two-dimensional contour map by interpolating
scattered data on a grid.
Parameters
----------
xall : ndarray(T)
Sample x-coordinates.
yall : ndarray(T)
Sample y-coordinates.
zall : ndarray(T)
Sample z-coordinates.
ax : matplotlib.Axes object, optional, default=None
The ax to plot to; if ax=None, a new ax (and fig) is created.
cmap : matplotlib colormap, optional, default=None
The color map to use.
ncontours : int, optional, default=100
Number of contour levels.
vmin : float, optional, default=None
Lowest z-value to be plotted.
vmax : float, optional, default=None
Highest z-value to be plotted.
levels : iterable of float, optional, default=None
Contour levels to plot; use legacy style calculation
if 'legacy'.
cbar : boolean, optional, default=True
Plot a color bar.
cax : matplotlib.Axes object, optional, default=None
Plot the colorbar into a custom axes object instead of
stealing space from ax.
cbar_label : str, optional, default=None
Colorbar label string; use None to suppress it.
cbar_orientation : str, optional, default='vertical'
Colorbar orientation; choose 'vertical' or 'horizontal'.
norm : matplotlib norm, optional, default=None
Use a norm when coloring the contour plot.
nbins : int, optional, default=100
Number of grid points used in each dimension.
method : str, optional, default='nearest'
Assignment method; scipy.interpolate.griddata supports the
methods 'nearest', 'linear', and 'cubic'.
mask : boolean, optional, default=False
Hide unsampled areas is True.
Optional parameters for contourf (**kwargs)
-------------------------------------------
corner_mask : boolean, optional
Enable/disable corner masking, which only has an effect if
z is a masked array. If False, any quad touching a masked
point is masked out. If True, only the triangular corners
of quads nearest those points are always masked out, other
triangular corners comprising three unmasked points are
contoured as usual.
Defaults to rcParams['contour.corner_mask'], which
defaults to True.
alpha : float
The alpha blending value.
locator : [ None | ticker.Locator subclass ]
If locator is None, the default MaxNLocator is used. The
locator is used to determine the contour levels if they are
not given explicitly via the levels argument.
extend : [ ‘neither’ | ‘both’ | ‘min’ | ‘max’ ]
Unless this is ‘neither’, contour levels are automatically
added to one or both ends of the range so that all data are
included. These added ranges are then mapped to the special
colormap values which default to the ends of the
colormap range, but can be set via
matplotlib.colors.Colormap.set_under() and
matplotlib.colors.Colormap.set_over() methods.
xunits, yunits : [ None | registered units ]
Override axis units by specifying an instance of a
matplotlib.units.ConversionInterface.
antialiased : boolean, optional
Enable antialiasing, overriding the defaults. For filled
contours, the default is True. For line contours, it is
taken from rcParams[‘lines.antialiased’].
nchunk : [ 0 | integer ]
If 0, no subdivision of the domain. Specify a positive
integer to divide the domain into subdomains of nchunk by
nchunk quads. Chunking reduces the maximum length of polygons
generated by the contouring algorithm which reduces the
rendering workload passed on to the backend and also requires
slightly less RAM. It can however introduce rendering
artifacts at chunk boundaries depending on the backend, the
antialiased flag and value of alpha.
hatches :
A list of cross hatch patterns to use on the filled areas.
If None, no hatching will be added to the contour. Hatching
is supported in the PostScript, PDF, SVG and Agg backends
only.
zorder : float
Set the zorder for the artist. Artists with lower zorder
values are drawn first.
Returns
-------
fig : matplotlib.Figure object
The figure in which the used ax resides.
ax : matplotlib.Axes object
The ax in which the map was plotted.
misc : dict
Contains a matplotlib.contour.QuadContourSet 'mappable' and,
if requested, a matplotlib.Colorbar object 'cbar'.
"""
x, y, z = get_grid_data(
xall, yall, zall, nbins=nbins, method=method)
if vmin is None:
vmin = _np.min(zall[zall > -_np.inf])
if vmax is None:
vmax = _np.max(zall[zall < _np.inf])
if levels == 'legacy':
eps = (vmax - vmin) / float(ncontours)
levels = _np.linspace(vmin - eps, vmax + eps)
if mask:
_, _, counts = get_histogram(
xall, yall, nbins=nbins, weights=None,
avoid_zero_count=None)
z = _np.ma.masked_where(counts.T <= 0, z)
return plot_map(
x, y, z, ax=ax, cmap=cmap,
ncontours=ncontours, vmin=vmin, vmax=vmax, levels=levels,
cbar=cbar, cax=cax, cbar_label=cbar_label,
cbar_orientation=cbar_orientation, norm=norm,
**kwargs)
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Jaymon/prom | prom/query.py | CacheNamespace.ttl | def ttl(self):
"""how long you should cache results for cacheable queries"""
ret = 3600
cn = self.get_process()
if "ttl" in cn:
ret = cn["ttl"]
return ret | python | def ttl(self):
"""how long you should cache results for cacheable queries"""
ret = 3600
cn = self.get_process()
if "ttl" in cn:
ret = cn["ttl"]
return ret | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
how long you should cache results for cacheable queries
### Response:
def ttl(self):
"""how long you should cache results for cacheable queries"""
ret = 3600
cn = self.get_process()
if "ttl" in cn:
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INM-6/hybridLFPy | hybridLFPy/gdf.py | GDF.create | def create(self, re='brunel-py-ex-*.gdf', index=True):
"""
Create db from list of gdf file glob
Parameters
----------
re : str
File glob to load.
index : bool
Create index on neurons for speed.
Returns
-------
None
See also
--------
sqlite3.connect.cursor, sqlite3.connect
"""
self.cursor.execute('CREATE TABLE IF NOT EXISTS spikes (neuron INT UNSIGNED, time REAL)')
tic = now()
for f in glob.glob(re):
print(f)
while True:
try:
for data in self._blockread(f):
self.cursor.executemany('INSERT INTO spikes VALUES (?, ?)', data)
self.conn.commit()
except:
continue
break
toc = now()
if self.debug: print('Inserts took %g seconds.' % (toc-tic))
# Optionally, create index for speed
if index:
tic = now()
self.cursor.execute('CREATE INDEX neuron_index on spikes (neuron)')
toc = now()
if self.debug: print('Indexed db in %g seconds.' % (toc-tic)) | python | def create(self, re='brunel-py-ex-*.gdf', index=True):
"""
Create db from list of gdf file glob
Parameters
----------
re : str
File glob to load.
index : bool
Create index on neurons for speed.
Returns
-------
None
See also
--------
sqlite3.connect.cursor, sqlite3.connect
"""
self.cursor.execute('CREATE TABLE IF NOT EXISTS spikes (neuron INT UNSIGNED, time REAL)')
tic = now()
for f in glob.glob(re):
print(f)
while True:
try:
for data in self._blockread(f):
self.cursor.executemany('INSERT INTO spikes VALUES (?, ?)', data)
self.conn.commit()
except:
continue
break
toc = now()
if self.debug: print('Inserts took %g seconds.' % (toc-tic))
# Optionally, create index for speed
if index:
tic = now()
self.cursor.execute('CREATE INDEX neuron_index on spikes (neuron)')
toc = now()
if self.debug: print('Indexed db in %g seconds.' % (toc-tic)) | [
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See also
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sqlite3.connect.cursor, sqlite3.connect | [
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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 db from list of gdf file glob
Parameters
----------
re : str
File glob to load.
index : bool
Create index on neurons for speed.
Returns
-------
None
See also
--------
sqlite3.connect.cursor, sqlite3.connect
### Response:
def create(self, re='brunel-py-ex-*.gdf', index=True):
"""
Create db from list of gdf file glob
Parameters
----------
re : str
File glob to load.
index : bool
Create index on neurons for speed.
Returns
-------
None
See also
--------
sqlite3.connect.cursor, sqlite3.connect
"""
self.cursor.execute('CREATE TABLE IF NOT EXISTS spikes (neuron INT UNSIGNED, time REAL)')
tic = now()
for f in glob.glob(re):
print(f)
while True:
try:
for data in self._blockread(f):
self.cursor.executemany('INSERT INTO spikes VALUES (?, ?)', data)
self.conn.commit()
except:
continue
break
toc = now()
if self.debug: print('Inserts took %g seconds.' % (toc-tic))
# Optionally, create index for speed
if index:
tic = now()
self.cursor.execute('CREATE INDEX neuron_index on spikes (neuron)')
toc = now()
if self.debug: print('Indexed db in %g seconds.' % (toc-tic))
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Esri/ArcREST | src/arcrest/ags/layer.py | FeatureLayer_Depricated._chunks | def _chunks(self, l, n):
""" Yield n successive chunks from a list l.
"""
l.sort()
newn = int(1.0 * len(l) / n + 0.5)
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yield l[i*newn:i*newn+newn]
yield l[n*newn-newn:] | python | def _chunks(self, l, n):
""" Yield n successive chunks from a list l.
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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:
Yield n successive chunks from a list l.
### Response:
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cs50/python-cs50 | src/cs50/cs50.py | eprint | def eprint(*args, **kwargs):
"""
Print an error message to standard error, prefixing it with
file name and line number from which method was called.
"""
end = kwargs.get("end", "\n")
sep = kwargs.get("sep", " ")
(filename, lineno) = inspect.stack()[1][1:3]
print("{}:{}: ".format(filename, lineno), end="")
print(*args, end=end, file=sys.stderr, sep=sep) | python | def eprint(*args, **kwargs):
"""
Print an error message to standard error, prefixing it with
file name and line number from which method was called.
"""
end = kwargs.get("end", "\n")
sep = kwargs.get("sep", " ")
(filename, lineno) = inspect.stack()[1][1:3]
print("{}:{}: ".format(filename, lineno), end="")
print(*args, end=end, file=sys.stderr, sep=sep) | [
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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:
Print an error message to standard error, prefixing it with
file name and line number from which method was called.
### Response:
def eprint(*args, **kwargs):
"""
Print an error message to standard error, prefixing it with
file name and line number from which method was called.
"""
end = kwargs.get("end", "\n")
sep = kwargs.get("sep", " ")
(filename, lineno) = inspect.stack()[1][1:3]
print("{}:{}: ".format(filename, lineno), end="")
print(*args, end=end, file=sys.stderr, sep=sep)
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pandas-dev/pandas | pandas/core/generic.py | NDFrame._reindex_axes | def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,
copy):
"""Perform the reindex for all the axes."""
obj = self
for a in self._AXIS_ORDERS:
labels = axes[a]
if labels is None:
continue
ax = self._get_axis(a)
new_index, indexer = ax.reindex(labels, level=level, limit=limit,
tolerance=tolerance, method=method)
axis = self._get_axis_number(a)
obj = obj._reindex_with_indexers({axis: [new_index, indexer]},
fill_value=fill_value,
copy=copy, allow_dups=False)
return obj | python | def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,
copy):
"""Perform the reindex for all the axes."""
obj = self
for a in self._AXIS_ORDERS:
labels = axes[a]
if labels is None:
continue
ax = self._get_axis(a)
new_index, indexer = ax.reindex(labels, level=level, limit=limit,
tolerance=tolerance, method=method)
axis = self._get_axis_number(a)
obj = obj._reindex_with_indexers({axis: [new_index, indexer]},
fill_value=fill_value,
copy=copy, allow_dups=False)
return obj | [
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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 the reindex for all the axes.
### Response:
def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,
copy):
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obj = self
for a in self._AXIS_ORDERS:
labels = axes[a]
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axis = self._get_axis_number(a)
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olt/corsa | corsa/app.py | parse_bind_address | def parse_bind_address(address, default=('localhost', 8080)):
"""
>>> parse_bind_address('80')
('localhost', 80)
>>> parse_bind_address('0.0.0.0')
('0.0.0.0', 8080)
>>> parse_bind_address('0.0.0.0:8081')
('0.0.0.0', 8081)
"""
if ':' in address:
host, port = address.split(':', 1)
port = int(port)
elif re.match('^\d+$', address):
host = default[0]
port = int(address)
else:
host = address
port = default[1]
return host, port | python | def parse_bind_address(address, default=('localhost', 8080)):
"""
>>> parse_bind_address('80')
('localhost', 80)
>>> parse_bind_address('0.0.0.0')
('0.0.0.0', 8080)
>>> parse_bind_address('0.0.0.0:8081')
('0.0.0.0', 8081)
"""
if ':' in address:
host, port = address.split(':', 1)
port = int(port)
elif re.match('^\d+$', address):
host = default[0]
port = int(address)
else:
host = address
port = default[1]
return host, port | [
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('localhost', 80)
>>> parse_bind_address('0.0.0.0')
('0.0.0.0', 8080)
>>> parse_bind_address('0.0.0.0:8081')
('0.0.0.0', 8081) | [
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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_bind_address('80')
('localhost', 80)
>>> parse_bind_address('0.0.0.0')
('0.0.0.0', 8080)
>>> parse_bind_address('0.0.0.0:8081')
('0.0.0.0', 8081)
### Response:
def parse_bind_address(address, default=('localhost', 8080)):
"""
>>> parse_bind_address('80')
('localhost', 80)
>>> parse_bind_address('0.0.0.0')
('0.0.0.0', 8080)
>>> parse_bind_address('0.0.0.0:8081')
('0.0.0.0', 8081)
"""
if ':' in address:
host, port = address.split(':', 1)
port = int(port)
elif re.match('^\d+$', address):
host = default[0]
port = int(address)
else:
host = address
port = default[1]
return host, port
<|endoftext|> | [
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a1ezzz/wasp-general | wasp_general/task/thread_tracker.py | WSimpleTrackerStorage.register_start | def register_start(self, task, event_details=None):
""" :meth:`.WSimpleTrackerStorage.register_start` method implementation
"""
if self.record_start() is True:
record_type = WTrackerEvents.start
record = WSimpleTrackerStorage.Record(record_type, task, event_details=event_details)
self.__store_record(record) | python | def register_start(self, task, event_details=None):
""" :meth:`.WSimpleTrackerStorage.register_start` method implementation
"""
if self.record_start() is True:
record_type = WTrackerEvents.start
record = WSimpleTrackerStorage.Record(record_type, task, event_details=event_details)
self.__store_record(record) | [
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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:
:meth:`.WSimpleTrackerStorage.register_start` method implementation
### Response:
def register_start(self, task, event_details=None):
""" :meth:`.WSimpleTrackerStorage.register_start` method implementation
"""
if self.record_start() is True:
record_type = WTrackerEvents.start
record = WSimpleTrackerStorage.Record(record_type, task, event_details=event_details)
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ryanmcgrath/twython | twython/streaming/types.py | TwythonStreamerTypes.site | def site(self, **params):
"""Stream site
Accepted params found at:
https://dev.twitter.com/docs/api/1.1/get/site
"""
url = 'https://sitestream.twitter.com/%s/site.json' \
% self.streamer.api_version
self.streamer._request(url, params=params) | python | def site(self, **params):
"""Stream site
Accepted params found at:
https://dev.twitter.com/docs/api/1.1/get/site
"""
url = 'https://sitestream.twitter.com/%s/site.json' \
% self.streamer.api_version
self.streamer._request(url, params=params) | [
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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:
Stream site
Accepted params found at:
https://dev.twitter.com/docs/api/1.1/get/site
### Response:
def site(self, **params):
"""Stream site
Accepted params found at:
https://dev.twitter.com/docs/api/1.1/get/site
"""
url = 'https://sitestream.twitter.com/%s/site.json' \
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self.streamer._request(url, params=params)
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brocade/pynos | pynos/versions/ver_7/ver_7_1_0/yang/brocade_firmware.py | brocade_firmware.logical_chassis_fwdl_sanity_output_cluster_output_rbridge_id | def logical_chassis_fwdl_sanity_output_cluster_output_rbridge_id(self, **kwargs):
"""Auto Generated Code
"""
config = ET.Element("config")
logical_chassis_fwdl_sanity = ET.Element("logical_chassis_fwdl_sanity")
config = logical_chassis_fwdl_sanity
output = ET.SubElement(logical_chassis_fwdl_sanity, "output")
cluster_output = ET.SubElement(output, "cluster-output")
rbridge_id = ET.SubElement(cluster_output, "rbridge-id")
rbridge_id.text = kwargs.pop('rbridge_id')
callback = kwargs.pop('callback', self._callback)
return callback(config) | python | def logical_chassis_fwdl_sanity_output_cluster_output_rbridge_id(self, **kwargs):
"""Auto Generated Code
"""
config = ET.Element("config")
logical_chassis_fwdl_sanity = ET.Element("logical_chassis_fwdl_sanity")
config = logical_chassis_fwdl_sanity
output = ET.SubElement(logical_chassis_fwdl_sanity, "output")
cluster_output = ET.SubElement(output, "cluster-output")
rbridge_id = ET.SubElement(cluster_output, "rbridge-id")
rbridge_id.text = kwargs.pop('rbridge_id')
callback = kwargs.pop('callback', self._callback)
return callback(config) | [
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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 logical_chassis_fwdl_sanity_output_cluster_output_rbridge_id(self, **kwargs):
"""Auto Generated Code
"""
config = ET.Element("config")
logical_chassis_fwdl_sanity = ET.Element("logical_chassis_fwdl_sanity")
config = logical_chassis_fwdl_sanity
output = ET.SubElement(logical_chassis_fwdl_sanity, "output")
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rbridge_id = ET.SubElement(cluster_output, "rbridge-id")
rbridge_id.text = kwargs.pop('rbridge_id')
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eyurtsev/FlowCytometryTools | FlowCytometryTools/gui/fc_widget.py | BaseVertex.update_coordinates | def update_coordinates(self, new_coordinates):
"""
new_coordinates : dict
"""
for k, v in new_coordinates.items():
if k in self.coordinates:
self.coordinates[k] = v
for svertex in self.spawn_list:
verts = tuple([self.coordinates.get(ch, None) for ch in svertex.channels])
if len(svertex.channels) == 1: # This means a histogram
svertex.update_position(verts[0], None)
else:
svertex.update_position(verts[0], verts[1])
self.callback(Event(Event.BASE_GATE_CHANGED)) | python | def update_coordinates(self, new_coordinates):
"""
new_coordinates : dict
"""
for k, v in new_coordinates.items():
if k in self.coordinates:
self.coordinates[k] = v
for svertex in self.spawn_list:
verts = tuple([self.coordinates.get(ch, None) for ch in svertex.channels])
if len(svertex.channels) == 1: # This means a histogram
svertex.update_position(verts[0], None)
else:
svertex.update_position(verts[0], verts[1])
self.callback(Event(Event.BASE_GATE_CHANGED)) | [
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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:
new_coordinates : dict
### Response:
def update_coordinates(self, new_coordinates):
"""
new_coordinates : dict
"""
for k, v in new_coordinates.items():
if k in self.coordinates:
self.coordinates[k] = v
for svertex in self.spawn_list:
verts = tuple([self.coordinates.get(ch, None) for ch in svertex.channels])
if len(svertex.channels) == 1: # This means a histogram
svertex.update_position(verts[0], None)
else:
svertex.update_position(verts[0], verts[1])
self.callback(Event(Event.BASE_GATE_CHANGED))
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dropbox/stone | stone/ir/api.py | ApiNamespace.get_namespaces_imported_by_route_io | def get_namespaces_imported_by_route_io(self):
# type: () -> typing.List[ApiNamespace]
"""
Returns a list of Namespace objects. A namespace is a member of this
list if it is imported by the current namespace and has a data type
from it referenced as an argument, result, or error of a route.
Namespaces are in ASCII order by name.
"""
namespace_data_types = sorted(self.get_route_io_data_types(),
key=lambda dt: dt.name)
referenced_namespaces = set()
for data_type in namespace_data_types:
if data_type.namespace != self:
referenced_namespaces.add(data_type.namespace)
return sorted(referenced_namespaces, key=lambda n: n.name) | python | def get_namespaces_imported_by_route_io(self):
# type: () -> typing.List[ApiNamespace]
"""
Returns a list of Namespace objects. A namespace is a member of this
list if it is imported by the current namespace and has a data type
from it referenced as an argument, result, or error of a route.
Namespaces are in ASCII order by name.
"""
namespace_data_types = sorted(self.get_route_io_data_types(),
key=lambda dt: dt.name)
referenced_namespaces = set()
for data_type in namespace_data_types:
if data_type.namespace != self:
referenced_namespaces.add(data_type.namespace)
return sorted(referenced_namespaces, key=lambda n: n.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 a list of Namespace objects. A namespace is a member of this
list if it is imported by the current namespace and has a data type
from it referenced as an argument, result, or error of a route.
Namespaces are in ASCII order by name.
### Response:
def get_namespaces_imported_by_route_io(self):
# type: () -> typing.List[ApiNamespace]
"""
Returns a list of Namespace objects. A namespace is a member of this
list if it is imported by the current namespace and has a data type
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Namespaces are in ASCII order by name.
"""
namespace_data_types = sorted(self.get_route_io_data_types(),
key=lambda dt: dt.name)
referenced_namespaces = set()
for data_type in namespace_data_types:
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vmalloc/dessert | dessert/rewrite.py | _read_pyc | def _read_pyc(source, pyc):
"""Possibly read a pytest pyc containing rewritten code.
Return rewritten code if successful or None if not.
"""
try:
fp = open(pyc, "rb")
except IOError:
return None
try:
try:
mtime = int(os.stat(source).st_mtime)
data = fp.read(8)
except EnvironmentError:
return None
# Check for invalid or out of date pyc file.
if (len(data) != 8 or data[:4] != imp.get_magic() or
struct.unpack("<l", data[4:])[0] != mtime):
return None
co = marshal.load(fp)
if not isinstance(co, types.CodeType):
# That's interesting....
return None
return co
finally:
fp.close() | python | def _read_pyc(source, pyc):
"""Possibly read a pytest pyc containing rewritten code.
Return rewritten code if successful or None if not.
"""
try:
fp = open(pyc, "rb")
except IOError:
return None
try:
try:
mtime = int(os.stat(source).st_mtime)
data = fp.read(8)
except EnvironmentError:
return None
# Check for invalid or out of date pyc file.
if (len(data) != 8 or data[:4] != imp.get_magic() or
struct.unpack("<l", data[4:])[0] != mtime):
return None
co = marshal.load(fp)
if not isinstance(co, types.CodeType):
# That's interesting....
return None
return co
finally:
fp.close() | [
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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:
Possibly read a pytest pyc containing rewritten code.
Return rewritten code if successful or None if not.
### Response:
def _read_pyc(source, pyc):
"""Possibly read a pytest pyc containing rewritten code.
Return rewritten code if successful or None if not.
"""
try:
fp = open(pyc, "rb")
except IOError:
return None
try:
try:
mtime = int(os.stat(source).st_mtime)
data = fp.read(8)
except EnvironmentError:
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# Check for invalid or out of date pyc file.
if (len(data) != 8 or data[:4] != imp.get_magic() or
struct.unpack("<l", data[4:])[0] != mtime):
return None
co = marshal.load(fp)
if not isinstance(co, types.CodeType):
# That's interesting....
return None
return co
finally:
fp.close()
<|endoftext|> | [
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infothrill/python-launchd | launchd/plist.py | write | def write(label, plist, scope=USER):
'''
Writes the given property list to the appropriate file on disk and returns
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:param plist: dict
:param label: string
:param scope: oneOf(USER, USER_ADMIN, DAEMON_ADMIN, USER_OS, DAEMON_OS)
'''
fname = compute_filename(label, scope)
with open(fname, "wb") as f:
plistlib.writePlist(plist, f)
return fname | python | def write(label, plist, scope=USER):
'''
Writes the given property list to the appropriate file on disk and returns
the absolute filename.
:param plist: dict
:param label: string
:param scope: oneOf(USER, USER_ADMIN, DAEMON_ADMIN, USER_OS, DAEMON_OS)
'''
fname = compute_filename(label, scope)
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plistlib.writePlist(plist, f)
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Writes the given property list to the appropriate file on disk and returns
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pymc-devs/pymc | pymc/Matplot.py | autocorrelation | def autocorrelation(
data, name, maxlags=100, format='png', reflected=False, suffix='-acf', path='./',
fontmap=None, new=True, last=True, rows=1, columns=1, num=1, verbose=1):
"""
Generate bar plot of the autocorrelation function for a series (usually an MCMC trace).
:Arguments:
data: PyMC object, trace or array
A trace from an MCMC sample or a PyMC object with one or more traces.
name: string
The name of the object.
maxlags (optional): int
The largest discrete value for the autocorrelation to be calculated (defaults to 100).
format (optional): string
Graphic output format (defaults to png).
suffix (optional): string
Filename suffix.
path (optional): string
Specifies location for saving plots (defaults to local directory).
fontmap (optional): dict
Font mapping for plot labels; most users should not specify this.
verbose (optional): int
Level of output verbosity.
"""
# Internal plotting specification for handling nested arrays
if fontmap is None:
fontmap = {1: 10, 2: 8, 3: 6, 4: 5, 5: 4}
# Stand-alone plot or subplot?
standalone = rows == 1 and columns == 1 and num == 1
if standalone:
if verbose > 0:
print_('Plotting', name)
figure()
subplot(rows, columns, num)
if ndim(data) == 1:
maxlags = min(len(data) - 1, maxlags)
try:
acorr(data, detrend=mlab.detrend_mean, maxlags=maxlags)
except:
print_('Cannot plot autocorrelation for %s' % name)
return
# Set axis bounds
ylim(-.1, 1.1)
xlim(-maxlags * reflected or 0, maxlags)
# Plot options
title(
'\n\n %s acorr' %
name,
x=0.,
y=1.,
ha='left',
va='top',
fontsize='small')
# Smaller tick labels
tlabels = gca().get_xticklabels()
setp(tlabels, 'fontsize', fontmap[1])
tlabels = gca().get_yticklabels()
setp(tlabels, 'fontsize', fontmap[1])
elif ndim(data) == 2:
# generate acorr plot for each dimension
rows = data.shape[1]
for j in range(rows):
autocorrelation(
data[:,
j],
'%s_%d' % (name,
j),
maxlags,
fontmap=fontmap,
rows=rows,
columns=1,
num=j + 1)
else:
raise ValueError(
'Only 1- and 2- dimensional functions can be displayed')
if standalone:
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
# Save to fiel
savefig("%s%s%s.%s" % (path, name, suffix, format)) | python | def autocorrelation(
data, name, maxlags=100, format='png', reflected=False, suffix='-acf', path='./',
fontmap=None, new=True, last=True, rows=1, columns=1, num=1, verbose=1):
"""
Generate bar plot of the autocorrelation function for a series (usually an MCMC trace).
:Arguments:
data: PyMC object, trace or array
A trace from an MCMC sample or a PyMC object with one or more traces.
name: string
The name of the object.
maxlags (optional): int
The largest discrete value for the autocorrelation to be calculated (defaults to 100).
format (optional): string
Graphic output format (defaults to png).
suffix (optional): string
Filename suffix.
path (optional): string
Specifies location for saving plots (defaults to local directory).
fontmap (optional): dict
Font mapping for plot labels; most users should not specify this.
verbose (optional): int
Level of output verbosity.
"""
# Internal plotting specification for handling nested arrays
if fontmap is None:
fontmap = {1: 10, 2: 8, 3: 6, 4: 5, 5: 4}
# Stand-alone plot or subplot?
standalone = rows == 1 and columns == 1 and num == 1
if standalone:
if verbose > 0:
print_('Plotting', name)
figure()
subplot(rows, columns, num)
if ndim(data) == 1:
maxlags = min(len(data) - 1, maxlags)
try:
acorr(data, detrend=mlab.detrend_mean, maxlags=maxlags)
except:
print_('Cannot plot autocorrelation for %s' % name)
return
# Set axis bounds
ylim(-.1, 1.1)
xlim(-maxlags * reflected or 0, maxlags)
# Plot options
title(
'\n\n %s acorr' %
name,
x=0.,
y=1.,
ha='left',
va='top',
fontsize='small')
# Smaller tick labels
tlabels = gca().get_xticklabels()
setp(tlabels, 'fontsize', fontmap[1])
tlabels = gca().get_yticklabels()
setp(tlabels, 'fontsize', fontmap[1])
elif ndim(data) == 2:
# generate acorr plot for each dimension
rows = data.shape[1]
for j in range(rows):
autocorrelation(
data[:,
j],
'%s_%d' % (name,
j),
maxlags,
fontmap=fontmap,
rows=rows,
columns=1,
num=j + 1)
else:
raise ValueError(
'Only 1- and 2- dimensional functions can be displayed')
if standalone:
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
# Save to fiel
savefig("%s%s%s.%s" % (path, name, suffix, format)) | [
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] | Generate bar plot of the autocorrelation function for a series (usually an MCMC trace).
:Arguments:
data: PyMC object, trace or array
A trace from an MCMC sample or a PyMC object with one or more traces.
name: string
The name of the object.
maxlags (optional): int
The largest discrete value for the autocorrelation to be calculated (defaults to 100).
format (optional): string
Graphic output format (defaults to png).
suffix (optional): string
Filename suffix.
path (optional): string
Specifies location for saving plots (defaults to local directory).
fontmap (optional): dict
Font mapping for plot labels; most users should not specify this.
verbose (optional): int
Level of output verbosity. | [
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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 bar plot of the autocorrelation function for a series (usually an MCMC trace).
:Arguments:
data: PyMC object, trace or array
A trace from an MCMC sample or a PyMC object with one or more traces.
name: string
The name of the object.
maxlags (optional): int
The largest discrete value for the autocorrelation to be calculated (defaults to 100).
format (optional): string
Graphic output format (defaults to png).
suffix (optional): string
Filename suffix.
path (optional): string
Specifies location for saving plots (defaults to local directory).
fontmap (optional): dict
Font mapping for plot labels; most users should not specify this.
verbose (optional): int
Level of output verbosity.
### Response:
def autocorrelation(
data, name, maxlags=100, format='png', reflected=False, suffix='-acf', path='./',
fontmap=None, new=True, last=True, rows=1, columns=1, num=1, verbose=1):
"""
Generate bar plot of the autocorrelation function for a series (usually an MCMC trace).
:Arguments:
data: PyMC object, trace or array
A trace from an MCMC sample or a PyMC object with one or more traces.
name: string
The name of the object.
maxlags (optional): int
The largest discrete value for the autocorrelation to be calculated (defaults to 100).
format (optional): string
Graphic output format (defaults to png).
suffix (optional): string
Filename suffix.
path (optional): string
Specifies location for saving plots (defaults to local directory).
fontmap (optional): dict
Font mapping for plot labels; most users should not specify this.
verbose (optional): int
Level of output verbosity.
"""
# Internal plotting specification for handling nested arrays
if fontmap is None:
fontmap = {1: 10, 2: 8, 3: 6, 4: 5, 5: 4}
# Stand-alone plot or subplot?
standalone = rows == 1 and columns == 1 and num == 1
if standalone:
if verbose > 0:
print_('Plotting', name)
figure()
subplot(rows, columns, num)
if ndim(data) == 1:
maxlags = min(len(data) - 1, maxlags)
try:
acorr(data, detrend=mlab.detrend_mean, maxlags=maxlags)
except:
print_('Cannot plot autocorrelation for %s' % name)
return
# Set axis bounds
ylim(-.1, 1.1)
xlim(-maxlags * reflected or 0, maxlags)
# Plot options
title(
'\n\n %s acorr' %
name,
x=0.,
y=1.,
ha='left',
va='top',
fontsize='small')
# Smaller tick labels
tlabels = gca().get_xticklabels()
setp(tlabels, 'fontsize', fontmap[1])
tlabels = gca().get_yticklabels()
setp(tlabels, 'fontsize', fontmap[1])
elif ndim(data) == 2:
# generate acorr plot for each dimension
rows = data.shape[1]
for j in range(rows):
autocorrelation(
data[:,
j],
'%s_%d' % (name,
j),
maxlags,
fontmap=fontmap,
rows=rows,
columns=1,
num=j + 1)
else:
raise ValueError(
'Only 1- and 2- dimensional functions can be displayed')
if standalone:
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
# Save to fiel
savefig("%s%s%s.%s" % (path, name, suffix, format))
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specialunderwear/django-easymode | easymode/tree/xml/serializers.py | RecursiveXmlSerializer.serialize | def serialize(self, queryset, **options):
"""
Serialize a queryset.
THE OUTPUT OF THIS SERIALIZER IS NOT MEANT TO BE SERIALIZED BACK
INTO THE DB.
"""
self.options = options
self.stream = options.get("stream", StringIO())
self.selected_fields = options.get("fields")
self.use_natural_keys = options.get("use_natural_keys", True)
self.xml = options.get("xml", None)
self.root = (self.xml == None)
self.start_serialization()
for obj in queryset:
# hook for having custom serialization
if hasattr(obj, '__serialize__'):
obj.__serialize__(self.xml)
else:
self.serialize_object(obj)
self.end_serialization()
return self.getvalue() | python | def serialize(self, queryset, **options):
"""
Serialize a queryset.
THE OUTPUT OF THIS SERIALIZER IS NOT MEANT TO BE SERIALIZED BACK
INTO THE DB.
"""
self.options = options
self.stream = options.get("stream", StringIO())
self.selected_fields = options.get("fields")
self.use_natural_keys = options.get("use_natural_keys", True)
self.xml = options.get("xml", None)
self.root = (self.xml == None)
self.start_serialization()
for obj in queryset:
# hook for having custom serialization
if hasattr(obj, '__serialize__'):
obj.__serialize__(self.xml)
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self.serialize_object(obj)
self.end_serialization()
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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:
Serialize a queryset.
THE OUTPUT OF THIS SERIALIZER IS NOT MEANT TO BE SERIALIZED BACK
INTO THE DB.
### Response:
def serialize(self, queryset, **options):
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Serialize a queryset.
THE OUTPUT OF THIS SERIALIZER IS NOT MEANT TO BE SERIALIZED BACK
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MacHu-GWU/angora-project | angora/text/strtemplate.py | StrTemplate.indented_show | def indented_show(text, howmany=1):
"""Print a formatted indented text.
"""
print(StrTemplate.pad_indent(text=text, howmany=howmany)) | python | def indented_show(text, howmany=1):
"""Print a formatted indented text.
"""
print(StrTemplate.pad_indent(text=text, howmany=howmany)) | [
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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:
Print a formatted indented text.
### Response:
def indented_show(text, howmany=1):
"""Print a formatted indented text.
"""
print(StrTemplate.pad_indent(text=text, howmany=howmany))
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houtianze/bypy | bypy/requester.py | UrllibRequester.request | def request(cls, method, url, **kwargs):
"""
:type method: str
"""
methodupper = method.upper()
hasdata = 'data' in kwargs
if methodupper == 'GET':
if hasdata:
print("ERROR: Can't do HTTP GET when the 'data' parameter presents")
assert False
resp = ulr.urlopen(url, **kwargs)
elif methodupper == 'POST':
if hasdata:
resp = ulr.urlopen(url, **kwargs)
else:
resp = ulr.urlopen(url, data = '', **kwargs)
else:
raise NotImplementedError()
return resp | python | def request(cls, method, url, **kwargs):
"""
:type method: str
"""
methodupper = method.upper()
hasdata = 'data' in kwargs
if methodupper == 'GET':
if hasdata:
print("ERROR: Can't do HTTP GET when the 'data' parameter presents")
assert False
resp = ulr.urlopen(url, **kwargs)
elif methodupper == 'POST':
if hasdata:
resp = ulr.urlopen(url, **kwargs)
else:
resp = ulr.urlopen(url, data = '', **kwargs)
else:
raise NotImplementedError()
return resp | [
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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:
:type method: str
### Response:
def request(cls, method, url, **kwargs):
"""
:type method: str
"""
methodupper = method.upper()
hasdata = 'data' in kwargs
if methodupper == 'GET':
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print("ERROR: Can't do HTTP GET when the 'data' parameter presents")
assert False
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if hasdata:
resp = ulr.urlopen(url, **kwargs)
else:
resp = ulr.urlopen(url, data = '', **kwargs)
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Phylliade/ikpy | contrib/transformations.py | pose_to_list | def pose_to_list(pose):
"""
Convert a Pose or PoseStamped in Python list ((position), (quaternion))
:param pose: geometry_msgs.msg.PoseStamped or geometry_msgs.msg.Pose
:return: the equivalent in list ((position), (quaternion))
"""
if type(pose) == geometry_msgs.msg.PoseStamped:
return [[
pose.pose.position.x, pose.pose.position.y, pose.pose.position.z
], [
pose.pose.orientation.x, pose.pose.orientation.y,
pose.pose.orientation.z, pose.pose.orientation.w
]]
elif type(pose) == geometry_msgs.msg.Pose:
return [[pose.position.x, pose.position.y, pose.position.z], [
pose.orientation.x, pose.orientation.y, pose.orientation.z,
pose.orientation.w
]]
else:
raise Exception("pose_to_list: parameter of type %s unexpected",
str(type(pose))) | python | def pose_to_list(pose):
"""
Convert a Pose or PoseStamped in Python list ((position), (quaternion))
:param pose: geometry_msgs.msg.PoseStamped or geometry_msgs.msg.Pose
:return: the equivalent in list ((position), (quaternion))
"""
if type(pose) == geometry_msgs.msg.PoseStamped:
return [[
pose.pose.position.x, pose.pose.position.y, pose.pose.position.z
], [
pose.pose.orientation.x, pose.pose.orientation.y,
pose.pose.orientation.z, pose.pose.orientation.w
]]
elif type(pose) == geometry_msgs.msg.Pose:
return [[pose.position.x, pose.position.y, pose.position.z], [
pose.orientation.x, pose.orientation.y, pose.orientation.z,
pose.orientation.w
]]
else:
raise Exception("pose_to_list: parameter of type %s unexpected",
str(type(pose))) | [
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:param pose: geometry_msgs.msg.PoseStamped or geometry_msgs.msg.Pose
:return: the equivalent in list ((position), (quaternion)) | [
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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 a Pose or PoseStamped in Python list ((position), (quaternion))
:param pose: geometry_msgs.msg.PoseStamped or geometry_msgs.msg.Pose
:return: the equivalent in list ((position), (quaternion))
### Response:
def pose_to_list(pose):
"""
Convert a Pose or PoseStamped in Python list ((position), (quaternion))
:param pose: geometry_msgs.msg.PoseStamped or geometry_msgs.msg.Pose
:return: the equivalent in list ((position), (quaternion))
"""
if type(pose) == geometry_msgs.msg.PoseStamped:
return [[
pose.pose.position.x, pose.pose.position.y, pose.pose.position.z
], [
pose.pose.orientation.x, pose.pose.orientation.y,
pose.pose.orientation.z, pose.pose.orientation.w
]]
elif type(pose) == geometry_msgs.msg.Pose:
return [[pose.position.x, pose.position.y, pose.position.z], [
pose.orientation.x, pose.orientation.y, pose.orientation.z,
pose.orientation.w
]]
else:
raise Exception("pose_to_list: parameter of type %s unexpected",
str(type(pose)))
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fmfn/BayesianOptimization | bayes_opt/bayesian_optimization.py | BayesianOptimization.suggest | def suggest(self, utility_function):
"""Most promissing point to probe next"""
if len(self._space) == 0:
return self._space.array_to_params(self._space.random_sample())
# Sklearn's GP throws a large number of warnings at times, but
# we don't really need to see them here.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
self._gp.fit(self._space.params, self._space.target)
# Finding argmax of the acquisition function.
suggestion = acq_max(
ac=utility_function.utility,
gp=self._gp,
y_max=self._space.target.max(),
bounds=self._space.bounds,
random_state=self._random_state
)
return self._space.array_to_params(suggestion) | python | def suggest(self, utility_function):
"""Most promissing point to probe next"""
if len(self._space) == 0:
return self._space.array_to_params(self._space.random_sample())
# Sklearn's GP throws a large number of warnings at times, but
# we don't really need to see them here.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
self._gp.fit(self._space.params, self._space.target)
# Finding argmax of the acquisition function.
suggestion = acq_max(
ac=utility_function.utility,
gp=self._gp,
y_max=self._space.target.max(),
bounds=self._space.bounds,
random_state=self._random_state
)
return self._space.array_to_params(suggestion) | [
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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:
Most promissing point to probe next
### Response:
def suggest(self, utility_function):
"""Most promissing point to probe next"""
if len(self._space) == 0:
return self._space.array_to_params(self._space.random_sample())
# Sklearn's GP throws a large number of warnings at times, but
# we don't really need to see them here.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
self._gp.fit(self._space.params, self._space.target)
# Finding argmax of the acquisition function.
suggestion = acq_max(
ac=utility_function.utility,
gp=self._gp,
y_max=self._space.target.max(),
bounds=self._space.bounds,
random_state=self._random_state
)
return self._space.array_to_params(suggestion)
<|endoftext|> | [
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