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apache/incubator-mxnet | python/mxnet/metric.py | np | def np(numpy_feval, name=None, allow_extra_outputs=False):
"""Creates a custom evaluation metric that receives its inputs as numpy arrays.
Parameters
----------
numpy_feval : callable(label, pred)
Custom evaluation function that receives labels and predictions for a minibatch
as numpy arrays and returns the corresponding custom metric as a floating point number.
name : str, optional
Name of the custom metric.
allow_extra_outputs : bool, optional
Whether prediction output is allowed to have extra outputs. This is useful in cases
like RNN where states are also part of output which can then be fed back to the RNN
in the next step. By default, extra outputs are not allowed.
Returns
-------
float
Custom metric corresponding to the provided labels and predictions.
Example
-------
>>> def custom_metric(label, pred):
... return np.mean(np.abs(label-pred))
...
>>> metric = mx.metric.np(custom_metric)
"""
def feval(label, pred):
"""Internal eval function."""
return numpy_feval(label, pred)
feval.__name__ = numpy_feval.__name__
return CustomMetric(feval, name, allow_extra_outputs) | python | def np(numpy_feval, name=None, allow_extra_outputs=False):
"""Creates a custom evaluation metric that receives its inputs as numpy arrays.
Parameters
----------
numpy_feval : callable(label, pred)
Custom evaluation function that receives labels and predictions for a minibatch
as numpy arrays and returns the corresponding custom metric as a floating point number.
name : str, optional
Name of the custom metric.
allow_extra_outputs : bool, optional
Whether prediction output is allowed to have extra outputs. This is useful in cases
like RNN where states are also part of output which can then be fed back to the RNN
in the next step. By default, extra outputs are not allowed.
Returns
-------
float
Custom metric corresponding to the provided labels and predictions.
Example
-------
>>> def custom_metric(label, pred):
... return np.mean(np.abs(label-pred))
...
>>> metric = mx.metric.np(custom_metric)
"""
def feval(label, pred):
"""Internal eval function."""
return numpy_feval(label, pred)
feval.__name__ = numpy_feval.__name__
return CustomMetric(feval, name, allow_extra_outputs) | [
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Custom metric corresponding to the provided labels and predictions.
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Creates a custom evaluation metric that receives its inputs as numpy arrays.
Parameters
----------
numpy_feval : callable(label, pred)
Custom evaluation function that receives labels and predictions for a minibatch
as numpy arrays and returns the corresponding custom metric as a floating point number.
name : str, optional
Name of the custom metric.
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Whether prediction output is allowed to have extra outputs. This is useful in cases
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Custom metric corresponding to the provided labels and predictions.
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>>> def custom_metric(label, pred):
... return np.mean(np.abs(label-pred))
...
>>> metric = mx.metric.np(custom_metric)
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def np(numpy_feval, name=None, allow_extra_outputs=False):
"""Creates a custom evaluation metric that receives its inputs as numpy arrays.
Parameters
----------
numpy_feval : callable(label, pred)
Custom evaluation function that receives labels and predictions for a minibatch
as numpy arrays and returns the corresponding custom metric as a floating point number.
name : str, optional
Name of the custom metric.
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Whether prediction output is allowed to have extra outputs. This is useful in cases
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in the next step. By default, extra outputs are not allowed.
Returns
-------
float
Custom metric corresponding to the provided labels and predictions.
Example
-------
>>> def custom_metric(label, pred):
... return np.mean(np.abs(label-pred))
...
>>> metric = mx.metric.np(custom_metric)
"""
def feval(label, pred):
"""Internal eval function."""
return numpy_feval(label, pred)
feval.__name__ = numpy_feval.__name__
return CustomMetric(feval, name, allow_extra_outputs)
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asweigart/pyautogui | pyautogui/_pyautogui_win.py | _scroll | def _scroll(clicks, x=None, y=None):
"""Send the mouse vertical scroll event to Windows by calling the
mouse_event() win32 function.
Args:
clicks (int): The amount of scrolling to do. A positive value is the mouse
wheel moving forward (scrolling up), a negative value is backwards (down).
x (int): The x position of the mouse event.
y (int): The y position of the mouse event.
Returns:
None
"""
startx, starty = _position()
width, height = _size()
if x is None:
x = startx
else:
if x < 0:
x = 0
elif x >= width:
x = width - 1
if y is None:
y = starty
else:
if y < 0:
y = 0
elif y >= height:
y = height - 1
try:
_sendMouseEvent(MOUSEEVENTF_WHEEL, x, y, dwData=clicks)
except (PermissionError, OSError): # TODO: We need to figure out how to prevent these errors, see https://github.com/asweigart/pyautogui/issues/60
pass | python | def _scroll(clicks, x=None, y=None):
"""Send the mouse vertical scroll event to Windows by calling the
mouse_event() win32 function.
Args:
clicks (int): The amount of scrolling to do. A positive value is the mouse
wheel moving forward (scrolling up), a negative value is backwards (down).
x (int): The x position of the mouse event.
y (int): The y position of the mouse event.
Returns:
None
"""
startx, starty = _position()
width, height = _size()
if x is None:
x = startx
else:
if x < 0:
x = 0
elif x >= width:
x = width - 1
if y is None:
y = starty
else:
if y < 0:
y = 0
elif y >= height:
y = height - 1
try:
_sendMouseEvent(MOUSEEVENTF_WHEEL, x, y, dwData=clicks)
except (PermissionError, OSError): # TODO: We need to figure out how to prevent these errors, see https://github.com/asweigart/pyautogui/issues/60
pass | [
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Args:
clicks (int): The amount of scrolling to do. A positive value is the mouse
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x (int): The x position of the mouse event.
y (int): The y position of the mouse event.
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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:
Send the mouse vertical scroll event to Windows by calling the
mouse_event() win32 function.
Args:
clicks (int): The amount of scrolling to do. A positive value is the mouse
wheel moving forward (scrolling up), a negative value is backwards (down).
x (int): The x position of the mouse event.
y (int): The y position of the mouse event.
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### Response:
def _scroll(clicks, x=None, y=None):
"""Send the mouse vertical scroll event to Windows by calling the
mouse_event() win32 function.
Args:
clicks (int): The amount of scrolling to do. A positive value is the mouse
wheel moving forward (scrolling up), a negative value is backwards (down).
x (int): The x position of the mouse event.
y (int): The y position of the mouse event.
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None
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startx, starty = _position()
width, height = _size()
if x is None:
x = startx
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x = 0
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x = width - 1
if y is None:
y = starty
else:
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y = 0
elif y >= height:
y = height - 1
try:
_sendMouseEvent(MOUSEEVENTF_WHEEL, x, y, dwData=clicks)
except (PermissionError, OSError): # TODO: We need to figure out how to prevent these errors, see https://github.com/asweigart/pyautogui/issues/60
pass
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deepmind/pysc2 | pysc2/env/lan_sc2_env.py | tcp_client | def tcp_client(tcp_addr):
"""Connect to the tcp server, and return the settings."""
family = socket.AF_INET6 if ":" in tcp_addr.ip else socket.AF_INET
sock = socket.socket(family, socket.SOCK_STREAM, socket.IPPROTO_TCP)
for i in range(300):
logging.info("Connecting to: %s, attempt %d", tcp_addr, i)
try:
sock.connect(tcp_addr)
break
except socket.error:
time.sleep(1)
else:
sock.connect(tcp_addr) # One last try, but don't catch this error.
logging.info("Connected.")
map_data = read_tcp(sock)
settings_str = read_tcp(sock)
if not settings_str:
raise socket.error("Failed to read")
settings = json.loads(settings_str.decode())
logging.info("Got settings. map_name: %s.", settings["map_name"])
logging.debug("settings: %s", settings)
settings["map_data"] = map_data
return sock, settings | python | def tcp_client(tcp_addr):
"""Connect to the tcp server, and return the settings."""
family = socket.AF_INET6 if ":" in tcp_addr.ip else socket.AF_INET
sock = socket.socket(family, socket.SOCK_STREAM, socket.IPPROTO_TCP)
for i in range(300):
logging.info("Connecting to: %s, attempt %d", tcp_addr, i)
try:
sock.connect(tcp_addr)
break
except socket.error:
time.sleep(1)
else:
sock.connect(tcp_addr) # One last try, but don't catch this error.
logging.info("Connected.")
map_data = read_tcp(sock)
settings_str = read_tcp(sock)
if not settings_str:
raise socket.error("Failed to read")
settings = json.loads(settings_str.decode())
logging.info("Got settings. map_name: %s.", settings["map_name"])
logging.debug("settings: %s", settings)
settings["map_data"] = map_data
return sock, settings | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Connect to the tcp server, and return the settings.
### Response:
def tcp_client(tcp_addr):
"""Connect to the tcp server, and return the settings."""
family = socket.AF_INET6 if ":" in tcp_addr.ip else socket.AF_INET
sock = socket.socket(family, socket.SOCK_STREAM, socket.IPPROTO_TCP)
for i in range(300):
logging.info("Connecting to: %s, attempt %d", tcp_addr, i)
try:
sock.connect(tcp_addr)
break
except socket.error:
time.sleep(1)
else:
sock.connect(tcp_addr) # One last try, but don't catch this error.
logging.info("Connected.")
map_data = read_tcp(sock)
settings_str = read_tcp(sock)
if not settings_str:
raise socket.error("Failed to read")
settings = json.loads(settings_str.decode())
logging.info("Got settings. map_name: %s.", settings["map_name"])
logging.debug("settings: %s", settings)
settings["map_data"] = map_data
return sock, settings
<|endoftext|> | [
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openid/python-openid | openid/yadis/xri.py | _escape_xref | def _escape_xref(xref_match):
"""Escape things that need to be escaped if they're in a cross-reference.
"""
xref = xref_match.group()
xref = xref.replace('/', '%2F')
xref = xref.replace('?', '%3F')
xref = xref.replace('#', '%23')
return xref | python | def _escape_xref(xref_match):
"""Escape things that need to be escaped if they're in a cross-reference.
"""
xref = xref_match.group()
xref = xref.replace('/', '%2F')
xref = xref.replace('?', '%3F')
xref = xref.replace('#', '%23')
return xref | [
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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:
Escape things that need to be escaped if they're in a cross-reference.
### Response:
def _escape_xref(xref_match):
"""Escape things that need to be escaped if they're in a cross-reference.
"""
xref = xref_match.group()
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biolink/biolink-model | metamodel/generators/yumlgen.py | YumlGenerator.prop_modifier | def prop_modifier(self, cls: ClassDefinition, slot: SlotDefinition) -> str:
""" Return the modifiers for the slot:
(i) - inherited
(m) - inherited through mixin
(a) - injected
(pk) - primary ckey
@param cls:
@param slot:
@return:
"""
pk = '(pk)' if slot.primary_key else ''
inherited = slot.name not in cls.slots
mixin = inherited and slot.name in \
[mslot.name for mslot in [self.schema.classes[m] for m in cls.mixins]]
injected = cls.name in self.synopsis.applytos and \
slot.name in [aslot.name for aslot in
[self.schema.classes[a] for a in sorted(self.synopsis.applytos[cls.name].classrefs)]]
return pk + '(a)' if injected else '(m)' if mixin else '(i)' if inherited else '' | python | def prop_modifier(self, cls: ClassDefinition, slot: SlotDefinition) -> str:
""" Return the modifiers for the slot:
(i) - inherited
(m) - inherited through mixin
(a) - injected
(pk) - primary ckey
@param cls:
@param slot:
@return:
"""
pk = '(pk)' if slot.primary_key else ''
inherited = slot.name not in cls.slots
mixin = inherited and slot.name in \
[mslot.name for mslot in [self.schema.classes[m] for m in cls.mixins]]
injected = cls.name in self.synopsis.applytos and \
slot.name in [aslot.name for aslot in
[self.schema.classes[a] for a in sorted(self.synopsis.applytos[cls.name].classrefs)]]
return pk + '(a)' if injected else '(m)' if mixin else '(i)' if inherited else '' | [
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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 modifiers for the slot:
(i) - inherited
(m) - inherited through mixin
(a) - injected
(pk) - primary ckey
@param cls:
@param slot:
@return:
### Response:
def prop_modifier(self, cls: ClassDefinition, slot: SlotDefinition) -> str:
""" Return the modifiers for the slot:
(i) - inherited
(m) - inherited through mixin
(a) - injected
(pk) - primary ckey
@param cls:
@param slot:
@return:
"""
pk = '(pk)' if slot.primary_key else ''
inherited = slot.name not in cls.slots
mixin = inherited and slot.name in \
[mslot.name for mslot in [self.schema.classes[m] for m in cls.mixins]]
injected = cls.name in self.synopsis.applytos and \
slot.name in [aslot.name for aslot in
[self.schema.classes[a] for a in sorted(self.synopsis.applytos[cls.name].classrefs)]]
return pk + '(a)' if injected else '(m)' if mixin else '(i)' if inherited else ''
<|endoftext|> | [
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scot-dev/scot | scot/ooapi.py | Workspace.optimize_var | def optimize_var(self):
""" Optimize the VAR model's hyperparameters (such as regularization).
Returns
-------
self : Workspace
The Workspace object.
Raises
------
RuntimeError
If the :class:`Workspace` instance does not contain source activations.
"""
if self.activations_ is None:
raise RuntimeError("VAR fitting requires source activations (run do_mvarica first)")
self.var_.optimize(self.activations_[self.trial_mask_, :, :])
return self | python | def optimize_var(self):
""" Optimize the VAR model's hyperparameters (such as regularization).
Returns
-------
self : Workspace
The Workspace object.
Raises
------
RuntimeError
If the :class:`Workspace` instance does not contain source activations.
"""
if self.activations_ is None:
raise RuntimeError("VAR fitting requires source activations (run do_mvarica first)")
self.var_.optimize(self.activations_[self.trial_mask_, :, :])
return self | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Optimize the VAR model's hyperparameters (such as regularization).
Returns
-------
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The Workspace object.
Raises
------
RuntimeError
If the :class:`Workspace` instance does not contain source activations.
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saltstack/salt | salt/modules/boto_s3_bucket.py | delete_website | def delete_website(Bucket,
region=None, key=None, keyid=None, profile=None):
'''
Remove the website configuration from the given bucket
Returns {deleted: true} if website configuration was deleted and returns
{deleted: False} if website configuration was not deleted.
CLI Example:
.. code-block:: bash
salt myminion boto_s3_bucket.delete_website my_bucket
'''
try:
conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile)
conn.delete_bucket_website(Bucket=Bucket)
return {'deleted': True, 'name': Bucket}
except ClientError as e:
return {'deleted': False, 'error': __utils__['boto3.get_error'](e)} | python | def delete_website(Bucket,
region=None, key=None, keyid=None, profile=None):
'''
Remove the website configuration from the given bucket
Returns {deleted: true} if website configuration was deleted and returns
{deleted: False} if website configuration was not deleted.
CLI Example:
.. code-block:: bash
salt myminion boto_s3_bucket.delete_website my_bucket
'''
try:
conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile)
conn.delete_bucket_website(Bucket=Bucket)
return {'deleted': True, 'name': Bucket}
except ClientError as e:
return {'deleted': False, 'error': __utils__['boto3.get_error'](e)} | [
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CLI Example:
.. code-block:: bash
salt myminion boto_s3_bucket.delete_website my_bucket | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Remove the website configuration from the given bucket
Returns {deleted: true} if website configuration was deleted and returns
{deleted: False} if website configuration was not deleted.
CLI Example:
.. code-block:: bash
salt myminion boto_s3_bucket.delete_website my_bucket
### Response:
def delete_website(Bucket,
region=None, key=None, keyid=None, profile=None):
'''
Remove the website configuration from the given bucket
Returns {deleted: true} if website configuration was deleted and returns
{deleted: False} if website configuration was not deleted.
CLI Example:
.. code-block:: bash
salt myminion boto_s3_bucket.delete_website my_bucket
'''
try:
conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile)
conn.delete_bucket_website(Bucket=Bucket)
return {'deleted': True, 'name': Bucket}
except ClientError as e:
return {'deleted': False, 'error': __utils__['boto3.get_error'](e)}
<|endoftext|> | [
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fracpete/python-weka-wrapper3 | python/weka/core/packages.py | install_package | def install_package(pkge, version="Latest"):
"""
The list of packages to install.
:param pkge: the name of the repository package, a URL (http/https) or a zip file
:type pkge: str
:param version: in case of the repository packages, the version
:type version: str
:return: whether successfully installed
:rtype: bool
"""
establish_cache()
if pkge.startswith("http://") or pkge.startswith("https://"):
url = javabridge.make_instance(
"java/net/URL", "(Ljava/lang/String;)V", javabridge.get_env().new_string_utf(pkge))
return not javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromURL",
"(Ljava/net/URL;[Ljava/io/PrintStream;)Ljava/lang/String;", url, []) is None
elif pkge.lower().endswith(".zip"):
return not javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromArchive",
"(Ljava/lang/String;[Ljava/io/PrintStream;)Ljava/lang/String;", pkge, []) is None
else:
return javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromRepository",
"(Ljava/lang/String;Ljava/lang/String;[Ljava/io/PrintStream;)Z", pkge, version, []) | python | def install_package(pkge, version="Latest"):
"""
The list of packages to install.
:param pkge: the name of the repository package, a URL (http/https) or a zip file
:type pkge: str
:param version: in case of the repository packages, the version
:type version: str
:return: whether successfully installed
:rtype: bool
"""
establish_cache()
if pkge.startswith("http://") or pkge.startswith("https://"):
url = javabridge.make_instance(
"java/net/URL", "(Ljava/lang/String;)V", javabridge.get_env().new_string_utf(pkge))
return not javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromURL",
"(Ljava/net/URL;[Ljava/io/PrintStream;)Ljava/lang/String;", url, []) is None
elif pkge.lower().endswith(".zip"):
return not javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromArchive",
"(Ljava/lang/String;[Ljava/io/PrintStream;)Ljava/lang/String;", pkge, []) is None
else:
return javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromRepository",
"(Ljava/lang/String;Ljava/lang/String;[Ljava/io/PrintStream;)Z", pkge, version, []) | [
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:param pkge: the name of the repository package, a URL (http/https) or a zip file
:type pkge: str
:param version: in case of the repository packages, the version
:type version: str
:return: whether successfully installed
:rtype: bool | [
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] | train | https://github.com/fracpete/python-weka-wrapper3/blob/d850ab1bdb25fbd5a8d86e99f34a397975425838/python/weka/core/packages.py#L279-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:
The list of packages to install.
:param pkge: the name of the repository package, a URL (http/https) or a zip file
:type pkge: str
:param version: in case of the repository packages, the version
:type version: str
:return: whether successfully installed
:rtype: bool
### Response:
def install_package(pkge, version="Latest"):
"""
The list of packages to install.
:param pkge: the name of the repository package, a URL (http/https) or a zip file
:type pkge: str
:param version: in case of the repository packages, the version
:type version: str
:return: whether successfully installed
:rtype: bool
"""
establish_cache()
if pkge.startswith("http://") or pkge.startswith("https://"):
url = javabridge.make_instance(
"java/net/URL", "(Ljava/lang/String;)V", javabridge.get_env().new_string_utf(pkge))
return not javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromURL",
"(Ljava/net/URL;[Ljava/io/PrintStream;)Ljava/lang/String;", url, []) is None
elif pkge.lower().endswith(".zip"):
return not javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromArchive",
"(Ljava/lang/String;[Ljava/io/PrintStream;)Ljava/lang/String;", pkge, []) is None
else:
return javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromRepository",
"(Ljava/lang/String;Ljava/lang/String;[Ljava/io/PrintStream;)Z", pkge, version, [])
<|endoftext|> | [
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yfpeng/bioc | bioc/validator.py | validate | def validate(collection, onerror: Callable[[str, List], None] = None):
"""Validate BioC data structure."""
BioCValidator(onerror).validate(collection) | python | def validate(collection, onerror: Callable[[str, List], None] = None):
"""Validate BioC data structure."""
BioCValidator(onerror).validate(collection) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Validate BioC data structure.
### Response:
def validate(collection, onerror: Callable[[str, List], None] = None):
"""Validate BioC data structure."""
BioCValidator(onerror).validate(collection)
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Microsoft/azure-devops-python-api | azure-devops/azure/devops/v5_1/work_item_tracking/work_item_tracking_client.py | WorkItemTrackingClient.delete_field | def delete_field(self, field_name_or_ref_name, project=None):
"""DeleteField.
[Preview API] Deletes the field.
:param str field_name_or_ref_name: Field simple name or reference name
:param str project: Project ID or project name
"""
route_values = {}
if project is not None:
route_values['project'] = self._serialize.url('project', project, 'str')
if field_name_or_ref_name is not None:
route_values['fieldNameOrRefName'] = self._serialize.url('field_name_or_ref_name', field_name_or_ref_name, 'str')
self._send(http_method='DELETE',
location_id='b51fd764-e5c2-4b9b-aaf7-3395cf4bdd94',
version='5.1-preview.2',
route_values=route_values) | python | def delete_field(self, field_name_or_ref_name, project=None):
"""DeleteField.
[Preview API] Deletes the field.
:param str field_name_or_ref_name: Field simple name or reference name
:param str project: Project ID or project name
"""
route_values = {}
if project is not None:
route_values['project'] = self._serialize.url('project', project, 'str')
if field_name_or_ref_name is not None:
route_values['fieldNameOrRefName'] = self._serialize.url('field_name_or_ref_name', field_name_or_ref_name, 'str')
self._send(http_method='DELETE',
location_id='b51fd764-e5c2-4b9b-aaf7-3395cf4bdd94',
version='5.1-preview.2',
route_values=route_values) | [
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:param str project: Project ID or project 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:
DeleteField.
[Preview API] Deletes the field.
:param str field_name_or_ref_name: Field simple name or reference name
:param str project: Project ID or project name
### Response:
def delete_field(self, field_name_or_ref_name, project=None):
"""DeleteField.
[Preview API] Deletes the field.
:param str field_name_or_ref_name: Field simple name or reference name
:param str project: Project ID or project name
"""
route_values = {}
if project is not None:
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route_values['fieldNameOrRefName'] = self._serialize.url('field_name_or_ref_name', field_name_or_ref_name, 'str')
self._send(http_method='DELETE',
location_id='b51fd764-e5c2-4b9b-aaf7-3395cf4bdd94',
version='5.1-preview.2',
route_values=route_values)
<|endoftext|> | [
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cggh/scikit-allel | allel/stats/selection.py | moving_hfs_rank | def moving_hfs_rank(h, size, start=0, stop=None):
"""Helper function for plotting haplotype frequencies in moving windows.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
Returns
-------
hr : ndarray, int, shape (n_windows, n_haplotypes)
Haplotype rank array.
"""
# determine windows
windows = np.asarray(list(index_windows(h, size=size, start=start,
stop=stop, step=None)))
# setup output
hr = np.zeros((windows.shape[0], h.shape[1]), dtype='i4')
# iterate over windows
for i, (window_start, window_stop) in enumerate(windows):
# extract haplotypes for the current window
hw = h[window_start:window_stop]
# count haplotypes
hc = hw.distinct_counts()
# ensure sorted descending
hc.sort()
hc = hc[::-1]
# compute ranks for non-singleton haplotypes
cp = 0
for j, c in enumerate(hc):
if c > 1:
hr[i, cp:cp+c] = j+1
cp += c
return hr | python | def moving_hfs_rank(h, size, start=0, stop=None):
"""Helper function for plotting haplotype frequencies in moving windows.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
Returns
-------
hr : ndarray, int, shape (n_windows, n_haplotypes)
Haplotype rank array.
"""
# determine windows
windows = np.asarray(list(index_windows(h, size=size, start=start,
stop=stop, step=None)))
# setup output
hr = np.zeros((windows.shape[0], h.shape[1]), dtype='i4')
# iterate over windows
for i, (window_start, window_stop) in enumerate(windows):
# extract haplotypes for the current window
hw = h[window_start:window_stop]
# count haplotypes
hc = hw.distinct_counts()
# ensure sorted descending
hc.sort()
hc = hc[::-1]
# compute ranks for non-singleton haplotypes
cp = 0
for j, c in enumerate(hc):
if c > 1:
hr[i, cp:cp+c] = j+1
cp += c
return hr | [
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Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
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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 for plotting haplotype frequencies in moving windows.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
Returns
-------
hr : ndarray, int, shape (n_windows, n_haplotypes)
Haplotype rank array.
### Response:
def moving_hfs_rank(h, size, start=0, stop=None):
"""Helper function for plotting haplotype frequencies in moving windows.
Parameters
----------
h : array_like, int, shape (n_variants, n_haplotypes)
Haplotype array.
size : int
The window size (number of variants).
start : int, optional
The index at which to start.
stop : int, optional
The index at which to stop.
Returns
-------
hr : ndarray, int, shape (n_windows, n_haplotypes)
Haplotype rank array.
"""
# determine windows
windows = np.asarray(list(index_windows(h, size=size, start=start,
stop=stop, step=None)))
# setup output
hr = np.zeros((windows.shape[0], h.shape[1]), dtype='i4')
# iterate over windows
for i, (window_start, window_stop) in enumerate(windows):
# extract haplotypes for the current window
hw = h[window_start:window_stop]
# count haplotypes
hc = hw.distinct_counts()
# ensure sorted descending
hc.sort()
hc = hc[::-1]
# compute ranks for non-singleton haplotypes
cp = 0
for j, c in enumerate(hc):
if c > 1:
hr[i, cp:cp+c] = j+1
cp += c
return hr
<|endoftext|> | [
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underworldcode/stripy | stripy-src/stripy/spherical.py | sTriangulation._generate_permutation | def _generate_permutation(self, npoints):
"""
Create shuffle and deshuffle vectors
"""
i = np.arange(0, npoints)
# permutation
p = np.random.permutation(npoints)
ip = np.empty_like(p)
# inverse permutation
ip[p[i]] = i
return p, ip | python | def _generate_permutation(self, npoints):
"""
Create shuffle and deshuffle vectors
"""
i = np.arange(0, npoints)
# permutation
p = np.random.permutation(npoints)
ip = np.empty_like(p)
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Create shuffle and deshuffle vectors
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ioos/compliance-checker | compliance_checker/cfutil.py | get_z_variables | def get_z_variables(nc):
'''
Returns a list of all variables matching definitions for Z
:param netcdf4.dataset nc: an open netcdf dataset object
'''
z_variables = []
# Vertical coordinates will be identifiable by units of pressure or the
# presence of the positive attribute with a value of up/down
# optionally, the vertical type may be indicated by providing the
# standard_name attribute or axis='Z'
total_coords = get_coordinate_variables(nc) + get_auxiliary_coordinate_variables(nc)
for coord_name in total_coords:
if coord_name in z_variables:
continue
coord_var = nc.variables[coord_name]
units = getattr(coord_var, 'units', None)
positive = getattr(coord_var, 'positive', None)
standard_name = getattr(coord_var, 'standard_name', None)
axis = getattr(coord_var, 'axis', None)
# If there are no units, we can't identify it as a vertical coordinate
# by checking pressure or positive
if units is not None:
if units_convertible(units, 'bar'):
z_variables.append(coord_name)
elif isinstance(positive, basestring):
if positive.lower() in ['up', 'down']:
z_variables.append(coord_name)
# if axis='Z' we're good
if coord_name not in z_variables and axis == 'Z':
z_variables.append(coord_name)
if coord_name not in z_variables and standard_name in ('depth', 'height', 'altitude'):
z_variables.append(coord_name)
if coord_name not in z_variables and standard_name in DIMENSIONLESS_VERTICAL_COORDINATES:
z_variables.append(coord_name)
return z_variables | python | def get_z_variables(nc):
'''
Returns a list of all variables matching definitions for Z
:param netcdf4.dataset nc: an open netcdf dataset object
'''
z_variables = []
# Vertical coordinates will be identifiable by units of pressure or the
# presence of the positive attribute with a value of up/down
# optionally, the vertical type may be indicated by providing the
# standard_name attribute or axis='Z'
total_coords = get_coordinate_variables(nc) + get_auxiliary_coordinate_variables(nc)
for coord_name in total_coords:
if coord_name in z_variables:
continue
coord_var = nc.variables[coord_name]
units = getattr(coord_var, 'units', None)
positive = getattr(coord_var, 'positive', None)
standard_name = getattr(coord_var, 'standard_name', None)
axis = getattr(coord_var, 'axis', None)
# If there are no units, we can't identify it as a vertical coordinate
# by checking pressure or positive
if units is not None:
if units_convertible(units, 'bar'):
z_variables.append(coord_name)
elif isinstance(positive, basestring):
if positive.lower() in ['up', 'down']:
z_variables.append(coord_name)
# if axis='Z' we're good
if coord_name not in z_variables and axis == 'Z':
z_variables.append(coord_name)
if coord_name not in z_variables and standard_name in ('depth', 'height', 'altitude'):
z_variables.append(coord_name)
if coord_name not in z_variables and standard_name in DIMENSIONLESS_VERTICAL_COORDINATES:
z_variables.append(coord_name)
return z_variables | [
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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 all variables matching definitions for Z
:param netcdf4.dataset nc: an open netcdf dataset object
### Response:
def get_z_variables(nc):
'''
Returns a list of all variables matching definitions for Z
:param netcdf4.dataset nc: an open netcdf dataset object
'''
z_variables = []
# Vertical coordinates will be identifiable by units of pressure or the
# presence of the positive attribute with a value of up/down
# optionally, the vertical type may be indicated by providing the
# standard_name attribute or axis='Z'
total_coords = get_coordinate_variables(nc) + get_auxiliary_coordinate_variables(nc)
for coord_name in total_coords:
if coord_name in z_variables:
continue
coord_var = nc.variables[coord_name]
units = getattr(coord_var, 'units', None)
positive = getattr(coord_var, 'positive', None)
standard_name = getattr(coord_var, 'standard_name', None)
axis = getattr(coord_var, 'axis', None)
# If there are no units, we can't identify it as a vertical coordinate
# by checking pressure or positive
if units is not None:
if units_convertible(units, 'bar'):
z_variables.append(coord_name)
elif isinstance(positive, basestring):
if positive.lower() in ['up', 'down']:
z_variables.append(coord_name)
# if axis='Z' we're good
if coord_name not in z_variables and axis == 'Z':
z_variables.append(coord_name)
if coord_name not in z_variables and standard_name in ('depth', 'height', 'altitude'):
z_variables.append(coord_name)
if coord_name not in z_variables and standard_name in DIMENSIONLESS_VERTICAL_COORDINATES:
z_variables.append(coord_name)
return z_variables
<|endoftext|> | [
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openearth/bmi-python | bmi/wrapper.py | BMIWrapper._load_library | def _load_library(self):
"""Return the fortran library, loaded with """
path = self._library_path()
logger.info("Loading library from path {}".format(path))
library_dir = os.path.dirname(path)
if platform.system() == 'Windows':
import win32api
olddir = os.getcwd()
os.chdir(library_dir)
win32api.SetDllDirectory('.')
result = cdll.LoadLibrary(path)
if platform.system() == 'Windows':
os.chdir(olddir)
return result | python | def _load_library(self):
"""Return the fortran library, loaded with """
path = self._library_path()
logger.info("Loading library from path {}".format(path))
library_dir = os.path.dirname(path)
if platform.system() == 'Windows':
import win32api
olddir = os.getcwd()
os.chdir(library_dir)
win32api.SetDllDirectory('.')
result = cdll.LoadLibrary(path)
if platform.system() == 'Windows':
os.chdir(olddir)
return result | [
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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 fortran library, loaded with
### Response:
def _load_library(self):
"""Return the fortran library, loaded with """
path = self._library_path()
logger.info("Loading library from path {}".format(path))
library_dir = os.path.dirname(path)
if platform.system() == 'Windows':
import win32api
olddir = os.getcwd()
os.chdir(library_dir)
win32api.SetDllDirectory('.')
result = cdll.LoadLibrary(path)
if platform.system() == 'Windows':
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dropbox/stone | stone/backends/obj_c_types.py | ObjCTypesBackend._generate_struct_cstor | def _generate_struct_cstor(self, struct):
"""Emits struct standard constructor."""
with self.block_func(
func=self._cstor_name_from_fields(struct.all_fields),
args=fmt_func_args_from_fields(struct.all_fields),
return_type='instancetype'):
for field in struct.all_fields:
self._generate_validator(field)
self.emit()
super_fields = [
f for f in struct.all_fields if f not in struct.fields
]
if super_fields:
super_args = fmt_func_args([(fmt_var(f.name), fmt_var(f.name))
for f in super_fields])
self.emit('self = [super {}:{}];'.format(
self._cstor_name_from_fields(super_fields), super_args))
else:
if struct.parent_type:
self.emit('self = [super initDefault];')
else:
self.emit('self = [super init];')
with self.block_init():
for field in struct.fields:
field_name = fmt_var(field.name)
if field.has_default:
self.emit('_{} = {} ?: {};'.format(
field_name, field_name, fmt_default_value(field)))
else:
self.emit('_{} = {};'.format(field_name, field_name))
self.emit() | python | def _generate_struct_cstor(self, struct):
"""Emits struct standard constructor."""
with self.block_func(
func=self._cstor_name_from_fields(struct.all_fields),
args=fmt_func_args_from_fields(struct.all_fields),
return_type='instancetype'):
for field in struct.all_fields:
self._generate_validator(field)
self.emit()
super_fields = [
f for f in struct.all_fields if f not in struct.fields
]
if super_fields:
super_args = fmt_func_args([(fmt_var(f.name), fmt_var(f.name))
for f in super_fields])
self.emit('self = [super {}:{}];'.format(
self._cstor_name_from_fields(super_fields), super_args))
else:
if struct.parent_type:
self.emit('self = [super initDefault];')
else:
self.emit('self = [super init];')
with self.block_init():
for field in struct.fields:
field_name = fmt_var(field.name)
if field.has_default:
self.emit('_{} = {} ?: {};'.format(
field_name, field_name, fmt_default_value(field)))
else:
self.emit('_{} = {};'.format(field_name, field_name))
self.emit() | [
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] | train | https://github.com/dropbox/stone/blob/2e95cbcd1c48e05cca68c919fd8d24adec6b0f58/stone/backends/obj_c_types.py#L461-L495 | <|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:
Emits struct standard constructor.
### Response:
def _generate_struct_cstor(self, struct):
"""Emits struct standard constructor."""
with self.block_func(
func=self._cstor_name_from_fields(struct.all_fields),
args=fmt_func_args_from_fields(struct.all_fields),
return_type='instancetype'):
for field in struct.all_fields:
self._generate_validator(field)
self.emit()
super_fields = [
f for f in struct.all_fields if f not in struct.fields
]
if super_fields:
super_args = fmt_func_args([(fmt_var(f.name), fmt_var(f.name))
for f in super_fields])
self.emit('self = [super {}:{}];'.format(
self._cstor_name_from_fields(super_fields), super_args))
else:
if struct.parent_type:
self.emit('self = [super initDefault];')
else:
self.emit('self = [super init];')
with self.block_init():
for field in struct.fields:
field_name = fmt_var(field.name)
if field.has_default:
self.emit('_{} = {} ?: {};'.format(
field_name, field_name, fmt_default_value(field)))
else:
self.emit('_{} = {};'.format(field_name, field_name))
self.emit()
<|endoftext|> | [
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tensorflow/tensor2tensor | tensor2tensor/data_generators/wikisum/wikisum.py | rank_reference_paragraphs | def rank_reference_paragraphs(wiki_title, references_content, normalize=True):
"""Rank and return reference paragraphs by tf-idf score on title tokens."""
normalized_title = _normalize_text(wiki_title)
title_tokens = _tokens_to_score(
set(tokenizer.encode(text_encoder.native_to_unicode(normalized_title))))
ref_paragraph_info = []
doc_counts = collections.defaultdict(int)
for ref in references_content:
for paragraph in ref.split("\n"):
normalized_paragraph = _normalize_text(paragraph)
if cc_utils.filter_paragraph(normalized_paragraph):
# Skip paragraph
continue
counts = _token_counts(normalized_paragraph, title_tokens)
for token in title_tokens:
if counts[token]:
doc_counts[token] += 1
content = normalized_paragraph if normalize else paragraph
info = {"content": content, "counts": counts}
ref_paragraph_info.append(info)
for info in ref_paragraph_info:
score = 0.
for token in title_tokens:
term_frequency = info["counts"][token]
inv_doc_frequency = (
float(len(ref_paragraph_info)) / max(doc_counts[token], 1))
score += term_frequency * math.log(inv_doc_frequency)
info["score"] = score
ref_paragraph_info.sort(key=lambda el: el["score"], reverse=True)
return [info["content"] for info in ref_paragraph_info] | python | def rank_reference_paragraphs(wiki_title, references_content, normalize=True):
"""Rank and return reference paragraphs by tf-idf score on title tokens."""
normalized_title = _normalize_text(wiki_title)
title_tokens = _tokens_to_score(
set(tokenizer.encode(text_encoder.native_to_unicode(normalized_title))))
ref_paragraph_info = []
doc_counts = collections.defaultdict(int)
for ref in references_content:
for paragraph in ref.split("\n"):
normalized_paragraph = _normalize_text(paragraph)
if cc_utils.filter_paragraph(normalized_paragraph):
# Skip paragraph
continue
counts = _token_counts(normalized_paragraph, title_tokens)
for token in title_tokens:
if counts[token]:
doc_counts[token] += 1
content = normalized_paragraph if normalize else paragraph
info = {"content": content, "counts": counts}
ref_paragraph_info.append(info)
for info in ref_paragraph_info:
score = 0.
for token in title_tokens:
term_frequency = info["counts"][token]
inv_doc_frequency = (
float(len(ref_paragraph_info)) / max(doc_counts[token], 1))
score += term_frequency * math.log(inv_doc_frequency)
info["score"] = score
ref_paragraph_info.sort(key=lambda el: el["score"], reverse=True)
return [info["content"] for info in ref_paragraph_info] | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Rank and return reference paragraphs by tf-idf score on title tokens.
### Response:
def rank_reference_paragraphs(wiki_title, references_content, normalize=True):
"""Rank and return reference paragraphs by tf-idf score on title tokens."""
normalized_title = _normalize_text(wiki_title)
title_tokens = _tokens_to_score(
set(tokenizer.encode(text_encoder.native_to_unicode(normalized_title))))
ref_paragraph_info = []
doc_counts = collections.defaultdict(int)
for ref in references_content:
for paragraph in ref.split("\n"):
normalized_paragraph = _normalize_text(paragraph)
if cc_utils.filter_paragraph(normalized_paragraph):
# Skip paragraph
continue
counts = _token_counts(normalized_paragraph, title_tokens)
for token in title_tokens:
if counts[token]:
doc_counts[token] += 1
content = normalized_paragraph if normalize else paragraph
info = {"content": content, "counts": counts}
ref_paragraph_info.append(info)
for info in ref_paragraph_info:
score = 0.
for token in title_tokens:
term_frequency = info["counts"][token]
inv_doc_frequency = (
float(len(ref_paragraph_info)) / max(doc_counts[token], 1))
score += term_frequency * math.log(inv_doc_frequency)
info["score"] = score
ref_paragraph_info.sort(key=lambda el: el["score"], reverse=True)
return [info["content"] for info in ref_paragraph_info]
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trailofbits/protofuzz | protofuzz/gen.py | Permuter._resolve_child | def _resolve_child(self, path):
'Return a member generator by a dot-delimited path'
obj = self
for component in path.split('.'):
ptr = obj
if not isinstance(ptr, Permuter):
raise self.MessageNotFound("Bad element path [wrong type]")
# pylint: disable=protected-access
found_gen = (_ for _ in ptr._generators if _.name() == component)
obj = next(found_gen, None)
if not obj:
raise self.MessageNotFound("Path '{}' unresolved to member."
.format(path))
return ptr, obj | python | def _resolve_child(self, path):
'Return a member generator by a dot-delimited path'
obj = self
for component in path.split('.'):
ptr = obj
if not isinstance(ptr, Permuter):
raise self.MessageNotFound("Bad element path [wrong type]")
# pylint: disable=protected-access
found_gen = (_ for _ in ptr._generators if _.name() == component)
obj = next(found_gen, None)
if not obj:
raise self.MessageNotFound("Path '{}' unresolved to member."
.format(path))
return ptr, obj | [
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] | train | https://github.com/trailofbits/protofuzz/blob/589492d34de9a0da6cc5554094e2588b893b2fd8/protofuzz/gen.py#L111-L128 | <|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 member generator by a dot-delimited path
### Response:
def _resolve_child(self, path):
'Return a member generator by a dot-delimited path'
obj = self
for component in path.split('.'):
ptr = obj
if not isinstance(ptr, Permuter):
raise self.MessageNotFound("Bad element path [wrong type]")
# pylint: disable=protected-access
found_gen = (_ for _ in ptr._generators if _.name() == component)
obj = next(found_gen, None)
if not obj:
raise self.MessageNotFound("Path '{}' unresolved to member."
.format(path))
return ptr, obj
<|endoftext|> | [
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brocade/pynos | pynos/versions/base/bgp.py | BGP.enabled | def enabled(self):
"""bool: ``True`` if BGP is enabled; ``False`` if BGP is disabled.
"""
namespace = 'urn:ietf:params:xml:ns:netconf:base:1.0'
bgp_filter = 'rbridge-id/router/bgp'
bgp_config = ET.Element('get-config', xmlns="%s" % namespace)
source = ET.SubElement(bgp_config, 'source')
ET.SubElement(source, 'running')
ET.SubElement(bgp_config, 'filter',
type="xpath", select="%s" % bgp_filter)
bgp_config = self._callback(bgp_config, handler='get')
namespace = 'urn:brocade.com:mgmt:brocade-bgp'
enabled = bgp_config.find('.//*{%s}bgp' % namespace)
if enabled is not None:
return True
return False | python | def enabled(self):
"""bool: ``True`` if BGP is enabled; ``False`` if BGP is disabled.
"""
namespace = 'urn:ietf:params:xml:ns:netconf:base:1.0'
bgp_filter = 'rbridge-id/router/bgp'
bgp_config = ET.Element('get-config', xmlns="%s" % namespace)
source = ET.SubElement(bgp_config, 'source')
ET.SubElement(source, 'running')
ET.SubElement(bgp_config, 'filter',
type="xpath", select="%s" % bgp_filter)
bgp_config = self._callback(bgp_config, handler='get')
namespace = 'urn:brocade.com:mgmt:brocade-bgp'
enabled = bgp_config.find('.//*{%s}bgp' % namespace)
if enabled is not None:
return True
return 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:
bool: ``True`` if BGP is enabled; ``False`` if BGP is disabled.
### Response:
def enabled(self):
"""bool: ``True`` if BGP is enabled; ``False`` if BGP is disabled.
"""
namespace = 'urn:ietf:params:xml:ns:netconf:base:1.0'
bgp_filter = 'rbridge-id/router/bgp'
bgp_config = ET.Element('get-config', xmlns="%s" % namespace)
source = ET.SubElement(bgp_config, 'source')
ET.SubElement(source, 'running')
ET.SubElement(bgp_config, 'filter',
type="xpath", select="%s" % bgp_filter)
bgp_config = self._callback(bgp_config, handler='get')
namespace = 'urn:brocade.com:mgmt:brocade-bgp'
enabled = bgp_config.find('.//*{%s}bgp' % namespace)
if enabled is not None:
return True
return False
<|endoftext|> | [
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dswah/pyGAM | pygam/pygam.py | LogisticGAM.accuracy | def accuracy(self, X=None, y=None, mu=None):
"""
computes the accuracy of the LogisticGAM
Parameters
----------
note: X or mu must be defined. defaults to mu
X : array-like of shape (n_samples, m_features), optional (default=None)
containing input data
y : array-like of shape (n,)
containing target data
mu : array-like of shape (n_samples,), optional (default=None
expected value of the targets given the model and inputs
Returns
-------
float in [0, 1]
"""
if not self._is_fitted:
raise AttributeError('GAM has not been fitted. Call fit first.')
y = check_y(y, self.link, self.distribution, verbose=self.verbose)
if X is not None:
X = check_X(X, n_feats=self.statistics_['m_features'],
edge_knots=self.edge_knots_, dtypes=self.dtype,
features=self.feature, verbose=self.verbose)
if mu is None:
mu = self.predict_mu(X)
check_X_y(mu, y)
return ((mu > 0.5).astype(int) == y).mean() | python | def accuracy(self, X=None, y=None, mu=None):
"""
computes the accuracy of the LogisticGAM
Parameters
----------
note: X or mu must be defined. defaults to mu
X : array-like of shape (n_samples, m_features), optional (default=None)
containing input data
y : array-like of shape (n,)
containing target data
mu : array-like of shape (n_samples,), optional (default=None
expected value of the targets given the model and inputs
Returns
-------
float in [0, 1]
"""
if not self._is_fitted:
raise AttributeError('GAM has not been fitted. Call fit first.')
y = check_y(y, self.link, self.distribution, verbose=self.verbose)
if X is not None:
X = check_X(X, n_feats=self.statistics_['m_features'],
edge_knots=self.edge_knots_, dtypes=self.dtype,
features=self.feature, verbose=self.verbose)
if mu is None:
mu = self.predict_mu(X)
check_X_y(mu, y)
return ((mu > 0.5).astype(int) == y).mean() | [
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] | computes the accuracy of the LogisticGAM
Parameters
----------
note: X or mu must be defined. defaults to mu
X : array-like of shape (n_samples, m_features), optional (default=None)
containing input data
y : array-like of shape (n,)
containing target data
mu : array-like of shape (n_samples,), optional (default=None
expected value of the targets given the model and inputs
Returns
-------
float in [0, 1] | [
"computes",
"the",
"accuracy",
"of",
"the",
"LogisticGAM"
] | train | https://github.com/dswah/pyGAM/blob/b3e5c3cd580f0a3ad69f9372861624f67760c325/pygam/pygam.py#L2395-L2426 | <|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:
computes the accuracy of the LogisticGAM
Parameters
----------
note: X or mu must be defined. defaults to mu
X : array-like of shape (n_samples, m_features), optional (default=None)
containing input data
y : array-like of shape (n,)
containing target data
mu : array-like of shape (n_samples,), optional (default=None
expected value of the targets given the model and inputs
Returns
-------
float in [0, 1]
### Response:
def accuracy(self, X=None, y=None, mu=None):
"""
computes the accuracy of the LogisticGAM
Parameters
----------
note: X or mu must be defined. defaults to mu
X : array-like of shape (n_samples, m_features), optional (default=None)
containing input data
y : array-like of shape (n,)
containing target data
mu : array-like of shape (n_samples,), optional (default=None
expected value of the targets given the model and inputs
Returns
-------
float in [0, 1]
"""
if not self._is_fitted:
raise AttributeError('GAM has not been fitted. Call fit first.')
y = check_y(y, self.link, self.distribution, verbose=self.verbose)
if X is not None:
X = check_X(X, n_feats=self.statistics_['m_features'],
edge_knots=self.edge_knots_, dtypes=self.dtype,
features=self.feature, verbose=self.verbose)
if mu is None:
mu = self.predict_mu(X)
check_X_y(mu, y)
return ((mu > 0.5).astype(int) == y).mean()
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batiste/django-page-cms | pages/placeholders.py | MarkdownPlaceholderNode.render | def render(self, context):
"""Render markdown."""
import markdown
content = self.get_content_from_context(context)
return markdown.markdown(content) | python | def render(self, context):
"""Render markdown."""
import markdown
content = self.get_content_from_context(context)
return markdown.markdown(content) | [
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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:
Render markdown.
### Response:
def render(self, context):
"""Render markdown."""
import markdown
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tanghaibao/goatools | goatools/grouper/aart_geneproducts_one.py | AArtGeneProductSetsOne.prt_report_grp1 | def prt_report_grp1(self, prt=sys.stdout, **kws_grp):
"""Print full GO/gene report with grouping."""
summaryline = self.str_summaryline()
# Print grouped GO IDs
prt.write("{SUMMARY}\n".format(SUMMARY=summaryline))
self.prt_gos_grouped(prt, **kws_grp)
# genes
genes = sorted(self.gene2gos.keys())
prt.write("\n\n{SUMMARY}\n\n".format(SUMMARY=summaryline))
self.prt_section_key(prt)
self.prt_gene_aart(genes, prt)
# Sort genes
prt.write("\n\n{SUMMARY}\n\n".format(SUMMARY=summaryline))
self.prt_gene_aart_details(genes, prt)
return (self.name, self.get_section_marks()) | python | def prt_report_grp1(self, prt=sys.stdout, **kws_grp):
"""Print full GO/gene report with grouping."""
summaryline = self.str_summaryline()
# Print grouped GO IDs
prt.write("{SUMMARY}\n".format(SUMMARY=summaryline))
self.prt_gos_grouped(prt, **kws_grp)
# genes
genes = sorted(self.gene2gos.keys())
prt.write("\n\n{SUMMARY}\n\n".format(SUMMARY=summaryline))
self.prt_section_key(prt)
self.prt_gene_aart(genes, prt)
# Sort genes
prt.write("\n\n{SUMMARY}\n\n".format(SUMMARY=summaryline))
self.prt_gene_aart_details(genes, prt)
return (self.name, self.get_section_marks()) | [
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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 full GO/gene report with grouping.
### Response:
def prt_report_grp1(self, prt=sys.stdout, **kws_grp):
"""Print full GO/gene report with grouping."""
summaryline = self.str_summaryline()
# Print grouped GO IDs
prt.write("{SUMMARY}\n".format(SUMMARY=summaryline))
self.prt_gos_grouped(prt, **kws_grp)
# genes
genes = sorted(self.gene2gos.keys())
prt.write("\n\n{SUMMARY}\n\n".format(SUMMARY=summaryline))
self.prt_section_key(prt)
self.prt_gene_aart(genes, prt)
# Sort genes
prt.write("\n\n{SUMMARY}\n\n".format(SUMMARY=summaryline))
self.prt_gene_aart_details(genes, prt)
return (self.name, self.get_section_marks())
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xtrementl/focus | focus/task.py | Task.exists | def exists(self, task_name):
""" Determines if task directory exists.
`task_name`
Task name.
Returns ``True`` if task exists.
"""
try:
return os.path.exists(self._get_task_dir(task_name))
except OSError:
return False | python | def exists(self, task_name):
""" Determines if task directory exists.
`task_name`
Task name.
Returns ``True`` if task exists.
"""
try:
return os.path.exists(self._get_task_dir(task_name))
except OSError:
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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:
Determines if task directory exists.
`task_name`
Task name.
Returns ``True`` if task exists.
### Response:
def exists(self, task_name):
""" Determines if task directory exists.
`task_name`
Task name.
Returns ``True`` if task exists.
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nornir-automation/nornir | nornir/core/inventory.py | Host.get_connection_state | def get_connection_state(self, connection: str) -> Dict[str, Any]:
"""
For an already established connection return its state.
"""
if connection not in self.connections:
raise ConnectionNotOpen(connection)
return self.connections[connection].state | python | def get_connection_state(self, connection: str) -> Dict[str, Any]:
"""
For an already established connection return its state.
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raise ConnectionNotOpen(connection)
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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 an already established connection return its state.
### Response:
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For an already established connection return its state.
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welbornprod/colr | colr/__main__.py | list_known_codes | def list_known_codes(s, unique=True, rgb_mode=False):
""" Find and print all known escape codes in a string,
using get_known_codes.
"""
total = 0
for codedesc in get_known_codes(s, unique=unique, rgb_mode=rgb_mode):
total += 1
print(codedesc)
plural = 'code' if total == 1 else 'codes'
codetype = ' unique' if unique else ''
print('\nFound {}{} escape {}.'.format(total, codetype, plural))
return 0 if total > 0 else 1 | python | def list_known_codes(s, unique=True, rgb_mode=False):
""" Find and print all known escape codes in a string,
using get_known_codes.
"""
total = 0
for codedesc in get_known_codes(s, unique=unique, rgb_mode=rgb_mode):
total += 1
print(codedesc)
plural = 'code' if total == 1 else 'codes'
codetype = ' unique' if unique else ''
print('\nFound {}{} escape {}.'.format(total, codetype, plural))
return 0 if total > 0 else 1 | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Find and print all known escape codes in a string,
using get_known_codes.
### Response:
def list_known_codes(s, unique=True, rgb_mode=False):
""" Find and print all known escape codes in a string,
using get_known_codes.
"""
total = 0
for codedesc in get_known_codes(s, unique=unique, rgb_mode=rgb_mode):
total += 1
print(codedesc)
plural = 'code' if total == 1 else 'codes'
codetype = ' unique' if unique else ''
print('\nFound {}{} escape {}.'.format(total, codetype, plural))
return 0 if total > 0 else 1
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google/grr | grr/core/grr_response_core/lib/config_lib.py | ParseConfigCommandLine | def ParseConfigCommandLine():
"""Parse all the command line options which control the config system."""
# The user may specify the primary config file on the command line.
if flags.FLAGS.config:
_CONFIG.Initialize(filename=flags.FLAGS.config, must_exist=True)
else:
raise RuntimeError("A config file is not specified.")
# Allow secondary configuration files to be specified.
if flags.FLAGS.secondary_configs:
for config_file in flags.FLAGS.secondary_configs:
_CONFIG.LoadSecondaryConfig(config_file)
# Allow individual options to be specified as global overrides.
for statement in flags.FLAGS.parameter:
if "=" not in statement:
raise RuntimeError("statement %s on command line not valid." % statement)
name, value = statement.split("=", 1)
_CONFIG.global_override[name] = value
# Load additional contexts from the command line.
for context in flags.FLAGS.context:
if context:
_CONFIG.AddContext(context)
if _CONFIG["Config.writeback"]:
_CONFIG.SetWriteBack(_CONFIG["Config.writeback"])
# Does the user want to dump help? We do this after the config system is
# initialized so the user can examine what we think the value of all the
# parameters are.
if flags.FLAGS.config_help:
print("Configuration overview.")
_CONFIG.PrintHelp()
sys.exit(0) | python | def ParseConfigCommandLine():
"""Parse all the command line options which control the config system."""
# The user may specify the primary config file on the command line.
if flags.FLAGS.config:
_CONFIG.Initialize(filename=flags.FLAGS.config, must_exist=True)
else:
raise RuntimeError("A config file is not specified.")
# Allow secondary configuration files to be specified.
if flags.FLAGS.secondary_configs:
for config_file in flags.FLAGS.secondary_configs:
_CONFIG.LoadSecondaryConfig(config_file)
# Allow individual options to be specified as global overrides.
for statement in flags.FLAGS.parameter:
if "=" not in statement:
raise RuntimeError("statement %s on command line not valid." % statement)
name, value = statement.split("=", 1)
_CONFIG.global_override[name] = value
# Load additional contexts from the command line.
for context in flags.FLAGS.context:
if context:
_CONFIG.AddContext(context)
if _CONFIG["Config.writeback"]:
_CONFIG.SetWriteBack(_CONFIG["Config.writeback"])
# Does the user want to dump help? We do this after the config system is
# initialized so the user can examine what we think the value of all the
# parameters are.
if flags.FLAGS.config_help:
print("Configuration overview.")
_CONFIG.PrintHelp()
sys.exit(0) | [
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] | train | https://github.com/google/grr/blob/5cef4e8e2f0d5df43ea4877e9c798e0bf60bfe74/grr/core/grr_response_core/lib/config_lib.py#L1704-L1740 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Parse all the command line options which control the config system.
### Response:
def ParseConfigCommandLine():
"""Parse all the command line options which control the config system."""
# The user may specify the primary config file on the command line.
if flags.FLAGS.config:
_CONFIG.Initialize(filename=flags.FLAGS.config, must_exist=True)
else:
raise RuntimeError("A config file is not specified.")
# Allow secondary configuration files to be specified.
if flags.FLAGS.secondary_configs:
for config_file in flags.FLAGS.secondary_configs:
_CONFIG.LoadSecondaryConfig(config_file)
# Allow individual options to be specified as global overrides.
for statement in flags.FLAGS.parameter:
if "=" not in statement:
raise RuntimeError("statement %s on command line not valid." % statement)
name, value = statement.split("=", 1)
_CONFIG.global_override[name] = value
# Load additional contexts from the command line.
for context in flags.FLAGS.context:
if context:
_CONFIG.AddContext(context)
if _CONFIG["Config.writeback"]:
_CONFIG.SetWriteBack(_CONFIG["Config.writeback"])
# Does the user want to dump help? We do this after the config system is
# initialized so the user can examine what we think the value of all the
# parameters are.
if flags.FLAGS.config_help:
print("Configuration overview.")
_CONFIG.PrintHelp()
sys.exit(0)
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gabstopper/smc-python | smc/api/session.py | Session._build_auth_request | def _build_auth_request(self, verify=False, **kwargs):
"""
Build the authentication request to SMC
"""
json = {
'domain': self.domain
}
credential = self.credential
params = {}
if credential.provider_name.startswith('lms'):
params = dict(
login=credential._login,
pwd=credential._pwd)
else:
json.update(authenticationkey=credential._api_key)
if kwargs:
json.update(**kwargs)
self._extra_args.update(**kwargs) # Store in case we need to rebuild later
request = dict(
url=self.credential.get_provider_entry_point(self.url, self.api_version),
json=json,
params=params,
headers={'content-type': 'application/json'},
verify=verify)
return request | python | def _build_auth_request(self, verify=False, **kwargs):
"""
Build the authentication request to SMC
"""
json = {
'domain': self.domain
}
credential = self.credential
params = {}
if credential.provider_name.startswith('lms'):
params = dict(
login=credential._login,
pwd=credential._pwd)
else:
json.update(authenticationkey=credential._api_key)
if kwargs:
json.update(**kwargs)
self._extra_args.update(**kwargs) # Store in case we need to rebuild later
request = dict(
url=self.credential.get_provider_entry_point(self.url, self.api_version),
json=json,
params=params,
headers={'content-type': 'application/json'},
verify=verify)
return request | [
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"Build",
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] | train | https://github.com/gabstopper/smc-python/blob/e027b8a5dcfaf884eada32d113d41c1e56b32457/smc/api/session.py#L521-L550 | <|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:
Build the authentication request to SMC
### Response:
def _build_auth_request(self, verify=False, **kwargs):
"""
Build the authentication request to SMC
"""
json = {
'domain': self.domain
}
credential = self.credential
params = {}
if credential.provider_name.startswith('lms'):
params = dict(
login=credential._login,
pwd=credential._pwd)
else:
json.update(authenticationkey=credential._api_key)
if kwargs:
json.update(**kwargs)
self._extra_args.update(**kwargs) # Store in case we need to rebuild later
request = dict(
url=self.credential.get_provider_entry_point(self.url, self.api_version),
json=json,
params=params,
headers={'content-type': 'application/json'},
verify=verify)
return request
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evhub/coconut | coconut/compiler/grammar.py | subscriptgroup_handle | def subscriptgroup_handle(tokens):
"""Process subscriptgroups."""
internal_assert(0 < len(tokens) <= 3, "invalid slice args", tokens)
args = []
for arg in tokens:
if not arg:
arg = "None"
args.append(arg)
if len(args) == 1:
return args[0]
else:
return "_coconut.slice(" + ", ".join(args) + ")" | python | def subscriptgroup_handle(tokens):
"""Process subscriptgroups."""
internal_assert(0 < len(tokens) <= 3, "invalid slice args", tokens)
args = []
for arg in tokens:
if not arg:
arg = "None"
args.append(arg)
if len(args) == 1:
return args[0]
else:
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"subscriptgroups",
"."
] | train | https://github.com/evhub/coconut/blob/ff97177344e7604e89a0a98a977a87ed2a56fc6d/coconut/compiler/grammar.py#L563-L574 | <|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:
Process subscriptgroups.
### Response:
def subscriptgroup_handle(tokens):
"""Process subscriptgroups."""
internal_assert(0 < len(tokens) <= 3, "invalid slice args", tokens)
args = []
for arg in tokens:
if not arg:
arg = "None"
args.append(arg)
if len(args) == 1:
return args[0]
else:
return "_coconut.slice(" + ", ".join(args) + ")"
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ANTsX/ANTsPy | ants/utils/invariant_image_similarity.py | invariant_image_similarity | def invariant_image_similarity(image1, image2,
local_search_iterations=0, metric='MI',
thetas=np.linspace(0,360,5),
thetas2=np.linspace(0,360,5),
thetas3=np.linspace(0,360,5),
scale_image=1, do_reflection=False,
txfn=None, transform='Affine'):
"""
Similarity metrics between two images as a function of geometry
Compute similarity metric between two images as image is rotated about its
center w/ or w/o optimization
ANTsR function: `invariantImageSimilarity`
Arguments
---------
image1 : ANTsImage
reference image
image2 : ANTsImage
moving image
local_search_iterations : integer
integer controlling local search in multistart
metric : string
which metric to use
MI
GC
thetas : 1D-ndarray/list/tuple
numeric vector of search angles in degrees
thetas2 : 1D-ndarray/list/tuple
numeric vector of search angles in degrees around principal axis 2 (3D)
thetas3 : 1D-ndarray/list/tuple
numeric vector of search angles in degrees around principal axis 3 (3D)
scale_image : scalar
global scale
do_reflection : boolean
whether to reflect image about principal axis
txfn : string (optional)
if present, write optimal tx to .mat file
transform : string
type of transform to use
Rigid
Similarity
Affine
Returns
-------
pd.DataFrame
dataframe with metric values and transformation parameters
Example
-------
>>> import ants
>>> img1 = ants.image_read(ants.get_ants_data('r16'))
>>> img2 = ants.image_read(ants.get_ants_data('r64'))
>>> metric = ants.invariant_image_similarity(img1,img2)
"""
if transform not in {'Rigid', 'Similarity', 'Affine'}:
raise ValueError('transform must be one of Rigid/Similarity/Affine')
if image1.pixeltype != 'float':
image1 = image1.clone('float')
if image2.pixeltype != 'float':
image2 = image2.clone('float')
if txfn is None:
txfn = mktemp(suffix='.mat')
# convert thetas to radians
thetain = (thetas * math.pi) / 180.
thetain2 = (thetas2 * math.pi) / 180.
thetain3 = (thetas3 * math.pi) / 180.
image1 = utils.iMath(image1, 'Normalize')
image2 = utils.iMath(image2, 'Normalize')
idim = image1.dimension
fpname = ['FixedParam%i'%i for i in range(1,idim+1)]
if not do_reflection:
libfn = utils.get_lib_fn('invariantImageSimilarity_%s%iD' % (transform, idim))
r1 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
int(do_reflection),
txfn)
r1 = np.asarray(r1)
pnames = ['Param%i'%i for i in range(1,r1.shape[1])]
pnames[(len(pnames)-idim):len(pnames)] = fpname
r1 = pd.DataFrame(r1, columns=['MetricValue']+pnames)
return r1, txfn
else:
txfn1 = mktemp(suffix='.mat')
txfn2 = mktemp(suffix='.mat')
txfn3 = mktemp(suffix='.mat')
txfn4 = mktemp(suffix='.mat')
libfn = utils.get_lib_fn('invariantImageSimilarity_%s%iD' % (transform, idim))
## R1 ##
r1 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
0,
txfn1)
r1 = np.asarray(r1)
pnames = ['Param%i'%i for i in range(1,r1.shape[1])]
pnames[(len(pnames)-idim):len(pnames)] = fpname
r1 = pd.DataFrame(r1, columns=['MetricValue']+pnames)
## R2 ##
r2 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
1,
txfn2)
r2 = np.asarray(r2)
r2 = pd.DataFrame(r2, columns=['MetricValue']+pnames)
## R3 ##
r3 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
2,
txfn3)
r3 = np.asarray(r3)
r3 = pd.DataFrame(r3, columns=['MetricValue']+pnames)
## R4 ##
r4 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
3,
txfn4)
r4 = np.asarray(r4)
r4 = pd.DataFrame(r4, columns=['MetricValue']+pnames)
rmins = [np.min(r1.iloc[:,0]), np.min(r2.iloc[:,0]), np.min(r3.iloc[:,0]), np.min(r4.iloc[:,0])]
ww = np.argmin(rmins)
if ww == 0:
return r1, txfn1
elif ww == 1:
return r2, txfn2
elif ww == 2:
return r3, txfn3
elif ww == 3:
return r4, txfn4 | python | def invariant_image_similarity(image1, image2,
local_search_iterations=0, metric='MI',
thetas=np.linspace(0,360,5),
thetas2=np.linspace(0,360,5),
thetas3=np.linspace(0,360,5),
scale_image=1, do_reflection=False,
txfn=None, transform='Affine'):
"""
Similarity metrics between two images as a function of geometry
Compute similarity metric between two images as image is rotated about its
center w/ or w/o optimization
ANTsR function: `invariantImageSimilarity`
Arguments
---------
image1 : ANTsImage
reference image
image2 : ANTsImage
moving image
local_search_iterations : integer
integer controlling local search in multistart
metric : string
which metric to use
MI
GC
thetas : 1D-ndarray/list/tuple
numeric vector of search angles in degrees
thetas2 : 1D-ndarray/list/tuple
numeric vector of search angles in degrees around principal axis 2 (3D)
thetas3 : 1D-ndarray/list/tuple
numeric vector of search angles in degrees around principal axis 3 (3D)
scale_image : scalar
global scale
do_reflection : boolean
whether to reflect image about principal axis
txfn : string (optional)
if present, write optimal tx to .mat file
transform : string
type of transform to use
Rigid
Similarity
Affine
Returns
-------
pd.DataFrame
dataframe with metric values and transformation parameters
Example
-------
>>> import ants
>>> img1 = ants.image_read(ants.get_ants_data('r16'))
>>> img2 = ants.image_read(ants.get_ants_data('r64'))
>>> metric = ants.invariant_image_similarity(img1,img2)
"""
if transform not in {'Rigid', 'Similarity', 'Affine'}:
raise ValueError('transform must be one of Rigid/Similarity/Affine')
if image1.pixeltype != 'float':
image1 = image1.clone('float')
if image2.pixeltype != 'float':
image2 = image2.clone('float')
if txfn is None:
txfn = mktemp(suffix='.mat')
# convert thetas to radians
thetain = (thetas * math.pi) / 180.
thetain2 = (thetas2 * math.pi) / 180.
thetain3 = (thetas3 * math.pi) / 180.
image1 = utils.iMath(image1, 'Normalize')
image2 = utils.iMath(image2, 'Normalize')
idim = image1.dimension
fpname = ['FixedParam%i'%i for i in range(1,idim+1)]
if not do_reflection:
libfn = utils.get_lib_fn('invariantImageSimilarity_%s%iD' % (transform, idim))
r1 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
int(do_reflection),
txfn)
r1 = np.asarray(r1)
pnames = ['Param%i'%i for i in range(1,r1.shape[1])]
pnames[(len(pnames)-idim):len(pnames)] = fpname
r1 = pd.DataFrame(r1, columns=['MetricValue']+pnames)
return r1, txfn
else:
txfn1 = mktemp(suffix='.mat')
txfn2 = mktemp(suffix='.mat')
txfn3 = mktemp(suffix='.mat')
txfn4 = mktemp(suffix='.mat')
libfn = utils.get_lib_fn('invariantImageSimilarity_%s%iD' % (transform, idim))
## R1 ##
r1 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
0,
txfn1)
r1 = np.asarray(r1)
pnames = ['Param%i'%i for i in range(1,r1.shape[1])]
pnames[(len(pnames)-idim):len(pnames)] = fpname
r1 = pd.DataFrame(r1, columns=['MetricValue']+pnames)
## R2 ##
r2 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
1,
txfn2)
r2 = np.asarray(r2)
r2 = pd.DataFrame(r2, columns=['MetricValue']+pnames)
## R3 ##
r3 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
2,
txfn3)
r3 = np.asarray(r3)
r3 = pd.DataFrame(r3, columns=['MetricValue']+pnames)
## R4 ##
r4 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
3,
txfn4)
r4 = np.asarray(r4)
r4 = pd.DataFrame(r4, columns=['MetricValue']+pnames)
rmins = [np.min(r1.iloc[:,0]), np.min(r2.iloc[:,0]), np.min(r3.iloc[:,0]), np.min(r4.iloc[:,0])]
ww = np.argmin(rmins)
if ww == 0:
return r1, txfn1
elif ww == 1:
return r2, txfn2
elif ww == 2:
return r3, txfn3
elif ww == 3:
return r4, txfn4 | [
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] | Similarity metrics between two images as a function of geometry
Compute similarity metric between two images as image is rotated about its
center w/ or w/o optimization
ANTsR function: `invariantImageSimilarity`
Arguments
---------
image1 : ANTsImage
reference image
image2 : ANTsImage
moving image
local_search_iterations : integer
integer controlling local search in multistart
metric : string
which metric to use
MI
GC
thetas : 1D-ndarray/list/tuple
numeric vector of search angles in degrees
thetas2 : 1D-ndarray/list/tuple
numeric vector of search angles in degrees around principal axis 2 (3D)
thetas3 : 1D-ndarray/list/tuple
numeric vector of search angles in degrees around principal axis 3 (3D)
scale_image : scalar
global scale
do_reflection : boolean
whether to reflect image about principal axis
txfn : string (optional)
if present, write optimal tx to .mat file
transform : string
type of transform to use
Rigid
Similarity
Affine
Returns
-------
pd.DataFrame
dataframe with metric values and transformation parameters
Example
-------
>>> import ants
>>> img1 = ants.image_read(ants.get_ants_data('r16'))
>>> img2 = ants.image_read(ants.get_ants_data('r64'))
>>> metric = ants.invariant_image_similarity(img1,img2) | [
"Similarity",
"metrics",
"between",
"two",
"images",
"as",
"a",
"function",
"of",
"geometry"
] | train | https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/invariant_image_similarity.py#L15-L197 | <|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:
Similarity metrics between two images as a function of geometry
Compute similarity metric between two images as image is rotated about its
center w/ or w/o optimization
ANTsR function: `invariantImageSimilarity`
Arguments
---------
image1 : ANTsImage
reference image
image2 : ANTsImage
moving image
local_search_iterations : integer
integer controlling local search in multistart
metric : string
which metric to use
MI
GC
thetas : 1D-ndarray/list/tuple
numeric vector of search angles in degrees
thetas2 : 1D-ndarray/list/tuple
numeric vector of search angles in degrees around principal axis 2 (3D)
thetas3 : 1D-ndarray/list/tuple
numeric vector of search angles in degrees around principal axis 3 (3D)
scale_image : scalar
global scale
do_reflection : boolean
whether to reflect image about principal axis
txfn : string (optional)
if present, write optimal tx to .mat file
transform : string
type of transform to use
Rigid
Similarity
Affine
Returns
-------
pd.DataFrame
dataframe with metric values and transformation parameters
Example
-------
>>> import ants
>>> img1 = ants.image_read(ants.get_ants_data('r16'))
>>> img2 = ants.image_read(ants.get_ants_data('r64'))
>>> metric = ants.invariant_image_similarity(img1,img2)
### Response:
def invariant_image_similarity(image1, image2,
local_search_iterations=0, metric='MI',
thetas=np.linspace(0,360,5),
thetas2=np.linspace(0,360,5),
thetas3=np.linspace(0,360,5),
scale_image=1, do_reflection=False,
txfn=None, transform='Affine'):
"""
Similarity metrics between two images as a function of geometry
Compute similarity metric between two images as image is rotated about its
center w/ or w/o optimization
ANTsR function: `invariantImageSimilarity`
Arguments
---------
image1 : ANTsImage
reference image
image2 : ANTsImage
moving image
local_search_iterations : integer
integer controlling local search in multistart
metric : string
which metric to use
MI
GC
thetas : 1D-ndarray/list/tuple
numeric vector of search angles in degrees
thetas2 : 1D-ndarray/list/tuple
numeric vector of search angles in degrees around principal axis 2 (3D)
thetas3 : 1D-ndarray/list/tuple
numeric vector of search angles in degrees around principal axis 3 (3D)
scale_image : scalar
global scale
do_reflection : boolean
whether to reflect image about principal axis
txfn : string (optional)
if present, write optimal tx to .mat file
transform : string
type of transform to use
Rigid
Similarity
Affine
Returns
-------
pd.DataFrame
dataframe with metric values and transformation parameters
Example
-------
>>> import ants
>>> img1 = ants.image_read(ants.get_ants_data('r16'))
>>> img2 = ants.image_read(ants.get_ants_data('r64'))
>>> metric = ants.invariant_image_similarity(img1,img2)
"""
if transform not in {'Rigid', 'Similarity', 'Affine'}:
raise ValueError('transform must be one of Rigid/Similarity/Affine')
if image1.pixeltype != 'float':
image1 = image1.clone('float')
if image2.pixeltype != 'float':
image2 = image2.clone('float')
if txfn is None:
txfn = mktemp(suffix='.mat')
# convert thetas to radians
thetain = (thetas * math.pi) / 180.
thetain2 = (thetas2 * math.pi) / 180.
thetain3 = (thetas3 * math.pi) / 180.
image1 = utils.iMath(image1, 'Normalize')
image2 = utils.iMath(image2, 'Normalize')
idim = image1.dimension
fpname = ['FixedParam%i'%i for i in range(1,idim+1)]
if not do_reflection:
libfn = utils.get_lib_fn('invariantImageSimilarity_%s%iD' % (transform, idim))
r1 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
int(do_reflection),
txfn)
r1 = np.asarray(r1)
pnames = ['Param%i'%i for i in range(1,r1.shape[1])]
pnames[(len(pnames)-idim):len(pnames)] = fpname
r1 = pd.DataFrame(r1, columns=['MetricValue']+pnames)
return r1, txfn
else:
txfn1 = mktemp(suffix='.mat')
txfn2 = mktemp(suffix='.mat')
txfn3 = mktemp(suffix='.mat')
txfn4 = mktemp(suffix='.mat')
libfn = utils.get_lib_fn('invariantImageSimilarity_%s%iD' % (transform, idim))
## R1 ##
r1 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
0,
txfn1)
r1 = np.asarray(r1)
pnames = ['Param%i'%i for i in range(1,r1.shape[1])]
pnames[(len(pnames)-idim):len(pnames)] = fpname
r1 = pd.DataFrame(r1, columns=['MetricValue']+pnames)
## R2 ##
r2 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
1,
txfn2)
r2 = np.asarray(r2)
r2 = pd.DataFrame(r2, columns=['MetricValue']+pnames)
## R3 ##
r3 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
2,
txfn3)
r3 = np.asarray(r3)
r3 = pd.DataFrame(r3, columns=['MetricValue']+pnames)
## R4 ##
r4 = libfn(image1.pointer,
image2.pointer,
list(thetain),
list(thetain2),
list(thetain3),
local_search_iterations,
metric,
scale_image,
3,
txfn4)
r4 = np.asarray(r4)
r4 = pd.DataFrame(r4, columns=['MetricValue']+pnames)
rmins = [np.min(r1.iloc[:,0]), np.min(r2.iloc[:,0]), np.min(r3.iloc[:,0]), np.min(r4.iloc[:,0])]
ww = np.argmin(rmins)
if ww == 0:
return r1, txfn1
elif ww == 1:
return r2, txfn2
elif ww == 2:
return r3, txfn3
elif ww == 3:
return r4, txfn4
<|endoftext|> | [
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bigchaindb/bigchaindb-driver | bigchaindb_driver/driver.py | TransactionsEndpoint.prepare | def prepare(*, operation='CREATE', signers=None,
recipients=None, asset=None, metadata=None, inputs=None):
"""Prepares a transaction payload, ready to be fulfilled.
Args:
operation (str): The operation to perform. Must be ``'CREATE'``
or ``'TRANSFER'``. Case insensitive. Defaults to ``'CREATE'``.
signers (:obj:`list` | :obj:`tuple` | :obj:`str`, optional):
One or more public keys representing the issuer(s) of
the asset being created. Only applies for ``'CREATE'``
operations. Defaults to ``None``.
recipients (:obj:`list` | :obj:`tuple` | :obj:`str`, optional):
One or more public keys representing the new recipients(s)
of the asset being created or transferred.
Defaults to ``None``.
asset (:obj:`dict`, optional): The asset to be created or
transferred. MUST be supplied for ``'TRANSFER'`` operations.
Defaults to ``None``.
metadata (:obj:`dict`, optional): Metadata associated with the
transaction. Defaults to ``None``.
inputs (:obj:`dict` | :obj:`list` | :obj:`tuple`, optional):
One or more inputs holding the condition(s) that this
transaction intends to fulfill. Each input is expected to
be a :obj:`dict`. Only applies to, and MUST be supplied for,
``'TRANSFER'`` operations.
Returns:
dict: The prepared transaction.
Raises:
:class:`~.exceptions.BigchaindbException`: If ``operation`` is
not ``'CREATE'`` or ``'TRANSFER'``.
.. important::
**CREATE operations**
* ``signers`` MUST be set.
* ``recipients``, ``asset``, and ``metadata`` MAY be set.
* If ``asset`` is set, it MUST be in the form of::
{
'data': {
...
}
}
* The argument ``inputs`` is ignored.
* If ``recipients`` is not given, or evaluates to
``False``, it will be set equal to ``signers``::
if not recipients:
recipients = signers
**TRANSFER operations**
* ``recipients``, ``asset``, and ``inputs`` MUST be set.
* ``asset`` MUST be in the form of::
{
'id': '<Asset ID (i.e. TX ID of its CREATE transaction)>'
}
* ``metadata`` MAY be set.
* The argument ``signers`` is ignored.
"""
return prepare_transaction(
operation=operation,
signers=signers,
recipients=recipients,
asset=asset,
metadata=metadata,
inputs=inputs,
) | python | def prepare(*, operation='CREATE', signers=None,
recipients=None, asset=None, metadata=None, inputs=None):
"""Prepares a transaction payload, ready to be fulfilled.
Args:
operation (str): The operation to perform. Must be ``'CREATE'``
or ``'TRANSFER'``. Case insensitive. Defaults to ``'CREATE'``.
signers (:obj:`list` | :obj:`tuple` | :obj:`str`, optional):
One or more public keys representing the issuer(s) of
the asset being created. Only applies for ``'CREATE'``
operations. Defaults to ``None``.
recipients (:obj:`list` | :obj:`tuple` | :obj:`str`, optional):
One or more public keys representing the new recipients(s)
of the asset being created or transferred.
Defaults to ``None``.
asset (:obj:`dict`, optional): The asset to be created or
transferred. MUST be supplied for ``'TRANSFER'`` operations.
Defaults to ``None``.
metadata (:obj:`dict`, optional): Metadata associated with the
transaction. Defaults to ``None``.
inputs (:obj:`dict` | :obj:`list` | :obj:`tuple`, optional):
One or more inputs holding the condition(s) that this
transaction intends to fulfill. Each input is expected to
be a :obj:`dict`. Only applies to, and MUST be supplied for,
``'TRANSFER'`` operations.
Returns:
dict: The prepared transaction.
Raises:
:class:`~.exceptions.BigchaindbException`: If ``operation`` is
not ``'CREATE'`` or ``'TRANSFER'``.
.. important::
**CREATE operations**
* ``signers`` MUST be set.
* ``recipients``, ``asset``, and ``metadata`` MAY be set.
* If ``asset`` is set, it MUST be in the form of::
{
'data': {
...
}
}
* The argument ``inputs`` is ignored.
* If ``recipients`` is not given, or evaluates to
``False``, it will be set equal to ``signers``::
if not recipients:
recipients = signers
**TRANSFER operations**
* ``recipients``, ``asset``, and ``inputs`` MUST be set.
* ``asset`` MUST be in the form of::
{
'id': '<Asset ID (i.e. TX ID of its CREATE transaction)>'
}
* ``metadata`` MAY be set.
* The argument ``signers`` is ignored.
"""
return prepare_transaction(
operation=operation,
signers=signers,
recipients=recipients,
asset=asset,
metadata=metadata,
inputs=inputs,
) | [
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signers (:obj:`list` | :obj:`tuple` | :obj:`str`, optional):
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metadata (:obj:`dict`, optional): Metadata associated with the
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be a :obj:`dict`. Only applies to, and MUST be supplied for,
``'TRANSFER'`` operations.
Returns:
dict: The prepared transaction.
Raises:
:class:`~.exceptions.BigchaindbException`: If ``operation`` is
not ``'CREATE'`` or ``'TRANSFER'``.
.. important::
**CREATE operations**
* ``signers`` MUST be set.
* ``recipients``, ``asset``, and ``metadata`` MAY be set.
* If ``asset`` is set, it MUST be in the form of::
{
'data': {
...
}
}
* The argument ``inputs`` is ignored.
* If ``recipients`` is not given, or evaluates to
``False``, it will be set equal to ``signers``::
if not recipients:
recipients = signers
**TRANSFER operations**
* ``recipients``, ``asset``, and ``inputs`` MUST be set.
* ``asset`` MUST be in the form of::
{
'id': '<Asset ID (i.e. TX ID of its CREATE transaction)>'
}
* ``metadata`` MAY be set.
* The argument ``signers`` is ignored. | [
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] | train | https://github.com/bigchaindb/bigchaindb-driver/blob/c294a535f0696bd19483ae11a4882b74e6fc061e/bigchaindb_driver/driver.py#L181-L255 | <|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:
Prepares a transaction payload, ready to be fulfilled.
Args:
operation (str): The operation to perform. Must be ``'CREATE'``
or ``'TRANSFER'``. Case insensitive. Defaults to ``'CREATE'``.
signers (:obj:`list` | :obj:`tuple` | :obj:`str`, optional):
One or more public keys representing the issuer(s) of
the asset being created. Only applies for ``'CREATE'``
operations. Defaults to ``None``.
recipients (:obj:`list` | :obj:`tuple` | :obj:`str`, optional):
One or more public keys representing the new recipients(s)
of the asset being created or transferred.
Defaults to ``None``.
asset (:obj:`dict`, optional): The asset to be created or
transferred. MUST be supplied for ``'TRANSFER'`` operations.
Defaults to ``None``.
metadata (:obj:`dict`, optional): Metadata associated with the
transaction. Defaults to ``None``.
inputs (:obj:`dict` | :obj:`list` | :obj:`tuple`, optional):
One or more inputs holding the condition(s) that this
transaction intends to fulfill. Each input is expected to
be a :obj:`dict`. Only applies to, and MUST be supplied for,
``'TRANSFER'`` operations.
Returns:
dict: The prepared transaction.
Raises:
:class:`~.exceptions.BigchaindbException`: If ``operation`` is
not ``'CREATE'`` or ``'TRANSFER'``.
.. important::
**CREATE operations**
* ``signers`` MUST be set.
* ``recipients``, ``asset``, and ``metadata`` MAY be set.
* If ``asset`` is set, it MUST be in the form of::
{
'data': {
...
}
}
* The argument ``inputs`` is ignored.
* If ``recipients`` is not given, or evaluates to
``False``, it will be set equal to ``signers``::
if not recipients:
recipients = signers
**TRANSFER operations**
* ``recipients``, ``asset``, and ``inputs`` MUST be set.
* ``asset`` MUST be in the form of::
{
'id': '<Asset ID (i.e. TX ID of its CREATE transaction)>'
}
* ``metadata`` MAY be set.
* The argument ``signers`` is ignored.
### Response:
def prepare(*, operation='CREATE', signers=None,
recipients=None, asset=None, metadata=None, inputs=None):
"""Prepares a transaction payload, ready to be fulfilled.
Args:
operation (str): The operation to perform. Must be ``'CREATE'``
or ``'TRANSFER'``. Case insensitive. Defaults to ``'CREATE'``.
signers (:obj:`list` | :obj:`tuple` | :obj:`str`, optional):
One or more public keys representing the issuer(s) of
the asset being created. Only applies for ``'CREATE'``
operations. Defaults to ``None``.
recipients (:obj:`list` | :obj:`tuple` | :obj:`str`, optional):
One or more public keys representing the new recipients(s)
of the asset being created or transferred.
Defaults to ``None``.
asset (:obj:`dict`, optional): The asset to be created or
transferred. MUST be supplied for ``'TRANSFER'`` operations.
Defaults to ``None``.
metadata (:obj:`dict`, optional): Metadata associated with the
transaction. Defaults to ``None``.
inputs (:obj:`dict` | :obj:`list` | :obj:`tuple`, optional):
One or more inputs holding the condition(s) that this
transaction intends to fulfill. Each input is expected to
be a :obj:`dict`. Only applies to, and MUST be supplied for,
``'TRANSFER'`` operations.
Returns:
dict: The prepared transaction.
Raises:
:class:`~.exceptions.BigchaindbException`: If ``operation`` is
not ``'CREATE'`` or ``'TRANSFER'``.
.. important::
**CREATE operations**
* ``signers`` MUST be set.
* ``recipients``, ``asset``, and ``metadata`` MAY be set.
* If ``asset`` is set, it MUST be in the form of::
{
'data': {
...
}
}
* The argument ``inputs`` is ignored.
* If ``recipients`` is not given, or evaluates to
``False``, it will be set equal to ``signers``::
if not recipients:
recipients = signers
**TRANSFER operations**
* ``recipients``, ``asset``, and ``inputs`` MUST be set.
* ``asset`` MUST be in the form of::
{
'id': '<Asset ID (i.e. TX ID of its CREATE transaction)>'
}
* ``metadata`` MAY be set.
* The argument ``signers`` is ignored.
"""
return prepare_transaction(
operation=operation,
signers=signers,
recipients=recipients,
asset=asset,
metadata=metadata,
inputs=inputs,
)
<|endoftext|> | [
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zsimic/runez | src/runez/click.py | debug | def debug(*args, **attrs):
"""Show debugging information."""
attrs.setdefault("is_flag", True)
attrs.setdefault("default", None)
return option(debug, *args, **attrs) | python | def debug(*args, **attrs):
"""Show debugging information."""
attrs.setdefault("is_flag", True)
attrs.setdefault("default", None)
return option(debug, *args, **attrs) | [
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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:
Show debugging information.
### Response:
def debug(*args, **attrs):
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attrs.setdefault("is_flag", True)
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celiao/rtsimple | rtsimple/movies.py | Movies.cast | def cast(self, **kwargs):
"""Get the cast for a movie specified by id from the API.
Returns:
A dict respresentation of the JSON returned from the API.
"""
path = self._get_id_path('cast')
response = self._GET(path, kwargs)
self._set_attrs_to_values(response)
return response | python | def cast(self, **kwargs):
"""Get the cast for a movie specified by id from the API.
Returns:
A dict respresentation of the JSON returned from the API.
"""
path = self._get_id_path('cast')
response = self._GET(path, kwargs)
self._set_attrs_to_values(response)
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Get the cast for a movie specified by id from the API.
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A dict respresentation of the JSON returned from the API.
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mbj4668/pyang | pyang/plugins/sample-xml-skeleton.py | SampleXMLSkeletonPlugin.leaf | def leaf(self, node, elem, module, path):
"""Create a sample leaf element."""
if node.i_default is None:
nel, newm, path = self.sample_element(node, elem, module, path)
if path is None:
return
if self.annots:
nel.append(etree.Comment(
" type: %s " % node.search_one("type").arg))
elif self.defaults:
nel, newm, path = self.sample_element(node, elem, module, path)
if path is None:
return
nel.text = str(node.i_default_str) | python | def leaf(self, node, elem, module, path):
"""Create a sample leaf element."""
if node.i_default is None:
nel, newm, path = self.sample_element(node, elem, module, path)
if path is None:
return
if self.annots:
nel.append(etree.Comment(
" type: %s " % node.search_one("type").arg))
elif self.defaults:
nel, newm, path = self.sample_element(node, elem, module, path)
if path is None:
return
nel.text = str(node.i_default_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:
Create a sample leaf element.
### Response:
def leaf(self, node, elem, module, path):
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gabstopper/smc-python | smc/core/collection.py | PhysicalInterfaceCollection.add_layer3_vlan_interface | def add_layer3_vlan_interface(self, interface_id, vlan_id, address=None,
network_value=None, virtual_mapping=None, virtual_resource_name=None,
zone_ref=None, comment=None, **kw):
"""
Add a Layer 3 VLAN interface. Optionally specify an address and network if
assigning an IP to the VLAN. This method will also assign an IP address to
an existing VLAN, or add an additional address to an existing VLAN. This
method may commonly be used on a Master Engine to create VLANs for virtual
firewall engines.
Example of creating a VLAN and passing kwargs to define a DHCP server
service on the VLAN interface::
engine = Engine('engine1')
engine.physical_interface.add_layer3_vlan_interface(interface_id=20, vlan_id=20,
address='20.20.20.20', network_value='20.20.20.0/24', comment='foocomment',
dhcp_server_on_interface={
'default_gateway': '20.20.20.1',
'default_lease_time': 7200,
'dhcp_address_range': '20.20.20.101-20.20.20.120',
'dhcp_range_per_node': [],
'primary_dns_server': '8.8.8.8'})
:param str,int interface_id: interface identifier
:param int vlan_id: vlan identifier
:param str address: optional IP address to assign to VLAN
:param str network_value: network cidr if address is specified. In
format: 10.10.10.0/24.
:param str zone_ref: zone to use, by name, href, or Zone
:param str comment: optional comment for VLAN level of interface
:param int virtual_mapping: virtual engine mapping id
See :class:`smc.core.engine.VirtualResource.vfw_id`
:param str virtual_resource_name: name of virtual resource
See :class:`smc.core.engine.VirtualResource.name`
:param dict kw: keyword arguments are passed to top level of VLAN interface,
not the base level physical interface. This is useful if you want to
pass in a configuration that enables the DHCP server on a VLAN for example.
:raises EngineCommandFailed: failure creating interface
:return: None
"""
interfaces = {'nodes': [{'address': address, 'network_value': network_value}] if address
and network_value else [], 'zone_ref': zone_ref, 'virtual_mapping': virtual_mapping,
'virtual_resource_name': virtual_resource_name, 'comment': comment}
interfaces.update(**kw)
_interface = {'interface_id': interface_id, 'interfaces': [interfaces]}
if 'single_fw' in self._engine.type: # L2FW / IPS
_interface.update(interface='single_node_interface')
try:
interface = self._engine.interface.get(interface_id)
vlan = interface.vlan_interface.get(vlan_id)
# Interface exists, so we need to update but check if VLAN already exists
if vlan is None:
interfaces.update(vlan_id=vlan_id)
interface._add_interface(**_interface)
else:
_interface.update(interface_id='{}.{}'.format(interface_id, vlan_id))
vlan._add_interface(**_interface)
return interface.update()
except InterfaceNotFound:
interfaces.update(vlan_id=vlan_id)
interface = Layer3PhysicalInterface(**_interface)
return self._engine.add_interface(interface) | python | def add_layer3_vlan_interface(self, interface_id, vlan_id, address=None,
network_value=None, virtual_mapping=None, virtual_resource_name=None,
zone_ref=None, comment=None, **kw):
"""
Add a Layer 3 VLAN interface. Optionally specify an address and network if
assigning an IP to the VLAN. This method will also assign an IP address to
an existing VLAN, or add an additional address to an existing VLAN. This
method may commonly be used on a Master Engine to create VLANs for virtual
firewall engines.
Example of creating a VLAN and passing kwargs to define a DHCP server
service on the VLAN interface::
engine = Engine('engine1')
engine.physical_interface.add_layer3_vlan_interface(interface_id=20, vlan_id=20,
address='20.20.20.20', network_value='20.20.20.0/24', comment='foocomment',
dhcp_server_on_interface={
'default_gateway': '20.20.20.1',
'default_lease_time': 7200,
'dhcp_address_range': '20.20.20.101-20.20.20.120',
'dhcp_range_per_node': [],
'primary_dns_server': '8.8.8.8'})
:param str,int interface_id: interface identifier
:param int vlan_id: vlan identifier
:param str address: optional IP address to assign to VLAN
:param str network_value: network cidr if address is specified. In
format: 10.10.10.0/24.
:param str zone_ref: zone to use, by name, href, or Zone
:param str comment: optional comment for VLAN level of interface
:param int virtual_mapping: virtual engine mapping id
See :class:`smc.core.engine.VirtualResource.vfw_id`
:param str virtual_resource_name: name of virtual resource
See :class:`smc.core.engine.VirtualResource.name`
:param dict kw: keyword arguments are passed to top level of VLAN interface,
not the base level physical interface. This is useful if you want to
pass in a configuration that enables the DHCP server on a VLAN for example.
:raises EngineCommandFailed: failure creating interface
:return: None
"""
interfaces = {'nodes': [{'address': address, 'network_value': network_value}] if address
and network_value else [], 'zone_ref': zone_ref, 'virtual_mapping': virtual_mapping,
'virtual_resource_name': virtual_resource_name, 'comment': comment}
interfaces.update(**kw)
_interface = {'interface_id': interface_id, 'interfaces': [interfaces]}
if 'single_fw' in self._engine.type: # L2FW / IPS
_interface.update(interface='single_node_interface')
try:
interface = self._engine.interface.get(interface_id)
vlan = interface.vlan_interface.get(vlan_id)
# Interface exists, so we need to update but check if VLAN already exists
if vlan is None:
interfaces.update(vlan_id=vlan_id)
interface._add_interface(**_interface)
else:
_interface.update(interface_id='{}.{}'.format(interface_id, vlan_id))
vlan._add_interface(**_interface)
return interface.update()
except InterfaceNotFound:
interfaces.update(vlan_id=vlan_id)
interface = Layer3PhysicalInterface(**_interface)
return self._engine.add_interface(interface) | [
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method may commonly be used on a Master Engine to create VLANs for virtual
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Example of creating a VLAN and passing kwargs to define a DHCP server
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address='20.20.20.20', network_value='20.20.20.0/24', comment='foocomment',
dhcp_server_on_interface={
'default_gateway': '20.20.20.1',
'default_lease_time': 7200,
'dhcp_address_range': '20.20.20.101-20.20.20.120',
'dhcp_range_per_node': [],
'primary_dns_server': '8.8.8.8'})
:param str,int interface_id: interface identifier
:param int vlan_id: vlan identifier
:param str address: optional IP address to assign to VLAN
:param str network_value: network cidr if address is specified. In
format: 10.10.10.0/24.
:param str zone_ref: zone to use, by name, href, or Zone
:param str comment: optional comment for VLAN level of interface
:param int virtual_mapping: virtual engine mapping id
See :class:`smc.core.engine.VirtualResource.vfw_id`
:param str virtual_resource_name: name of virtual resource
See :class:`smc.core.engine.VirtualResource.name`
:param dict kw: keyword arguments are passed to top level of VLAN interface,
not the base level physical interface. This is useful if you want to
pass in a configuration that enables the DHCP server on a VLAN for example.
:raises EngineCommandFailed: failure creating interface
:return: None | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Add a Layer 3 VLAN interface. Optionally specify an address and network if
assigning an IP to the VLAN. This method will also assign an IP address to
an existing VLAN, or add an additional address to an existing VLAN. This
method may commonly be used on a Master Engine to create VLANs for virtual
firewall engines.
Example of creating a VLAN and passing kwargs to define a DHCP server
service on the VLAN interface::
engine = Engine('engine1')
engine.physical_interface.add_layer3_vlan_interface(interface_id=20, vlan_id=20,
address='20.20.20.20', network_value='20.20.20.0/24', comment='foocomment',
dhcp_server_on_interface={
'default_gateway': '20.20.20.1',
'default_lease_time': 7200,
'dhcp_address_range': '20.20.20.101-20.20.20.120',
'dhcp_range_per_node': [],
'primary_dns_server': '8.8.8.8'})
:param str,int interface_id: interface identifier
:param int vlan_id: vlan identifier
:param str address: optional IP address to assign to VLAN
:param str network_value: network cidr if address is specified. In
format: 10.10.10.0/24.
:param str zone_ref: zone to use, by name, href, or Zone
:param str comment: optional comment for VLAN level of interface
:param int virtual_mapping: virtual engine mapping id
See :class:`smc.core.engine.VirtualResource.vfw_id`
:param str virtual_resource_name: name of virtual resource
See :class:`smc.core.engine.VirtualResource.name`
:param dict kw: keyword arguments are passed to top level of VLAN interface,
not the base level physical interface. This is useful if you want to
pass in a configuration that enables the DHCP server on a VLAN for example.
:raises EngineCommandFailed: failure creating interface
:return: None
### Response:
def add_layer3_vlan_interface(self, interface_id, vlan_id, address=None,
network_value=None, virtual_mapping=None, virtual_resource_name=None,
zone_ref=None, comment=None, **kw):
"""
Add a Layer 3 VLAN interface. Optionally specify an address and network if
assigning an IP to the VLAN. This method will also assign an IP address to
an existing VLAN, or add an additional address to an existing VLAN. This
method may commonly be used on a Master Engine to create VLANs for virtual
firewall engines.
Example of creating a VLAN and passing kwargs to define a DHCP server
service on the VLAN interface::
engine = Engine('engine1')
engine.physical_interface.add_layer3_vlan_interface(interface_id=20, vlan_id=20,
address='20.20.20.20', network_value='20.20.20.0/24', comment='foocomment',
dhcp_server_on_interface={
'default_gateway': '20.20.20.1',
'default_lease_time': 7200,
'dhcp_address_range': '20.20.20.101-20.20.20.120',
'dhcp_range_per_node': [],
'primary_dns_server': '8.8.8.8'})
:param str,int interface_id: interface identifier
:param int vlan_id: vlan identifier
:param str address: optional IP address to assign to VLAN
:param str network_value: network cidr if address is specified. In
format: 10.10.10.0/24.
:param str zone_ref: zone to use, by name, href, or Zone
:param str comment: optional comment for VLAN level of interface
:param int virtual_mapping: virtual engine mapping id
See :class:`smc.core.engine.VirtualResource.vfw_id`
:param str virtual_resource_name: name of virtual resource
See :class:`smc.core.engine.VirtualResource.name`
:param dict kw: keyword arguments are passed to top level of VLAN interface,
not the base level physical interface. This is useful if you want to
pass in a configuration that enables the DHCP server on a VLAN for example.
:raises EngineCommandFailed: failure creating interface
:return: None
"""
interfaces = {'nodes': [{'address': address, 'network_value': network_value}] if address
and network_value else [], 'zone_ref': zone_ref, 'virtual_mapping': virtual_mapping,
'virtual_resource_name': virtual_resource_name, 'comment': comment}
interfaces.update(**kw)
_interface = {'interface_id': interface_id, 'interfaces': [interfaces]}
if 'single_fw' in self._engine.type: # L2FW / IPS
_interface.update(interface='single_node_interface')
try:
interface = self._engine.interface.get(interface_id)
vlan = interface.vlan_interface.get(vlan_id)
# Interface exists, so we need to update but check if VLAN already exists
if vlan is None:
interfaces.update(vlan_id=vlan_id)
interface._add_interface(**_interface)
else:
_interface.update(interface_id='{}.{}'.format(interface_id, vlan_id))
vlan._add_interface(**_interface)
return interface.update()
except InterfaceNotFound:
interfaces.update(vlan_id=vlan_id)
interface = Layer3PhysicalInterface(**_interface)
return self._engine.add_interface(interface)
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jjgomera/iapws | iapws/iapws95.py | MEoS._phir | def _phir(self, tau, delta):
"""Residual contribution to the free Helmholtz energy
Parameters
----------
tau : float
Inverse reduced temperature Tc/T, [-]
delta : float
Reduced density rho/rhoc, [-]
Returns
-------
prop : dict
Dictionary with residual adimensional helmholtz energy and deriv:
* fir
* firt: ∂fir/∂τ|δ,x
* fird: ∂fir/∂δ|τ,x
* firtt: ∂²fir/∂τ²|δ,x
* firdt: ∂²fir/∂τ∂δ|x
* firdd: ∂²fir/∂δ²|τ,x
References
----------
IAPWS, Revised Release on the IAPWS Formulation 1995 for the
Thermodynamic Properties of Ordinary Water Substance for General and
Scientific Use, September 2016, Table 5
http://www.iapws.org/relguide/IAPWS-95.html
"""
fir = fird = firdd = firt = firtt = firdt = 0
# Polinomial terms
nr1 = self._constants.get("nr1", [])
d1 = self._constants.get("d1", [])
t1 = self._constants.get("t1", [])
for n, d, t in zip(nr1, d1, t1):
fir += n*delta**d*tau**t
fird += n*d*delta**(d-1)*tau**t
firdd += n*d*(d-1)*delta**(d-2)*tau**t
firt += n*t*delta**d*tau**(t-1)
firtt += n*t*(t-1)*delta**d*tau**(t-2)
firdt += n*t*d*delta**(d-1)*tau**(t-1)
# Exponential terms
nr2 = self._constants.get("nr2", [])
d2 = self._constants.get("d2", [])
g2 = self._constants.get("gamma2", [])
t2 = self._constants.get("t2", [])
c2 = self._constants.get("c2", [])
for n, d, g, t, c in zip(nr2, d2, g2, t2, c2):
fir += n*delta**d*tau**t*exp(-g*delta**c)
fird += n*exp(-g*delta**c)*delta**(d-1)*tau**t*(d-g*c*delta**c)
firdd += n*exp(-g*delta**c)*delta**(d-2)*tau**t * \
((d-g*c*delta**c)*(d-1-g*c*delta**c)-g**2*c**2*delta**c)
firt += n*t*delta**d*tau**(t-1)*exp(-g*delta**c)
firtt += n*t*(t-1)*delta**d*tau**(t-2)*exp(-g*delta**c)
firdt += n*t*delta**(d-1)*tau**(t-1)*(d-g*c*delta**c)*exp(
-g*delta**c)
# Gaussian terms
nr3 = self._constants.get("nr3", [])
d3 = self._constants.get("d3", [])
t3 = self._constants.get("t3", [])
a3 = self._constants.get("alfa3", [])
e3 = self._constants.get("epsilon3", [])
b3 = self._constants.get("beta3", [])
g3 = self._constants.get("gamma3", [])
for n, d, t, a, e, b, g in zip(nr3, d3, t3, a3, e3, b3, g3):
fir += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)
fird += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
d/delta-2*a*(delta-e))
firdd += n*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
-2*a*delta**d + 4*a**2*delta**d*(delta-e)**2 -
4*d*a*delta**(d-1)*(delta-e) + d*(d-1)*delta**(d-2))
firt += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
t/tau-2*b*(tau-g))
firtt += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
(t/tau-2*b*(tau-g))**2-t/tau**2-2*b)
firdt += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
t/tau-2*b*(tau-g))*(d/delta-2*a*(delta-e))
# Non analitic terms
nr4 = self._constants.get("nr4", [])
a4 = self._constants.get("a4", [])
b4 = self._constants.get("b4", [])
Ai = self._constants.get("A", [])
Bi = self._constants.get("B", [])
Ci = self._constants.get("C", [])
Di = self._constants.get("D", [])
bt4 = self._constants.get("beta4", [])
for n, a, b, A, B, C, D, bt in zip(nr4, a4, b4, Ai, Bi, Ci, Di, bt4):
Tita = (1-tau)+A*((delta-1)**2)**(0.5/bt)
F = exp(-C*(delta-1)**2-D*(tau-1)**2)
Fd = -2*C*F*(delta-1)
Fdd = 2*C*F*(2*C*(delta-1)**2-1)
Ft = -2*D*F*(tau-1)
Ftt = 2*D*F*(2*D*(tau-1)**2-1)
Fdt = 4*C*D*F*(delta-1)*(tau-1)
Delta = Tita**2+B*((delta-1)**2)**a
Deltad = (delta-1)*(A*Tita*2/bt*((delta-1)**2)**(0.5/bt-1) +
2*B*a*((delta-1)**2)**(a-1))
if delta == 1:
Deltadd = 0
else:
Deltadd = Deltad/(delta-1)+(delta-1)**2*(
4*B*a*(a-1)*((delta-1)**2)**(a-2) +
2*A**2/bt**2*(((delta-1)**2)**(0.5/bt-1))**2 +
A*Tita*4/bt*(0.5/bt-1)*((delta-1)**2)**(0.5/bt-2))
DeltaBd = b*Delta**(b-1)*Deltad
DeltaBdd = b*(Delta**(b-1)*Deltadd+(b-1)*Delta**(b-2)*Deltad**2)
DeltaBt = -2*Tita*b*Delta**(b-1)
DeltaBtt = 2*b*Delta**(b-1)+4*Tita**2*b*(b-1)*Delta**(b-2)
DeltaBdt = -A*b*2/bt*Delta**(b-1)*(delta-1)*((delta-1)**2)**(
0.5/bt-1)-2*Tita*b*(b-1)*Delta**(b-2)*Deltad
fir += n*Delta**b*delta*F
fird += n*(Delta**b*(F+delta*Fd)+DeltaBd*delta*F)
firdd += n*(Delta**b*(2*Fd+delta*Fdd) + 2*DeltaBd*(F+delta*Fd) +
DeltaBdd*delta*F)
firt += n*delta*(DeltaBt*F+Delta**b*Ft)
firtt += n*delta*(DeltaBtt*F+2*DeltaBt*Ft+Delta**b*Ftt)
firdt += n*(Delta**b*(Ft+delta*Fdt)+delta*DeltaBd*Ft +
DeltaBt*(F+delta*Fd)+DeltaBdt*delta*F)
prop = {}
prop["fir"] = fir
prop["firt"] = firt
prop["firtt"] = firtt
prop["fird"] = fird
prop["firdd"] = firdd
prop["firdt"] = firdt
return prop | python | def _phir(self, tau, delta):
"""Residual contribution to the free Helmholtz energy
Parameters
----------
tau : float
Inverse reduced temperature Tc/T, [-]
delta : float
Reduced density rho/rhoc, [-]
Returns
-------
prop : dict
Dictionary with residual adimensional helmholtz energy and deriv:
* fir
* firt: ∂fir/∂τ|δ,x
* fird: ∂fir/∂δ|τ,x
* firtt: ∂²fir/∂τ²|δ,x
* firdt: ∂²fir/∂τ∂δ|x
* firdd: ∂²fir/∂δ²|τ,x
References
----------
IAPWS, Revised Release on the IAPWS Formulation 1995 for the
Thermodynamic Properties of Ordinary Water Substance for General and
Scientific Use, September 2016, Table 5
http://www.iapws.org/relguide/IAPWS-95.html
"""
fir = fird = firdd = firt = firtt = firdt = 0
# Polinomial terms
nr1 = self._constants.get("nr1", [])
d1 = self._constants.get("d1", [])
t1 = self._constants.get("t1", [])
for n, d, t in zip(nr1, d1, t1):
fir += n*delta**d*tau**t
fird += n*d*delta**(d-1)*tau**t
firdd += n*d*(d-1)*delta**(d-2)*tau**t
firt += n*t*delta**d*tau**(t-1)
firtt += n*t*(t-1)*delta**d*tau**(t-2)
firdt += n*t*d*delta**(d-1)*tau**(t-1)
# Exponential terms
nr2 = self._constants.get("nr2", [])
d2 = self._constants.get("d2", [])
g2 = self._constants.get("gamma2", [])
t2 = self._constants.get("t2", [])
c2 = self._constants.get("c2", [])
for n, d, g, t, c in zip(nr2, d2, g2, t2, c2):
fir += n*delta**d*tau**t*exp(-g*delta**c)
fird += n*exp(-g*delta**c)*delta**(d-1)*tau**t*(d-g*c*delta**c)
firdd += n*exp(-g*delta**c)*delta**(d-2)*tau**t * \
((d-g*c*delta**c)*(d-1-g*c*delta**c)-g**2*c**2*delta**c)
firt += n*t*delta**d*tau**(t-1)*exp(-g*delta**c)
firtt += n*t*(t-1)*delta**d*tau**(t-2)*exp(-g*delta**c)
firdt += n*t*delta**(d-1)*tau**(t-1)*(d-g*c*delta**c)*exp(
-g*delta**c)
# Gaussian terms
nr3 = self._constants.get("nr3", [])
d3 = self._constants.get("d3", [])
t3 = self._constants.get("t3", [])
a3 = self._constants.get("alfa3", [])
e3 = self._constants.get("epsilon3", [])
b3 = self._constants.get("beta3", [])
g3 = self._constants.get("gamma3", [])
for n, d, t, a, e, b, g in zip(nr3, d3, t3, a3, e3, b3, g3):
fir += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)
fird += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
d/delta-2*a*(delta-e))
firdd += n*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
-2*a*delta**d + 4*a**2*delta**d*(delta-e)**2 -
4*d*a*delta**(d-1)*(delta-e) + d*(d-1)*delta**(d-2))
firt += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
t/tau-2*b*(tau-g))
firtt += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
(t/tau-2*b*(tau-g))**2-t/tau**2-2*b)
firdt += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
t/tau-2*b*(tau-g))*(d/delta-2*a*(delta-e))
# Non analitic terms
nr4 = self._constants.get("nr4", [])
a4 = self._constants.get("a4", [])
b4 = self._constants.get("b4", [])
Ai = self._constants.get("A", [])
Bi = self._constants.get("B", [])
Ci = self._constants.get("C", [])
Di = self._constants.get("D", [])
bt4 = self._constants.get("beta4", [])
for n, a, b, A, B, C, D, bt in zip(nr4, a4, b4, Ai, Bi, Ci, Di, bt4):
Tita = (1-tau)+A*((delta-1)**2)**(0.5/bt)
F = exp(-C*(delta-1)**2-D*(tau-1)**2)
Fd = -2*C*F*(delta-1)
Fdd = 2*C*F*(2*C*(delta-1)**2-1)
Ft = -2*D*F*(tau-1)
Ftt = 2*D*F*(2*D*(tau-1)**2-1)
Fdt = 4*C*D*F*(delta-1)*(tau-1)
Delta = Tita**2+B*((delta-1)**2)**a
Deltad = (delta-1)*(A*Tita*2/bt*((delta-1)**2)**(0.5/bt-1) +
2*B*a*((delta-1)**2)**(a-1))
if delta == 1:
Deltadd = 0
else:
Deltadd = Deltad/(delta-1)+(delta-1)**2*(
4*B*a*(a-1)*((delta-1)**2)**(a-2) +
2*A**2/bt**2*(((delta-1)**2)**(0.5/bt-1))**2 +
A*Tita*4/bt*(0.5/bt-1)*((delta-1)**2)**(0.5/bt-2))
DeltaBd = b*Delta**(b-1)*Deltad
DeltaBdd = b*(Delta**(b-1)*Deltadd+(b-1)*Delta**(b-2)*Deltad**2)
DeltaBt = -2*Tita*b*Delta**(b-1)
DeltaBtt = 2*b*Delta**(b-1)+4*Tita**2*b*(b-1)*Delta**(b-2)
DeltaBdt = -A*b*2/bt*Delta**(b-1)*(delta-1)*((delta-1)**2)**(
0.5/bt-1)-2*Tita*b*(b-1)*Delta**(b-2)*Deltad
fir += n*Delta**b*delta*F
fird += n*(Delta**b*(F+delta*Fd)+DeltaBd*delta*F)
firdd += n*(Delta**b*(2*Fd+delta*Fdd) + 2*DeltaBd*(F+delta*Fd) +
DeltaBdd*delta*F)
firt += n*delta*(DeltaBt*F+Delta**b*Ft)
firtt += n*delta*(DeltaBtt*F+2*DeltaBt*Ft+Delta**b*Ftt)
firdt += n*(Delta**b*(Ft+delta*Fdt)+delta*DeltaBd*Ft +
DeltaBt*(F+delta*Fd)+DeltaBdt*delta*F)
prop = {}
prop["fir"] = fir
prop["firt"] = firt
prop["firtt"] = firtt
prop["fird"] = fird
prop["firdd"] = firdd
prop["firdt"] = firdt
return prop | [
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] | Residual contribution to the free Helmholtz energy
Parameters
----------
tau : float
Inverse reduced temperature Tc/T, [-]
delta : float
Reduced density rho/rhoc, [-]
Returns
-------
prop : dict
Dictionary with residual adimensional helmholtz energy and deriv:
* fir
* firt: ∂fir/∂τ|δ,x
* fird: ∂fir/∂δ|τ,x
* firtt: ∂²fir/∂τ²|δ,x
* firdt: ∂²fir/∂τ∂δ|x
* firdd: ∂²fir/∂δ²|τ,x
References
----------
IAPWS, Revised Release on the IAPWS Formulation 1995 for the
Thermodynamic Properties of Ordinary Water Substance for General and
Scientific Use, September 2016, Table 5
http://www.iapws.org/relguide/IAPWS-95.html | [
"Residual",
"contribution",
"to",
"the",
"free",
"Helmholtz",
"energy"
] | train | https://github.com/jjgomera/iapws/blob/1e5812aab38212fb8a63736f61cdcfa427d223b1/iapws/iapws95.py#L1758-L1890 | <|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:
Residual contribution to the free Helmholtz energy
Parameters
----------
tau : float
Inverse reduced temperature Tc/T, [-]
delta : float
Reduced density rho/rhoc, [-]
Returns
-------
prop : dict
Dictionary with residual adimensional helmholtz energy and deriv:
* fir
* firt: ∂fir/∂τ|δ,x
* fird: ∂fir/∂δ|τ,x
* firtt: ∂²fir/∂τ²|δ,x
* firdt: ∂²fir/∂τ∂δ|x
* firdd: ∂²fir/∂δ²|τ,x
References
----------
IAPWS, Revised Release on the IAPWS Formulation 1995 for the
Thermodynamic Properties of Ordinary Water Substance for General and
Scientific Use, September 2016, Table 5
http://www.iapws.org/relguide/IAPWS-95.html
### Response:
def _phir(self, tau, delta):
"""Residual contribution to the free Helmholtz energy
Parameters
----------
tau : float
Inverse reduced temperature Tc/T, [-]
delta : float
Reduced density rho/rhoc, [-]
Returns
-------
prop : dict
Dictionary with residual adimensional helmholtz energy and deriv:
* fir
* firt: ∂fir/∂τ|δ,x
* fird: ∂fir/∂δ|τ,x
* firtt: ∂²fir/∂τ²|δ,x
* firdt: ∂²fir/∂τ∂δ|x
* firdd: ∂²fir/∂δ²|τ,x
References
----------
IAPWS, Revised Release on the IAPWS Formulation 1995 for the
Thermodynamic Properties of Ordinary Water Substance for General and
Scientific Use, September 2016, Table 5
http://www.iapws.org/relguide/IAPWS-95.html
"""
fir = fird = firdd = firt = firtt = firdt = 0
# Polinomial terms
nr1 = self._constants.get("nr1", [])
d1 = self._constants.get("d1", [])
t1 = self._constants.get("t1", [])
for n, d, t in zip(nr1, d1, t1):
fir += n*delta**d*tau**t
fird += n*d*delta**(d-1)*tau**t
firdd += n*d*(d-1)*delta**(d-2)*tau**t
firt += n*t*delta**d*tau**(t-1)
firtt += n*t*(t-1)*delta**d*tau**(t-2)
firdt += n*t*d*delta**(d-1)*tau**(t-1)
# Exponential terms
nr2 = self._constants.get("nr2", [])
d2 = self._constants.get("d2", [])
g2 = self._constants.get("gamma2", [])
t2 = self._constants.get("t2", [])
c2 = self._constants.get("c2", [])
for n, d, g, t, c in zip(nr2, d2, g2, t2, c2):
fir += n*delta**d*tau**t*exp(-g*delta**c)
fird += n*exp(-g*delta**c)*delta**(d-1)*tau**t*(d-g*c*delta**c)
firdd += n*exp(-g*delta**c)*delta**(d-2)*tau**t * \
((d-g*c*delta**c)*(d-1-g*c*delta**c)-g**2*c**2*delta**c)
firt += n*t*delta**d*tau**(t-1)*exp(-g*delta**c)
firtt += n*t*(t-1)*delta**d*tau**(t-2)*exp(-g*delta**c)
firdt += n*t*delta**(d-1)*tau**(t-1)*(d-g*c*delta**c)*exp(
-g*delta**c)
# Gaussian terms
nr3 = self._constants.get("nr3", [])
d3 = self._constants.get("d3", [])
t3 = self._constants.get("t3", [])
a3 = self._constants.get("alfa3", [])
e3 = self._constants.get("epsilon3", [])
b3 = self._constants.get("beta3", [])
g3 = self._constants.get("gamma3", [])
for n, d, t, a, e, b, g in zip(nr3, d3, t3, a3, e3, b3, g3):
fir += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)
fird += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
d/delta-2*a*(delta-e))
firdd += n*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
-2*a*delta**d + 4*a**2*delta**d*(delta-e)**2 -
4*d*a*delta**(d-1)*(delta-e) + d*(d-1)*delta**(d-2))
firt += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
t/tau-2*b*(tau-g))
firtt += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
(t/tau-2*b*(tau-g))**2-t/tau**2-2*b)
firdt += n*delta**d*tau**t*exp(-a*(delta-e)**2-b*(tau-g)**2)*(
t/tau-2*b*(tau-g))*(d/delta-2*a*(delta-e))
# Non analitic terms
nr4 = self._constants.get("nr4", [])
a4 = self._constants.get("a4", [])
b4 = self._constants.get("b4", [])
Ai = self._constants.get("A", [])
Bi = self._constants.get("B", [])
Ci = self._constants.get("C", [])
Di = self._constants.get("D", [])
bt4 = self._constants.get("beta4", [])
for n, a, b, A, B, C, D, bt in zip(nr4, a4, b4, Ai, Bi, Ci, Di, bt4):
Tita = (1-tau)+A*((delta-1)**2)**(0.5/bt)
F = exp(-C*(delta-1)**2-D*(tau-1)**2)
Fd = -2*C*F*(delta-1)
Fdd = 2*C*F*(2*C*(delta-1)**2-1)
Ft = -2*D*F*(tau-1)
Ftt = 2*D*F*(2*D*(tau-1)**2-1)
Fdt = 4*C*D*F*(delta-1)*(tau-1)
Delta = Tita**2+B*((delta-1)**2)**a
Deltad = (delta-1)*(A*Tita*2/bt*((delta-1)**2)**(0.5/bt-1) +
2*B*a*((delta-1)**2)**(a-1))
if delta == 1:
Deltadd = 0
else:
Deltadd = Deltad/(delta-1)+(delta-1)**2*(
4*B*a*(a-1)*((delta-1)**2)**(a-2) +
2*A**2/bt**2*(((delta-1)**2)**(0.5/bt-1))**2 +
A*Tita*4/bt*(0.5/bt-1)*((delta-1)**2)**(0.5/bt-2))
DeltaBd = b*Delta**(b-1)*Deltad
DeltaBdd = b*(Delta**(b-1)*Deltadd+(b-1)*Delta**(b-2)*Deltad**2)
DeltaBt = -2*Tita*b*Delta**(b-1)
DeltaBtt = 2*b*Delta**(b-1)+4*Tita**2*b*(b-1)*Delta**(b-2)
DeltaBdt = -A*b*2/bt*Delta**(b-1)*(delta-1)*((delta-1)**2)**(
0.5/bt-1)-2*Tita*b*(b-1)*Delta**(b-2)*Deltad
fir += n*Delta**b*delta*F
fird += n*(Delta**b*(F+delta*Fd)+DeltaBd*delta*F)
firdd += n*(Delta**b*(2*Fd+delta*Fdd) + 2*DeltaBd*(F+delta*Fd) +
DeltaBdd*delta*F)
firt += n*delta*(DeltaBt*F+Delta**b*Ft)
firtt += n*delta*(DeltaBtt*F+2*DeltaBt*Ft+Delta**b*Ftt)
firdt += n*(Delta**b*(Ft+delta*Fdt)+delta*DeltaBd*Ft +
DeltaBt*(F+delta*Fd)+DeltaBdt*delta*F)
prop = {}
prop["fir"] = fir
prop["firt"] = firt
prop["firtt"] = firtt
prop["fird"] = fird
prop["firdd"] = firdd
prop["firdt"] = firdt
return prop
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cebel/pyuniprot | src/pyuniprot/manager/query.py | QueryManager.function | def function(self, text=None, entry_name=None, limit=None, as_df=False):
"""Method to query :class:`.models.Function` objects in database
:param text: description(s) of function(s)
:type text: str or tuple(str) or None
:param entry_name: name(s) in :class:`.models.Entry`
:type entry_name: str or tuple(str) or None
:param limit:
- if `isinstance(limit,int)==True` -> limit
- if `isinstance(limit,tuple)==True` -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
:type limit: int or tuple(int) or None
:param bool as_df: if `True` results are returned as :class:`pandas.DataFrame`
:return:
- if `as_df == False` -> list(:class:`.models.Function`)
- if `as_df == True` -> :class:`pandas.DataFrame`
:rtype: list(:class:`.models.Function`) or :class:`pandas.DataFrame`
"""
q = self.session.query(models.Function)
model_queries_config = (
(text, models.Function.text),
)
q = self.get_model_queries(q, model_queries_config)
q = self.get_one_to_many_queries(q, ((entry_name, models.Entry.name),))
return self._limit_and_df(q, limit, as_df) | python | def function(self, text=None, entry_name=None, limit=None, as_df=False):
"""Method to query :class:`.models.Function` objects in database
:param text: description(s) of function(s)
:type text: str or tuple(str) or None
:param entry_name: name(s) in :class:`.models.Entry`
:type entry_name: str or tuple(str) or None
:param limit:
- if `isinstance(limit,int)==True` -> limit
- if `isinstance(limit,tuple)==True` -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
:type limit: int or tuple(int) or None
:param bool as_df: if `True` results are returned as :class:`pandas.DataFrame`
:return:
- if `as_df == False` -> list(:class:`.models.Function`)
- if `as_df == True` -> :class:`pandas.DataFrame`
:rtype: list(:class:`.models.Function`) or :class:`pandas.DataFrame`
"""
q = self.session.query(models.Function)
model_queries_config = (
(text, models.Function.text),
)
q = self.get_model_queries(q, model_queries_config)
q = self.get_one_to_many_queries(q, ((entry_name, models.Entry.name),))
return self._limit_and_df(q, limit, as_df) | [
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:param text: description(s) of function(s)
:type text: str or tuple(str) or None
:param entry_name: name(s) in :class:`.models.Entry`
:type entry_name: str or tuple(str) or None
:param limit:
- if `isinstance(limit,int)==True` -> limit
- if `isinstance(limit,tuple)==True` -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
:type limit: int or tuple(int) or None
:param bool as_df: if `True` results are returned as :class:`pandas.DataFrame`
:return:
- if `as_df == False` -> list(:class:`.models.Function`)
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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:
Method to query :class:`.models.Function` objects in database
:param text: description(s) of function(s)
:type text: str or tuple(str) or None
:param entry_name: name(s) in :class:`.models.Entry`
:type entry_name: str or tuple(str) or None
:param limit:
- if `isinstance(limit,int)==True` -> limit
- if `isinstance(limit,tuple)==True` -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
:type limit: int or tuple(int) or None
:param bool as_df: if `True` results are returned as :class:`pandas.DataFrame`
:return:
- if `as_df == False` -> list(:class:`.models.Function`)
- if `as_df == True` -> :class:`pandas.DataFrame`
:rtype: list(:class:`.models.Function`) or :class:`pandas.DataFrame`
### Response:
def function(self, text=None, entry_name=None, limit=None, as_df=False):
"""Method to query :class:`.models.Function` objects in database
:param text: description(s) of function(s)
:type text: str or tuple(str) or None
:param entry_name: name(s) in :class:`.models.Entry`
:type entry_name: str or tuple(str) or None
:param limit:
- if `isinstance(limit,int)==True` -> limit
- if `isinstance(limit,tuple)==True` -> format:= tuple(page_number, results_per_page)
- if limit == None -> all results
:type limit: int or tuple(int) or None
:param bool as_df: if `True` results are returned as :class:`pandas.DataFrame`
:return:
- if `as_df == False` -> list(:class:`.models.Function`)
- if `as_df == True` -> :class:`pandas.DataFrame`
:rtype: list(:class:`.models.Function`) or :class:`pandas.DataFrame`
"""
q = self.session.query(models.Function)
model_queries_config = (
(text, models.Function.text),
)
q = self.get_model_queries(q, model_queries_config)
q = self.get_one_to_many_queries(q, ((entry_name, models.Entry.name),))
return self._limit_and_df(q, limit, as_df)
<|endoftext|> | [
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Autodesk/aomi | aomi/cli.py | ux_actions | def ux_actions(parser, args):
"""Handle some human triggers actions"""
# cryptorito uses native logging (as aomi should tbh)
normal_fmt = '%(message)s'
if hasattr(args, 'verbose') and args.verbose and args.verbose >= 2:
logging.basicConfig(level=logging.DEBUG)
elif hasattr(args, 'verbose') and args.verbose >= 1:
logging.basicConfig(level=logging.INFO, format=normal_fmt)
else:
logging.basicConfig(level=logging.WARN, format=normal_fmt)
if args.operation == 'help':
help_me(parser, args) | python | def ux_actions(parser, args):
"""Handle some human triggers actions"""
# cryptorito uses native logging (as aomi should tbh)
normal_fmt = '%(message)s'
if hasattr(args, 'verbose') and args.verbose and args.verbose >= 2:
logging.basicConfig(level=logging.DEBUG)
elif hasattr(args, 'verbose') and args.verbose >= 1:
logging.basicConfig(level=logging.INFO, format=normal_fmt)
else:
logging.basicConfig(level=logging.WARN, format=normal_fmt)
if args.operation == 'help':
help_me(parser, args) | [
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] | train | https://github.com/Autodesk/aomi/blob/84da2dfb0424837adf9c4ddc1aa352e942bb7a4a/aomi/cli.py#L388-L400 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Handle some human triggers actions
### Response:
def ux_actions(parser, args):
"""Handle some human triggers actions"""
# cryptorito uses native logging (as aomi should tbh)
normal_fmt = '%(message)s'
if hasattr(args, 'verbose') and args.verbose and args.verbose >= 2:
logging.basicConfig(level=logging.DEBUG)
elif hasattr(args, 'verbose') and args.verbose >= 1:
logging.basicConfig(level=logging.INFO, format=normal_fmt)
else:
logging.basicConfig(level=logging.WARN, format=normal_fmt)
if args.operation == 'help':
help_me(parser, args)
<|endoftext|> | [
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crocs-muni/roca | roca/detect.py | RocaFingerprinter.process_ssh | def process_ssh(self, data, name):
"""
Processes SSH keys
:param data:
:param name:
:return:
"""
if data is None or len(data) == 0:
return
ret = []
try:
lines = [x.strip() for x in data.split(b'\n')]
for idx, line in enumerate(lines):
ret.append(self.process_ssh_line(line, name, idx))
except Exception as e:
logger.debug('Exception in processing SSH public key %s : %s' % (name, e))
self.trace_logger.log(e)
return ret | python | def process_ssh(self, data, name):
"""
Processes SSH keys
:param data:
:param name:
:return:
"""
if data is None or len(data) == 0:
return
ret = []
try:
lines = [x.strip() for x in data.split(b'\n')]
for idx, line in enumerate(lines):
ret.append(self.process_ssh_line(line, name, idx))
except Exception as e:
logger.debug('Exception in processing SSH public key %s : %s' % (name, e))
self.trace_logger.log(e)
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:
Processes SSH keys
:param data:
:param name:
:return:
### Response:
def process_ssh(self, data, name):
"""
Processes SSH keys
:param data:
:param name:
:return:
"""
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ret = []
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self.trace_logger.log(e)
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"""
Moved into a function for testability.
"""
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def set_closure_cell(cell, value):
cell.__setstate__((value,))
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try:
ctypes = import_ctypes()
set_closure_cell = ctypes.pythonapi.PyCell_Set
set_closure_cell.argtypes = (ctypes.py_object, ctypes.py_object)
set_closure_cell.restype = ctypes.c_int
except Exception:
# We try best effort to set the cell, but sometimes it's not
# possible. For example on Jython or on GAE.
set_closure_cell = just_warn
return set_closure_cell | python | def make_set_closure_cell():
"""
Moved into a function for testability.
"""
if PYPY: # pragma: no cover
def set_closure_cell(cell, value):
cell.__setstate__((value,))
else:
try:
ctypes = import_ctypes()
set_closure_cell = ctypes.pythonapi.PyCell_Set
set_closure_cell.argtypes = (ctypes.py_object, ctypes.py_object)
set_closure_cell.restype = ctypes.c_int
except Exception:
# We try best effort to set the cell, but sometimes it's not
# possible. For example on Jython or on GAE.
set_closure_cell = just_warn
return set_closure_cell | [
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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:
Moved into a function for testability.
### Response:
def make_set_closure_cell():
"""
Moved into a function for testability.
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if PYPY: # pragma: no cover
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except Exception:
# We try best effort to set the cell, but sometimes it's not
# possible. For example on Jython or on GAE.
set_closure_cell = just_warn
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sander76/aio-powerview-api | aiopvapi/hub.py | Hub.query_firmware | async def query_firmware(self):
"""Query the firmware versions."""
_version = await self.request.get(join_path(self._base_path, "/fwversion"))
_fw = _version.get("firmware")
if _fw:
_main = _fw.get("mainProcessor")
if _main:
self._main_processor_version = self._make_version(_main)
_radio = _fw.get("radio")
if _radio:
self._radio_version = self._make_version(_radio) | python | async def query_firmware(self):
"""Query the firmware versions."""
_version = await self.request.get(join_path(self._base_path, "/fwversion"))
_fw = _version.get("firmware")
if _fw:
_main = _fw.get("mainProcessor")
if _main:
self._main_processor_version = self._make_version(_main)
_radio = _fw.get("radio")
if _radio:
self._radio_version = self._make_version(_radio) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Query the firmware versions.
### Response:
async def query_firmware(self):
"""Query the firmware versions."""
_version = await self.request.get(join_path(self._base_path, "/fwversion"))
_fw = _version.get("firmware")
if _fw:
_main = _fw.get("mainProcessor")
if _main:
self._main_processor_version = self._make_version(_main)
_radio = _fw.get("radio")
if _radio:
self._radio_version = self._make_version(_radio)
<|endoftext|> | [
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siznax/wptools | wptools/wikidata.py | WPToolsWikidata._update_what | def _update_what(self):
"""
set what this thing is! "instance of (P31)"
"""
if 'P31' not in self.data['claims']: # missing Wikidata
msg = ("Note: Wikidata item %s" % self.data['wikibase'],
"missing 'instance of' (P31)")
utils.stderr(" ".join(msg))
return
instance_of = self.data['claims']['P31'][0]
labels = self.data['labels']
if instance_of in labels:
self.data['what'] = labels[instance_of] | python | def _update_what(self):
"""
set what this thing is! "instance of (P31)"
"""
if 'P31' not in self.data['claims']: # missing Wikidata
msg = ("Note: Wikidata item %s" % self.data['wikibase'],
"missing 'instance of' (P31)")
utils.stderr(" ".join(msg))
return
instance_of = self.data['claims']['P31'][0]
labels = self.data['labels']
if instance_of in labels:
self.data['what'] = labels[instance_of] | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
set what this thing is! "instance of (P31)"
### Response:
def _update_what(self):
"""
set what this thing is! "instance of (P31)"
"""
if 'P31' not in self.data['claims']: # missing Wikidata
msg = ("Note: Wikidata item %s" % self.data['wikibase'],
"missing 'instance of' (P31)")
utils.stderr(" ".join(msg))
return
instance_of = self.data['claims']['P31'][0]
labels = self.data['labels']
if instance_of in labels:
self.data['what'] = labels[instance_of]
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brentp/cruzdb | cruzdb/sqlsoup.py | SQLSoup.join | def join(self, left, right, onclause=None, isouter=False,
base=None, **mapper_args):
"""Create an :func:`.expression.join` and map to it.
The class and its mapping are not cached and will
be discarded once dereferenced (as of 0.6.6).
:param left: a mapped class or table object.
:param right: a mapped class or table object.
:param onclause: optional "ON" clause construct..
:param isouter: if True, the join will be an OUTER join.
:param base: a Python class which will be used as the
base for the mapped class. If ``None``, the "base"
argument specified by this :class:`.SQLSoup`
instance's constructor will be used, which defaults to
``object``.
:param mapper_args: Dictionary of arguments which will
be passed directly to :func:`.orm.mapper`.
"""
j = join(left, right, onclause=onclause, isouter=isouter)
return self.map(j, base=base, **mapper_args) | python | def join(self, left, right, onclause=None, isouter=False,
base=None, **mapper_args):
"""Create an :func:`.expression.join` and map to it.
The class and its mapping are not cached and will
be discarded once dereferenced (as of 0.6.6).
:param left: a mapped class or table object.
:param right: a mapped class or table object.
:param onclause: optional "ON" clause construct..
:param isouter: if True, the join will be an OUTER join.
:param base: a Python class which will be used as the
base for the mapped class. If ``None``, the "base"
argument specified by this :class:`.SQLSoup`
instance's constructor will be used, which defaults to
``object``.
:param mapper_args: Dictionary of arguments which will
be passed directly to :func:`.orm.mapper`.
"""
j = join(left, right, onclause=onclause, isouter=isouter)
return self.map(j, base=base, **mapper_args) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Create an :func:`.expression.join` and map to it.
The class and its mapping are not cached and will
be discarded once dereferenced (as of 0.6.6).
:param left: a mapped class or table object.
:param right: a mapped class or table object.
:param onclause: optional "ON" clause construct..
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:param base: a Python class which will be used as the
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:param mapper_args: Dictionary of arguments which will
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### Response:
def join(self, left, right, onclause=None, isouter=False,
base=None, **mapper_args):
"""Create an :func:`.expression.join` and map to it.
The class and its mapping are not cached and will
be discarded once dereferenced (as of 0.6.6).
:param left: a mapped class or table object.
:param right: a mapped class or table object.
:param onclause: optional "ON" clause construct..
:param isouter: if True, the join will be an OUTER join.
:param base: a Python class which will be used as the
base for the mapped class. If ``None``, the "base"
argument specified by this :class:`.SQLSoup`
instance's constructor will be used, which defaults to
``object``.
:param mapper_args: Dictionary of arguments which will
be passed directly to :func:`.orm.mapper`.
"""
j = join(left, right, onclause=onclause, isouter=isouter)
return self.map(j, base=base, **mapper_args)
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Zaeb0s/epoll-socket-server | esockets/socket_server.py | SocketServer._mainthread_accept_clients | def _mainthread_accept_clients(self):
"""Accepts new clients and sends them to the to _handle_accepted within a subthread
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except socket.error:
pass | python | def _mainthread_accept_clients(self):
"""Accepts new clients and sends them to the to _handle_accepted within a subthread
"""
try:
if self._accept_selector.select(timeout=self.block_time):
client = self._server_socket.accept()
logging.info('Client connected: {}'.format(client[1]))
self._threads_limiter.start_thread(target=self._subthread_handle_accepted,
args=(client,))
except socket.error:
pass | [
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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:
Accepts new clients and sends them to the to _handle_accepted within a subthread
### Response:
def _mainthread_accept_clients(self):
"""Accepts new clients and sends them to the to _handle_accepted within a subthread
"""
try:
if self._accept_selector.select(timeout=self.block_time):
client = self._server_socket.accept()
logging.info('Client connected: {}'.format(client[1]))
self._threads_limiter.start_thread(target=self._subthread_handle_accepted,
args=(client,))
except socket.error:
pass
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uogbuji/versa | tools/py/pipeline/core_actions.py | res | def res(arg):
'''
Convert the argument into an IRI ref
'''
def _res(ctx):
_arg = arg(ctx) if callable(arg) else arg
return I(arg)
return _res | python | def res(arg):
'''
Convert the argument into an IRI ref
'''
def _res(ctx):
_arg = arg(ctx) if callable(arg) else arg
return I(arg)
return _res | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Convert the argument into an IRI ref
### Response:
def res(arg):
'''
Convert the argument into an IRI ref
'''
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RJT1990/pyflux | pyflux/families/t.py | t.second_order_score | def second_order_score(y, mean, scale, shape, skewness):
""" GAS t Update term potentially using second-order information - native Python function
Parameters
----------
y : float
datapoint for the time series
mean : float
location parameter for the t distribution
scale : float
scale parameter for the t distribution
shape : float
tail thickness parameter for the t distribution
skewness : float
skewness parameter for the t distribution
Returns
----------
- Adjusted score of the t family
"""
return ((shape+1)/shape)*(y-mean)/(np.power(scale,2) + (np.power(y-mean,2)/shape))/((shape+1)*((np.power(scale,2)*shape) - np.power(y-mean,2))/np.power((np.power(scale,2)*shape) + np.power(y-mean,2),2)) | python | def second_order_score(y, mean, scale, shape, skewness):
""" GAS t Update term potentially using second-order information - native Python function
Parameters
----------
y : float
datapoint for the time series
mean : float
location parameter for the t distribution
scale : float
scale parameter for the t distribution
shape : float
tail thickness parameter for the t distribution
skewness : float
skewness parameter for the t distribution
Returns
----------
- Adjusted score of the t family
"""
return ((shape+1)/shape)*(y-mean)/(np.power(scale,2) + (np.power(y-mean,2)/shape))/((shape+1)*((np.power(scale,2)*shape) - np.power(y-mean,2))/np.power((np.power(scale,2)*shape) + np.power(y-mean,2),2)) | [
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shape : float
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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:
GAS t Update term potentially using second-order information - native Python function
Parameters
----------
y : float
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mean : float
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scale : float
scale parameter for the t distribution
shape : float
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skewness : float
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def second_order_score(y, mean, scale, shape, skewness):
""" GAS t Update term potentially using second-order information - native Python function
Parameters
----------
y : float
datapoint for the time series
mean : float
location parameter for the t distribution
scale : float
scale parameter for the t distribution
shape : float
tail thickness parameter for the t distribution
skewness : float
skewness parameter for the t distribution
Returns
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- Adjusted score of the t family
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return ((shape+1)/shape)*(y-mean)/(np.power(scale,2) + (np.power(y-mean,2)/shape))/((shape+1)*((np.power(scale,2)*shape) - np.power(y-mean,2))/np.power((np.power(scale,2)*shape) + np.power(y-mean,2),2))
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googleapis/google-cloud-python | translate/google/cloud/translate_v3beta1/gapic/translation_service_client.py | TranslationServiceClient.translate_text | def translate_text(
self,
contents,
target_language_code,
mime_type=None,
source_language_code=None,
parent=None,
model=None,
glossary_config=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None,
):
"""
Translates input text and returns translated text.
Example:
>>> from google.cloud import translate_v3beta1
>>>
>>> client = translate_v3beta1.TranslationServiceClient()
>>>
>>> # TODO: Initialize `contents`:
>>> contents = []
>>>
>>> # TODO: Initialize `target_language_code`:
>>> target_language_code = ''
>>>
>>> response = client.translate_text(contents, target_language_code)
Args:
contents (list[str]): Required. The content of the input in string format.
We recommend the total contents to be less than 30k codepoints.
Please use BatchTranslateText for larger text.
target_language_code (str): Required. The BCP-47 language code to use for translation of the input
text, set to one of the language codes listed in Language Support.
mime_type (str): Optional. The format of the source text, for example, "text/html",
"text/plain". If left blank, the MIME type is assumed to be "text/html".
source_language_code (str): Optional. The BCP-47 language code of the input text if
known, for example, "en-US" or "sr-Latn". Supported language codes are
listed in Language Support. If the source language isn't specified, the API
attempts to identify the source language automatically and returns the
the source language within the response.
parent (str): Optional. Only used when making regionalized call.
Format:
projects/{project-id}/locations/{location-id}.
Only custom model/glossary within the same location-id can be used.
Otherwise 400 is returned.
model (str): Optional. The ``model`` type requested for this translation.
The format depends on model type:
1. Custom models:
projects/{project-id}/locations/{location-id}/models/{model-id}.
2. General (built-in) models:
projects/{project-id}/locations/{location-id}/models/general/nmt
projects/{project-id}/locations/{location-id}/models/general/base
For global (non-regionalized) requests, use {location-id} 'global'. For
example, projects/{project-id}/locations/global/models/general/nmt
If missing, the system decides which google base model to use.
glossary_config (Union[dict, ~google.cloud.translate_v3beta1.types.TranslateTextGlossaryConfig]): Optional. Glossary to be applied. The glossary needs to be in the same
region as the model, otherwise an INVALID\_ARGUMENT error is returned.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.translate_v3beta1.types.TranslateTextGlossaryConfig`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.translate_v3beta1.types.TranslateTextResponse` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if "translate_text" not in self._inner_api_calls:
self._inner_api_calls[
"translate_text"
] = google.api_core.gapic_v1.method.wrap_method(
self.transport.translate_text,
default_retry=self._method_configs["TranslateText"].retry,
default_timeout=self._method_configs["TranslateText"].timeout,
client_info=self._client_info,
)
request = translation_service_pb2.TranslateTextRequest(
contents=contents,
target_language_code=target_language_code,
mime_type=mime_type,
source_language_code=source_language_code,
parent=parent,
model=model,
glossary_config=glossary_config,
)
if metadata is None:
metadata = []
metadata = list(metadata)
try:
routing_header = [("parent", parent)]
except AttributeError:
pass
else:
routing_metadata = google.api_core.gapic_v1.routing_header.to_grpc_metadata(
routing_header
)
metadata.append(routing_metadata)
return self._inner_api_calls["translate_text"](
request, retry=retry, timeout=timeout, metadata=metadata
) | python | def translate_text(
self,
contents,
target_language_code,
mime_type=None,
source_language_code=None,
parent=None,
model=None,
glossary_config=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None,
):
"""
Translates input text and returns translated text.
Example:
>>> from google.cloud import translate_v3beta1
>>>
>>> client = translate_v3beta1.TranslationServiceClient()
>>>
>>> # TODO: Initialize `contents`:
>>> contents = []
>>>
>>> # TODO: Initialize `target_language_code`:
>>> target_language_code = ''
>>>
>>> response = client.translate_text(contents, target_language_code)
Args:
contents (list[str]): Required. The content of the input in string format.
We recommend the total contents to be less than 30k codepoints.
Please use BatchTranslateText for larger text.
target_language_code (str): Required. The BCP-47 language code to use for translation of the input
text, set to one of the language codes listed in Language Support.
mime_type (str): Optional. The format of the source text, for example, "text/html",
"text/plain". If left blank, the MIME type is assumed to be "text/html".
source_language_code (str): Optional. The BCP-47 language code of the input text if
known, for example, "en-US" or "sr-Latn". Supported language codes are
listed in Language Support. If the source language isn't specified, the API
attempts to identify the source language automatically and returns the
the source language within the response.
parent (str): Optional. Only used when making regionalized call.
Format:
projects/{project-id}/locations/{location-id}.
Only custom model/glossary within the same location-id can be used.
Otherwise 400 is returned.
model (str): Optional. The ``model`` type requested for this translation.
The format depends on model type:
1. Custom models:
projects/{project-id}/locations/{location-id}/models/{model-id}.
2. General (built-in) models:
projects/{project-id}/locations/{location-id}/models/general/nmt
projects/{project-id}/locations/{location-id}/models/general/base
For global (non-regionalized) requests, use {location-id} 'global'. For
example, projects/{project-id}/locations/global/models/general/nmt
If missing, the system decides which google base model to use.
glossary_config (Union[dict, ~google.cloud.translate_v3beta1.types.TranslateTextGlossaryConfig]): Optional. Glossary to be applied. The glossary needs to be in the same
region as the model, otherwise an INVALID\_ARGUMENT error is returned.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.translate_v3beta1.types.TranslateTextGlossaryConfig`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.translate_v3beta1.types.TranslateTextResponse` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if "translate_text" not in self._inner_api_calls:
self._inner_api_calls[
"translate_text"
] = google.api_core.gapic_v1.method.wrap_method(
self.transport.translate_text,
default_retry=self._method_configs["TranslateText"].retry,
default_timeout=self._method_configs["TranslateText"].timeout,
client_info=self._client_info,
)
request = translation_service_pb2.TranslateTextRequest(
contents=contents,
target_language_code=target_language_code,
mime_type=mime_type,
source_language_code=source_language_code,
parent=parent,
model=model,
glossary_config=glossary_config,
)
if metadata is None:
metadata = []
metadata = list(metadata)
try:
routing_header = [("parent", parent)]
except AttributeError:
pass
else:
routing_metadata = google.api_core.gapic_v1.routing_header.to_grpc_metadata(
routing_header
)
metadata.append(routing_metadata)
return self._inner_api_calls["translate_text"](
request, retry=retry, timeout=timeout, metadata=metadata
) | [
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] | Translates input text and returns translated text.
Example:
>>> from google.cloud import translate_v3beta1
>>>
>>> client = translate_v3beta1.TranslationServiceClient()
>>>
>>> # TODO: Initialize `contents`:
>>> contents = []
>>>
>>> # TODO: Initialize `target_language_code`:
>>> target_language_code = ''
>>>
>>> response = client.translate_text(contents, target_language_code)
Args:
contents (list[str]): Required. The content of the input in string format.
We recommend the total contents to be less than 30k codepoints.
Please use BatchTranslateText for larger text.
target_language_code (str): Required. The BCP-47 language code to use for translation of the input
text, set to one of the language codes listed in Language Support.
mime_type (str): Optional. The format of the source text, for example, "text/html",
"text/plain". If left blank, the MIME type is assumed to be "text/html".
source_language_code (str): Optional. The BCP-47 language code of the input text if
known, for example, "en-US" or "sr-Latn". Supported language codes are
listed in Language Support. If the source language isn't specified, the API
attempts to identify the source language automatically and returns the
the source language within the response.
parent (str): Optional. Only used when making regionalized call.
Format:
projects/{project-id}/locations/{location-id}.
Only custom model/glossary within the same location-id can be used.
Otherwise 400 is returned.
model (str): Optional. The ``model`` type requested for this translation.
The format depends on model type:
1. Custom models:
projects/{project-id}/locations/{location-id}/models/{model-id}.
2. General (built-in) models:
projects/{project-id}/locations/{location-id}/models/general/nmt
projects/{project-id}/locations/{location-id}/models/general/base
For global (non-regionalized) requests, use {location-id} 'global'. For
example, projects/{project-id}/locations/global/models/general/nmt
If missing, the system decides which google base model to use.
glossary_config (Union[dict, ~google.cloud.translate_v3beta1.types.TranslateTextGlossaryConfig]): Optional. Glossary to be applied. The glossary needs to be in the same
region as the model, otherwise an INVALID\_ARGUMENT error is returned.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.translate_v3beta1.types.TranslateTextGlossaryConfig`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.translate_v3beta1.types.TranslateTextResponse` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid. | [
"Translates",
"input",
"text",
"and",
"returns",
"translated",
"text",
"."
] | train | https://github.com/googleapis/google-cloud-python/blob/85e80125a59cb10f8cb105f25ecc099e4b940b50/translate/google/cloud/translate_v3beta1/gapic/translation_service_client.py#L196-L317 | <|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:
Translates input text and returns translated text.
Example:
>>> from google.cloud import translate_v3beta1
>>>
>>> client = translate_v3beta1.TranslationServiceClient()
>>>
>>> # TODO: Initialize `contents`:
>>> contents = []
>>>
>>> # TODO: Initialize `target_language_code`:
>>> target_language_code = ''
>>>
>>> response = client.translate_text(contents, target_language_code)
Args:
contents (list[str]): Required. The content of the input in string format.
We recommend the total contents to be less than 30k codepoints.
Please use BatchTranslateText for larger text.
target_language_code (str): Required. The BCP-47 language code to use for translation of the input
text, set to one of the language codes listed in Language Support.
mime_type (str): Optional. The format of the source text, for example, "text/html",
"text/plain". If left blank, the MIME type is assumed to be "text/html".
source_language_code (str): Optional. The BCP-47 language code of the input text if
known, for example, "en-US" or "sr-Latn". Supported language codes are
listed in Language Support. If the source language isn't specified, the API
attempts to identify the source language automatically and returns the
the source language within the response.
parent (str): Optional. Only used when making regionalized call.
Format:
projects/{project-id}/locations/{location-id}.
Only custom model/glossary within the same location-id can be used.
Otherwise 400 is returned.
model (str): Optional. The ``model`` type requested for this translation.
The format depends on model type:
1. Custom models:
projects/{project-id}/locations/{location-id}/models/{model-id}.
2. General (built-in) models:
projects/{project-id}/locations/{location-id}/models/general/nmt
projects/{project-id}/locations/{location-id}/models/general/base
For global (non-regionalized) requests, use {location-id} 'global'. For
example, projects/{project-id}/locations/global/models/general/nmt
If missing, the system decides which google base model to use.
glossary_config (Union[dict, ~google.cloud.translate_v3beta1.types.TranslateTextGlossaryConfig]): Optional. Glossary to be applied. The glossary needs to be in the same
region as the model, otherwise an INVALID\_ARGUMENT error is returned.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.translate_v3beta1.types.TranslateTextGlossaryConfig`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.translate_v3beta1.types.TranslateTextResponse` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
### Response:
def translate_text(
self,
contents,
target_language_code,
mime_type=None,
source_language_code=None,
parent=None,
model=None,
glossary_config=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None,
):
"""
Translates input text and returns translated text.
Example:
>>> from google.cloud import translate_v3beta1
>>>
>>> client = translate_v3beta1.TranslationServiceClient()
>>>
>>> # TODO: Initialize `contents`:
>>> contents = []
>>>
>>> # TODO: Initialize `target_language_code`:
>>> target_language_code = ''
>>>
>>> response = client.translate_text(contents, target_language_code)
Args:
contents (list[str]): Required. The content of the input in string format.
We recommend the total contents to be less than 30k codepoints.
Please use BatchTranslateText for larger text.
target_language_code (str): Required. The BCP-47 language code to use for translation of the input
text, set to one of the language codes listed in Language Support.
mime_type (str): Optional. The format of the source text, for example, "text/html",
"text/plain". If left blank, the MIME type is assumed to be "text/html".
source_language_code (str): Optional. The BCP-47 language code of the input text if
known, for example, "en-US" or "sr-Latn". Supported language codes are
listed in Language Support. If the source language isn't specified, the API
attempts to identify the source language automatically and returns the
the source language within the response.
parent (str): Optional. Only used when making regionalized call.
Format:
projects/{project-id}/locations/{location-id}.
Only custom model/glossary within the same location-id can be used.
Otherwise 400 is returned.
model (str): Optional. The ``model`` type requested for this translation.
The format depends on model type:
1. Custom models:
projects/{project-id}/locations/{location-id}/models/{model-id}.
2. General (built-in) models:
projects/{project-id}/locations/{location-id}/models/general/nmt
projects/{project-id}/locations/{location-id}/models/general/base
For global (non-regionalized) requests, use {location-id} 'global'. For
example, projects/{project-id}/locations/global/models/general/nmt
If missing, the system decides which google base model to use.
glossary_config (Union[dict, ~google.cloud.translate_v3beta1.types.TranslateTextGlossaryConfig]): Optional. Glossary to be applied. The glossary needs to be in the same
region as the model, otherwise an INVALID\_ARGUMENT error is returned.
If a dict is provided, it must be of the same form as the protobuf
message :class:`~google.cloud.translate_v3beta1.types.TranslateTextGlossaryConfig`
retry (Optional[google.api_core.retry.Retry]): A retry object used
to retry requests. If ``None`` is specified, requests will not
be retried.
timeout (Optional[float]): The amount of time, in seconds, to wait
for the request to complete. Note that if ``retry`` is
specified, the timeout applies to each individual attempt.
metadata (Optional[Sequence[Tuple[str, str]]]): Additional metadata
that is provided to the method.
Returns:
A :class:`~google.cloud.translate_v3beta1.types.TranslateTextResponse` instance.
Raises:
google.api_core.exceptions.GoogleAPICallError: If the request
failed for any reason.
google.api_core.exceptions.RetryError: If the request failed due
to a retryable error and retry attempts failed.
ValueError: If the parameters are invalid.
"""
# Wrap the transport method to add retry and timeout logic.
if "translate_text" not in self._inner_api_calls:
self._inner_api_calls[
"translate_text"
] = google.api_core.gapic_v1.method.wrap_method(
self.transport.translate_text,
default_retry=self._method_configs["TranslateText"].retry,
default_timeout=self._method_configs["TranslateText"].timeout,
client_info=self._client_info,
)
request = translation_service_pb2.TranslateTextRequest(
contents=contents,
target_language_code=target_language_code,
mime_type=mime_type,
source_language_code=source_language_code,
parent=parent,
model=model,
glossary_config=glossary_config,
)
if metadata is None:
metadata = []
metadata = list(metadata)
try:
routing_header = [("parent", parent)]
except AttributeError:
pass
else:
routing_metadata = google.api_core.gapic_v1.routing_header.to_grpc_metadata(
routing_header
)
metadata.append(routing_metadata)
return self._inner_api_calls["translate_text"](
request, retry=retry, timeout=timeout, metadata=metadata
)
<|endoftext|> | [
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vmware/pyvmomi | pyVim/connect.py | VimSessionOrientedStub.makeUserLoginMethod | def makeUserLoginMethod(username, password, locale=None):
'''Return a function that will call the vim.SessionManager.Login() method
with the given parameters. The result of this function can be passed as
the "loginMethod" to a SessionOrientedStub constructor.'''
def _doLogin(soapStub):
si = vim.ServiceInstance("ServiceInstance", soapStub)
sm = si.content.sessionManager
if not sm.currentSession:
si.content.sessionManager.Login(username, password, locale)
return _doLogin | python | def makeUserLoginMethod(username, password, locale=None):
'''Return a function that will call the vim.SessionManager.Login() method
with the given parameters. The result of this function can be passed as
the "loginMethod" to a SessionOrientedStub constructor.'''
def _doLogin(soapStub):
si = vim.ServiceInstance("ServiceInstance", soapStub)
sm = si.content.sessionManager
if not sm.currentSession:
si.content.sessionManager.Login(username, password, locale)
return _doLogin | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Return a function that will call the vim.SessionManager.Login() method
with the given parameters. The result of this function can be passed as
the "loginMethod" to a SessionOrientedStub constructor.
### Response:
def makeUserLoginMethod(username, password, locale=None):
'''Return a function that will call the vim.SessionManager.Login() method
with the given parameters. The result of this function can be passed as
the "loginMethod" to a SessionOrientedStub constructor.'''
def _doLogin(soapStub):
si = vim.ServiceInstance("ServiceInstance", soapStub)
sm = si.content.sessionManager
if not sm.currentSession:
si.content.sessionManager.Login(username, password, locale)
return _doLogin
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apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | _Recommender.recommend | def recommend(self, users=None, k=10, exclude=None, items=None,
new_observation_data=None, new_user_data=None, new_item_data=None,
exclude_known=True, diversity=0, random_seed=None,
verbose=True):
"""
Recommend the ``k`` highest scored items for each user.
Parameters
----------
users : SArray, SFrame, or list, optional
Users or observation queries for which to make recommendations.
For list, SArray, and single-column inputs, this is simply a set
of user IDs. By default, recommendations are returned for all
users present when the model was trained. However, if the
recommender model was created with additional features in the
``observation_data`` SFrame, then a corresponding SFrame of
observation queries -- observation data without item or target
columns -- can be passed to this method. For example, a model
trained with user ID, item ID, time, and rating columns may be
queried using an SFrame with user ID and time columns. In this
case, the user ID column must be present, and all column names
should match those in the ``observation_data`` SFrame passed to
``create.``
k : int, optional
The number of recommendations to generate for each user.
items : SArray, SFrame, or list, optional
Restricts the items from which recommendations can be made. If
``items`` is an SArray, list, or SFrame with a single column,
only items from the given set will be recommended. This can be
used, for example, to restrict the recommendations to items
within a particular category or genre. If ``items`` is an
SFrame with user ID and item ID columns, then the item
restriction is specialized to each user. For example, if
``items`` contains 3 rows with user U1 -- (U1, I1), (U1, I2),
and (U1, I3) -- then the recommendations for user U1 are
chosen from items I1, I2, and I3. By default, recommendations
are made from all items present when the model was trained.
new_observation_data : SFrame, optional
``new_observation_data`` gives additional observation data
to the model, which may be used by the models to improve
score and recommendation accuracy. Must be in the same
format as the observation data passed to ``create``. How
this data is used varies by model.
new_user_data : SFrame, optional
``new_user_data`` may give additional user data to the
model. If present, scoring is done with reference to this
new information. If there is any overlap with the side
information present at training time, then this new side
data is preferred. Must be in the same format as the user
data passed to ``create``.
new_item_data : SFrame, optional
``new_item_data`` may give additional item data to the
model. If present, scoring is done with reference to this
new information. If there is any overlap with the side
information present at training time, then this new side
data is preferred. Must be in the same format as the item
data passed to ``create``.
exclude : SFrame, optional
An :class:`~turicreate.SFrame` of user / item pairs. The
column names must be equal to the user and item columns of
the main data, and it provides the model with user/item
pairs to exclude from the recommendations. These
user-item-pairs are always excluded from the predictions,
even if exclude_known is False.
exclude_known : bool, optional
By default, all user-item interactions previously seen in
the training data, or in any new data provided using
new_observation_data.., are excluded from the
recommendations. Passing in ``exclude_known = False``
overrides this behavior.
diversity : non-negative float, optional
If given, then the recommend function attempts chooses a set
of `k` items that are both highly scored and different from
other items in that set. It does this by first retrieving
``k*(1+diversity)`` recommended items, then randomly
choosing a diverse set from these items. Suggested values
for diversity are between 1 and 3.
random_seed : int, optional
If diversity is larger than 0, then some randomness is used;
this controls the random seed to use for randomization. If
None, will be different each time.
verbose : bool, optional
If True, print the progress of generating recommendation.
Returns
-------
out : SFrame
A SFrame with the top ranked items for each user. The
columns are: ``user_id``, ``item_id``, *score*,
and *rank*, where ``user_id`` and ``item_id``
match the user and item column names specified at training
time. The rank column is between 1 and ``k`` and gives
the relative score of that item. The value of score
depends on the method used for recommendations.
See Also
--------
recommend_from_interactions
predict
evaluate
"""
from turicreate._cython.cy_server import QuietProgress
assert type(k) == int
column_types = self._get_data_schema()
user_id = self.user_id
item_id = self.item_id
user_type = column_types[user_id]
item_type = column_types[item_id]
__null_sframe = _SFrame()
if users is None:
users = __null_sframe
if exclude is None:
exclude = __null_sframe
if items is None:
items = __null_sframe
if new_observation_data is None:
new_observation_data = __null_sframe
if new_user_data is None:
new_user_data = __null_sframe
if new_item_data is None:
new_item_data = __null_sframe
if isinstance(users, list) or (_HAS_NUMPY and isinstance(users, _numpy.ndarray)):
users = _SArray(users)
# allow to take a list of dictionaries of the form [{'user_id':1,'time':10}] etc.
if users.dtype == dict:
users = users.unpack(column_name_prefix='')
if isinstance(users, _SArray):
users = _SFrame({user_id: users})
if isinstance(items, list) or (_HAS_NUMPY and isinstance(items, _numpy.ndarray)):
items = _SArray(items, dtype = item_type)
if isinstance(items, _SArray):
items = _SFrame({item_id: items})
# Check type of incoming data.
def check_type(arg, arg_name, required_type, allowed_types):
if not isinstance(arg, required_type):
raise TypeError("Parameter " + arg_name + " must be of type(s) "
+ (", ".join(allowed_types))
+ "; Type '" + str(type(arg)) + "' not recognized.")
check_type(users, "users", _SFrame, ["SArray", "list", "SFrame", "numpy.ndarray"])
check_type(exclude, "exclude", _SFrame, ["SFrame"])
check_type(items, "items", _SFrame, ["SFrame", "SArray", "list", "numpy.ndarray"])
check_type(new_observation_data, "new_observation_data", _SFrame, ["SFrame"])
check_type(new_user_data, "new_user_data", _SFrame, ["SFrame"])
check_type(new_item_data, "new_item_data", _SFrame, ["SFrame"])
# See if we are in the situation where there are no users
# originally. In this case, the default type of the user
# column is string, so we have to be mindful of that when
# making recommendations and possibly cast it to string if
# needed.
# The only case where we need to deal with the user id is when
# it's used to link with rated items in new_observation_data,
# thus handle that case explicitly and error out in others.
cast_user_to_string_type = False
if self.num_users == 0:
cast_user_to_string_type = True
if users.num_rows() != 0:
# In this case, the user column has actually been set to a
# string type, so we need to make sure that we cast
# everything back and forth to that to preserve type.
if new_observation_data.num_rows() == 0:
raise ValueError("When users are not specified with the model, "
"new_observation_data must be set in order to make recommendations.")
new_observation_data[user_id] = new_observation_data[user_id].astype(user_type)
else:
print("WARNING: No users specified to model at creation time, so "
"calling recommend() for all users returns empty SFrame.")
# Cast to the appropriate type if necessary.
if users.num_rows() != 0:
try:
user_column = users[user_id]
except RuntimeError:
raise _ToolkitError("User column '%s' not present in input user data." % user_id)
if cast_user_to_string_type:
assert new_observation_data.num_rows() != 0
original_user_type = user_column.dtype
users[user_id] = user_column.astype(str)
user_type=str
elif user_column.dtype != user_type:
users[user_id] = user_column.astype(user_type)
# Cast user specified in exclude to the appropriate type if necessary.
if user_id in exclude.column_names() and exclude[user_id].dtype!=user_type:
exclude[user_id] = exclude[user_id].astype(user_type)
try:
diversity = float(diversity)
except Exception:
raise TypeError("Parameter diversity must be a floating point value equal to or larger than 0.")
if diversity < 0:
raise TypeError("Parameter diversity must be a floating point value equal to or larger than 0.")
if random_seed is None:
random_seed = hash("%.20f" % _time.time())
else:
try:
random_seed = int(random_seed)
except TypeError:
raise TypeError("random_seed must be integer.")
opt = {'model': self.__proxy__,
'query': users,
'top_k': k,
'exclude': exclude,
'restrictions': items,
'new_data': new_observation_data,
'new_user_data': new_user_data,
'new_item_data': new_item_data,
'exclude_known': exclude_known,
'diversity' : diversity,
'random_seed' : random_seed
}
with QuietProgress(verbose):
recs = self.__proxy__.recommend(users, exclude, items, new_observation_data, new_user_data,
new_item_data, exclude_known, k, diversity, random_seed)
if cast_user_to_string_type:
recs[user_id] = recs[user_id].astype(original_user_type)
return recs | python | def recommend(self, users=None, k=10, exclude=None, items=None,
new_observation_data=None, new_user_data=None, new_item_data=None,
exclude_known=True, diversity=0, random_seed=None,
verbose=True):
"""
Recommend the ``k`` highest scored items for each user.
Parameters
----------
users : SArray, SFrame, or list, optional
Users or observation queries for which to make recommendations.
For list, SArray, and single-column inputs, this is simply a set
of user IDs. By default, recommendations are returned for all
users present when the model was trained. However, if the
recommender model was created with additional features in the
``observation_data`` SFrame, then a corresponding SFrame of
observation queries -- observation data without item or target
columns -- can be passed to this method. For example, a model
trained with user ID, item ID, time, and rating columns may be
queried using an SFrame with user ID and time columns. In this
case, the user ID column must be present, and all column names
should match those in the ``observation_data`` SFrame passed to
``create.``
k : int, optional
The number of recommendations to generate for each user.
items : SArray, SFrame, or list, optional
Restricts the items from which recommendations can be made. If
``items`` is an SArray, list, or SFrame with a single column,
only items from the given set will be recommended. This can be
used, for example, to restrict the recommendations to items
within a particular category or genre. If ``items`` is an
SFrame with user ID and item ID columns, then the item
restriction is specialized to each user. For example, if
``items`` contains 3 rows with user U1 -- (U1, I1), (U1, I2),
and (U1, I3) -- then the recommendations for user U1 are
chosen from items I1, I2, and I3. By default, recommendations
are made from all items present when the model was trained.
new_observation_data : SFrame, optional
``new_observation_data`` gives additional observation data
to the model, which may be used by the models to improve
score and recommendation accuracy. Must be in the same
format as the observation data passed to ``create``. How
this data is used varies by model.
new_user_data : SFrame, optional
``new_user_data`` may give additional user data to the
model. If present, scoring is done with reference to this
new information. If there is any overlap with the side
information present at training time, then this new side
data is preferred. Must be in the same format as the user
data passed to ``create``.
new_item_data : SFrame, optional
``new_item_data`` may give additional item data to the
model. If present, scoring is done with reference to this
new information. If there is any overlap with the side
information present at training time, then this new side
data is preferred. Must be in the same format as the item
data passed to ``create``.
exclude : SFrame, optional
An :class:`~turicreate.SFrame` of user / item pairs. The
column names must be equal to the user and item columns of
the main data, and it provides the model with user/item
pairs to exclude from the recommendations. These
user-item-pairs are always excluded from the predictions,
even if exclude_known is False.
exclude_known : bool, optional
By default, all user-item interactions previously seen in
the training data, or in any new data provided using
new_observation_data.., are excluded from the
recommendations. Passing in ``exclude_known = False``
overrides this behavior.
diversity : non-negative float, optional
If given, then the recommend function attempts chooses a set
of `k` items that are both highly scored and different from
other items in that set. It does this by first retrieving
``k*(1+diversity)`` recommended items, then randomly
choosing a diverse set from these items. Suggested values
for diversity are between 1 and 3.
random_seed : int, optional
If diversity is larger than 0, then some randomness is used;
this controls the random seed to use for randomization. If
None, will be different each time.
verbose : bool, optional
If True, print the progress of generating recommendation.
Returns
-------
out : SFrame
A SFrame with the top ranked items for each user. The
columns are: ``user_id``, ``item_id``, *score*,
and *rank*, where ``user_id`` and ``item_id``
match the user and item column names specified at training
time. The rank column is between 1 and ``k`` and gives
the relative score of that item. The value of score
depends on the method used for recommendations.
See Also
--------
recommend_from_interactions
predict
evaluate
"""
from turicreate._cython.cy_server import QuietProgress
assert type(k) == int
column_types = self._get_data_schema()
user_id = self.user_id
item_id = self.item_id
user_type = column_types[user_id]
item_type = column_types[item_id]
__null_sframe = _SFrame()
if users is None:
users = __null_sframe
if exclude is None:
exclude = __null_sframe
if items is None:
items = __null_sframe
if new_observation_data is None:
new_observation_data = __null_sframe
if new_user_data is None:
new_user_data = __null_sframe
if new_item_data is None:
new_item_data = __null_sframe
if isinstance(users, list) or (_HAS_NUMPY and isinstance(users, _numpy.ndarray)):
users = _SArray(users)
# allow to take a list of dictionaries of the form [{'user_id':1,'time':10}] etc.
if users.dtype == dict:
users = users.unpack(column_name_prefix='')
if isinstance(users, _SArray):
users = _SFrame({user_id: users})
if isinstance(items, list) or (_HAS_NUMPY and isinstance(items, _numpy.ndarray)):
items = _SArray(items, dtype = item_type)
if isinstance(items, _SArray):
items = _SFrame({item_id: items})
# Check type of incoming data.
def check_type(arg, arg_name, required_type, allowed_types):
if not isinstance(arg, required_type):
raise TypeError("Parameter " + arg_name + " must be of type(s) "
+ (", ".join(allowed_types))
+ "; Type '" + str(type(arg)) + "' not recognized.")
check_type(users, "users", _SFrame, ["SArray", "list", "SFrame", "numpy.ndarray"])
check_type(exclude, "exclude", _SFrame, ["SFrame"])
check_type(items, "items", _SFrame, ["SFrame", "SArray", "list", "numpy.ndarray"])
check_type(new_observation_data, "new_observation_data", _SFrame, ["SFrame"])
check_type(new_user_data, "new_user_data", _SFrame, ["SFrame"])
check_type(new_item_data, "new_item_data", _SFrame, ["SFrame"])
# See if we are in the situation where there are no users
# originally. In this case, the default type of the user
# column is string, so we have to be mindful of that when
# making recommendations and possibly cast it to string if
# needed.
# The only case where we need to deal with the user id is when
# it's used to link with rated items in new_observation_data,
# thus handle that case explicitly and error out in others.
cast_user_to_string_type = False
if self.num_users == 0:
cast_user_to_string_type = True
if users.num_rows() != 0:
# In this case, the user column has actually been set to a
# string type, so we need to make sure that we cast
# everything back and forth to that to preserve type.
if new_observation_data.num_rows() == 0:
raise ValueError("When users are not specified with the model, "
"new_observation_data must be set in order to make recommendations.")
new_observation_data[user_id] = new_observation_data[user_id].astype(user_type)
else:
print("WARNING: No users specified to model at creation time, so "
"calling recommend() for all users returns empty SFrame.")
# Cast to the appropriate type if necessary.
if users.num_rows() != 0:
try:
user_column = users[user_id]
except RuntimeError:
raise _ToolkitError("User column '%s' not present in input user data." % user_id)
if cast_user_to_string_type:
assert new_observation_data.num_rows() != 0
original_user_type = user_column.dtype
users[user_id] = user_column.astype(str)
user_type=str
elif user_column.dtype != user_type:
users[user_id] = user_column.astype(user_type)
# Cast user specified in exclude to the appropriate type if necessary.
if user_id in exclude.column_names() and exclude[user_id].dtype!=user_type:
exclude[user_id] = exclude[user_id].astype(user_type)
try:
diversity = float(diversity)
except Exception:
raise TypeError("Parameter diversity must be a floating point value equal to or larger than 0.")
if diversity < 0:
raise TypeError("Parameter diversity must be a floating point value equal to or larger than 0.")
if random_seed is None:
random_seed = hash("%.20f" % _time.time())
else:
try:
random_seed = int(random_seed)
except TypeError:
raise TypeError("random_seed must be integer.")
opt = {'model': self.__proxy__,
'query': users,
'top_k': k,
'exclude': exclude,
'restrictions': items,
'new_data': new_observation_data,
'new_user_data': new_user_data,
'new_item_data': new_item_data,
'exclude_known': exclude_known,
'diversity' : diversity,
'random_seed' : random_seed
}
with QuietProgress(verbose):
recs = self.__proxy__.recommend(users, exclude, items, new_observation_data, new_user_data,
new_item_data, exclude_known, k, diversity, random_seed)
if cast_user_to_string_type:
recs[user_id] = recs[user_id].astype(original_user_type)
return recs | [
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] | Recommend the ``k`` highest scored items for each user.
Parameters
----------
users : SArray, SFrame, or list, optional
Users or observation queries for which to make recommendations.
For list, SArray, and single-column inputs, this is simply a set
of user IDs. By default, recommendations are returned for all
users present when the model was trained. However, if the
recommender model was created with additional features in the
``observation_data`` SFrame, then a corresponding SFrame of
observation queries -- observation data without item or target
columns -- can be passed to this method. For example, a model
trained with user ID, item ID, time, and rating columns may be
queried using an SFrame with user ID and time columns. In this
case, the user ID column must be present, and all column names
should match those in the ``observation_data`` SFrame passed to
``create.``
k : int, optional
The number of recommendations to generate for each user.
items : SArray, SFrame, or list, optional
Restricts the items from which recommendations can be made. If
``items`` is an SArray, list, or SFrame with a single column,
only items from the given set will be recommended. This can be
used, for example, to restrict the recommendations to items
within a particular category or genre. If ``items`` is an
SFrame with user ID and item ID columns, then the item
restriction is specialized to each user. For example, if
``items`` contains 3 rows with user U1 -- (U1, I1), (U1, I2),
and (U1, I3) -- then the recommendations for user U1 are
chosen from items I1, I2, and I3. By default, recommendations
are made from all items present when the model was trained.
new_observation_data : SFrame, optional
``new_observation_data`` gives additional observation data
to the model, which may be used by the models to improve
score and recommendation accuracy. Must be in the same
format as the observation data passed to ``create``. How
this data is used varies by model.
new_user_data : SFrame, optional
``new_user_data`` may give additional user data to the
model. If present, scoring is done with reference to this
new information. If there is any overlap with the side
information present at training time, then this new side
data is preferred. Must be in the same format as the user
data passed to ``create``.
new_item_data : SFrame, optional
``new_item_data`` may give additional item data to the
model. If present, scoring is done with reference to this
new information. If there is any overlap with the side
information present at training time, then this new side
data is preferred. Must be in the same format as the item
data passed to ``create``.
exclude : SFrame, optional
An :class:`~turicreate.SFrame` of user / item pairs. The
column names must be equal to the user and item columns of
the main data, and it provides the model with user/item
pairs to exclude from the recommendations. These
user-item-pairs are always excluded from the predictions,
even if exclude_known is False.
exclude_known : bool, optional
By default, all user-item interactions previously seen in
the training data, or in any new data provided using
new_observation_data.., are excluded from the
recommendations. Passing in ``exclude_known = False``
overrides this behavior.
diversity : non-negative float, optional
If given, then the recommend function attempts chooses a set
of `k` items that are both highly scored and different from
other items in that set. It does this by first retrieving
``k*(1+diversity)`` recommended items, then randomly
choosing a diverse set from these items. Suggested values
for diversity are between 1 and 3.
random_seed : int, optional
If diversity is larger than 0, then some randomness is used;
this controls the random seed to use for randomization. If
None, will be different each time.
verbose : bool, optional
If True, print the progress of generating recommendation.
Returns
-------
out : SFrame
A SFrame with the top ranked items for each user. The
columns are: ``user_id``, ``item_id``, *score*,
and *rank*, where ``user_id`` and ``item_id``
match the user and item column names specified at training
time. The rank column is between 1 and ``k`` and gives
the relative score of that item. The value of score
depends on the method used for recommendations.
See Also
--------
recommend_from_interactions
predict
evaluate | [
"Recommend",
"the",
"k",
"highest",
"scored",
"items",
"for",
"each",
"user",
"."
] | train | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/src/unity/python/turicreate/toolkits/recommender/util.py#L1056-L1308 | <|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:
Recommend the ``k`` highest scored items for each user.
Parameters
----------
users : SArray, SFrame, or list, optional
Users or observation queries for which to make recommendations.
For list, SArray, and single-column inputs, this is simply a set
of user IDs. By default, recommendations are returned for all
users present when the model was trained. However, if the
recommender model was created with additional features in the
``observation_data`` SFrame, then a corresponding SFrame of
observation queries -- observation data without item or target
columns -- can be passed to this method. For example, a model
trained with user ID, item ID, time, and rating columns may be
queried using an SFrame with user ID and time columns. In this
case, the user ID column must be present, and all column names
should match those in the ``observation_data`` SFrame passed to
``create.``
k : int, optional
The number of recommendations to generate for each user.
items : SArray, SFrame, or list, optional
Restricts the items from which recommendations can be made. If
``items`` is an SArray, list, or SFrame with a single column,
only items from the given set will be recommended. This can be
used, for example, to restrict the recommendations to items
within a particular category or genre. If ``items`` is an
SFrame with user ID and item ID columns, then the item
restriction is specialized to each user. For example, if
``items`` contains 3 rows with user U1 -- (U1, I1), (U1, I2),
and (U1, I3) -- then the recommendations for user U1 are
chosen from items I1, I2, and I3. By default, recommendations
are made from all items present when the model was trained.
new_observation_data : SFrame, optional
``new_observation_data`` gives additional observation data
to the model, which may be used by the models to improve
score and recommendation accuracy. Must be in the same
format as the observation data passed to ``create``. How
this data is used varies by model.
new_user_data : SFrame, optional
``new_user_data`` may give additional user data to the
model. If present, scoring is done with reference to this
new information. If there is any overlap with the side
information present at training time, then this new side
data is preferred. Must be in the same format as the user
data passed to ``create``.
new_item_data : SFrame, optional
``new_item_data`` may give additional item data to the
model. If present, scoring is done with reference to this
new information. If there is any overlap with the side
information present at training time, then this new side
data is preferred. Must be in the same format as the item
data passed to ``create``.
exclude : SFrame, optional
An :class:`~turicreate.SFrame` of user / item pairs. The
column names must be equal to the user and item columns of
the main data, and it provides the model with user/item
pairs to exclude from the recommendations. These
user-item-pairs are always excluded from the predictions,
even if exclude_known is False.
exclude_known : bool, optional
By default, all user-item interactions previously seen in
the training data, or in any new data provided using
new_observation_data.., are excluded from the
recommendations. Passing in ``exclude_known = False``
overrides this behavior.
diversity : non-negative float, optional
If given, then the recommend function attempts chooses a set
of `k` items that are both highly scored and different from
other items in that set. It does this by first retrieving
``k*(1+diversity)`` recommended items, then randomly
choosing a diverse set from these items. Suggested values
for diversity are between 1 and 3.
random_seed : int, optional
If diversity is larger than 0, then some randomness is used;
this controls the random seed to use for randomization. If
None, will be different each time.
verbose : bool, optional
If True, print the progress of generating recommendation.
Returns
-------
out : SFrame
A SFrame with the top ranked items for each user. The
columns are: ``user_id``, ``item_id``, *score*,
and *rank*, where ``user_id`` and ``item_id``
match the user and item column names specified at training
time. The rank column is between 1 and ``k`` and gives
the relative score of that item. The value of score
depends on the method used for recommendations.
See Also
--------
recommend_from_interactions
predict
evaluate
### Response:
def recommend(self, users=None, k=10, exclude=None, items=None,
new_observation_data=None, new_user_data=None, new_item_data=None,
exclude_known=True, diversity=0, random_seed=None,
verbose=True):
"""
Recommend the ``k`` highest scored items for each user.
Parameters
----------
users : SArray, SFrame, or list, optional
Users or observation queries for which to make recommendations.
For list, SArray, and single-column inputs, this is simply a set
of user IDs. By default, recommendations are returned for all
users present when the model was trained. However, if the
recommender model was created with additional features in the
``observation_data`` SFrame, then a corresponding SFrame of
observation queries -- observation data without item or target
columns -- can be passed to this method. For example, a model
trained with user ID, item ID, time, and rating columns may be
queried using an SFrame with user ID and time columns. In this
case, the user ID column must be present, and all column names
should match those in the ``observation_data`` SFrame passed to
``create.``
k : int, optional
The number of recommendations to generate for each user.
items : SArray, SFrame, or list, optional
Restricts the items from which recommendations can be made. If
``items`` is an SArray, list, or SFrame with a single column,
only items from the given set will be recommended. This can be
used, for example, to restrict the recommendations to items
within a particular category or genre. If ``items`` is an
SFrame with user ID and item ID columns, then the item
restriction is specialized to each user. For example, if
``items`` contains 3 rows with user U1 -- (U1, I1), (U1, I2),
and (U1, I3) -- then the recommendations for user U1 are
chosen from items I1, I2, and I3. By default, recommendations
are made from all items present when the model was trained.
new_observation_data : SFrame, optional
``new_observation_data`` gives additional observation data
to the model, which may be used by the models to improve
score and recommendation accuracy. Must be in the same
format as the observation data passed to ``create``. How
this data is used varies by model.
new_user_data : SFrame, optional
``new_user_data`` may give additional user data to the
model. If present, scoring is done with reference to this
new information. If there is any overlap with the side
information present at training time, then this new side
data is preferred. Must be in the same format as the user
data passed to ``create``.
new_item_data : SFrame, optional
``new_item_data`` may give additional item data to the
model. If present, scoring is done with reference to this
new information. If there is any overlap with the side
information present at training time, then this new side
data is preferred. Must be in the same format as the item
data passed to ``create``.
exclude : SFrame, optional
An :class:`~turicreate.SFrame` of user / item pairs. The
column names must be equal to the user and item columns of
the main data, and it provides the model with user/item
pairs to exclude from the recommendations. These
user-item-pairs are always excluded from the predictions,
even if exclude_known is False.
exclude_known : bool, optional
By default, all user-item interactions previously seen in
the training data, or in any new data provided using
new_observation_data.., are excluded from the
recommendations. Passing in ``exclude_known = False``
overrides this behavior.
diversity : non-negative float, optional
If given, then the recommend function attempts chooses a set
of `k` items that are both highly scored and different from
other items in that set. It does this by first retrieving
``k*(1+diversity)`` recommended items, then randomly
choosing a diverse set from these items. Suggested values
for diversity are between 1 and 3.
random_seed : int, optional
If diversity is larger than 0, then some randomness is used;
this controls the random seed to use for randomization. If
None, will be different each time.
verbose : bool, optional
If True, print the progress of generating recommendation.
Returns
-------
out : SFrame
A SFrame with the top ranked items for each user. The
columns are: ``user_id``, ``item_id``, *score*,
and *rank*, where ``user_id`` and ``item_id``
match the user and item column names specified at training
time. The rank column is between 1 and ``k`` and gives
the relative score of that item. The value of score
depends on the method used for recommendations.
See Also
--------
recommend_from_interactions
predict
evaluate
"""
from turicreate._cython.cy_server import QuietProgress
assert type(k) == int
column_types = self._get_data_schema()
user_id = self.user_id
item_id = self.item_id
user_type = column_types[user_id]
item_type = column_types[item_id]
__null_sframe = _SFrame()
if users is None:
users = __null_sframe
if exclude is None:
exclude = __null_sframe
if items is None:
items = __null_sframe
if new_observation_data is None:
new_observation_data = __null_sframe
if new_user_data is None:
new_user_data = __null_sframe
if new_item_data is None:
new_item_data = __null_sframe
if isinstance(users, list) or (_HAS_NUMPY and isinstance(users, _numpy.ndarray)):
users = _SArray(users)
# allow to take a list of dictionaries of the form [{'user_id':1,'time':10}] etc.
if users.dtype == dict:
users = users.unpack(column_name_prefix='')
if isinstance(users, _SArray):
users = _SFrame({user_id: users})
if isinstance(items, list) or (_HAS_NUMPY and isinstance(items, _numpy.ndarray)):
items = _SArray(items, dtype = item_type)
if isinstance(items, _SArray):
items = _SFrame({item_id: items})
# Check type of incoming data.
def check_type(arg, arg_name, required_type, allowed_types):
if not isinstance(arg, required_type):
raise TypeError("Parameter " + arg_name + " must be of type(s) "
+ (", ".join(allowed_types))
+ "; Type '" + str(type(arg)) + "' not recognized.")
check_type(users, "users", _SFrame, ["SArray", "list", "SFrame", "numpy.ndarray"])
check_type(exclude, "exclude", _SFrame, ["SFrame"])
check_type(items, "items", _SFrame, ["SFrame", "SArray", "list", "numpy.ndarray"])
check_type(new_observation_data, "new_observation_data", _SFrame, ["SFrame"])
check_type(new_user_data, "new_user_data", _SFrame, ["SFrame"])
check_type(new_item_data, "new_item_data", _SFrame, ["SFrame"])
# See if we are in the situation where there are no users
# originally. In this case, the default type of the user
# column is string, so we have to be mindful of that when
# making recommendations and possibly cast it to string if
# needed.
# The only case where we need to deal with the user id is when
# it's used to link with rated items in new_observation_data,
# thus handle that case explicitly and error out in others.
cast_user_to_string_type = False
if self.num_users == 0:
cast_user_to_string_type = True
if users.num_rows() != 0:
# In this case, the user column has actually been set to a
# string type, so we need to make sure that we cast
# everything back and forth to that to preserve type.
if new_observation_data.num_rows() == 0:
raise ValueError("When users are not specified with the model, "
"new_observation_data must be set in order to make recommendations.")
new_observation_data[user_id] = new_observation_data[user_id].astype(user_type)
else:
print("WARNING: No users specified to model at creation time, so "
"calling recommend() for all users returns empty SFrame.")
# Cast to the appropriate type if necessary.
if users.num_rows() != 0:
try:
user_column = users[user_id]
except RuntimeError:
raise _ToolkitError("User column '%s' not present in input user data." % user_id)
if cast_user_to_string_type:
assert new_observation_data.num_rows() != 0
original_user_type = user_column.dtype
users[user_id] = user_column.astype(str)
user_type=str
elif user_column.dtype != user_type:
users[user_id] = user_column.astype(user_type)
# Cast user specified in exclude to the appropriate type if necessary.
if user_id in exclude.column_names() and exclude[user_id].dtype!=user_type:
exclude[user_id] = exclude[user_id].astype(user_type)
try:
diversity = float(diversity)
except Exception:
raise TypeError("Parameter diversity must be a floating point value equal to or larger than 0.")
if diversity < 0:
raise TypeError("Parameter diversity must be a floating point value equal to or larger than 0.")
if random_seed is None:
random_seed = hash("%.20f" % _time.time())
else:
try:
random_seed = int(random_seed)
except TypeError:
raise TypeError("random_seed must be integer.")
opt = {'model': self.__proxy__,
'query': users,
'top_k': k,
'exclude': exclude,
'restrictions': items,
'new_data': new_observation_data,
'new_user_data': new_user_data,
'new_item_data': new_item_data,
'exclude_known': exclude_known,
'diversity' : diversity,
'random_seed' : random_seed
}
with QuietProgress(verbose):
recs = self.__proxy__.recommend(users, exclude, items, new_observation_data, new_user_data,
new_item_data, exclude_known, k, diversity, random_seed)
if cast_user_to_string_type:
recs[user_id] = recs[user_id].astype(original_user_type)
return recs
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10gen/mongo-orchestration | mongo_orchestration/servers.py | Servers.remove | def remove(self, server_id):
"""remove server and data stuff
Args:
server_id - server identity
"""
server = self._storage.pop(server_id)
server.stop()
server.cleanup() | python | def remove(self, server_id):
"""remove server and data stuff
Args:
server_id - server identity
"""
server = self._storage.pop(server_id)
server.stop()
server.cleanup() | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
remove server and data stuff
Args:
server_id - server identity
### Response:
def remove(self, server_id):
"""remove server and data stuff
Args:
server_id - server identity
"""
server = self._storage.pop(server_id)
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server.cleanup()
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inasafe/inasafe | safe_extras/pydispatch/dispatcher.py | _removeOldBackRefs | def _removeOldBackRefs(senderkey, signal, receiver, receivers):
"""Kill old sendersBack references from receiver
This guards against multiple registration of the same
receiver for a given signal and sender leaking memory
as old back reference records build up.
Also removes old receiver instance from receivers
"""
try:
index = receivers.index(receiver)
# need to scan back references here and remove senderkey
except ValueError:
return False
else:
oldReceiver = receivers[index]
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signals = connections.get(signal)
if signals is not None:
for sig,recs in connections.get(signal,{}).items():
if sig != signal:
for rec in recs:
if rec is oldReceiver:
found = 1
break
if not found:
_killBackref( oldReceiver, senderkey )
return True
return False | python | def _removeOldBackRefs(senderkey, signal, receiver, receivers):
"""Kill old sendersBack references from receiver
This guards against multiple registration of the same
receiver for a given signal and sender leaking memory
as old back reference records build up.
Also removes old receiver instance from receivers
"""
try:
index = receivers.index(receiver)
# need to scan back references here and remove senderkey
except ValueError:
return False
else:
oldReceiver = receivers[index]
del receivers[index]
found = 0
signals = connections.get(signal)
if signals is not None:
for sig,recs in connections.get(signal,{}).items():
if sig != signal:
for rec in recs:
if rec is oldReceiver:
found = 1
break
if not found:
_killBackref( oldReceiver, senderkey )
return True
return 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:
Kill old sendersBack references from receiver
This guards against multiple registration of the same
receiver for a given signal and sender leaking memory
as old back reference records build up.
Also removes old receiver instance from receivers
### Response:
def _removeOldBackRefs(senderkey, signal, receiver, receivers):
"""Kill old sendersBack references from receiver
This guards against multiple registration of the same
receiver for a given signal and sender leaking memory
as old back reference records build up.
Also removes old receiver instance from receivers
"""
try:
index = receivers.index(receiver)
# need to scan back references here and remove senderkey
except ValueError:
return False
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for rec in recs:
if rec is oldReceiver:
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if not found:
_killBackref( oldReceiver, senderkey )
return True
return False
<|endoftext|> | [
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JukeboxPipeline/jukebox-core | src/jukeboxcore/release.py | Release.delete_releasefile | def delete_releasefile(self, release):
"""Delete the releasefile of the given release
This is inteded to be used in a action unit.
:param release: the release with the releasefile
:type release: :class:`Release`
:returns: an action status
:rtype: :class:`ActionStatus`
:raises: None
"""
fp = release._releasefile.get_fullpath()
log.info("Deleting release file %s", fp)
delete_file(release._releasefile)
return ActionStatus(ActionStatus.SUCCESS,
msg="Deleted %s" % fp) | python | def delete_releasefile(self, release):
"""Delete the releasefile of the given release
This is inteded to be used in a action unit.
:param release: the release with the releasefile
:type release: :class:`Release`
:returns: an action status
:rtype: :class:`ActionStatus`
:raises: None
"""
fp = release._releasefile.get_fullpath()
log.info("Deleting release file %s", fp)
delete_file(release._releasefile)
return ActionStatus(ActionStatus.SUCCESS,
msg="Deleted %s" % fp) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Delete the releasefile of the given release
This is inteded to be used in a action unit.
:param release: the release with the releasefile
:type release: :class:`Release`
:returns: an action status
:rtype: :class:`ActionStatus`
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def delete_releasefile(self, release):
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:param release: the release with the releasefile
:type release: :class:`Release`
:returns: an action status
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quantmind/pulsar | pulsar/apps/http/stream.py | HttpStream.read | async def read(self, n=None):
"""Read all content
"""
if self._streamed:
return b''
buffer = []
async for body in self:
buffer.append(body)
return b''.join(buffer) | python | async def read(self, n=None):
"""Read all content
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Read all content
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session["oauth_user_attrs"] = dict(provider=provider, **attrs)
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redirect_url = request.args.get("next")
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"in",
"via",
"oauth"
] | train | https://github.com/frascoweb/frasco-users/blob/16591ca466de5b7c80d7a2384327d9cf2d919c41/frasco_users/__init__.py#L863-L870 | <|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:
Start the signup process after having logged in via oauth
### Response:
def oauth_signup(self, provider, attrs, defaults, redirect_url=None):
"""Start the signup process after having logged in via oauth
"""
session["oauth_user_defaults"] = defaults
session["oauth_user_attrs"] = dict(provider=provider, **attrs)
if not redirect_url:
redirect_url = request.args.get("next")
return redirect(url_for('users.oauth_signup', next=redirect_url))
<|endoftext|> | [
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saltstack/salt | salt/modules/kernelpkg_linux_apt.py | cleanup | def cleanup(keep_latest=True):
'''
Remove all unused kernel packages from the system.
keep_latest : True
In the event that the active kernel is not the latest one installed, setting this to True
will retain the latest kernel package, in addition to the active one. If False, all kernel
packages other than the active one will be removed.
CLI Example:
.. code-block:: bash
salt '*' kernelpkg.cleanup
'''
removed = []
# Loop over all installed kernel packages
for kernel in list_installed():
# Keep the active kernel package
if kernel == active():
continue
# Optionally keep the latest kernel package
if keep_latest and kernel == latest_installed():
continue
# Remove the kernel package
removed.extend(remove(kernel)['removed'])
return {'removed': removed} | python | def cleanup(keep_latest=True):
'''
Remove all unused kernel packages from the system.
keep_latest : True
In the event that the active kernel is not the latest one installed, setting this to True
will retain the latest kernel package, in addition to the active one. If False, all kernel
packages other than the active one will be removed.
CLI Example:
.. code-block:: bash
salt '*' kernelpkg.cleanup
'''
removed = []
# Loop over all installed kernel packages
for kernel in list_installed():
# Keep the active kernel package
if kernel == active():
continue
# Optionally keep the latest kernel package
if keep_latest and kernel == latest_installed():
continue
# Remove the kernel package
removed.extend(remove(kernel)['removed'])
return {'removed': removed} | [
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CLI Example:
.. code-block:: bash
salt '*' kernelpkg.cleanup | [
"Remove",
"all",
"unused",
"kernel",
"packages",
"from",
"the",
"system",
"."
] | train | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/modules/kernelpkg_linux_apt.py#L231-L262 | <|startoftext|>
### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Remove all unused kernel packages from the system.
keep_latest : True
In the event that the active kernel is not the latest one installed, setting this to True
will retain the latest kernel package, in addition to the active one. If False, all kernel
packages other than the active one will be removed.
CLI Example:
.. code-block:: bash
salt '*' kernelpkg.cleanup
### Response:
def cleanup(keep_latest=True):
'''
Remove all unused kernel packages from the system.
keep_latest : True
In the event that the active kernel is not the latest one installed, setting this to True
will retain the latest kernel package, in addition to the active one. If False, all kernel
packages other than the active one will be removed.
CLI Example:
.. code-block:: bash
salt '*' kernelpkg.cleanup
'''
removed = []
# Loop over all installed kernel packages
for kernel in list_installed():
# Keep the active kernel package
if kernel == active():
continue
# Optionally keep the latest kernel package
if keep_latest and kernel == latest_installed():
continue
# Remove the kernel package
removed.extend(remove(kernel)['removed'])
return {'removed': removed}
<|endoftext|> | [
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icometrix/dicom2nifti | dicom2nifti/convert_philips.py | _singleframe_to_block | def _singleframe_to_block(grouped_dicoms):
"""
Generate a full datablock containing all timepoints
"""
# For each slice / mosaic create a data volume block
data_blocks = []
for index in range(0, len(grouped_dicoms)):
logger.info('Creating block %s of %s' % (index + 1, len(grouped_dicoms)))
current_block = _stack_to_block(grouped_dicoms[index])
current_block = current_block[:, :, :, numpy.newaxis]
data_blocks.append(current_block)
try:
full_block = numpy.concatenate(data_blocks, axis=3)
except:
traceback.print_exc()
raise ConversionError("MISSING_DICOM_FILES")
# Apply the rescaling if needed
common.apply_scaling(full_block, grouped_dicoms[0][0])
return full_block | python | def _singleframe_to_block(grouped_dicoms):
"""
Generate a full datablock containing all timepoints
"""
# For each slice / mosaic create a data volume block
data_blocks = []
for index in range(0, len(grouped_dicoms)):
logger.info('Creating block %s of %s' % (index + 1, len(grouped_dicoms)))
current_block = _stack_to_block(grouped_dicoms[index])
current_block = current_block[:, :, :, numpy.newaxis]
data_blocks.append(current_block)
try:
full_block = numpy.concatenate(data_blocks, axis=3)
except:
traceback.print_exc()
raise ConversionError("MISSING_DICOM_FILES")
# Apply the rescaling if needed
common.apply_scaling(full_block, grouped_dicoms[0][0])
return full_block | [
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"Generate",
"a",
"full",
"datablock",
"containing",
"all",
"timepoints"
] | train | https://github.com/icometrix/dicom2nifti/blob/1462ae5dd979fa3f276fe7a78ceb9b028121536f/dicom2nifti/convert_philips.py#L325-L346 | <|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:
Generate a full datablock containing all timepoints
### Response:
def _singleframe_to_block(grouped_dicoms):
"""
Generate a full datablock containing all timepoints
"""
# For each slice / mosaic create a data volume block
data_blocks = []
for index in range(0, len(grouped_dicoms)):
logger.info('Creating block %s of %s' % (index + 1, len(grouped_dicoms)))
current_block = _stack_to_block(grouped_dicoms[index])
current_block = current_block[:, :, :, numpy.newaxis]
data_blocks.append(current_block)
try:
full_block = numpy.concatenate(data_blocks, axis=3)
except:
traceback.print_exc()
raise ConversionError("MISSING_DICOM_FILES")
# Apply the rescaling if needed
common.apply_scaling(full_block, grouped_dicoms[0][0])
return full_block
<|endoftext|> | [
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python-cmd2/cmd2 | examples/async_printing.py | AlerterApp._generate_alert_str | def _generate_alert_str(self) -> str:
"""
Combines alerts into one string that can be printed to the terminal
:return: the alert string
"""
global ALERTS
alert_str = ''
alerts = self._get_alerts()
longest_alert = max(ALERTS, key=len)
num_asterisks = len(longest_alert) + 8
for i, cur_alert in enumerate(alerts):
# Use padding to center the alert
padding = ' ' * int((num_asterisks - len(cur_alert)) / 2)
if i > 0:
alert_str += '\n'
alert_str += '*' * num_asterisks + '\n'
alert_str += padding + cur_alert + padding + '\n'
alert_str += '*' * num_asterisks + '\n'
return alert_str | python | def _generate_alert_str(self) -> str:
"""
Combines alerts into one string that can be printed to the terminal
:return: the alert string
"""
global ALERTS
alert_str = ''
alerts = self._get_alerts()
longest_alert = max(ALERTS, key=len)
num_asterisks = len(longest_alert) + 8
for i, cur_alert in enumerate(alerts):
# Use padding to center the alert
padding = ' ' * int((num_asterisks - len(cur_alert)) / 2)
if i > 0:
alert_str += '\n'
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Combines alerts into one string that can be printed to the terminal
:return: the alert string
### Response:
def _generate_alert_str(self) -> str:
"""
Combines alerts into one string that can be printed to the terminal
:return: the alert string
"""
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JoeVirtual/KonFoo | konfoo/core.py | Structure.index_fields | def index_fields(self, index=Index(), **options):
""" Indexes all fields in the `Structure` starting with the given
*index* and returns the :class:`Index` after the last :class:`Field`
in the `Structure`.
:param Index index: start :class:`Index` for the first :class:`Field`
in the `Structure`.
:keyword bool nested: if ``True`` all :class:`Pointer` fields of the
`Structure` indexes their referenced :attr:`~Pointer.data` object
fields as well (chained method call).
"""
for name, item in self.items():
# Container
if is_container(item):
index = item.index_fields(index, **options)
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index = item.index_field(index)
item.index_data()
# Field
elif is_field(item):
index = item.index_field(index)
else:
raise MemberTypeError(self, item, name, index)
return index | python | def index_fields(self, index=Index(), **options):
""" Indexes all fields in the `Structure` starting with the given
*index* and returns the :class:`Index` after the last :class:`Field`
in the `Structure`.
:param Index index: start :class:`Index` for the first :class:`Field`
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:keyword bool nested: if ``True`` all :class:`Pointer` fields of the
`Structure` indexes their referenced :attr:`~Pointer.data` object
fields as well (chained method call).
"""
for name, item in self.items():
# Container
if is_container(item):
index = item.index_fields(index, **options)
# Pointer
elif is_pointer(item) and get_nested(options):
index = item.index_field(index)
item.index_data()
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index = item.index_field(index)
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Indexes all fields in the `Structure` starting with the given
*index* and returns the :class:`Index` after the last :class:`Field`
in the `Structure`.
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def index_fields(self, index=Index(), **options):
""" Indexes all fields in the `Structure` starting with the given
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in the `Structure`.
:param Index index: start :class:`Index` for the first :class:`Field`
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:keyword bool nested: if ``True`` all :class:`Pointer` fields of the
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"""
for name, item in self.items():
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index = item.index_field(index)
item.index_data()
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index = item.index_field(index)
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gwastro/pycbc | pycbc/workflow/datafind.py | setup_datafind_server_connection | def setup_datafind_server_connection(cp, tags=None):
"""
This function is resposible for setting up the connection with the datafind
server.
Parameters
-----------
cp : pycbc.workflow.configuration.WorkflowConfigParser
The memory representation of the ConfigParser
Returns
--------
connection
The open connection to the datafind server.
"""
if tags is None:
tags = []
if cp.has_option_tags("workflow-datafind",
"datafind-ligo-datafind-server", tags):
datafind_server = cp.get_opt_tags("workflow-datafind",
"datafind-ligo-datafind-server", tags)
else:
datafind_server = None
return datafind_connection(datafind_server) | python | def setup_datafind_server_connection(cp, tags=None):
"""
This function is resposible for setting up the connection with the datafind
server.
Parameters
-----------
cp : pycbc.workflow.configuration.WorkflowConfigParser
The memory representation of the ConfigParser
Returns
--------
connection
The open connection to the datafind server.
"""
if tags is None:
tags = []
if cp.has_option_tags("workflow-datafind",
"datafind-ligo-datafind-server", tags):
datafind_server = cp.get_opt_tags("workflow-datafind",
"datafind-ligo-datafind-server", tags)
else:
datafind_server = None
return datafind_connection(datafind_server) | [
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Bogdanp/dramatiq | dramatiq/rate_limits/rate_limiter.py | RateLimiter.acquire | def acquire(self, *, raise_on_failure=True):
"""Attempt to acquire a slot under this rate limiter.
Parameters:
raise_on_failure(bool): Whether or not failures should raise an
exception. If this is false, the context manager will instead
return a boolean value representing whether or not the rate
limit slot was acquired.
Returns:
bool: Whether or not the slot could be acquired.
"""
acquired = False
try:
acquired = self._acquire()
if raise_on_failure and not acquired:
raise RateLimitExceeded("rate limit exceeded for key %(key)r" % vars(self))
yield acquired
finally:
if acquired:
self._release() | python | def acquire(self, *, raise_on_failure=True):
"""Attempt to acquire a slot under this rate limiter.
Parameters:
raise_on_failure(bool): Whether or not failures should raise an
exception. If this is false, the context manager will instead
return a boolean value representing whether or not the rate
limit slot was acquired.
Returns:
bool: Whether or not the slot could be acquired.
"""
acquired = False
try:
acquired = self._acquire()
if raise_on_failure and not acquired:
raise RateLimitExceeded("rate limit exceeded for key %(key)r" % vars(self))
yield acquired
finally:
if acquired:
self._release() | [
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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:
Attempt to acquire a slot under this rate limiter.
Parameters:
raise_on_failure(bool): Whether or not failures should raise an
exception. If this is false, the context manager will instead
return a boolean value representing whether or not the rate
limit slot was acquired.
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bool: Whether or not the slot could be acquired.
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def acquire(self, *, raise_on_failure=True):
"""Attempt to acquire a slot under this rate limiter.
Parameters:
raise_on_failure(bool): Whether or not failures should raise an
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notpushkin/rock | rock_cli/cli/register.py | cmd_register | def cmd_register(phone):
"""
Войти в приложение с помощью SMS.
"""
if phone is None:
phone = click.prompt("Номер телефона")
r = rocket.devices.register.post(data={"phone": phone})
r = handle_error(r)
id = r.json()["sms_verification"]["id"]
code = click.prompt("Введите код из SMS", type=int)
r = rocket.sms_verifications[id]["verify"].patch(data={"code": code})
r = handle_error(r)
j = r.json()
click.secho("Добро пожаловать, {}!".format(j["user"]["first_name"]), fg="green")
config.email = j["user"]["email"]
config.write() | python | def cmd_register(phone):
"""
Войти в приложение с помощью SMS.
"""
if phone is None:
phone = click.prompt("Номер телефона")
r = rocket.devices.register.post(data={"phone": phone})
r = handle_error(r)
id = r.json()["sms_verification"]["id"]
code = click.prompt("Введите код из SMS", type=int)
r = rocket.sms_verifications[id]["verify"].patch(data={"code": code})
r = handle_error(r)
j = r.json()
click.secho("Добро пожаловать, {}!".format(j["user"]["first_name"]), fg="green")
config.email = j["user"]["email"]
config.write() | [
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"SMS",
"."
] | train | https://github.com/notpushkin/rock/blob/c1451a7604b462c19e4929f0eb171cbcf23e50ab/rock_cli/cli/register.py#L7-L27 | <|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:
Войти в приложение с помощью SMS.
### Response:
def cmd_register(phone):
"""
Войти в приложение с помощью SMS.
"""
if phone is None:
phone = click.prompt("Номер телефона")
r = rocket.devices.register.post(data={"phone": phone})
r = handle_error(r)
id = r.json()["sms_verification"]["id"]
code = click.prompt("Введите код из SMS", type=int)
r = rocket.sms_verifications[id]["verify"].patch(data={"code": code})
r = handle_error(r)
j = r.json()
click.secho("Добро пожаловать, {}!".format(j["user"]["first_name"]), fg="green")
config.email = j["user"]["email"]
config.write()
<|endoftext|> | [
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JdeRobot/base | src/drivers/MAVLinkServer/MAVProxy/pymavlink/dialects/v20/ardupilotmega.py | MAVLink.data64_send | def data64_send(self, type, len, data, force_mavlink1=False):
'''
Data packet, size 64
type : data type (uint8_t)
len : data length (uint8_t)
data : raw data (uint8_t)
'''
return self.send(self.data64_encode(type, len, data), force_mavlink1=force_mavlink1) | python | def data64_send(self, type, len, data, force_mavlink1=False):
'''
Data packet, size 64
type : data type (uint8_t)
len : data length (uint8_t)
data : raw data (uint8_t)
'''
return self.send(self.data64_encode(type, len, data), force_mavlink1=force_mavlink1) | [
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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:
Data packet, size 64
type : data type (uint8_t)
len : data length (uint8_t)
data : raw data (uint8_t)
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def data64_send(self, type, len, data, force_mavlink1=False):
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Data packet, size 64
type : data type (uint8_t)
len : data length (uint8_t)
data : raw data (uint8_t)
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pypa/pipenv | pipenv/patched/notpip/_internal/req/req_install.py | InstallRequirement.remove_temporary_source | def remove_temporary_source(self):
# type: () -> None
"""Remove the source files from this requirement, if they are marked
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if self.source_dir and os.path.exists(
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rmtree(self.source_dir)
self.source_dir = None
self._temp_build_dir.cleanup()
self.build_env.cleanup() | python | def remove_temporary_source(self):
# type: () -> None
"""Remove the source files from this requirement, if they are marked
for deletion"""
if self.source_dir and os.path.exists(
os.path.join(self.source_dir, PIP_DELETE_MARKER_FILENAME)):
logger.debug('Removing source in %s', self.source_dir)
rmtree(self.source_dir)
self.source_dir = None
self._temp_build_dir.cleanup()
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Remove the source files from this requirement, if they are marked
for deletion
### Response:
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idlesign/uwsgiconf | uwsgiconf/options/python.py | Python.set_app_args | def set_app_args(self, *args):
"""Sets ``sys.argv`` for python apps.
Examples:
* pyargv="one two three" will set ``sys.argv`` to ``('one', 'two', 'three')``.
:param args:
"""
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self._set('pyargv', ' '.join(args))
return self._section | python | def set_app_args(self, *args):
"""Sets ``sys.argv`` for python apps.
Examples:
* pyargv="one two three" will set ``sys.argv`` to ``('one', 'two', 'three')``.
:param args:
"""
if args:
self._set('pyargv', ' '.join(args))
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quokkaproject/flask-htmlbuilder | flask_htmlbuilder/htmlbuilder.py | render | def render(element, level=0):
"""Renders the HTML builder `element` and it's children to a string.
Example::
>>> print(
... render(
... html.form(action='/search', name='f')(
... html.input(name='q'),
... html.input(name='search_button', type='submit')
... )
... )
... )
<form action="/search" name="f">
<input name="q" />
<input type="submit" name="search_button" />
</form>
The :func:`render` function accepts the following arguments:
:param element: element created through the `html` builder instance, a list
of such elements, or just a string.
:param level: indentation level of the rendered element with a step of two
spaces. If `level` is `None`, the `element` will be rendered
without indentation and new line transferring, passing the
same rule to the child elements.
"""
if hasattr(element, 'render'):
return element.render(level)
elif isinstance(element, basestring):
return _render_string(element, level)
elif hasattr(element, '__iter__'):
return _render_iteratable(element, level)
elif element is None:
return ''
raise TypeError('Cannot render %r' % element) | python | def render(element, level=0):
"""Renders the HTML builder `element` and it's children to a string.
Example::
>>> print(
... render(
... html.form(action='/search', name='f')(
... html.input(name='q'),
... html.input(name='search_button', type='submit')
... )
... )
... )
<form action="/search" name="f">
<input name="q" />
<input type="submit" name="search_button" />
</form>
The :func:`render` function accepts the following arguments:
:param element: element created through the `html` builder instance, a list
of such elements, or just a string.
:param level: indentation level of the rendered element with a step of two
spaces. If `level` is `None`, the `element` will be rendered
without indentation and new line transferring, passing the
same rule to the child elements.
"""
if hasattr(element, 'render'):
return element.render(level)
elif isinstance(element, basestring):
return _render_string(element, level)
elif hasattr(element, '__iter__'):
return _render_iteratable(element, level)
elif element is None:
return ''
raise TypeError('Cannot render %r' % element) | [
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... html.input(name='search_button', type='submit')
... )
... )
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Renders the HTML builder `element` and it's children to a string.
Example::
>>> print(
... render(
... html.form(action='/search', name='f')(
... html.input(name='q'),
... html.input(name='search_button', type='submit')
... )
... )
... )
<form action="/search" name="f">
<input name="q" />
<input type="submit" name="search_button" />
</form>
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:param element: element created through the `html` builder instance, a list
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:param level: indentation level of the rendered element with a step of two
spaces. If `level` is `None`, the `element` will be rendered
without indentation and new line transferring, passing the
same rule to the child elements.
### Response:
def render(element, level=0):
"""Renders the HTML builder `element` and it's children to a string.
Example::
>>> print(
... render(
... html.form(action='/search', name='f')(
... html.input(name='q'),
... html.input(name='search_button', type='submit')
... )
... )
... )
<form action="/search" name="f">
<input name="q" />
<input type="submit" name="search_button" />
</form>
The :func:`render` function accepts the following arguments:
:param element: element created through the `html` builder instance, a list
of such elements, or just a string.
:param level: indentation level of the rendered element with a step of two
spaces. If `level` is `None`, the `element` will be rendered
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"""
if hasattr(element, 'render'):
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return _render_string(element, level)
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return _render_iteratable(element, level)
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raise TypeError('Cannot render %r' % element)
<|endoftext|> | [
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return _read_array(stream, self.dtype(byte_order), 1)[0]
except IndexError:
raise StopIteration | python | def _read_bin(self, stream, byte_order):
'''
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upsight/doctor | doctor/schema.py | SchemaRefResolver._format_stack | def _format_stack(self, stack, current=None):
"""Prettifies a scope stack for use in error messages.
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return ' => '.join(stack) | python | def _format_stack(self, stack, current=None):
"""Prettifies a scope stack for use in error messages.
:param list(str) stack: List of scopes.
:param str current: The current scope. If specified, will be appended
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:returns: 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:
Prettifies a scope stack for use in error messages.
:param list(str) stack: List of scopes.
:param str current: The current scope. If specified, will be appended
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:returns: str
### Response:
def _format_stack(self, stack, current=None):
"""Prettifies a scope stack for use in error messages.
:param list(str) stack: List of scopes.
:param str current: The current scope. If specified, will be appended
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prefix = os.path.commonprefix(stack)
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django-salesforce/django-salesforce | salesforce/auth.py | SalesforceAuth.dynamic_start | def dynamic_start(self, access_token, instance_url=None, **kw):
"""
Set the access token dynamically according to the current user.
More parameters can be set.
"""
self.dynamic = {'access_token': str(access_token), 'instance_url': str(instance_url)}
self.dynamic.update(kw) | python | def dynamic_start(self, access_token, instance_url=None, **kw):
"""
Set the access token dynamically according to the current user.
More parameters can be set.
"""
self.dynamic = {'access_token': str(access_token), 'instance_url': str(instance_url)}
self.dynamic.update(kw) | [
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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:
Set the access token dynamically according to the current user.
More parameters can be set.
### Response:
def dynamic_start(self, access_token, instance_url=None, **kw):
"""
Set the access token dynamically according to the current user.
More parameters can be set.
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self.dynamic = {'access_token': str(access_token), 'instance_url': str(instance_url)}
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tBuLi/symfit | symfit/core/support.py | sympy_to_scipy | def sympy_to_scipy(func, vars, params):
"""
Convert a symbolic expression to one scipy digs. Not used by ``symfit`` any more.
:param func: sympy expression
:param vars: variables
:param params: parameters
:return: Scipy-style function to be used for numerical evaluation of the model.
"""
lambda_func = sympy_to_py(func, vars, params)
def f(x, p):
"""
Scipy style function.
:param x: list of arrays, NxM
:param p: tuple of parameter values.
"""
x = np.atleast_2d(x)
y = [x[i] for i in range(len(x))] if len(x[0]) else []
try:
ans = lambda_func(*(y + list(p)))
except TypeError:
# Possibly this is a constant function in which case it only has Parameters.
ans = lambda_func(*list(p))# * np.ones(x_shape)
return ans
return f | python | def sympy_to_scipy(func, vars, params):
"""
Convert a symbolic expression to one scipy digs. Not used by ``symfit`` any more.
:param func: sympy expression
:param vars: variables
:param params: parameters
:return: Scipy-style function to be used for numerical evaluation of the model.
"""
lambda_func = sympy_to_py(func, vars, params)
def f(x, p):
"""
Scipy style function.
:param x: list of arrays, NxM
:param p: tuple of parameter values.
"""
x = np.atleast_2d(x)
y = [x[i] for i in range(len(x))] if len(x[0]) else []
try:
ans = lambda_func(*(y + list(p)))
except TypeError:
# Possibly this is a constant function in which case it only has Parameters.
ans = lambda_func(*list(p))# * np.ones(x_shape)
return ans
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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 symbolic expression to one scipy digs. Not used by ``symfit`` any more.
:param func: sympy expression
:param vars: variables
:param params: parameters
:return: Scipy-style function to be used for numerical evaluation of the model.
### Response:
def sympy_to_scipy(func, vars, params):
"""
Convert a symbolic expression to one scipy digs. Not used by ``symfit`` any more.
:param func: sympy expression
:param vars: variables
:param params: parameters
:return: Scipy-style function to be used for numerical evaluation of the model.
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try:
ans = lambda_func(*(y + list(p)))
except TypeError:
# Possibly this is a constant function in which case it only has Parameters.
ans = lambda_func(*list(p))# * np.ones(x_shape)
return ans
return f
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toumorokoshi/sprinter | sprinter/formula/base.py | FormulaBase.validate | def validate(self):
"""
validates the feature configuration, and returns a list of errors (empty list if no error)
validate should:
* required variables
* warn on unused variables
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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:
validates the feature configuration, and returns a list of errors (empty list if no error)
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errors should either be reported via self._log_error(), or raise an exception
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'''Retrieve associations for a given URL.
The associations returned have the exact same structure as defined
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field contains the full tag name for the association.
'''
url = urllib.unquote_plus(url.decode('utf-8')).strip()
return {'associations': tags.assocs_by_url(url)} | python | def v1_url_associations(tags, url):
'''Retrieve associations for a given URL.
The associations returned have the exact same structure as defined
in the ``v1_tag_associate`` route with one addition: a ``tag``
field contains the full tag name for the association.
'''
url = urllib.unquote_plus(url.decode('utf-8')).strip()
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Retrieve associations for a given URL.
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bitesofcode/projexui | projexui/widgets/xtreewidget/xtreewidget.py | XTreeWidget.restoreXml | def restoreXml(self, xml):
"""
Restores properties for this tree widget from the inputed XML.
:param xml | <xml.etree.ElementTree.Element>
:return <bool> success
"""
if xml is None:
return False
self.setUpdatesEnabled(False)
self.blockSignals(True)
# restore column data
header = self.header()
xcolumns = xml.find('columns')
if xcolumns is not None:
for xcolumn in xcolumns:
name = xcolumn.get('name')
index = self.column(name)
vindex = int(xcolumn.get('visualIndex', index))
currvi = header.visualIndex(index)
# restore from name
if index == -1:
continue
if currvi != vindex:
header.moveSection(currvi, vindex)
self.setColumnHidden(index, xcolumn.get('hidden') == 'True')
if not self.isColumnHidden(index):
width = int(xcolumn.get('width', self.columnWidth(index)))
self.setColumnWidth(index, width)
# restore order data
headerState = xml.get('headerState')
if headerState is not None:
state = QtCore.QByteArray.fromBase64(str(headerState))
self.header().restoreState(state)
sortColumn = int(xml.get('sortColumn', 0))
sortingEnabled = xml.get('sortingEnabled') == 'True'
sortOrder = QtCore.Qt.SortOrder(int(xml.get('sortOrder', 0)))
if sortingEnabled:
self.setSortingEnabled(sortingEnabled)
self.sortByColumn(sortColumn, sortOrder)
self.setUpdatesEnabled(True)
self.blockSignals(False)
return True | python | def restoreXml(self, xml):
"""
Restores properties for this tree widget from the inputed XML.
:param xml | <xml.etree.ElementTree.Element>
:return <bool> success
"""
if xml is None:
return False
self.setUpdatesEnabled(False)
self.blockSignals(True)
# restore column data
header = self.header()
xcolumns = xml.find('columns')
if xcolumns is not None:
for xcolumn in xcolumns:
name = xcolumn.get('name')
index = self.column(name)
vindex = int(xcolumn.get('visualIndex', index))
currvi = header.visualIndex(index)
# restore from name
if index == -1:
continue
if currvi != vindex:
header.moveSection(currvi, vindex)
self.setColumnHidden(index, xcolumn.get('hidden') == 'True')
if not self.isColumnHidden(index):
width = int(xcolumn.get('width', self.columnWidth(index)))
self.setColumnWidth(index, width)
# restore order data
headerState = xml.get('headerState')
if headerState is not None:
state = QtCore.QByteArray.fromBase64(str(headerState))
self.header().restoreState(state)
sortColumn = int(xml.get('sortColumn', 0))
sortingEnabled = xml.get('sortingEnabled') == 'True'
sortOrder = QtCore.Qt.SortOrder(int(xml.get('sortOrder', 0)))
if sortingEnabled:
self.setSortingEnabled(sortingEnabled)
self.sortByColumn(sortColumn, sortOrder)
self.setUpdatesEnabled(True)
self.blockSignals(False)
return True | [
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:param xml | <xml.etree.ElementTree.Element>
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Restores properties for this tree widget from the inputed XML.
:param xml | <xml.etree.ElementTree.Element>
:return <bool> success
### Response:
def restoreXml(self, xml):
"""
Restores properties for this tree widget from the inputed XML.
:param xml | <xml.etree.ElementTree.Element>
:return <bool> success
"""
if xml is None:
return False
self.setUpdatesEnabled(False)
self.blockSignals(True)
# restore column data
header = self.header()
xcolumns = xml.find('columns')
if xcolumns is not None:
for xcolumn in xcolumns:
name = xcolumn.get('name')
index = self.column(name)
vindex = int(xcolumn.get('visualIndex', index))
currvi = header.visualIndex(index)
# restore from name
if index == -1:
continue
if currvi != vindex:
header.moveSection(currvi, vindex)
self.setColumnHidden(index, xcolumn.get('hidden') == 'True')
if not self.isColumnHidden(index):
width = int(xcolumn.get('width', self.columnWidth(index)))
self.setColumnWidth(index, width)
# restore order data
headerState = xml.get('headerState')
if headerState is not None:
state = QtCore.QByteArray.fromBase64(str(headerState))
self.header().restoreState(state)
sortColumn = int(xml.get('sortColumn', 0))
sortingEnabled = xml.get('sortingEnabled') == 'True'
sortOrder = QtCore.Qt.SortOrder(int(xml.get('sortOrder', 0)))
if sortingEnabled:
self.setSortingEnabled(sortingEnabled)
self.sortByColumn(sortColumn, sortOrder)
self.setUpdatesEnabled(True)
self.blockSignals(False)
return True
<|endoftext|> | [
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crackinglandia/pype32 | pype32/pype32.py | PE._parseExceptionDirectory | def _parseExceptionDirectory(self, rva, size, magic = consts.PE32):
"""
Parses the C{IMAGE_EXCEPTION_DIRECTORY} directory.
@type rva: int
@param rva: The RVA where the C{IMAGE_EXCEPTION_DIRECTORY} starts.
@type size: int
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@type magic: int
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@rtype: str
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"""
return self.getDataAtRva(rva, size) | python | def _parseExceptionDirectory(self, rva, size, magic = consts.PE32):
"""
Parses the C{IMAGE_EXCEPTION_DIRECTORY} directory.
@type rva: int
@param rva: The RVA where the C{IMAGE_EXCEPTION_DIRECTORY} starts.
@type size: int
@param size: The size of the C{IMAGE_EXCEPTION_DIRECTORY} directory.
@type magic: int
@param magic: (Optional) The type of PE. This value could be L{consts.PE32} or L{consts.PE64}.
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"""
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Parses the C{IMAGE_EXCEPTION_DIRECTORY} directory.
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UCSBarchlab/PyRTL | pyrtl/corecircuits.py | signed_mult | def signed_mult(a, b):
""" Return a*b where a and b are treated as signed values. """
a, b = as_wires(a), as_wires(b)
final_len = len(a) + len(b)
# sign extend both inputs to the final target length
a, b = a.sign_extended(final_len), b.sign_extended(final_len)
# the result is the multiplication of both, but truncated
# TODO: this may make estimates based on the multiplication overly
# pessimistic as half of the multiply result is thrown right away!
return (a * b)[0:final_len] | python | def signed_mult(a, b):
""" Return a*b where a and b are treated as signed values. """
a, b = as_wires(a), as_wires(b)
final_len = len(a) + len(b)
# sign extend both inputs to the final target length
a, b = a.sign_extended(final_len), b.sign_extended(final_len)
# the result is the multiplication of both, but truncated
# TODO: this may make estimates based on the multiplication overly
# pessimistic as half of the multiply result is thrown right away!
return (a * b)[0:final_len] | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Return a*b where a and b are treated as signed values.
### Response:
def signed_mult(a, b):
""" Return a*b where a and b are treated as signed values. """
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return (a * b)[0:final_len]
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JoseAntFer/pyny3d | pyny3d/geoms.py | Polygon.get_domain | def get_domain(self):
"""
:returns: opposite vertices of the bounding prism for this
object.
:rtype: ndarray([min], [max])
"""
if self.domain is None:
return np.array([self.points.min(axis=0),
self.points.max(axis=0)])
return self.domain | python | def get_domain(self):
"""
:returns: opposite vertices of the bounding prism for this
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:rtype: ndarray([min], [max])
"""
if self.domain is None:
return np.array([self.points.min(axis=0),
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You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
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:returns: opposite vertices of the bounding prism for this
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:rtype: ndarray([min], [max])
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JarryShaw/PyPCAPKit | src/toolkit/default.py | ipv4_reassembly | def ipv4_reassembly(frame):
"""Make data for IPv4 reassembly."""
if 'IPv4' in frame:
ipv4 = frame['IPv4'].info
if ipv4.flags.df: # dismiss not fragmented frame
return False, None
data = dict(
bufid=(
ipv4.src, # source IP address
ipv4.dst, # destination IP address
ipv4.id, # identification
ipv4.proto.name, # payload protocol type
),
num=frame.info.number, # original packet range number
fo=ipv4.frag_offset, # fragment offset
ihl=ipv4.hdr_len, # internet header length
mf=ipv4.flags.mf, # more fragment flag
tl=ipv4.len, # total length, header includes
header=bytearray(ipv4.packet.header), # raw bytearray type header
payload=bytearray(ipv4.packet.payload or b''), # raw bytearray type payload
)
return True, data
return False, None | python | def ipv4_reassembly(frame):
"""Make data for IPv4 reassembly."""
if 'IPv4' in frame:
ipv4 = frame['IPv4'].info
if ipv4.flags.df: # dismiss not fragmented frame
return False, None
data = dict(
bufid=(
ipv4.src, # source IP address
ipv4.dst, # destination IP address
ipv4.id, # identification
ipv4.proto.name, # payload protocol type
),
num=frame.info.number, # original packet range number
fo=ipv4.frag_offset, # fragment offset
ihl=ipv4.hdr_len, # internet header length
mf=ipv4.flags.mf, # more fragment flag
tl=ipv4.len, # total length, header includes
header=bytearray(ipv4.packet.header), # raw bytearray type header
payload=bytearray(ipv4.packet.payload or b''), # raw bytearray type payload
)
return True, data
return False, None | [
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] | train | https://github.com/JarryShaw/PyPCAPKit/blob/c7f0da9aebc2cf210bf8f8b912f7d3cbb98ca10e/src/toolkit/default.py#L12-L34 | <|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:
Make data for IPv4 reassembly.
### Response:
def ipv4_reassembly(frame):
"""Make data for IPv4 reassembly."""
if 'IPv4' in frame:
ipv4 = frame['IPv4'].info
if ipv4.flags.df: # dismiss not fragmented frame
return False, None
data = dict(
bufid=(
ipv4.src, # source IP address
ipv4.dst, # destination IP address
ipv4.id, # identification
ipv4.proto.name, # payload protocol type
),
num=frame.info.number, # original packet range number
fo=ipv4.frag_offset, # fragment offset
ihl=ipv4.hdr_len, # internet header length
mf=ipv4.flags.mf, # more fragment flag
tl=ipv4.len, # total length, header includes
header=bytearray(ipv4.packet.header), # raw bytearray type header
payload=bytearray(ipv4.packet.payload or b''), # raw bytearray type payload
)
return True, data
return False, None
<|endoftext|> | [
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totalgood/nlpia | src/nlpia/book/examples/ch04_sklearn_pca_source.py | _fit_full | def _fit_full(self=self, X=X, n_components=6):
"""Fit the model by computing full SVD on X"""
n_samples, n_features = X.shape
# Center data
self.mean_ = np.mean(X, axis=0)
print(self.mean_)
X -= self.mean_
print(X.round(2))
U, S, V = linalg.svd(X, full_matrices=False)
print(V.round(2))
# flip eigenvectors' sign to enforce deterministic output
U, V = svd_flip(U, V)
components_ = V
print(components_.round(2))
# Get variance explained by singular values
explained_variance_ = (S ** 2) / (n_samples - 1)
total_var = explained_variance_.sum()
explained_variance_ratio_ = explained_variance_ / total_var
singular_values_ = S.copy() # Store the singular values.
# Postprocess the number of components required
if n_components == 'mle':
n_components = \
_infer_dimension_(explained_variance_, n_samples, n_features)
elif 0 < n_components < 1.0:
# number of components for which the cumulated explained
# variance percentage is superior to the desired threshold
ratio_cumsum = stable_cumsum(explained_variance_ratio_)
n_components = np.searchsorted(ratio_cumsum, n_components) + 1
# Compute noise covariance using Probabilistic PCA model
# The sigma2 maximum likelihood (cf. eq. 12.46)
if n_components < min(n_features, n_samples):
self.noise_variance_ = explained_variance_[n_components:].mean()
else:
self.noise_variance_ = 0.
self.n_samples_, self.n_features_ = n_samples, n_features
self.components_ = components_[:n_components]
print(self.components_.round(2))
self.n_components_ = n_components
self.explained_variance_ = explained_variance_[:n_components]
self.explained_variance_ratio_ = \
explained_variance_ratio_[:n_components]
self.singular_values_ = singular_values_[:n_components]
return U, S, V | python | def _fit_full(self=self, X=X, n_components=6):
"""Fit the model by computing full SVD on X"""
n_samples, n_features = X.shape
# Center data
self.mean_ = np.mean(X, axis=0)
print(self.mean_)
X -= self.mean_
print(X.round(2))
U, S, V = linalg.svd(X, full_matrices=False)
print(V.round(2))
# flip eigenvectors' sign to enforce deterministic output
U, V = svd_flip(U, V)
components_ = V
print(components_.round(2))
# Get variance explained by singular values
explained_variance_ = (S ** 2) / (n_samples - 1)
total_var = explained_variance_.sum()
explained_variance_ratio_ = explained_variance_ / total_var
singular_values_ = S.copy() # Store the singular values.
# Postprocess the number of components required
if n_components == 'mle':
n_components = \
_infer_dimension_(explained_variance_, n_samples, n_features)
elif 0 < n_components < 1.0:
# number of components for which the cumulated explained
# variance percentage is superior to the desired threshold
ratio_cumsum = stable_cumsum(explained_variance_ratio_)
n_components = np.searchsorted(ratio_cumsum, n_components) + 1
# Compute noise covariance using Probabilistic PCA model
# The sigma2 maximum likelihood (cf. eq. 12.46)
if n_components < min(n_features, n_samples):
self.noise_variance_ = explained_variance_[n_components:].mean()
else:
self.noise_variance_ = 0.
self.n_samples_, self.n_features_ = n_samples, n_features
self.components_ = components_[:n_components]
print(self.components_.round(2))
self.n_components_ = n_components
self.explained_variance_ = explained_variance_[:n_components]
self.explained_variance_ratio_ = \
explained_variance_ratio_[:n_components]
self.singular_values_ = singular_values_[:n_components]
return U, S, V | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Fit the model by computing full SVD on X
### Response:
def _fit_full(self=self, X=X, n_components=6):
"""Fit the model by computing full SVD on X"""
n_samples, n_features = X.shape
# Center data
self.mean_ = np.mean(X, axis=0)
print(self.mean_)
X -= self.mean_
print(X.round(2))
U, S, V = linalg.svd(X, full_matrices=False)
print(V.round(2))
# flip eigenvectors' sign to enforce deterministic output
U, V = svd_flip(U, V)
components_ = V
print(components_.round(2))
# Get variance explained by singular values
explained_variance_ = (S ** 2) / (n_samples - 1)
total_var = explained_variance_.sum()
explained_variance_ratio_ = explained_variance_ / total_var
singular_values_ = S.copy() # Store the singular values.
# Postprocess the number of components required
if n_components == 'mle':
n_components = \
_infer_dimension_(explained_variance_, n_samples, n_features)
elif 0 < n_components < 1.0:
# number of components for which the cumulated explained
# variance percentage is superior to the desired threshold
ratio_cumsum = stable_cumsum(explained_variance_ratio_)
n_components = np.searchsorted(ratio_cumsum, n_components) + 1
# Compute noise covariance using Probabilistic PCA model
# The sigma2 maximum likelihood (cf. eq. 12.46)
if n_components < min(n_features, n_samples):
self.noise_variance_ = explained_variance_[n_components:].mean()
else:
self.noise_variance_ = 0.
self.n_samples_, self.n_features_ = n_samples, n_features
self.components_ = components_[:n_components]
print(self.components_.round(2))
self.n_components_ = n_components
self.explained_variance_ = explained_variance_[:n_components]
self.explained_variance_ratio_ = \
explained_variance_ratio_[:n_components]
self.singular_values_ = singular_values_[:n_components]
return U, S, V
<|endoftext|> | [
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"""
Select the best ScriptWriter for this environment.
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"""filter lets django managers use `objects.filter` on a hashable object."""
obj = kwargs.pop(self.object_property_name, None)
if obj is not None:
kwargs['object_hash'] = self.model._compute_hash(obj)
return super().filter(*args, **kwargs) | python | def filter(self, *args, **kwargs):
"""filter lets django managers use `objects.filter` on a hashable object."""
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filter lets django managers use `objects.filter` on a hashable object.
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rootpy/rootpy | rootpy/io/pickler.py | load | def load(root_file, use_proxy=True, key=None):
"""Load an object from a ROOT TFile.
`root_file` may be an open ROOT file or directory, or a string path to an
existing ROOT file.
"""
if isinstance(root_file, string_types):
root_file = root_open(root_file)
own_file = True
else:
own_file = False
obj = Unpickler(root_file, use_proxy).load(key)
if own_file:
root_file.Close()
return obj | python | def load(root_file, use_proxy=True, key=None):
"""Load an object from a ROOT TFile.
`root_file` may be an open ROOT file or directory, or a string path to an
existing ROOT file.
"""
if isinstance(root_file, string_types):
root_file = root_open(root_file)
own_file = True
else:
own_file = False
obj = Unpickler(root_file, use_proxy).load(key)
if own_file:
root_file.Close()
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:
Load an object from a ROOT TFile.
`root_file` may be an open ROOT file or directory, or a string path to an
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def load(root_file, use_proxy=True, key=None):
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PierreRust/apigpio | apigpio/apigpio.py | Pi.read | def read(self, gpio):
"""
Returns the GPIO level.
gpio:= 0-53.
...
yield from pi.set_mode(23, pigpio.INPUT)
yield from pi.set_pull_up_down(23, pigpio.PUD_DOWN)
print(yield from pi.read(23))
0
yield from pi.set_pull_up_down(23, pigpio.PUD_UP)
print(yield from pi.read(23))
1
...
"""
res = yield from self._pigpio_aio_command(_PI_CMD_READ, gpio, 0)
return _u2i(res) | python | def read(self, gpio):
"""
Returns the GPIO level.
gpio:= 0-53.
...
yield from pi.set_mode(23, pigpio.INPUT)
yield from pi.set_pull_up_down(23, pigpio.PUD_DOWN)
print(yield from pi.read(23))
0
yield from pi.set_pull_up_down(23, pigpio.PUD_UP)
print(yield from pi.read(23))
1
...
"""
res = yield from self._pigpio_aio_command(_PI_CMD_READ, gpio, 0)
return _u2i(res) | [
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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 the GPIO level.
gpio:= 0-53.
...
yield from pi.set_mode(23, pigpio.INPUT)
yield from pi.set_pull_up_down(23, pigpio.PUD_DOWN)
print(yield from pi.read(23))
0
yield from pi.set_pull_up_down(23, pigpio.PUD_UP)
print(yield from pi.read(23))
1
...
### Response:
def read(self, gpio):
"""
Returns the GPIO level.
gpio:= 0-53.
...
yield from pi.set_mode(23, pigpio.INPUT)
yield from pi.set_pull_up_down(23, pigpio.PUD_DOWN)
print(yield from pi.read(23))
0
yield from pi.set_pull_up_down(23, pigpio.PUD_UP)
print(yield from pi.read(23))
1
...
"""
res = yield from self._pigpio_aio_command(_PI_CMD_READ, gpio, 0)
return _u2i(res)
<|endoftext|> | [
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tensorflow/tensorboard | tensorboard/plugins/scalar/scalars_demo.py | run_all | def run_all(logdir, verbose=False):
"""Run simulations on a reasonable set of parameters.
Arguments:
logdir: the directory into which to store all the runs' data
verbose: if true, print out each run's name as it begins
"""
for initial_temperature in [270.0, 310.0, 350.0]:
for final_temperature in [270.0, 310.0, 350.0]:
for heat_coefficient in [0.001, 0.005]:
run_name = 'temperature:t0=%g,tA=%g,kH=%g' % (
initial_temperature, final_temperature, heat_coefficient)
if verbose:
print('--- Running: %s' % run_name)
run(logdir, run_name,
initial_temperature, final_temperature, heat_coefficient) | python | def run_all(logdir, verbose=False):
"""Run simulations on a reasonable set of parameters.
Arguments:
logdir: the directory into which to store all the runs' data
verbose: if true, print out each run's name as it begins
"""
for initial_temperature in [270.0, 310.0, 350.0]:
for final_temperature in [270.0, 310.0, 350.0]:
for heat_coefficient in [0.001, 0.005]:
run_name = 'temperature:t0=%g,tA=%g,kH=%g' % (
initial_temperature, final_temperature, heat_coefficient)
if verbose:
print('--- Running: %s' % run_name)
run(logdir, run_name,
initial_temperature, final_temperature, heat_coefficient) | [
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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 simulations on a reasonable set of parameters.
Arguments:
logdir: the directory into which to store all the runs' data
verbose: if true, print out each run's name as it begins
### Response:
def run_all(logdir, verbose=False):
"""Run simulations on a reasonable set of parameters.
Arguments:
logdir: the directory into which to store all the runs' data
verbose: if true, print out each run's name as it begins
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for initial_temperature in [270.0, 310.0, 350.0]:
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run(logdir, run_name,
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empymod/empymod | empymod/kernel.py | wavenumber | def wavenumber(zsrc, zrec, lsrc, lrec, depth, etaH, etaV, zetaH, zetaV, lambd,
ab, xdirect, msrc, mrec, use_ne_eval):
r"""Calculate wavenumber domain solution.
Return the wavenumber domain solutions ``PJ0``, ``PJ1``, and ``PJ0b``,
which have to be transformed with a Hankel transform to the frequency
domain. ``PJ0``/``PJ0b`` and ``PJ1`` have to be transformed with Bessel
functions of order 0 (:math:`J_0`) and 1 (:math:`J_1`), respectively.
This function corresponds loosely to equations 105--107, 111--116,
119--121, and 123--128 in [HuTS15]_, and equally loosely to the file
``kxwmod.c``.
[HuTS15]_ uses Bessel functions of orders 0, 1, and 2 (:math:`J_0, J_1,
J_2`). The implementations of the *Fast Hankel Transform* and the
*Quadrature-with-Extrapolation* in ``transform`` are set-up with Bessel
functions of order 0 and 1 only. This is achieved by applying the
recurrence formula
.. math:: J_2(kr) = \frac{2}{kr} J_1(kr) - J_0(kr) \ .
.. note::
``PJ0`` and ``PJ0b`` could theoretically be added here into one, and
then be transformed in one go. However, ``PJ0b`` has to be multiplied
by ``factAng`` later. This has to be done after the Hankel transform
for methods which make use of spline interpolation, in order to work
for offsets that are not in line with each other.
This function is called from one of the Hankel functions in
:mod:`transform`. Consult the modelling routines in :mod:`model` for a
description of the input and output parameters.
If you are solely interested in the wavenumber-domain solution you can call
this function directly. However, you have to make sure all input arguments
are correct, as no checks are carried out here.
"""
# ** CALCULATE GREEN'S FUNCTIONS
# Shape of PTM, PTE: (nfreq, noffs, nfilt)
PTM, PTE = greenfct(zsrc, zrec, lsrc, lrec, depth, etaH, etaV, zetaH,
zetaV, lambd, ab, xdirect, msrc, mrec, use_ne_eval)
# ** AB-SPECIFIC COLLECTION OF PJ0, PJ1, AND PJ0b
# Pre-allocate output
PJ0 = None
PJ1 = None
PJ0b = None
# Calculate Ptot which is used in all cases
Ptot = (PTM + PTE)/(4*np.pi)
# If rec is magnetic switch sign (reciprocity MM/ME => EE/EM).
if mrec:
sign = -1
else:
sign = 1
# Group into PJ0 and PJ1 for J0/J1 Hankel Transform
if ab in [11, 12, 21, 22, 14, 24, 15, 25]: # Eqs 105, 106, 111, 112,
# J2(kr) = 2/(kr)*J1(kr) - J0(kr) # 119, 120, 123, 124
if ab in [14, 22]:
sign *= -1
PJ0b = sign/2*Ptot*lambd
PJ1 = -sign*Ptot
if ab in [11, 22, 24, 15]:
if ab in [22, 24]:
sign *= -1
PJ0 = sign*(PTM - PTE)/(8*np.pi)*lambd
elif ab in [13, 23, 31, 32, 34, 35, 16, 26]: # Eqs 107, 113, 114, 115,
PJ1 = sign*Ptot*lambd*lambd # . 121, 125, 126, 127
if ab in [34, 26]:
PJ1 *= -1
elif ab in [33, ]: # Eq 116
PJ0 = sign*Ptot*lambd*lambd*lambd
# Return PJ0, PJ1, PJ0b
return PJ0, PJ1, PJ0b | python | def wavenumber(zsrc, zrec, lsrc, lrec, depth, etaH, etaV, zetaH, zetaV, lambd,
ab, xdirect, msrc, mrec, use_ne_eval):
r"""Calculate wavenumber domain solution.
Return the wavenumber domain solutions ``PJ0``, ``PJ1``, and ``PJ0b``,
which have to be transformed with a Hankel transform to the frequency
domain. ``PJ0``/``PJ0b`` and ``PJ1`` have to be transformed with Bessel
functions of order 0 (:math:`J_0`) and 1 (:math:`J_1`), respectively.
This function corresponds loosely to equations 105--107, 111--116,
119--121, and 123--128 in [HuTS15]_, and equally loosely to the file
``kxwmod.c``.
[HuTS15]_ uses Bessel functions of orders 0, 1, and 2 (:math:`J_0, J_1,
J_2`). The implementations of the *Fast Hankel Transform* and the
*Quadrature-with-Extrapolation* in ``transform`` are set-up with Bessel
functions of order 0 and 1 only. This is achieved by applying the
recurrence formula
.. math:: J_2(kr) = \frac{2}{kr} J_1(kr) - J_0(kr) \ .
.. note::
``PJ0`` and ``PJ0b`` could theoretically be added here into one, and
then be transformed in one go. However, ``PJ0b`` has to be multiplied
by ``factAng`` later. This has to be done after the Hankel transform
for methods which make use of spline interpolation, in order to work
for offsets that are not in line with each other.
This function is called from one of the Hankel functions in
:mod:`transform`. Consult the modelling routines in :mod:`model` for a
description of the input and output parameters.
If you are solely interested in the wavenumber-domain solution you can call
this function directly. However, you have to make sure all input arguments
are correct, as no checks are carried out here.
"""
# ** CALCULATE GREEN'S FUNCTIONS
# Shape of PTM, PTE: (nfreq, noffs, nfilt)
PTM, PTE = greenfct(zsrc, zrec, lsrc, lrec, depth, etaH, etaV, zetaH,
zetaV, lambd, ab, xdirect, msrc, mrec, use_ne_eval)
# ** AB-SPECIFIC COLLECTION OF PJ0, PJ1, AND PJ0b
# Pre-allocate output
PJ0 = None
PJ1 = None
PJ0b = None
# Calculate Ptot which is used in all cases
Ptot = (PTM + PTE)/(4*np.pi)
# If rec is magnetic switch sign (reciprocity MM/ME => EE/EM).
if mrec:
sign = -1
else:
sign = 1
# Group into PJ0 and PJ1 for J0/J1 Hankel Transform
if ab in [11, 12, 21, 22, 14, 24, 15, 25]: # Eqs 105, 106, 111, 112,
# J2(kr) = 2/(kr)*J1(kr) - J0(kr) # 119, 120, 123, 124
if ab in [14, 22]:
sign *= -1
PJ0b = sign/2*Ptot*lambd
PJ1 = -sign*Ptot
if ab in [11, 22, 24, 15]:
if ab in [22, 24]:
sign *= -1
PJ0 = sign*(PTM - PTE)/(8*np.pi)*lambd
elif ab in [13, 23, 31, 32, 34, 35, 16, 26]: # Eqs 107, 113, 114, 115,
PJ1 = sign*Ptot*lambd*lambd # . 121, 125, 126, 127
if ab in [34, 26]:
PJ1 *= -1
elif ab in [33, ]: # Eq 116
PJ0 = sign*Ptot*lambd*lambd*lambd
# Return PJ0, PJ1, PJ0b
return PJ0, PJ1, PJ0b | [
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which have to be transformed with a Hankel transform to the frequency
domain. ``PJ0``/``PJ0b`` and ``PJ1`` have to be transformed with Bessel
functions of order 0 (:math:`J_0`) and 1 (:math:`J_1`), respectively.
This function corresponds loosely to equations 105--107, 111--116,
119--121, and 123--128 in [HuTS15]_, and equally loosely to the file
``kxwmod.c``.
[HuTS15]_ uses Bessel functions of orders 0, 1, and 2 (:math:`J_0, J_1,
J_2`). The implementations of the *Fast Hankel Transform* and the
*Quadrature-with-Extrapolation* in ``transform`` are set-up with Bessel
functions of order 0 and 1 only. This is achieved by applying the
recurrence formula
.. math:: J_2(kr) = \frac{2}{kr} J_1(kr) - J_0(kr) \ .
.. note::
``PJ0`` and ``PJ0b`` could theoretically be added here into one, and
then be transformed in one go. However, ``PJ0b`` has to be multiplied
by ``factAng`` later. This has to be done after the Hankel transform
for methods which make use of spline interpolation, in order to work
for offsets that are not in line with each other.
This function is called from one of the Hankel functions in
:mod:`transform`. Consult the modelling routines in :mod:`model` for a
description of the input and output parameters.
If you are solely interested in the wavenumber-domain solution you can call
this function directly. However, you have to make sure all input arguments
are correct, as no checks are carried out here. | [
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] | train | https://github.com/empymod/empymod/blob/4a78ca4191ed4b4d42d019ce715a9a3889dba1bc/empymod/kernel.py#L47-L129 | <|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:
r"""Calculate wavenumber domain solution.
Return the wavenumber domain solutions ``PJ0``, ``PJ1``, and ``PJ0b``,
which have to be transformed with a Hankel transform to the frequency
domain. ``PJ0``/``PJ0b`` and ``PJ1`` have to be transformed with Bessel
functions of order 0 (:math:`J_0`) and 1 (:math:`J_1`), respectively.
This function corresponds loosely to equations 105--107, 111--116,
119--121, and 123--128 in [HuTS15]_, and equally loosely to the file
``kxwmod.c``.
[HuTS15]_ uses Bessel functions of orders 0, 1, and 2 (:math:`J_0, J_1,
J_2`). The implementations of the *Fast Hankel Transform* and the
*Quadrature-with-Extrapolation* in ``transform`` are set-up with Bessel
functions of order 0 and 1 only. This is achieved by applying the
recurrence formula
.. math:: J_2(kr) = \frac{2}{kr} J_1(kr) - J_0(kr) \ .
.. note::
``PJ0`` and ``PJ0b`` could theoretically be added here into one, and
then be transformed in one go. However, ``PJ0b`` has to be multiplied
by ``factAng`` later. This has to be done after the Hankel transform
for methods which make use of spline interpolation, in order to work
for offsets that are not in line with each other.
This function is called from one of the Hankel functions in
:mod:`transform`. Consult the modelling routines in :mod:`model` for a
description of the input and output parameters.
If you are solely interested in the wavenumber-domain solution you can call
this function directly. However, you have to make sure all input arguments
are correct, as no checks are carried out here.
### Response:
def wavenumber(zsrc, zrec, lsrc, lrec, depth, etaH, etaV, zetaH, zetaV, lambd,
ab, xdirect, msrc, mrec, use_ne_eval):
r"""Calculate wavenumber domain solution.
Return the wavenumber domain solutions ``PJ0``, ``PJ1``, and ``PJ0b``,
which have to be transformed with a Hankel transform to the frequency
domain. ``PJ0``/``PJ0b`` and ``PJ1`` have to be transformed with Bessel
functions of order 0 (:math:`J_0`) and 1 (:math:`J_1`), respectively.
This function corresponds loosely to equations 105--107, 111--116,
119--121, and 123--128 in [HuTS15]_, and equally loosely to the file
``kxwmod.c``.
[HuTS15]_ uses Bessel functions of orders 0, 1, and 2 (:math:`J_0, J_1,
J_2`). The implementations of the *Fast Hankel Transform* and the
*Quadrature-with-Extrapolation* in ``transform`` are set-up with Bessel
functions of order 0 and 1 only. This is achieved by applying the
recurrence formula
.. math:: J_2(kr) = \frac{2}{kr} J_1(kr) - J_0(kr) \ .
.. note::
``PJ0`` and ``PJ0b`` could theoretically be added here into one, and
then be transformed in one go. However, ``PJ0b`` has to be multiplied
by ``factAng`` later. This has to be done after the Hankel transform
for methods which make use of spline interpolation, in order to work
for offsets that are not in line with each other.
This function is called from one of the Hankel functions in
:mod:`transform`. Consult the modelling routines in :mod:`model` for a
description of the input and output parameters.
If you are solely interested in the wavenumber-domain solution you can call
this function directly. However, you have to make sure all input arguments
are correct, as no checks are carried out here.
"""
# ** CALCULATE GREEN'S FUNCTIONS
# Shape of PTM, PTE: (nfreq, noffs, nfilt)
PTM, PTE = greenfct(zsrc, zrec, lsrc, lrec, depth, etaH, etaV, zetaH,
zetaV, lambd, ab, xdirect, msrc, mrec, use_ne_eval)
# ** AB-SPECIFIC COLLECTION OF PJ0, PJ1, AND PJ0b
# Pre-allocate output
PJ0 = None
PJ1 = None
PJ0b = None
# Calculate Ptot which is used in all cases
Ptot = (PTM + PTE)/(4*np.pi)
# If rec is magnetic switch sign (reciprocity MM/ME => EE/EM).
if mrec:
sign = -1
else:
sign = 1
# Group into PJ0 and PJ1 for J0/J1 Hankel Transform
if ab in [11, 12, 21, 22, 14, 24, 15, 25]: # Eqs 105, 106, 111, 112,
# J2(kr) = 2/(kr)*J1(kr) - J0(kr) # 119, 120, 123, 124
if ab in [14, 22]:
sign *= -1
PJ0b = sign/2*Ptot*lambd
PJ1 = -sign*Ptot
if ab in [11, 22, 24, 15]:
if ab in [22, 24]:
sign *= -1
PJ0 = sign*(PTM - PTE)/(8*np.pi)*lambd
elif ab in [13, 23, 31, 32, 34, 35, 16, 26]: # Eqs 107, 113, 114, 115,
PJ1 = sign*Ptot*lambd*lambd # . 121, 125, 126, 127
if ab in [34, 26]:
PJ1 *= -1
elif ab in [33, ]: # Eq 116
PJ0 = sign*Ptot*lambd*lambd*lambd
# Return PJ0, PJ1, PJ0b
return PJ0, PJ1, PJ0b
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BYU-PCCL/holodeck | holodeck/holodeckclient.py | HolodeckClient.malloc | def malloc(self, key, shape, dtype):
"""Allocates a block of shared memory, and returns a numpy array whose data corresponds with that block.
Args:
key (str): The key to identify the block.
shape (list of int): The shape of the numpy array to allocate.
dtype (type): The numpy data type (e.g. np.float32).
Returns:
np.ndarray: The numpy array that is positioned on the shared memory.
"""
if key not in self._memory or self._memory[key].shape != shape or self._memory[key].dtype != dtype:
self._memory[key] = Shmem(key, shape, dtype, self._uuid)
return self._memory[key].np_array | python | def malloc(self, key, shape, dtype):
"""Allocates a block of shared memory, and returns a numpy array whose data corresponds with that block.
Args:
key (str): The key to identify the block.
shape (list of int): The shape of the numpy array to allocate.
dtype (type): The numpy data type (e.g. np.float32).
Returns:
np.ndarray: The numpy array that is positioned on the shared memory.
"""
if key not in self._memory or self._memory[key].shape != shape or self._memory[key].dtype != dtype:
self._memory[key] = Shmem(key, shape, dtype, self._uuid)
return self._memory[key].np_array | [
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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:
Allocates a block of shared memory, and returns a numpy array whose data corresponds with that block.
Args:
key (str): The key to identify the block.
shape (list of int): The shape of the numpy array to allocate.
dtype (type): The numpy data type (e.g. np.float32).
Returns:
np.ndarray: The numpy array that is positioned on the shared memory.
### Response:
def malloc(self, key, shape, dtype):
"""Allocates a block of shared memory, and returns a numpy array whose data corresponds with that block.
Args:
key (str): The key to identify the block.
shape (list of int): The shape of the numpy array to allocate.
dtype (type): The numpy data type (e.g. np.float32).
Returns:
np.ndarray: The numpy array that is positioned on the shared memory.
"""
if key not in self._memory or self._memory[key].shape != shape or self._memory[key].dtype != dtype:
self._memory[key] = Shmem(key, shape, dtype, self._uuid)
return self._memory[key].np_array
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timknip/pycsg | csg/core.py | CSG.cube | def cube(cls, center=[0,0,0], radius=[1,1,1]):
"""
Construct an axis-aligned solid cuboid. Optional parameters are `center` and
`radius`, which default to `[0, 0, 0]` and `[1, 1, 1]`. The radius can be
specified using a single number or a list of three numbers, one for each axis.
Example code::
cube = CSG.cube(
center=[0, 0, 0],
radius=1
)
"""
c = Vector(0, 0, 0)
r = [1, 1, 1]
if isinstance(center, list): c = Vector(center)
if isinstance(radius, list): r = radius
else: r = [radius, radius, radius]
polygons = list(map(
lambda v: Polygon(
list(map(lambda i:
Vertex(
Vector(
c.x + r[0] * (2 * bool(i & 1) - 1),
c.y + r[1] * (2 * bool(i & 2) - 1),
c.z + r[2] * (2 * bool(i & 4) - 1)
),
None
), v[0]))),
[
[[0, 4, 6, 2], [-1, 0, 0]],
[[1, 3, 7, 5], [+1, 0, 0]],
[[0, 1, 5, 4], [0, -1, 0]],
[[2, 6, 7, 3], [0, +1, 0]],
[[0, 2, 3, 1], [0, 0, -1]],
[[4, 5, 7, 6], [0, 0, +1]]
]))
return CSG.fromPolygons(polygons) | python | def cube(cls, center=[0,0,0], radius=[1,1,1]):
"""
Construct an axis-aligned solid cuboid. Optional parameters are `center` and
`radius`, which default to `[0, 0, 0]` and `[1, 1, 1]`. The radius can be
specified using a single number or a list of three numbers, one for each axis.
Example code::
cube = CSG.cube(
center=[0, 0, 0],
radius=1
)
"""
c = Vector(0, 0, 0)
r = [1, 1, 1]
if isinstance(center, list): c = Vector(center)
if isinstance(radius, list): r = radius
else: r = [radius, radius, radius]
polygons = list(map(
lambda v: Polygon(
list(map(lambda i:
Vertex(
Vector(
c.x + r[0] * (2 * bool(i & 1) - 1),
c.y + r[1] * (2 * bool(i & 2) - 1),
c.z + r[2] * (2 * bool(i & 4) - 1)
),
None
), v[0]))),
[
[[0, 4, 6, 2], [-1, 0, 0]],
[[1, 3, 7, 5], [+1, 0, 0]],
[[0, 1, 5, 4], [0, -1, 0]],
[[2, 6, 7, 3], [0, +1, 0]],
[[0, 2, 3, 1], [0, 0, -1]],
[[4, 5, 7, 6], [0, 0, +1]]
]))
return CSG.fromPolygons(polygons) | [
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`radius`, which default to `[0, 0, 0]` and `[1, 1, 1]`. The radius can be
specified using a single number or a list of three numbers, one for each axis.
Example code::
cube = CSG.cube(
center=[0, 0, 0],
radius=1
) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Construct an axis-aligned solid cuboid. Optional parameters are `center` and
`radius`, which default to `[0, 0, 0]` and `[1, 1, 1]`. The radius can be
specified using a single number or a list of three numbers, one for each axis.
Example code::
cube = CSG.cube(
center=[0, 0, 0],
radius=1
)
### Response:
def cube(cls, center=[0,0,0], radius=[1,1,1]):
"""
Construct an axis-aligned solid cuboid. Optional parameters are `center` and
`radius`, which default to `[0, 0, 0]` and `[1, 1, 1]`. The radius can be
specified using a single number or a list of three numbers, one for each axis.
Example code::
cube = CSG.cube(
center=[0, 0, 0],
radius=1
)
"""
c = Vector(0, 0, 0)
r = [1, 1, 1]
if isinstance(center, list): c = Vector(center)
if isinstance(radius, list): r = radius
else: r = [radius, radius, radius]
polygons = list(map(
lambda v: Polygon(
list(map(lambda i:
Vertex(
Vector(
c.x + r[0] * (2 * bool(i & 1) - 1),
c.y + r[1] * (2 * bool(i & 2) - 1),
c.z + r[2] * (2 * bool(i & 4) - 1)
),
None
), v[0]))),
[
[[0, 4, 6, 2], [-1, 0, 0]],
[[1, 3, 7, 5], [+1, 0, 0]],
[[0, 1, 5, 4], [0, -1, 0]],
[[2, 6, 7, 3], [0, +1, 0]],
[[0, 2, 3, 1], [0, 0, -1]],
[[4, 5, 7, 6], [0, 0, +1]]
]))
return CSG.fromPolygons(polygons)
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dnanexus/dx-toolkit | src/python/dxpy/bindings/dxglobalworkflow.py | DXGlobalWorkflow.describe_underlying_workflow | def describe_underlying_workflow(self, region, describe_output=None):
"""
:param region: region name
:type region: string
:param describe_output: description of a global workflow
:type describe_output: dict
:returns: object description of a workflow
:rtype: : dict
Returns an object description of an underlying workflow from a given region.
"""
assert(describe_output is None or describe_output.get('class', '') == 'globalworkflow')
if region is None:
raise DXError(
'DXGlobalWorkflow: region must be provided to get an underlying workflow')
# Perhaps we have cached it already
if region in self._workflow_desc_by_region:
return self._workflow_desc_by_region[region]
if not describe_output:
describe_output = self.describe()
if region not in describe_output['regionalOptions'].keys():
raise DXError('DXGlobalWorkflow: the global workflow {} is not enabled in region {}'.format(
self.get_id(), region))
underlying_workflow_id = describe_output['regionalOptions'][region]['workflow']
dxworkflow = dxpy.DXWorkflow(underlying_workflow_id)
dxworkflow_desc = dxworkflow.describe()
self._workflow_desc_by_region = dxworkflow_desc
return dxworkflow_desc | python | def describe_underlying_workflow(self, region, describe_output=None):
"""
:param region: region name
:type region: string
:param describe_output: description of a global workflow
:type describe_output: dict
:returns: object description of a workflow
:rtype: : dict
Returns an object description of an underlying workflow from a given region.
"""
assert(describe_output is None or describe_output.get('class', '') == 'globalworkflow')
if region is None:
raise DXError(
'DXGlobalWorkflow: region must be provided to get an underlying workflow')
# Perhaps we have cached it already
if region in self._workflow_desc_by_region:
return self._workflow_desc_by_region[region]
if not describe_output:
describe_output = self.describe()
if region not in describe_output['regionalOptions'].keys():
raise DXError('DXGlobalWorkflow: the global workflow {} is not enabled in region {}'.format(
self.get_id(), region))
underlying_workflow_id = describe_output['regionalOptions'][region]['workflow']
dxworkflow = dxpy.DXWorkflow(underlying_workflow_id)
dxworkflow_desc = dxworkflow.describe()
self._workflow_desc_by_region = dxworkflow_desc
return dxworkflow_desc | [
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:type region: string
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:type describe_output: dict
:returns: object description of a workflow
:rtype: : dict
Returns an object description of an underlying workflow from a given region. | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
:param region: region name
:type region: string
:param describe_output: description of a global workflow
:type describe_output: dict
:returns: object description of a workflow
:rtype: : dict
Returns an object description of an underlying workflow from a given region.
### Response:
def describe_underlying_workflow(self, region, describe_output=None):
"""
:param region: region name
:type region: string
:param describe_output: description of a global workflow
:type describe_output: dict
:returns: object description of a workflow
:rtype: : dict
Returns an object description of an underlying workflow from a given region.
"""
assert(describe_output is None or describe_output.get('class', '') == 'globalworkflow')
if region is None:
raise DXError(
'DXGlobalWorkflow: region must be provided to get an underlying workflow')
# Perhaps we have cached it already
if region in self._workflow_desc_by_region:
return self._workflow_desc_by_region[region]
if not describe_output:
describe_output = self.describe()
if region not in describe_output['regionalOptions'].keys():
raise DXError('DXGlobalWorkflow: the global workflow {} is not enabled in region {}'.format(
self.get_id(), region))
underlying_workflow_id = describe_output['regionalOptions'][region]['workflow']
dxworkflow = dxpy.DXWorkflow(underlying_workflow_id)
dxworkflow_desc = dxworkflow.describe()
self._workflow_desc_by_region = dxworkflow_desc
return dxworkflow_desc
<|endoftext|> | [
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saltstack/salt | salt/modules/boto_kms.py | decrypt | def decrypt(ciphertext_blob, encryption_context=None, grant_tokens=None,
region=None, key=None, keyid=None, profile=None):
'''
Decrypt ciphertext.
CLI example::
salt myminion boto_kms.decrypt encrypted_ciphertext
'''
conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile)
r = {}
try:
plaintext = conn.decrypt(
ciphertext_blob,
encryption_context=encryption_context,
grant_tokens=grant_tokens
)
r['plaintext'] = plaintext['Plaintext']
except boto.exception.BotoServerError as e:
r['error'] = __utils__['boto.get_error'](e)
return r | python | def decrypt(ciphertext_blob, encryption_context=None, grant_tokens=None,
region=None, key=None, keyid=None, profile=None):
'''
Decrypt ciphertext.
CLI example::
salt myminion boto_kms.decrypt encrypted_ciphertext
'''
conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile)
r = {}
try:
plaintext = conn.decrypt(
ciphertext_blob,
encryption_context=encryption_context,
grant_tokens=grant_tokens
)
r['plaintext'] = plaintext['Plaintext']
except boto.exception.BotoServerError as e:
r['error'] = __utils__['boto.get_error'](e)
return r | [
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CLI example::
salt myminion boto_kms.decrypt encrypted_ciphertext | [
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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:
Decrypt ciphertext.
CLI example::
salt myminion boto_kms.decrypt encrypted_ciphertext
### Response:
def decrypt(ciphertext_blob, encryption_context=None, grant_tokens=None,
region=None, key=None, keyid=None, profile=None):
'''
Decrypt ciphertext.
CLI example::
salt myminion boto_kms.decrypt encrypted_ciphertext
'''
conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile)
r = {}
try:
plaintext = conn.decrypt(
ciphertext_blob,
encryption_context=encryption_context,
grant_tokens=grant_tokens
)
r['plaintext'] = plaintext['Plaintext']
except boto.exception.BotoServerError as e:
r['error'] = __utils__['boto.get_error'](e)
return r
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gr33ndata/dysl | dysl/dyslib/lm.py | LM.add_doc | def add_doc(self, doc_id ='', doc_terms=[], doc_length=-1):
'''
Add new document to our Language Model (training phase)
doc_id is used here, so we build seperate LF for each doc_id
I.e. if you call it more than once with same doc_id,
then all terms given via doc_terms will contribute to same LM
doc_terms: list of words in document to be added
doc_length: the length of the document, you can provide it yourself,
otherwise, we use len(doc_terms) instead.
'''
if doc_length == -1:
self.update_lengths(doc_id=doc_id, doc_length=len(doc_terms))
else:
self.update_lengths(doc_id=doc_id, doc_length=int(doc_length))
for term in doc_terms:
self.vocabulary.add(term)
terms = self.lr_padding(doc_terms)
ngrams = self.to_ngrams(terms)
self.update_counts(doc_id, ngrams) | python | def add_doc(self, doc_id ='', doc_terms=[], doc_length=-1):
'''
Add new document to our Language Model (training phase)
doc_id is used here, so we build seperate LF for each doc_id
I.e. if you call it more than once with same doc_id,
then all terms given via doc_terms will contribute to same LM
doc_terms: list of words in document to be added
doc_length: the length of the document, you can provide it yourself,
otherwise, we use len(doc_terms) instead.
'''
if doc_length == -1:
self.update_lengths(doc_id=doc_id, doc_length=len(doc_terms))
else:
self.update_lengths(doc_id=doc_id, doc_length=int(doc_length))
for term in doc_terms:
self.vocabulary.add(term)
terms = self.lr_padding(doc_terms)
ngrams = self.to_ngrams(terms)
self.update_counts(doc_id, ngrams) | [
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doc_length: the length of the document, you can provide it yourself,
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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 new document to our Language Model (training phase)
doc_id is used here, so we build seperate LF for each doc_id
I.e. if you call it more than once with same doc_id,
then all terms given via doc_terms will contribute to same LM
doc_terms: list of words in document to be added
doc_length: the length of the document, you can provide it yourself,
otherwise, we use len(doc_terms) instead.
### Response:
def add_doc(self, doc_id ='', doc_terms=[], doc_length=-1):
'''
Add new document to our Language Model (training phase)
doc_id is used here, so we build seperate LF for each doc_id
I.e. if you call it more than once with same doc_id,
then all terms given via doc_terms will contribute to same LM
doc_terms: list of words in document to be added
doc_length: the length of the document, you can provide it yourself,
otherwise, we use len(doc_terms) instead.
'''
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self.update_lengths(doc_id=doc_id, doc_length=len(doc_terms))
else:
self.update_lengths(doc_id=doc_id, doc_length=int(doc_length))
for term in doc_terms:
self.vocabulary.add(term)
terms = self.lr_padding(doc_terms)
ngrams = self.to_ngrams(terms)
self.update_counts(doc_id, ngrams)
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HDI-Project/BTB | btb/selection/pure.py | PureBestKVelocity.compute_rewards | def compute_rewards(self, scores):
"""
Compute the "velocity" of (average distance between) the k+1 best
scores. Return a list with those k velocities padded out with zeros so
that the count remains the same.
"""
# get the k + 1 best scores in descending order
best_scores = sorted(scores, reverse=True)[:self.k + 1]
velocities = [best_scores[i] - best_scores[i + 1]
for i in range(len(best_scores) - 1)]
# pad the list out with zeros to maintain the length of the list
zeros = (len(scores) - self.k) * [0]
return velocities + zeros | python | def compute_rewards(self, scores):
"""
Compute the "velocity" of (average distance between) the k+1 best
scores. Return a list with those k velocities padded out with zeros so
that the count remains the same.
"""
# get the k + 1 best scores in descending order
best_scores = sorted(scores, reverse=True)[:self.k + 1]
velocities = [best_scores[i] - best_scores[i + 1]
for i in range(len(best_scores) - 1)]
# pad the list out with zeros to maintain the length of the list
zeros = (len(scores) - self.k) * [0]
return velocities + zeros | [
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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 the "velocity" of (average distance between) the k+1 best
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### Response:
def compute_rewards(self, scores):
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zeros = (len(scores) - self.k) * [0]
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python-gitlab/python-gitlab | gitlab/mixins.py | UpdateMixin.update | def update(self, id=None, new_data={}, **kwargs):
"""Update an object on the server.
Args:
id: ID of the object to update (can be None if not required)
new_data: the update data for the object
**kwargs: Extra options to send to the server (e.g. sudo)
Returns:
dict: The new object data (*not* a RESTObject)
Raises:
GitlabAuthenticationError: If authentication is not correct
GitlabUpdateError: If the server cannot perform the request
"""
if id is None:
path = self.path
else:
path = '%s/%s' % (self.path, id)
self._check_missing_update_attrs(new_data)
files = {}
# We get the attributes that need some special transformation
types = getattr(self, '_types', {})
if types:
# Duplicate data to avoid messing with what the user sent us
new_data = new_data.copy()
for attr_name, type_cls in types.items():
if attr_name in new_data.keys():
type_obj = type_cls(new_data[attr_name])
# if the type if FileAttribute we need to pass the data as
# file
if issubclass(type_cls, g_types.FileAttribute):
k = type_obj.get_file_name(attr_name)
files[attr_name] = (k, new_data.pop(attr_name))
else:
new_data[attr_name] = type_obj.get_for_api()
http_method = self._get_update_method()
return http_method(path, post_data=new_data, files=files, **kwargs) | python | def update(self, id=None, new_data={}, **kwargs):
"""Update an object on the server.
Args:
id: ID of the object to update (can be None if not required)
new_data: the update data for the object
**kwargs: Extra options to send to the server (e.g. sudo)
Returns:
dict: The new object data (*not* a RESTObject)
Raises:
GitlabAuthenticationError: If authentication is not correct
GitlabUpdateError: If the server cannot perform the request
"""
if id is None:
path = self.path
else:
path = '%s/%s' % (self.path, id)
self._check_missing_update_attrs(new_data)
files = {}
# We get the attributes that need some special transformation
types = getattr(self, '_types', {})
if types:
# Duplicate data to avoid messing with what the user sent us
new_data = new_data.copy()
for attr_name, type_cls in types.items():
if attr_name in new_data.keys():
type_obj = type_cls(new_data[attr_name])
# if the type if FileAttribute we need to pass the data as
# file
if issubclass(type_cls, g_types.FileAttribute):
k = type_obj.get_file_name(attr_name)
files[attr_name] = (k, new_data.pop(attr_name))
else:
new_data[attr_name] = type_obj.get_for_api()
http_method = self._get_update_method()
return http_method(path, post_data=new_data, files=files, **kwargs) | [
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**kwargs: Extra options to send to the server (e.g. sudo)
Returns:
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Update an object on the server.
Args:
id: ID of the object to update (can be None if not required)
new_data: the update data for the object
**kwargs: Extra options to send to the server (e.g. sudo)
Returns:
dict: The new object data (*not* a RESTObject)
Raises:
GitlabAuthenticationError: If authentication is not correct
GitlabUpdateError: If the server cannot perform the request
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def update(self, id=None, new_data={}, **kwargs):
"""Update an object on the server.
Args:
id: ID of the object to update (can be None if not required)
new_data: the update data for the object
**kwargs: Extra options to send to the server (e.g. sudo)
Returns:
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GitlabUpdateError: If the server cannot perform the request
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if id is None:
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self._check_missing_update_attrs(new_data)
files = {}
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# if the type if FileAttribute we need to pass the data as
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k = type_obj.get_file_name(attr_name)
files[attr_name] = (k, new_data.pop(attr_name))
else:
new_data[attr_name] = type_obj.get_for_api()
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<|endoftext|> | [
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saltstack/salt | salt/states/file.py | absent | def absent(name,
**kwargs):
'''
Make sure that the named file or directory is absent. If it exists, it will
be deleted. This will work to reverse any of the functions in the file
state module. If a directory is supplied, it will be recursively deleted.
name
The path which should be deleted
'''
name = os.path.expanduser(name)
ret = {'name': name,
'changes': {},
'result': True,
'comment': ''}
if not name:
return _error(ret, 'Must provide name to file.absent')
if not os.path.isabs(name):
return _error(
ret, 'Specified file {0} is not an absolute path'.format(name)
)
if name == '/':
return _error(ret, 'Refusing to make "/" absent')
if os.path.isfile(name) or os.path.islink(name):
if __opts__['test']:
ret['result'] = None
ret['changes']['removed'] = name
ret['comment'] = 'File {0} is set for removal'.format(name)
return ret
try:
__salt__['file.remove'](name, force=True)
ret['comment'] = 'Removed file {0}'.format(name)
ret['changes']['removed'] = name
return ret
except CommandExecutionError as exc:
return _error(ret, '{0}'.format(exc))
elif os.path.isdir(name):
if __opts__['test']:
ret['result'] = None
ret['changes']['removed'] = name
ret['comment'] = 'Directory {0} is set for removal'.format(name)
return ret
try:
__salt__['file.remove'](name, force=True)
ret['comment'] = 'Removed directory {0}'.format(name)
ret['changes']['removed'] = name
return ret
except (OSError, IOError):
return _error(ret, 'Failed to remove directory {0}'.format(name))
ret['comment'] = 'File {0} is not present'.format(name)
return ret | python | def absent(name,
**kwargs):
'''
Make sure that the named file or directory is absent. If it exists, it will
be deleted. This will work to reverse any of the functions in the file
state module. If a directory is supplied, it will be recursively deleted.
name
The path which should be deleted
'''
name = os.path.expanduser(name)
ret = {'name': name,
'changes': {},
'result': True,
'comment': ''}
if not name:
return _error(ret, 'Must provide name to file.absent')
if not os.path.isabs(name):
return _error(
ret, 'Specified file {0} is not an absolute path'.format(name)
)
if name == '/':
return _error(ret, 'Refusing to make "/" absent')
if os.path.isfile(name) or os.path.islink(name):
if __opts__['test']:
ret['result'] = None
ret['changes']['removed'] = name
ret['comment'] = 'File {0} is set for removal'.format(name)
return ret
try:
__salt__['file.remove'](name, force=True)
ret['comment'] = 'Removed file {0}'.format(name)
ret['changes']['removed'] = name
return ret
except CommandExecutionError as exc:
return _error(ret, '{0}'.format(exc))
elif os.path.isdir(name):
if __opts__['test']:
ret['result'] = None
ret['changes']['removed'] = name
ret['comment'] = 'Directory {0} is set for removal'.format(name)
return ret
try:
__salt__['file.remove'](name, force=True)
ret['comment'] = 'Removed directory {0}'.format(name)
ret['changes']['removed'] = name
return ret
except (OSError, IOError):
return _error(ret, 'Failed to remove directory {0}'.format(name))
ret['comment'] = 'File {0} is not present'.format(name)
return ret | [
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be deleted. This will work to reverse any of the functions in the file
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name
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] | train | https://github.com/saltstack/salt/blob/e8541fd6e744ab0df786c0f76102e41631f45d46/salt/states/file.py#L1717-L1770 | <|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:
Make sure that the named file or directory is absent. If it exists, it will
be deleted. This will work to reverse any of the functions in the file
state module. If a directory is supplied, it will be recursively deleted.
name
The path which should be deleted
### Response:
def absent(name,
**kwargs):
'''
Make sure that the named file or directory is absent. If it exists, it will
be deleted. This will work to reverse any of the functions in the file
state module. If a directory is supplied, it will be recursively deleted.
name
The path which should be deleted
'''
name = os.path.expanduser(name)
ret = {'name': name,
'changes': {},
'result': True,
'comment': ''}
if not name:
return _error(ret, 'Must provide name to file.absent')
if not os.path.isabs(name):
return _error(
ret, 'Specified file {0} is not an absolute path'.format(name)
)
if name == '/':
return _error(ret, 'Refusing to make "/" absent')
if os.path.isfile(name) or os.path.islink(name):
if __opts__['test']:
ret['result'] = None
ret['changes']['removed'] = name
ret['comment'] = 'File {0} is set for removal'.format(name)
return ret
try:
__salt__['file.remove'](name, force=True)
ret['comment'] = 'Removed file {0}'.format(name)
ret['changes']['removed'] = name
return ret
except CommandExecutionError as exc:
return _error(ret, '{0}'.format(exc))
elif os.path.isdir(name):
if __opts__['test']:
ret['result'] = None
ret['changes']['removed'] = name
ret['comment'] = 'Directory {0} is set for removal'.format(name)
return ret
try:
__salt__['file.remove'](name, force=True)
ret['comment'] = 'Removed directory {0}'.format(name)
ret['changes']['removed'] = name
return ret
except (OSError, IOError):
return _error(ret, 'Failed to remove directory {0}'.format(name))
ret['comment'] = 'File {0} is not present'.format(name)
return ret
<|endoftext|> | [
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maoaiz/django-admin-conf-vars | django_admin_conf_vars/global_vars.py | VariablesManager.set | def set(self, name, default=0, editable=True, description=""):
'''Define a variable in DB and in memory'''
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if created:
var.value = default
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var.description = description
var.save(reload=False)
# ATTRIBUTES is accesible by any instance of VariablesManager
self.ATTRIBUTES[var.name] = var.value | python | def set(self, name, default=0, editable=True, description=""):
'''Define a variable in DB and in memory'''
var, created = ConfigurationVariable.objects.get_or_create(name=name)
if created:
var.value = default
if not editable:
var.value = default
var.editable = editable
var.description = description
var.save(reload=False)
# ATTRIBUTES is accesible by any instance of VariablesManager
self.ATTRIBUTES[var.name] = var.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:
Define a variable in DB and in memory
### Response:
def set(self, name, default=0, editable=True, description=""):
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var, created = ConfigurationVariable.objects.get_or_create(name=name)
if created:
var.value = default
if not editable:
var.value = default
var.editable = editable
var.description = description
var.save(reload=False)
# ATTRIBUTES is accesible by any instance of VariablesManager
self.ATTRIBUTES[var.name] = var.value
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abilian/abilian-core | abilian/i18n.py | country_choices | def country_choices(first=None, default_country_first=True):
"""Return a list of (code, countries), alphabetically sorted on localized
country name.
:param first: Country code to be placed at the top
:param default_country_first:
:type default_country_first: bool
"""
locale = _get_locale()
territories = [
(code, name) for code, name in locale.territories.items() if len(code) == 2
] # skip 3-digit regions
if first is None and default_country_first:
first = default_country()
def sortkey(item):
if first is not None and item[0] == first:
return "0"
return to_lower_ascii(item[1])
territories.sort(key=sortkey)
return territories | python | def country_choices(first=None, default_country_first=True):
"""Return a list of (code, countries), alphabetically sorted on localized
country name.
:param first: Country code to be placed at the top
:param default_country_first:
:type default_country_first: bool
"""
locale = _get_locale()
territories = [
(code, name) for code, name in locale.territories.items() if len(code) == 2
] # skip 3-digit regions
if first is None and default_country_first:
first = default_country()
def sortkey(item):
if first is not None and item[0] == first:
return "0"
return to_lower_ascii(item[1])
territories.sort(key=sortkey)
return territories | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Return a list of (code, countries), alphabetically sorted on localized
country name.
:param first: Country code to be placed at the top
:param default_country_first:
:type default_country_first: bool
### Response:
def country_choices(first=None, default_country_first=True):
"""Return a list of (code, countries), alphabetically sorted on localized
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:param first: Country code to be placed at the top
:param default_country_first:
:type default_country_first: bool
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first = default_country()
def sortkey(item):
if first is not None and item[0] == first:
return "0"
return to_lower_ascii(item[1])
territories.sort(key=sortkey)
return territories
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ChrisCummins/labm8 | labtypes.py | dict_values | def dict_values(src):
"""
Recursively get values in dict.
Unlike the builtin dict.values() function, this method will descend into
nested dicts, returning all nested values.
Arguments:
src (dict): Source dict.
Returns:
list: List of values.
"""
for v in src.values():
if isinstance(v, dict):
for v in dict_values(v):
yield v
else:
yield v | python | def dict_values(src):
"""
Recursively get values in dict.
Unlike the builtin dict.values() function, this method will descend into
nested dicts, returning all nested values.
Arguments:
src (dict): Source dict.
Returns:
list: List of values.
"""
for v in src.values():
if isinstance(v, dict):
for v in dict_values(v):
yield v
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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:
Recursively get values in dict.
Unlike the builtin dict.values() function, this method will descend into
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Arguments:
src (dict): Source dict.
Returns:
list: List of values.
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def dict_values(src):
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Recursively get values in dict.
Unlike the builtin dict.values() function, this method will descend into
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src (dict): Source dict.
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planetarypy/pvl | pvl/decoder.py | PVLDecoder.parse_units | def parse_units(self, stream):
"""
UnitsExpression ::=
UnitsStart WhiteSpace* UnitsValue WhiteSpace* UnitsEnd
"""
value = b''
self.expect(stream, self.begin_units)
while not self.has_next(self.end_units, stream):
if self.has_eof(stream):
self.raise_unexpected_eof(stream)
value += stream.read(1)
self.expect(stream, self.end_units)
return value.strip(b''.join(self.whitespace)).decode('utf-8') | python | def parse_units(self, stream):
"""
UnitsExpression ::=
UnitsStart WhiteSpace* UnitsValue WhiteSpace* UnitsEnd
"""
value = b''
self.expect(stream, self.begin_units)
while not self.has_next(self.end_units, stream):
if self.has_eof(stream):
self.raise_unexpected_eof(stream)
value += stream.read(1)
self.expect(stream, self.end_units)
return value.strip(b''.join(self.whitespace)).decode('utf-8') | [
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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:
UnitsExpression ::=
UnitsStart WhiteSpace* UnitsValue WhiteSpace* UnitsEnd
### Response:
def parse_units(self, stream):
"""
UnitsExpression ::=
UnitsStart WhiteSpace* UnitsValue WhiteSpace* UnitsEnd
"""
value = b''
self.expect(stream, self.begin_units)
while not self.has_next(self.end_units, stream):
if self.has_eof(stream):
self.raise_unexpected_eof(stream)
value += stream.read(1)
self.expect(stream, self.end_units)
return value.strip(b''.join(self.whitespace)).decode('utf-8')
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dropbox/stone | stone/backends/obj_c_client.py | ObjCBackend._get_deprecation_warning | def _get_deprecation_warning(self, route):
"""Returns a deprecation tag / message, if route is deprecated."""
result = ''
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msg = '{} is deprecated.'.format(fmt_route_func(route))
if route.deprecated.by:
msg += ' Use {}.'.format(fmt_var(route.deprecated.by.name))
result = ' __deprecated_msg("{}")'.format(msg)
return result | python | def _get_deprecation_warning(self, route):
"""Returns a deprecation tag / message, if route is deprecated."""
result = ''
if route.deprecated:
msg = '{} is deprecated.'.format(fmt_route_func(route))
if route.deprecated.by:
msg += ' Use {}.'.format(fmt_var(route.deprecated.by.name))
result = ' __deprecated_msg("{}")'.format(msg)
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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 deprecation tag / message, if route is deprecated.
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def _get_deprecation_warning(self, route):
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androguard/androguard | androguard/decompiler/dad/control_flow.py | derived_sequence | def derived_sequence(graph):
"""
Compute the derived sequence of the graph G
The intervals of G are collapsed into nodes, intervals of these nodes are
built, and the process is repeated iteratively until we obtain a single
node (if the graph is not irreducible)
"""
deriv_seq = [graph]
deriv_interv = []
single_node = False
while not single_node:
interv_graph, interv_heads = intervals(graph)
deriv_interv.append(interv_heads)
single_node = len(interv_graph) == 1
if not single_node:
deriv_seq.append(interv_graph)
graph = interv_graph
graph.compute_rpo()
return deriv_seq, deriv_interv | python | def derived_sequence(graph):
"""
Compute the derived sequence of the graph G
The intervals of G are collapsed into nodes, intervals of these nodes are
built, and the process is repeated iteratively until we obtain a single
node (if the graph is not irreducible)
"""
deriv_seq = [graph]
deriv_interv = []
single_node = False
while not single_node:
interv_graph, interv_heads = intervals(graph)
deriv_interv.append(interv_heads)
single_node = len(interv_graph) == 1
if not single_node:
deriv_seq.append(interv_graph)
graph = interv_graph
graph.compute_rpo()
return deriv_seq, deriv_interv | [
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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 the derived sequence of the graph G
The intervals of G are collapsed into nodes, intervals of these nodes are
built, and the process is repeated iteratively until we obtain a single
node (if the graph is not irreducible)
### Response:
def derived_sequence(graph):
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Compute the derived sequence of the graph G
The intervals of G are collapsed into nodes, intervals of these nodes are
built, and the process is repeated iteratively until we obtain a single
node (if the graph is not irreducible)
"""
deriv_seq = [graph]
deriv_interv = []
single_node = False
while not single_node:
interv_graph, interv_heads = intervals(graph)
deriv_interv.append(interv_heads)
single_node = len(interv_graph) == 1
if not single_node:
deriv_seq.append(interv_graph)
graph = interv_graph
graph.compute_rpo()
return deriv_seq, deriv_interv
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tensorpack/tensorpack | examples/basics/export-model.py | export_compact | def export_compact(model_path):
"""Export trained model to use it as a frozen and pruned inference graph in
mobile applications. """
pred_config = PredictConfig(
session_init=get_model_loader(model_path),
model=Model(),
input_names=['input_img'],
output_names=['prediction_img'])
ModelExporter(pred_config).export_compact('/tmp/compact_graph.pb') | python | def export_compact(model_path):
"""Export trained model to use it as a frozen and pruned inference graph in
mobile applications. """
pred_config = PredictConfig(
session_init=get_model_loader(model_path),
model=Model(),
input_names=['input_img'],
output_names=['prediction_img'])
ModelExporter(pred_config).export_compact('/tmp/compact_graph.pb') | [
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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:
Export trained model to use it as a frozen and pruned inference graph in
mobile applications.
### Response:
def export_compact(model_path):
"""Export trained model to use it as a frozen and pruned inference graph in
mobile applications. """
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session_init=get_model_loader(model_path),
model=Model(),
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PBR/MQ2 | MQ2/mq2.py | cli_main | def cli_main(): # pragma: no cover
""" Main function when running from CLI. """
if '--debug' in sys.argv:
LOG.setLevel(logging.DEBUG)
elif '--verbose' in sys.argv:
LOG.setLevel(logging.INFO)
args = _get_arguments()
try:
plugin, folder = get_plugin_and_folder(
inputzip=args.inputzip,
inputdir=args.inputdir,
inputfile=args.inputfile)
LOG.debug('Plugin: %s -- Folder: %s' % (plugin.name, folder))
run_mq2(
plugin, folder, lod_threshold=args.lod, session=args.session)
except MQ2Exception as err:
print(err)
return 1
return 0 | python | def cli_main(): # pragma: no cover
""" Main function when running from CLI. """
if '--debug' in sys.argv:
LOG.setLevel(logging.DEBUG)
elif '--verbose' in sys.argv:
LOG.setLevel(logging.INFO)
args = _get_arguments()
try:
plugin, folder = get_plugin_and_folder(
inputzip=args.inputzip,
inputdir=args.inputdir,
inputfile=args.inputfile)
LOG.debug('Plugin: %s -- Folder: %s' % (plugin.name, folder))
run_mq2(
plugin, folder, lod_threshold=args.lod, session=args.session)
except MQ2Exception as err:
print(err)
return 1
return 0 | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Main function when running from CLI.
### Response:
def cli_main(): # pragma: no cover
""" Main function when running from CLI. """
if '--debug' in sys.argv:
LOG.setLevel(logging.DEBUG)
elif '--verbose' in sys.argv:
LOG.setLevel(logging.INFO)
args = _get_arguments()
try:
plugin, folder = get_plugin_and_folder(
inputzip=args.inputzip,
inputdir=args.inputdir,
inputfile=args.inputfile)
LOG.debug('Plugin: %s -- Folder: %s' % (plugin.name, folder))
run_mq2(
plugin, folder, lod_threshold=args.lod, session=args.session)
except MQ2Exception as err:
print(err)
return 1
return 0
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QUANTAXIS/QUANTAXIS | QUANTAXIS/QASU/main.py | QA_SU_save_stock_xdxr | def QA_SU_save_stock_xdxr(engine, client=DATABASE):
"""save stock_xdxr
Arguments:
engine {[type]} -- [description]
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
engine = select_save_engine(engine)
engine.QA_SU_save_stock_xdxr(client=client) | python | def QA_SU_save_stock_xdxr(engine, client=DATABASE):
"""save stock_xdxr
Arguments:
engine {[type]} -- [description]
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
engine = select_save_engine(engine)
engine.QA_SU_save_stock_xdxr(client=client) | [
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engine {[type]} -- [description]
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE}) | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
save stock_xdxr
Arguments:
engine {[type]} -- [description]
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
### Response:
def QA_SU_save_stock_xdxr(engine, client=DATABASE):
"""save stock_xdxr
Arguments:
engine {[type]} -- [description]
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
engine = select_save_engine(engine)
engine.QA_SU_save_stock_xdxr(client=client)
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geographika/mappyfile | mappyfile/pprint.py | Quoter.escape_quotes | def escape_quotes(self, val):
"""
Escape any quotes in a value
"""
if self.is_string(val) and self._in_quotes(val, self.quote):
# make sure any previously escaped quotes are not re-escaped
middle = self.remove_quotes(val).replace("\\" + self.quote, self.quote)
middle = middle.replace(self.quote, "\\" + self.quote)
val = self.add_quotes(middle)
return val | python | def escape_quotes(self, val):
"""
Escape any quotes in a value
"""
if self.is_string(val) and self._in_quotes(val, self.quote):
# make sure any previously escaped quotes are not re-escaped
middle = self.remove_quotes(val).replace("\\" + self.quote, self.quote)
middle = middle.replace(self.quote, "\\" + self.quote)
val = self.add_quotes(middle)
return val | [
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### Instruction:
You are a Senior Software Developer and a helpful AI programming assistant. Generate only Python code without explanations. Respond only in English.
### Task:
Escape any quotes in a value
### Response:
def escape_quotes(self, val):
"""
Escape any quotes in a value
"""
if self.is_string(val) and self._in_quotes(val, self.quote):
# make sure any previously escaped quotes are not re-escaped
middle = self.remove_quotes(val).replace("\\" + self.quote, self.quote)
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anjianshi/flask-restful-extend | flask_restful_extend/model_validates.py | _validate_handler | def _validate_handler(column_name, value, predicate_refs):
"""handle predicate's return value"""
# only does validate when attribute value is not None
# else, just return it, let sqlalchemy decide if the value was legal according to `nullable` argument's value
if value is not None:
for predicate_ref in predicate_refs:
predicate, predicate_name, predicate_args = _decode_predicate_ref(predicate_ref)
validate_result = predicate(value, *predicate_args)
if isinstance(validate_result, dict) and 'value' in validate_result:
value = validate_result['value']
elif type(validate_result) != bool:
raise Exception(
'predicate (name={}) can only return bool or dict(value=new_value) value'.format(predicate_name))
elif not validate_result:
raise ModelInvalid(u'db model validate failed: column={}, value={}, predicate={}, arguments={}'.format(
column_name, value, predicate_name, ','.join(map(str, predicate_args))
))
return value | python | def _validate_handler(column_name, value, predicate_refs):
"""handle predicate's return value"""
# only does validate when attribute value is not None
# else, just return it, let sqlalchemy decide if the value was legal according to `nullable` argument's value
if value is not None:
for predicate_ref in predicate_refs:
predicate, predicate_name, predicate_args = _decode_predicate_ref(predicate_ref)
validate_result = predicate(value, *predicate_args)
if isinstance(validate_result, dict) and 'value' in validate_result:
value = validate_result['value']
elif type(validate_result) != bool:
raise Exception(
'predicate (name={}) can only return bool or dict(value=new_value) value'.format(predicate_name))
elif not validate_result:
raise ModelInvalid(u'db model validate failed: column={}, value={}, predicate={}, arguments={}'.format(
column_name, value, predicate_name, ','.join(map(str, predicate_args))
))
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:
handle predicate's return value
### Response:
def _validate_handler(column_name, value, predicate_refs):
"""handle predicate's return value"""
# only does validate when attribute value is not None
# else, just return it, let sqlalchemy decide if the value was legal according to `nullable` argument's value
if value is not None:
for predicate_ref in predicate_refs:
predicate, predicate_name, predicate_args = _decode_predicate_ref(predicate_ref)
validate_result = predicate(value, *predicate_args)
if isinstance(validate_result, dict) and 'value' in validate_result:
value = validate_result['value']
elif type(validate_result) != bool:
raise Exception(
'predicate (name={}) can only return bool or dict(value=new_value) value'.format(predicate_name))
elif not validate_result:
raise ModelInvalid(u'db model validate failed: column={}, value={}, predicate={}, arguments={}'.format(
column_name, value, predicate_name, ','.join(map(str, predicate_args))
))
return value
<|endoftext|> | [
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