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def fetch(self):
'\n Fetch a FeedbackInstance\n\n :returns: Fetched FeedbackInstance\n :rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance\n '
params = values.of({})
payload = self._version.fetch('GET', self._uri, params=params)
return FeedbackInstance(self._version, payload, account_sid=self._solution['account_sid'], call_sid=self._solution['call_sid']) | -2,003,002,863,357,165,600 | Fetch a FeedbackInstance
:returns: Fetched FeedbackInstance
:rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | fetch | Jason-Paprocki/hacknjit | python | def fetch(self):
'\n Fetch a FeedbackInstance\n\n :returns: Fetched FeedbackInstance\n :rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance\n '
params = values.of({})
payload = self._version.fetch('GET', self._uri, params=params)
return FeedbackInstance(self._version, payload, account_sid=self._solution['account_sid'], call_sid=self._solution['call_sid']) |
def update(self, quality_score, issue=values.unset):
'\n Update the FeedbackInstance\n\n :param unicode quality_score: An integer from 1 to 5\n :param FeedbackInstance.Issues issue: Issues experienced during the call\n\n :returns: Updated FeedbackInstance\n :rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance\n '
data = values.of({'QualityScore': quality_score, 'Issue': issue})
payload = self._version.update('POST', self._uri, data=data)
return FeedbackInstance(self._version, payload, account_sid=self._solution['account_sid'], call_sid=self._solution['call_sid']) | 1,302,640,320,467,985,700 | Update the FeedbackInstance
:param unicode quality_score: An integer from 1 to 5
:param FeedbackInstance.Issues issue: Issues experienced during the call
:returns: Updated FeedbackInstance
:rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | update | Jason-Paprocki/hacknjit | python | def update(self, quality_score, issue=values.unset):
'\n Update the FeedbackInstance\n\n :param unicode quality_score: An integer from 1 to 5\n :param FeedbackInstance.Issues issue: Issues experienced during the call\n\n :returns: Updated FeedbackInstance\n :rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance\n '
data = values.of({'QualityScore': quality_score, 'Issue': issue})
payload = self._version.update('POST', self._uri, data=data)
return FeedbackInstance(self._version, payload, account_sid=self._solution['account_sid'], call_sid=self._solution['call_sid']) |
def __repr__(self):
'\n Provide a friendly representation\n\n :returns: Machine friendly representation\n :rtype: str\n '
context = ' '.join(('{}={}'.format(k, v) for (k, v) in self._solution.items()))
return '<Twilio.Api.V2010.FeedbackContext {}>'.format(context) | -4,387,753,038,428,856,000 | Provide a friendly representation
:returns: Machine friendly representation
:rtype: str | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | __repr__ | Jason-Paprocki/hacknjit | python | def __repr__(self):
'\n Provide a friendly representation\n\n :returns: Machine friendly representation\n :rtype: str\n '
context = ' '.join(('{}={}'.format(k, v) for (k, v) in self._solution.items()))
return '<Twilio.Api.V2010.FeedbackContext {}>'.format(context) |
def __init__(self, version, payload, account_sid, call_sid):
'\n Initialize the FeedbackInstance\n\n :returns: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance\n :rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance\n '
super(FeedbackInstance, self).__init__(version)
self._properties = {'account_sid': payload['account_sid'], 'date_created': deserialize.rfc2822_datetime(payload['date_created']), 'date_updated': deserialize.rfc2822_datetime(payload['date_updated']), 'issues': payload['issues'], 'quality_score': deserialize.integer(payload['quality_score']), 'sid': payload['sid']}
self._context = None
self._solution = {'account_sid': account_sid, 'call_sid': call_sid} | -801,141,970,854,188,800 | Initialize the FeedbackInstance
:returns: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance
:rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | __init__ | Jason-Paprocki/hacknjit | python | def __init__(self, version, payload, account_sid, call_sid):
'\n Initialize the FeedbackInstance\n\n :returns: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance\n :rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance\n '
super(FeedbackInstance, self).__init__(version)
self._properties = {'account_sid': payload['account_sid'], 'date_created': deserialize.rfc2822_datetime(payload['date_created']), 'date_updated': deserialize.rfc2822_datetime(payload['date_updated']), 'issues': payload['issues'], 'quality_score': deserialize.integer(payload['quality_score']), 'sid': payload['sid']}
self._context = None
self._solution = {'account_sid': account_sid, 'call_sid': call_sid} |
@property
def _proxy(self):
'\n Generate an instance context for the instance, the context is capable of\n performing various actions. All instance actions are proxied to the context\n\n :returns: FeedbackContext for this FeedbackInstance\n :rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackContext\n '
if (self._context is None):
self._context = FeedbackContext(self._version, account_sid=self._solution['account_sid'], call_sid=self._solution['call_sid'])
return self._context | -4,084,114,105,181,760,500 | Generate an instance context for the instance, the context is capable of
performing various actions. All instance actions are proxied to the context
:returns: FeedbackContext for this FeedbackInstance
:rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackContext | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | _proxy | Jason-Paprocki/hacknjit | python | @property
def _proxy(self):
'\n Generate an instance context for the instance, the context is capable of\n performing various actions. All instance actions are proxied to the context\n\n :returns: FeedbackContext for this FeedbackInstance\n :rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackContext\n '
if (self._context is None):
self._context = FeedbackContext(self._version, account_sid=self._solution['account_sid'], call_sid=self._solution['call_sid'])
return self._context |
@property
def account_sid(self):
'\n :returns: The account_sid\n :rtype: unicode\n '
return self._properties['account_sid'] | -6,875,424,500,779,715,000 | :returns: The account_sid
:rtype: unicode | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | account_sid | Jason-Paprocki/hacknjit | python | @property
def account_sid(self):
'\n :returns: The account_sid\n :rtype: unicode\n '
return self._properties['account_sid'] |
@property
def date_created(self):
'\n :returns: The date_created\n :rtype: datetime\n '
return self._properties['date_created'] | -250,917,683,243,838,050 | :returns: The date_created
:rtype: datetime | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | date_created | Jason-Paprocki/hacknjit | python | @property
def date_created(self):
'\n :returns: The date_created\n :rtype: datetime\n '
return self._properties['date_created'] |
@property
def date_updated(self):
'\n :returns: The date_updated\n :rtype: datetime\n '
return self._properties['date_updated'] | -8,425,627,007,073,683,000 | :returns: The date_updated
:rtype: datetime | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | date_updated | Jason-Paprocki/hacknjit | python | @property
def date_updated(self):
'\n :returns: The date_updated\n :rtype: datetime\n '
return self._properties['date_updated'] |
@property
def issues(self):
'\n :returns: The issues\n :rtype: FeedbackInstance.Issues\n '
return self._properties['issues'] | -594,813,764,122,206,500 | :returns: The issues
:rtype: FeedbackInstance.Issues | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | issues | Jason-Paprocki/hacknjit | python | @property
def issues(self):
'\n :returns: The issues\n :rtype: FeedbackInstance.Issues\n '
return self._properties['issues'] |
@property
def quality_score(self):
'\n :returns: 1 to 5 quality score\n :rtype: unicode\n '
return self._properties['quality_score'] | -4,891,796,970,587,220,000 | :returns: 1 to 5 quality score
:rtype: unicode | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | quality_score | Jason-Paprocki/hacknjit | python | @property
def quality_score(self):
'\n :returns: 1 to 5 quality score\n :rtype: unicode\n '
return self._properties['quality_score'] |
@property
def sid(self):
'\n :returns: The sid\n :rtype: unicode\n '
return self._properties['sid'] | -8,253,500,487,129,927,000 | :returns: The sid
:rtype: unicode | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | sid | Jason-Paprocki/hacknjit | python | @property
def sid(self):
'\n :returns: The sid\n :rtype: unicode\n '
return self._properties['sid'] |
def create(self, quality_score, issue=values.unset):
'\n Create a new FeedbackInstance\n\n :param unicode quality_score: The quality_score\n :param FeedbackInstance.Issues issue: The issue\n\n :returns: Newly created FeedbackInstance\n :rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance\n '
return self._proxy.create(quality_score, issue=issue) | -2,144,275,642,321,963,300 | Create a new FeedbackInstance
:param unicode quality_score: The quality_score
:param FeedbackInstance.Issues issue: The issue
:returns: Newly created FeedbackInstance
:rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | create | Jason-Paprocki/hacknjit | python | def create(self, quality_score, issue=values.unset):
'\n Create a new FeedbackInstance\n\n :param unicode quality_score: The quality_score\n :param FeedbackInstance.Issues issue: The issue\n\n :returns: Newly created FeedbackInstance\n :rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance\n '
return self._proxy.create(quality_score, issue=issue) |
def fetch(self):
'\n Fetch a FeedbackInstance\n\n :returns: Fetched FeedbackInstance\n :rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance\n '
return self._proxy.fetch() | -8,067,892,351,115,993,000 | Fetch a FeedbackInstance
:returns: Fetched FeedbackInstance
:rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | fetch | Jason-Paprocki/hacknjit | python | def fetch(self):
'\n Fetch a FeedbackInstance\n\n :returns: Fetched FeedbackInstance\n :rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance\n '
return self._proxy.fetch() |
def update(self, quality_score, issue=values.unset):
'\n Update the FeedbackInstance\n\n :param unicode quality_score: An integer from 1 to 5\n :param FeedbackInstance.Issues issue: Issues experienced during the call\n\n :returns: Updated FeedbackInstance\n :rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance\n '
return self._proxy.update(quality_score, issue=issue) | 969,629,373,209,911,300 | Update the FeedbackInstance
:param unicode quality_score: An integer from 1 to 5
:param FeedbackInstance.Issues issue: Issues experienced during the call
:returns: Updated FeedbackInstance
:rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | update | Jason-Paprocki/hacknjit | python | def update(self, quality_score, issue=values.unset):
'\n Update the FeedbackInstance\n\n :param unicode quality_score: An integer from 1 to 5\n :param FeedbackInstance.Issues issue: Issues experienced during the call\n\n :returns: Updated FeedbackInstance\n :rtype: twilio.rest.api.v2010.account.call.feedback.FeedbackInstance\n '
return self._proxy.update(quality_score, issue=issue) |
def __repr__(self):
'\n Provide a friendly representation\n\n :returns: Machine friendly representation\n :rtype: str\n '
context = ' '.join(('{}={}'.format(k, v) for (k, v) in self._solution.items()))
return '<Twilio.Api.V2010.FeedbackInstance {}>'.format(context) | -429,944,536,459,628,300 | Provide a friendly representation
:returns: Machine friendly representation
:rtype: str | lib/python2.7/site-packages/twilio/rest/api/v2010/account/call/feedback.py | __repr__ | Jason-Paprocki/hacknjit | python | def __repr__(self):
'\n Provide a friendly representation\n\n :returns: Machine friendly representation\n :rtype: str\n '
context = ' '.join(('{}={}'.format(k, v) for (k, v) in self._solution.items()))
return '<Twilio.Api.V2010.FeedbackInstance {}>'.format(context) |
def processLayoutText(self, layoutText):
'\n Coordinates are flipped from the input format to the (x,y) convention here\n\n The shape of the maze. Each character\n represents a different type of object.\n % - Wall\n . - Food\n o - Capsule\n G - Ghost\n P - Pacman\n Other characters are ignored.\n '
maxY = (self.height - 1)
for y in range(self.height):
for x in range(self.width):
layoutChar = layoutText[(maxY - y)][x]
self.processLayoutChar(x, y, layoutChar)
self.agentPositions.sort()
self.agentPositions = [((i == 0), pos) for (i, pos) in self.agentPositions] | 9,172,213,862,141,142,000 | Coordinates are flipped from the input format to the (x,y) convention here
The shape of the maze. Each character
represents a different type of object.
% - Wall
. - Food
o - Capsule
G - Ghost
P - Pacman
Other characters are ignored. | tracking/layout.py | processLayoutText | chibinz/CS181 | python | def processLayoutText(self, layoutText):
'\n Coordinates are flipped from the input format to the (x,y) convention here\n\n The shape of the maze. Each character\n represents a different type of object.\n % - Wall\n . - Food\n o - Capsule\n G - Ghost\n P - Pacman\n Other characters are ignored.\n '
maxY = (self.height - 1)
for y in range(self.height):
for x in range(self.width):
layoutChar = layoutText[(maxY - y)][x]
self.processLayoutChar(x, y, layoutChar)
self.agentPositions.sort()
self.agentPositions = [((i == 0), pos) for (i, pos) in self.agentPositions] |
def train(estimator: object, X: Iterable[Union[(int, float)]], y: Iterable):
'\n Train custom classifier model.\n\n Parameters:\n estimator: Unfitted estimator.\n X: Input training data.\n y: Labels for test data.\n\n Returns:\n Fitted estimator model.\n '
return estimator.fit(X=X, y=y) | -8,926,492,144,770,881,000 | Train custom classifier model.
Parameters:
estimator: Unfitted estimator.
X: Input training data.
y: Labels for test data.
Returns:
Fitted estimator model. | ml.py | train | Fennec2000GH/Poly-Finance | python | def train(estimator: object, X: Iterable[Union[(int, float)]], y: Iterable):
'\n Train custom classifier model.\n\n Parameters:\n estimator: Unfitted estimator.\n X: Input training data.\n y: Labels for test data.\n\n Returns:\n Fitted estimator model.\n '
return estimator.fit(X=X, y=y) |
def classify(estimator: object, X: Iterable[Union[(int, float)]]):
'\n Predict with custom classifier model.\n\n Parameters:\n estimator: Fitted estimator.\n X: Input test data.\n\n Returns:\n Predicted labels.\n '
return estimator.predict(X=X) | 7,964,491,657,674,355,000 | Predict with custom classifier model.
Parameters:
estimator: Fitted estimator.
X: Input test data.
Returns:
Predicted labels. | ml.py | classify | Fennec2000GH/Poly-Finance | python | def classify(estimator: object, X: Iterable[Union[(int, float)]]):
'\n Predict with custom classifier model.\n\n Parameters:\n estimator: Fitted estimator.\n X: Input test data.\n\n Returns:\n Predicted labels.\n '
return estimator.predict(X=X) |
def regress(estimator: object, X: Iterable[Union[(int, float)]], y: Iterable):
'\n Predict with custom regressor model.\n\n Parameters:\n estimator: Fitted estimator.\n X: Input test data.\n y: Labels for test data.\n\n Returns:\n Predicted labels.\n '
pass | 1,504,345,972,072,559,400 | Predict with custom regressor model.
Parameters:
estimator: Fitted estimator.
X: Input test data.
y: Labels for test data.
Returns:
Predicted labels. | ml.py | regress | Fennec2000GH/Poly-Finance | python | def regress(estimator: object, X: Iterable[Union[(int, float)]], y: Iterable):
'\n Predict with custom regressor model.\n\n Parameters:\n estimator: Fitted estimator.\n X: Input test data.\n y: Labels for test data.\n\n Returns:\n Predicted labels.\n '
pass |
def evaluate(estimator: object, X: Iterable[Union[(int, float)]], y: Iterable):
'\n Predict with custom classifier model.\n\n Parameters:\n estimator: Fitted estimator.\n X: Input test data.\n y: Labels for test data.\n\n Returns:\n Predicted labels.\n '
pred = estimator.predict(X=X)
report = classification_report(y_true=y, y_pred=pred)
st.write('Classification Report')
st.write(report)
(fpr, tpr, thresholds) = roc_curve(y, pred)
roc_auc = auc(fpr, tpr)
(_, _, figure) = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name=type(estimator))
st.pyplot(fig=figure) | -6,500,435,570,618,804,000 | Predict with custom classifier model.
Parameters:
estimator: Fitted estimator.
X: Input test data.
y: Labels for test data.
Returns:
Predicted labels. | ml.py | evaluate | Fennec2000GH/Poly-Finance | python | def evaluate(estimator: object, X: Iterable[Union[(int, float)]], y: Iterable):
'\n Predict with custom classifier model.\n\n Parameters:\n estimator: Fitted estimator.\n X: Input test data.\n y: Labels for test data.\n\n Returns:\n Predicted labels.\n '
pred = estimator.predict(X=X)
report = classification_report(y_true=y, y_pred=pred)
st.write('Classification Report')
st.write(report)
(fpr, tpr, thresholds) = roc_curve(y, pred)
roc_auc = auc(fpr, tpr)
(_, _, figure) = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name=type(estimator))
st.pyplot(fig=figure) |
def iter_linear_fit(xy, uv, wxy=None, wuv=None, fitgeom='general', center=None, nclip=3, sigma=(3.0, 'rmse'), clip_accum=False):
'\n Compute linear transformation parameters that "best" (in the sense of\n minimizing residuals) transform ``uv`` source position to ``xy``\n sources iteratively using sigma-clipping.\n\n More precisely, this functions attempts to find a ``2x2`` matrix ``F`` and\n a shift vector ``s`` that minimize the residuals between the *transformed*\n reference source coordinates ``uv``\n\n .. math::\n \\mathbf{xy}\'_k = \\mathbf{F}\\cdot(\\mathbf{uv}_k-\\mathbf{c})+\\\n \\mathbf{s} + \\mathbf{c}\n :label: ilf1\n\n and the "observed" source positions ``xy``:\n\n .. math::\n \\epsilon^2 = \\Sigma_k w_k \\|\\mathbf{xy}_k-\\mathbf{xy}\'_k\\|^2.\n :label: ilf2\n\n In the above equations, :math:`\\mathbf{F}` is a ``2x2`` matrix while\n :math:`\\mathbf{xy}_k` and :math:`\\mathbf{uv}_k` are the position\n coordinates of the ``k``-th source (row in input ``xy`` and ``uv`` arrays).\n\n One of the two catalogs (``xy`` or ``uv``) contains what we refer to as\n "image" source positions and the other one as "reference" source positions.\n The meaning assigned to ``xy`` and ``uv`` parameters are up to the\n caller of this function.\n\n Parameters\n ----------\n xy: numpy.ndarray\n A ``(N, 2)``-shaped array of source positions (one 2-coordinate\n position per line).\n\n uv: numpy.ndarray\n A ``(N, 2)``-shaped array of source positions (one 2-coordinate\n position per line). This array *must have* the same length (shape)\n as the ``xy`` array.\n\n wxy: numpy.ndarray, None, optional\n A 1-dimensional array of weights of the same length (``N``)\n as ``xy`` array indicating how much a given coordinate should be\n weighted in the fit. If not provided or set to `None`, all positions\n will be contribute equally to the fit if ``wuv`` is also set to `None`.\n See ``Notes`` section for more details.\n\n wuv: numpy.ndarray, None, optional\n A 1-dimensional array of weights of the same length (``N``)\n as ``xy`` array indicating how much a given coordinate should be\n weighted in the fit. If not provided or set to `None`, all positions\n will be contribute equally to the fit if ``wxy`` is also set to `None`.\n See ``Notes`` section for more details.\n\n fitgeom: {\'shift\', \'rscale\', \'general\'}, optional\n The fitting geometry to be used in fitting the matched object lists.\n This parameter is used in fitting the shifts (offsets), rotations\n and/or scale changes from the matched object lists. The \'general\'\n fit geometry allows for independent scale and rotation for each axis.\n\n center: tuple, list, numpy.ndarray, None, optional\n A list-like container with two ``X``- and ``Y``-positions of the center\n (origin) of rotations in the ``uv`` and ``xy`` coordinate frames.\n If not provided, ``center`` is estimated as a (weighted) mean position\n in the ``uv`` frame.\n\n nclip: int, None, optional\n Number (a non-negative integer) of clipping iterations in fit.\n Clipping will be turned off if ``nclip`` is either `None` or 0.\n\n sigma: float, tuple of the form (float, str), optional\n When a tuple is provided, first value (a positive number)\n indicates the number of "fit error estimates" to use for clipping.\n The second value (a string) indicates the statistic to be\n used for "fit error estimate". Currently the following values are\n supported: ``\'rmse\'``, ``\'mae\'``, and ``\'std\'``\n - see ``Notes`` section for more details.\n\n When ``sigma`` is a single number, it must be a positive number and\n the default error estimate ``\'rmse\'`` is assumed.\n\n This parameter is ignored when ``nclip`` is either `None` or 0.\n\n clip_accum: bool, optional\n Indicates whether or not to reset the list of "bad" (clipped out)\n sources after each clipping iteration. When set to `True` the list\n only grows with each iteration as "bad" positions never re-enter the\n pool of available position for the fit. By default the list of\n "bad" source positions is purged at each iteration.\n\n Returns\n -------\n fit: dict\n - ``\'shift\'``: A ``numpy.ndarray`` with two components of the\n computed shift.\n - ``\'shift_ld\'``: A ``numpy.ndarray`` with two components of the\n computed shift of type ``numpy.longdouble``.\n - ``\'matrix\'``: A ``2x2`` ``numpy.ndarray`` with the computed\n generalized rotation matrix.\n - ``\'matrix_ld\'``: A ``2x2`` ``numpy.ndarray`` with the computed\n generalized rotation matrix of type ``numpy.longdouble``.\n - ``\'proper_rot\'``: Rotation angle (degree) as if the rotation is\n proper.\n - ``\'rot\'``: A tuple of ``(rotx, roty)`` - the rotation angles with\n regard to the ``X`` and ``Y`` axes.\n - ``\'<rot>\'``: *Arithmetic mean* of the angles of rotation around\n ``X`` and ``Y`` axes.\n - ``\'scale\'``: A tuple of ``(sx, sy)`` - scale change in the direction\n of the ``X`` and ``Y`` axes.\n - ``\'<scale>\'``: *Geometric mean* of scales ``sx`` and ``sy``.\n - ``\'skew\'``: Computed skew.\n - ``\'proper\'``: a boolean indicating whether the rotation is proper.\n - ``\'fitgeom\'``: Fit geometry (allowed transformations) used for\n fitting data (to minimize residuals). This is copy of the input\n argument ``fitgeom``.\n - ``\'center\'``: Center of rotation\n - ``\'center_ld\'``: Center of rotation as a ``numpy.longdouble``.\n - ``\'fitmask\'``: A boolean array indicating which source positions\n where used for fitting (`True`) and which were clipped out\n (`False`). **NOTE** For weighted fits, positions with zero\n weights are automatically excluded from the fits.\n - ``\'eff_nclip\'``: Effective number of clipping iterations\n - ``\'rmse\'``: Root-Mean-Square Error\n - ``\'mae\'``: Mean Absolute Error\n - ``\'std\'``: Standard Deviation of the residuals\n - ``\'resids\'``: An array of residuals of the fit.\n **NOTE:** Only the residuals for the "valid" points are reported\n here. Therefore the length of this array may be smaller than the\n length of input arrays of positions.\n\n Notes\n -----\n **Weights**\n\n Weights can be provided for both "image" source positions and "reference"\n source positions. When no weights are given, all positions are weighted\n equally. When only one set of positions have weights (i.e., either ``wxy``\n or ``wuv`` is not `None`) then weights in :eq:`ilf2` are set to be equal\n to the provided set of weights. When weights for *both* "image" source\n positions and "reference" source positions are provided, then the\n combined weight that is used in :eq:`ilf2` is computed as:\n\n .. math::\n 1/w = 1/w_{xy} + 1/w_{uv}.\n\n **Statistics for clipping**\n\n Several statistics are available for clipping iterations and all of them\n are reported in the returned ``fit`` dictionary regardless of the\n setting in ``sigma``:\n\n .. math::\n \\mathrm{RMSE} = \\sqrt{\\Sigma_k w_k \\|\\mathbf{r}_k\\|^2}\n\n .. math::\n \\mathrm{MAE} = \\sqrt{\\Sigma_k w_k \\|\\mathbf{r}_k\\|}\n\n .. math::\n \\mathrm{STD} = \\sqrt{\\Sigma_k w_k \\|\\mathbf{r}_k - \\\n \\mathbf{\\overline{r}}\\|^2}/(1-V_2)\n\n where :math:`\\mathbf{r}_k=\\mathbf{xy}_k-\\mathbf{xy}\'_k`,\n :math:`\\Sigma_k w_k = 1`, and :math:`V_2=\\Sigma_k w_k^2`.\n\n '
if (fitgeom == 'general'):
linear_fit = fit_general
elif (fitgeom == 'rscale'):
linear_fit = fit_rscale
elif (fitgeom == 'shift'):
linear_fit = fit_shifts
else:
raise ValueError("Unsupported 'fitgeom' value: '{}'".format(fitgeom))
minobj_per_fitgeom = {'shift': 1, 'rscale': 2, 'general': 3}
minobj = minobj_per_fitgeom[fitgeom]
xy = np.array(xy, dtype=np.longdouble)
uv = np.array(uv, dtype=np.longdouble)
if ((len(xy.shape) != 2) or (xy.shape[1] != 2) or (uv.shape != xy.shape)):
raise ValueError("Input coordinate arrays 'xy' and 'uv' must be of shape (N, 2) where N is the number of coordinate points.")
wmask = np.ones(len(xy), dtype=np.bool_)
if (wxy is not None):
wxy = np.asarray(wxy)
if ((len(wxy.shape) != 1) or (wxy.shape[0] != xy.shape[0])):
raise ValueError("Weights 'wxy' must be a 1-dimensional vector of lengths equal to the number of input points.")
wmask *= (wxy > 0.0)
if (wuv is not None):
wuv = np.asarray(wuv)
if ((len(wuv.shape) != 1) or (wuv.shape[0] != xy.shape[0])):
raise ValueError("Weights 'wuv' must be a 1-dimensional vector of lengths equal to the number of input points.")
wmask *= (wuv > 0.0)
mask = wmask
if ((sigma is None) and (nclip is not None) and (nclip > 0)):
raise ValueError("Argument 'sigma' cannot be None when 'nclip' is a positive number.")
if isinstance(sigma, numbers.Number):
sigstat = 'rmse'
nsigma = float(sigma)
elif (sigma is not None):
nsigma = float(sigma[0])
sigstat = sigma[1]
if (sigstat not in ['rmse', 'mae', 'std']):
raise ValueError('Unsupported sigma statistics value.')
if ((sigma is not None) and (nsigma <= 0.0)):
raise ValueError('The value of sigma for clipping iterations must be positive.')
if (nclip is None):
nclip = 0
else:
if (nclip < 0):
raise ValueError("Argument 'nclip' must be non-negative.")
nclip = int(nclip)
if (np.count_nonzero(mask) == minobj):
log.warning("The number of sources for the fit is smaller than the minimum number of sources necessary for the requested 'fitgeom'.")
log.warning('Resetting number of clipping iterations to 0.')
nclip = 0
if (center is None):
center_ld = uv[mask].mean(axis=0, dtype=np.longdouble)
center = center_ld.astype(np.double)
else:
center_ld = np.longdouble(center)
xy[mask] -= center_ld
uv[mask] -= center_ld
log.info("Performing '{:s}' fit".format(fitgeom))
wmxy = (None if (wxy is None) else wxy[mask])
wmuv = (None if (wuv is None) else wuv[mask])
fit = linear_fit(xy[mask], uv[mask], wmxy, wmuv)
effective_nclip = 0
for n in range(nclip):
resids = fit['resids']
cutoff = (nsigma * fit[sigstat])
nonclipped = (np.linalg.norm(resids, axis=1) < cutoff)
if ((np.count_nonzero(nonclipped) < minobj) or nonclipped.all()):
break
effective_nclip += 1
prev_mask = mask
if (not clip_accum):
mask = np.array(wmask)
mask[prev_mask] *= nonclipped
wmxy = (None if (wxy is None) else wxy[mask])
wmuv = (None if (wuv is None) else wuv[mask])
fit = linear_fit(xy[mask], uv[mask], wmxy, wmuv)
fit['center'] = center
fit['center_ld'] = center_ld
fit['fitmask'] = mask
fit['eff_nclip'] = effective_nclip
return fit | -8,699,323,938,293,037,000 | Compute linear transformation parameters that "best" (in the sense of
minimizing residuals) transform ``uv`` source position to ``xy``
sources iteratively using sigma-clipping.
More precisely, this functions attempts to find a ``2x2`` matrix ``F`` and
a shift vector ``s`` that minimize the residuals between the *transformed*
reference source coordinates ``uv``
.. math::
\mathbf{xy}'_k = \mathbf{F}\cdot(\mathbf{uv}_k-\mathbf{c})+\
\mathbf{s} + \mathbf{c}
:label: ilf1
and the "observed" source positions ``xy``:
.. math::
\epsilon^2 = \Sigma_k w_k \|\mathbf{xy}_k-\mathbf{xy}'_k\|^2.
:label: ilf2
In the above equations, :math:`\mathbf{F}` is a ``2x2`` matrix while
:math:`\mathbf{xy}_k` and :math:`\mathbf{uv}_k` are the position
coordinates of the ``k``-th source (row in input ``xy`` and ``uv`` arrays).
One of the two catalogs (``xy`` or ``uv``) contains what we refer to as
"image" source positions and the other one as "reference" source positions.
The meaning assigned to ``xy`` and ``uv`` parameters are up to the
caller of this function.
Parameters
----------
xy: numpy.ndarray
A ``(N, 2)``-shaped array of source positions (one 2-coordinate
position per line).
uv: numpy.ndarray
A ``(N, 2)``-shaped array of source positions (one 2-coordinate
position per line). This array *must have* the same length (shape)
as the ``xy`` array.
wxy: numpy.ndarray, None, optional
A 1-dimensional array of weights of the same length (``N``)
as ``xy`` array indicating how much a given coordinate should be
weighted in the fit. If not provided or set to `None`, all positions
will be contribute equally to the fit if ``wuv`` is also set to `None`.
See ``Notes`` section for more details.
wuv: numpy.ndarray, None, optional
A 1-dimensional array of weights of the same length (``N``)
as ``xy`` array indicating how much a given coordinate should be
weighted in the fit. If not provided or set to `None`, all positions
will be contribute equally to the fit if ``wxy`` is also set to `None`.
See ``Notes`` section for more details.
fitgeom: {'shift', 'rscale', 'general'}, optional
The fitting geometry to be used in fitting the matched object lists.
This parameter is used in fitting the shifts (offsets), rotations
and/or scale changes from the matched object lists. The 'general'
fit geometry allows for independent scale and rotation for each axis.
center: tuple, list, numpy.ndarray, None, optional
A list-like container with two ``X``- and ``Y``-positions of the center
(origin) of rotations in the ``uv`` and ``xy`` coordinate frames.
If not provided, ``center`` is estimated as a (weighted) mean position
in the ``uv`` frame.
nclip: int, None, optional
Number (a non-negative integer) of clipping iterations in fit.
Clipping will be turned off if ``nclip`` is either `None` or 0.
sigma: float, tuple of the form (float, str), optional
When a tuple is provided, first value (a positive number)
indicates the number of "fit error estimates" to use for clipping.
The second value (a string) indicates the statistic to be
used for "fit error estimate". Currently the following values are
supported: ``'rmse'``, ``'mae'``, and ``'std'``
- see ``Notes`` section for more details.
When ``sigma`` is a single number, it must be a positive number and
the default error estimate ``'rmse'`` is assumed.
This parameter is ignored when ``nclip`` is either `None` or 0.
clip_accum: bool, optional
Indicates whether or not to reset the list of "bad" (clipped out)
sources after each clipping iteration. When set to `True` the list
only grows with each iteration as "bad" positions never re-enter the
pool of available position for the fit. By default the list of
"bad" source positions is purged at each iteration.
Returns
-------
fit: dict
- ``'shift'``: A ``numpy.ndarray`` with two components of the
computed shift.
- ``'shift_ld'``: A ``numpy.ndarray`` with two components of the
computed shift of type ``numpy.longdouble``.
- ``'matrix'``: A ``2x2`` ``numpy.ndarray`` with the computed
generalized rotation matrix.
- ``'matrix_ld'``: A ``2x2`` ``numpy.ndarray`` with the computed
generalized rotation matrix of type ``numpy.longdouble``.
- ``'proper_rot'``: Rotation angle (degree) as if the rotation is
proper.
- ``'rot'``: A tuple of ``(rotx, roty)`` - the rotation angles with
regard to the ``X`` and ``Y`` axes.
- ``'<rot>'``: *Arithmetic mean* of the angles of rotation around
``X`` and ``Y`` axes.
- ``'scale'``: A tuple of ``(sx, sy)`` - scale change in the direction
of the ``X`` and ``Y`` axes.
- ``'<scale>'``: *Geometric mean* of scales ``sx`` and ``sy``.
- ``'skew'``: Computed skew.
- ``'proper'``: a boolean indicating whether the rotation is proper.
- ``'fitgeom'``: Fit geometry (allowed transformations) used for
fitting data (to minimize residuals). This is copy of the input
argument ``fitgeom``.
- ``'center'``: Center of rotation
- ``'center_ld'``: Center of rotation as a ``numpy.longdouble``.
- ``'fitmask'``: A boolean array indicating which source positions
where used for fitting (`True`) and which were clipped out
(`False`). **NOTE** For weighted fits, positions with zero
weights are automatically excluded from the fits.
- ``'eff_nclip'``: Effective number of clipping iterations
- ``'rmse'``: Root-Mean-Square Error
- ``'mae'``: Mean Absolute Error
- ``'std'``: Standard Deviation of the residuals
- ``'resids'``: An array of residuals of the fit.
**NOTE:** Only the residuals for the "valid" points are reported
here. Therefore the length of this array may be smaller than the
length of input arrays of positions.
Notes
-----
**Weights**
Weights can be provided for both "image" source positions and "reference"
source positions. When no weights are given, all positions are weighted
equally. When only one set of positions have weights (i.e., either ``wxy``
or ``wuv`` is not `None`) then weights in :eq:`ilf2` are set to be equal
to the provided set of weights. When weights for *both* "image" source
positions and "reference" source positions are provided, then the
combined weight that is used in :eq:`ilf2` is computed as:
.. math::
1/w = 1/w_{xy} + 1/w_{uv}.
**Statistics for clipping**
Several statistics are available for clipping iterations and all of them
are reported in the returned ``fit`` dictionary regardless of the
setting in ``sigma``:
.. math::
\mathrm{RMSE} = \sqrt{\Sigma_k w_k \|\mathbf{r}_k\|^2}
.. math::
\mathrm{MAE} = \sqrt{\Sigma_k w_k \|\mathbf{r}_k\|}
.. math::
\mathrm{STD} = \sqrt{\Sigma_k w_k \|\mathbf{r}_k - \
\mathbf{\overline{r}}\|^2}/(1-V_2)
where :math:`\mathbf{r}_k=\mathbf{xy}_k-\mathbf{xy}'_k`,
:math:`\Sigma_k w_k = 1`, and :math:`V_2=\Sigma_k w_k^2`. | tweakwcs/linearfit.py | iter_linear_fit | jhunkeler/tweakwcs | python | def iter_linear_fit(xy, uv, wxy=None, wuv=None, fitgeom='general', center=None, nclip=3, sigma=(3.0, 'rmse'), clip_accum=False):
'\n Compute linear transformation parameters that "best" (in the sense of\n minimizing residuals) transform ``uv`` source position to ``xy``\n sources iteratively using sigma-clipping.\n\n More precisely, this functions attempts to find a ``2x2`` matrix ``F`` and\n a shift vector ``s`` that minimize the residuals between the *transformed*\n reference source coordinates ``uv``\n\n .. math::\n \\mathbf{xy}\'_k = \\mathbf{F}\\cdot(\\mathbf{uv}_k-\\mathbf{c})+\\\n \\mathbf{s} + \\mathbf{c}\n :label: ilf1\n\n and the "observed" source positions ``xy``:\n\n .. math::\n \\epsilon^2 = \\Sigma_k w_k \\|\\mathbf{xy}_k-\\mathbf{xy}\'_k\\|^2.\n :label: ilf2\n\n In the above equations, :math:`\\mathbf{F}` is a ``2x2`` matrix while\n :math:`\\mathbf{xy}_k` and :math:`\\mathbf{uv}_k` are the position\n coordinates of the ``k``-th source (row in input ``xy`` and ``uv`` arrays).\n\n One of the two catalogs (``xy`` or ``uv``) contains what we refer to as\n "image" source positions and the other one as "reference" source positions.\n The meaning assigned to ``xy`` and ``uv`` parameters are up to the\n caller of this function.\n\n Parameters\n ----------\n xy: numpy.ndarray\n A ``(N, 2)``-shaped array of source positions (one 2-coordinate\n position per line).\n\n uv: numpy.ndarray\n A ``(N, 2)``-shaped array of source positions (one 2-coordinate\n position per line). This array *must have* the same length (shape)\n as the ``xy`` array.\n\n wxy: numpy.ndarray, None, optional\n A 1-dimensional array of weights of the same length (``N``)\n as ``xy`` array indicating how much a given coordinate should be\n weighted in the fit. If not provided or set to `None`, all positions\n will be contribute equally to the fit if ``wuv`` is also set to `None`.\n See ``Notes`` section for more details.\n\n wuv: numpy.ndarray, None, optional\n A 1-dimensional array of weights of the same length (``N``)\n as ``xy`` array indicating how much a given coordinate should be\n weighted in the fit. If not provided or set to `None`, all positions\n will be contribute equally to the fit if ``wxy`` is also set to `None`.\n See ``Notes`` section for more details.\n\n fitgeom: {\'shift\', \'rscale\', \'general\'}, optional\n The fitting geometry to be used in fitting the matched object lists.\n This parameter is used in fitting the shifts (offsets), rotations\n and/or scale changes from the matched object lists. The \'general\'\n fit geometry allows for independent scale and rotation for each axis.\n\n center: tuple, list, numpy.ndarray, None, optional\n A list-like container with two ``X``- and ``Y``-positions of the center\n (origin) of rotations in the ``uv`` and ``xy`` coordinate frames.\n If not provided, ``center`` is estimated as a (weighted) mean position\n in the ``uv`` frame.\n\n nclip: int, None, optional\n Number (a non-negative integer) of clipping iterations in fit.\n Clipping will be turned off if ``nclip`` is either `None` or 0.\n\n sigma: float, tuple of the form (float, str), optional\n When a tuple is provided, first value (a positive number)\n indicates the number of "fit error estimates" to use for clipping.\n The second value (a string) indicates the statistic to be\n used for "fit error estimate". Currently the following values are\n supported: ``\'rmse\'``, ``\'mae\'``, and ``\'std\'``\n - see ``Notes`` section for more details.\n\n When ``sigma`` is a single number, it must be a positive number and\n the default error estimate ``\'rmse\'`` is assumed.\n\n This parameter is ignored when ``nclip`` is either `None` or 0.\n\n clip_accum: bool, optional\n Indicates whether or not to reset the list of "bad" (clipped out)\n sources after each clipping iteration. When set to `True` the list\n only grows with each iteration as "bad" positions never re-enter the\n pool of available position for the fit. By default the list of\n "bad" source positions is purged at each iteration.\n\n Returns\n -------\n fit: dict\n - ``\'shift\'``: A ``numpy.ndarray`` with two components of the\n computed shift.\n - ``\'shift_ld\'``: A ``numpy.ndarray`` with two components of the\n computed shift of type ``numpy.longdouble``.\n - ``\'matrix\'``: A ``2x2`` ``numpy.ndarray`` with the computed\n generalized rotation matrix.\n - ``\'matrix_ld\'``: A ``2x2`` ``numpy.ndarray`` with the computed\n generalized rotation matrix of type ``numpy.longdouble``.\n - ``\'proper_rot\'``: Rotation angle (degree) as if the rotation is\n proper.\n - ``\'rot\'``: A tuple of ``(rotx, roty)`` - the rotation angles with\n regard to the ``X`` and ``Y`` axes.\n - ``\'<rot>\'``: *Arithmetic mean* of the angles of rotation around\n ``X`` and ``Y`` axes.\n - ``\'scale\'``: A tuple of ``(sx, sy)`` - scale change in the direction\n of the ``X`` and ``Y`` axes.\n - ``\'<scale>\'``: *Geometric mean* of scales ``sx`` and ``sy``.\n - ``\'skew\'``: Computed skew.\n - ``\'proper\'``: a boolean indicating whether the rotation is proper.\n - ``\'fitgeom\'``: Fit geometry (allowed transformations) used for\n fitting data (to minimize residuals). This is copy of the input\n argument ``fitgeom``.\n - ``\'center\'``: Center of rotation\n - ``\'center_ld\'``: Center of rotation as a ``numpy.longdouble``.\n - ``\'fitmask\'``: A boolean array indicating which source positions\n where used for fitting (`True`) and which were clipped out\n (`False`). **NOTE** For weighted fits, positions with zero\n weights are automatically excluded from the fits.\n - ``\'eff_nclip\'``: Effective number of clipping iterations\n - ``\'rmse\'``: Root-Mean-Square Error\n - ``\'mae\'``: Mean Absolute Error\n - ``\'std\'``: Standard Deviation of the residuals\n - ``\'resids\'``: An array of residuals of the fit.\n **NOTE:** Only the residuals for the "valid" points are reported\n here. Therefore the length of this array may be smaller than the\n length of input arrays of positions.\n\n Notes\n -----\n **Weights**\n\n Weights can be provided for both "image" source positions and "reference"\n source positions. When no weights are given, all positions are weighted\n equally. When only one set of positions have weights (i.e., either ``wxy``\n or ``wuv`` is not `None`) then weights in :eq:`ilf2` are set to be equal\n to the provided set of weights. When weights for *both* "image" source\n positions and "reference" source positions are provided, then the\n combined weight that is used in :eq:`ilf2` is computed as:\n\n .. math::\n 1/w = 1/w_{xy} + 1/w_{uv}.\n\n **Statistics for clipping**\n\n Several statistics are available for clipping iterations and all of them\n are reported in the returned ``fit`` dictionary regardless of the\n setting in ``sigma``:\n\n .. math::\n \\mathrm{RMSE} = \\sqrt{\\Sigma_k w_k \\|\\mathbf{r}_k\\|^2}\n\n .. math::\n \\mathrm{MAE} = \\sqrt{\\Sigma_k w_k \\|\\mathbf{r}_k\\|}\n\n .. math::\n \\mathrm{STD} = \\sqrt{\\Sigma_k w_k \\|\\mathbf{r}_k - \\\n \\mathbf{\\overline{r}}\\|^2}/(1-V_2)\n\n where :math:`\\mathbf{r}_k=\\mathbf{xy}_k-\\mathbf{xy}\'_k`,\n :math:`\\Sigma_k w_k = 1`, and :math:`V_2=\\Sigma_k w_k^2`.\n\n '
if (fitgeom == 'general'):
linear_fit = fit_general
elif (fitgeom == 'rscale'):
linear_fit = fit_rscale
elif (fitgeom == 'shift'):
linear_fit = fit_shifts
else:
raise ValueError("Unsupported 'fitgeom' value: '{}'".format(fitgeom))
minobj_per_fitgeom = {'shift': 1, 'rscale': 2, 'general': 3}
minobj = minobj_per_fitgeom[fitgeom]
xy = np.array(xy, dtype=np.longdouble)
uv = np.array(uv, dtype=np.longdouble)
if ((len(xy.shape) != 2) or (xy.shape[1] != 2) or (uv.shape != xy.shape)):
raise ValueError("Input coordinate arrays 'xy' and 'uv' must be of shape (N, 2) where N is the number of coordinate points.")
wmask = np.ones(len(xy), dtype=np.bool_)
if (wxy is not None):
wxy = np.asarray(wxy)
if ((len(wxy.shape) != 1) or (wxy.shape[0] != xy.shape[0])):
raise ValueError("Weights 'wxy' must be a 1-dimensional vector of lengths equal to the number of input points.")
wmask *= (wxy > 0.0)
if (wuv is not None):
wuv = np.asarray(wuv)
if ((len(wuv.shape) != 1) or (wuv.shape[0] != xy.shape[0])):
raise ValueError("Weights 'wuv' must be a 1-dimensional vector of lengths equal to the number of input points.")
wmask *= (wuv > 0.0)
mask = wmask
if ((sigma is None) and (nclip is not None) and (nclip > 0)):
raise ValueError("Argument 'sigma' cannot be None when 'nclip' is a positive number.")
if isinstance(sigma, numbers.Number):
sigstat = 'rmse'
nsigma = float(sigma)
elif (sigma is not None):
nsigma = float(sigma[0])
sigstat = sigma[1]
if (sigstat not in ['rmse', 'mae', 'std']):
raise ValueError('Unsupported sigma statistics value.')
if ((sigma is not None) and (nsigma <= 0.0)):
raise ValueError('The value of sigma for clipping iterations must be positive.')
if (nclip is None):
nclip = 0
else:
if (nclip < 0):
raise ValueError("Argument 'nclip' must be non-negative.")
nclip = int(nclip)
if (np.count_nonzero(mask) == minobj):
log.warning("The number of sources for the fit is smaller than the minimum number of sources necessary for the requested 'fitgeom'.")
log.warning('Resetting number of clipping iterations to 0.')
nclip = 0
if (center is None):
center_ld = uv[mask].mean(axis=0, dtype=np.longdouble)
center = center_ld.astype(np.double)
else:
center_ld = np.longdouble(center)
xy[mask] -= center_ld
uv[mask] -= center_ld
log.info("Performing '{:s}' fit".format(fitgeom))
wmxy = (None if (wxy is None) else wxy[mask])
wmuv = (None if (wuv is None) else wuv[mask])
fit = linear_fit(xy[mask], uv[mask], wmxy, wmuv)
effective_nclip = 0
for n in range(nclip):
resids = fit['resids']
cutoff = (nsigma * fit[sigstat])
nonclipped = (np.linalg.norm(resids, axis=1) < cutoff)
if ((np.count_nonzero(nonclipped) < minobj) or nonclipped.all()):
break
effective_nclip += 1
prev_mask = mask
if (not clip_accum):
mask = np.array(wmask)
mask[prev_mask] *= nonclipped
wmxy = (None if (wxy is None) else wxy[mask])
wmuv = (None if (wuv is None) else wuv[mask])
fit = linear_fit(xy[mask], uv[mask], wmxy, wmuv)
fit['center'] = center
fit['center_ld'] = center_ld
fit['fitmask'] = mask
fit['eff_nclip'] = effective_nclip
return fit |
def fit_shifts(xy, uv, wxy=None, wuv=None):
" Fits (non-iteratively and without sigma-clipping) a displacement\n transformation only between input lists of positions ``xy`` and ``uv``.\n When weights are provided, a weighted fit is performed. Parameter\n descriptions and return values are identical to those in `iter_linear_fit`,\n except returned ``fit`` dictionary does not contain the following\n keys irrelevant to this function: ``'center'``, ``'fitmask'``, and\n ``'eff_nclip'``.\n\n "
if (xy.size == 0):
raise NotEnoughPointsError('At least one point is required to find shifts.')
diff_pts = np.subtract(xy, uv, dtype=np.longdouble)
if ((wxy is None) and (wuv is None)):
w = None
meanx = diff_pts[:, 0].mean(dtype=np.longdouble)
meany = diff_pts[:, 1].mean(dtype=np.longdouble)
else:
if (wxy is None):
w = np.array(wuv, dtype=np.longdouble)
elif (wuv is None):
w = np.array(wxy, dtype=np.longdouble)
else:
wuv = np.array(wuv, dtype=np.longdouble)
wxy = np.array(wxy, dtype=np.longdouble)
m = np.logical_and((wuv > 0), (wxy > 0))
w = np.zeros_like(wuv)
w[m] = ((wxy[m] * wuv[m]) / (wxy[m] + wuv[m]))
if np.any((w < 0.0)):
raise ValueError('Invalid weights: weights must be non-negative.')
if (not np.sum((w > 0), dtype=np.int)):
raise ValueError("Not enough valid data for 'shift' fit: too many weights are zero!")
w /= np.sum(w, dtype=np.longdouble)
meanx = np.dot(w, diff_pts[:, 0])
meany = np.dot(w, diff_pts[:, 1])
p = np.array([1.0, 0.0, meanx], dtype=np.longdouble)
q = np.array([0.0, 1.0, meany], dtype=np.longdouble)
fit = _build_fit(p, q, 'shift')
resids = (diff_pts - fit['shift'])
fit['resids'] = resids.astype(np.double)
_compute_stat(fit, residuals=resids, weights=w)
return fit | -1,491,896,026,331,747,000 | Fits (non-iteratively and without sigma-clipping) a displacement
transformation only between input lists of positions ``xy`` and ``uv``.
When weights are provided, a weighted fit is performed. Parameter
descriptions and return values are identical to those in `iter_linear_fit`,
except returned ``fit`` dictionary does not contain the following
keys irrelevant to this function: ``'center'``, ``'fitmask'``, and
``'eff_nclip'``. | tweakwcs/linearfit.py | fit_shifts | jhunkeler/tweakwcs | python | def fit_shifts(xy, uv, wxy=None, wuv=None):
" Fits (non-iteratively and without sigma-clipping) a displacement\n transformation only between input lists of positions ``xy`` and ``uv``.\n When weights are provided, a weighted fit is performed. Parameter\n descriptions and return values are identical to those in `iter_linear_fit`,\n except returned ``fit`` dictionary does not contain the following\n keys irrelevant to this function: ``'center'``, ``'fitmask'``, and\n ``'eff_nclip'``.\n\n "
if (xy.size == 0):
raise NotEnoughPointsError('At least one point is required to find shifts.')
diff_pts = np.subtract(xy, uv, dtype=np.longdouble)
if ((wxy is None) and (wuv is None)):
w = None
meanx = diff_pts[:, 0].mean(dtype=np.longdouble)
meany = diff_pts[:, 1].mean(dtype=np.longdouble)
else:
if (wxy is None):
w = np.array(wuv, dtype=np.longdouble)
elif (wuv is None):
w = np.array(wxy, dtype=np.longdouble)
else:
wuv = np.array(wuv, dtype=np.longdouble)
wxy = np.array(wxy, dtype=np.longdouble)
m = np.logical_and((wuv > 0), (wxy > 0))
w = np.zeros_like(wuv)
w[m] = ((wxy[m] * wuv[m]) / (wxy[m] + wuv[m]))
if np.any((w < 0.0)):
raise ValueError('Invalid weights: weights must be non-negative.')
if (not np.sum((w > 0), dtype=np.int)):
raise ValueError("Not enough valid data for 'shift' fit: too many weights are zero!")
w /= np.sum(w, dtype=np.longdouble)
meanx = np.dot(w, diff_pts[:, 0])
meany = np.dot(w, diff_pts[:, 1])
p = np.array([1.0, 0.0, meanx], dtype=np.longdouble)
q = np.array([0.0, 1.0, meany], dtype=np.longdouble)
fit = _build_fit(p, q, 'shift')
resids = (diff_pts - fit['shift'])
fit['resids'] = resids.astype(np.double)
_compute_stat(fit, residuals=resids, weights=w)
return fit |
def fit_rscale(xy, uv, wxy=None, wuv=None):
" Fits (non-iteratively and without sigma-clipping) a displacement,\n rotation and scale transformations between input lists of positions\n ``xy`` and ``uv``. When weights are provided, a weighted fit is performed.\n Parameter descriptions and return values are identical to those\n in `iter_linear_fit`, except returned ``fit`` dictionary does not contain\n the following keys irrelevant to this function: ``'center'``,\n ``'fitmask'``, and ``'eff_nclip'``.\n\n "
if (len(xy) < 2):
raise NotEnoughPointsError('At least two points are required to find shifts, rotation, and scale.')
x = np.array(xy[:, 0], dtype=np.longdouble)
y = np.array(xy[:, 1], dtype=np.longdouble)
u = np.array(uv[:, 0], dtype=np.longdouble)
v = np.array(uv[:, 1], dtype=np.longdouble)
if ((wxy is None) and (wuv is None)):
w = None
xm = np.mean(x)
ym = np.mean(y)
um = np.mean(u)
vm = np.mean(v)
x -= xm
y -= ym
u -= um
v -= vm
su2 = np.dot(u, u)
sv2 = np.dot(v, v)
sxv = np.dot(x, v)
syu = np.dot(y, u)
sxu = np.dot(x, u)
syv = np.dot(y, v)
su2v2 = (su2 + sv2)
else:
if (wxy is None):
w = np.array(wuv, dtype=np.longdouble)
elif (wuv is None):
w = np.array(wxy, dtype=np.longdouble)
else:
wuv = np.array(wuv, dtype=np.longdouble)
wxy = np.array(wxy, dtype=np.longdouble)
m = np.logical_and((wuv > 0), (wxy > 0))
w = np.zeros_like(wuv)
w[m] = ((wxy[m] * wuv[m]) / (wxy[m] + wuv[m]))
if np.any((w < 0.0)):
raise ValueError('Invalid weights: weights must be non-negative.')
if (np.sum((w > 0)) < 2):
raise ValueError("Not enough valid data for 'rscale' fit: too many weights are zero!")
w /= np.sum(w, dtype=np.longdouble)
xm = np.dot(w, x)
ym = np.dot(w, y)
um = np.dot(w, u)
vm = np.dot(w, v)
x -= xm
y -= ym
u -= um
v -= vm
su2 = np.dot(w, (u ** 2))
sv2 = np.dot(w, (v ** 2))
sxv = np.dot(w, (x * v))
syu = np.dot(w, (y * u))
sxu = np.dot(w, (x * u))
syv = np.dot(w, (y * v))
su2v2 = (su2 + sv2)
det = ((sxu * syv) - (sxv * syu))
if (det < 0):
rot_num = (sxv + syu)
rot_denom = (sxu - syv)
else:
rot_num = (sxv - syu)
rot_denom = (sxu + syv)
if (rot_num == rot_denom):
theta = 0.0
else:
theta = np.rad2deg(np.arctan2(rot_num, rot_denom))
if (theta < 0):
theta += 360.0
ctheta = np.cos(np.deg2rad(theta))
stheta = np.sin(np.deg2rad(theta))
s_num = ((rot_denom * ctheta) + (rot_num * stheta))
if (su2v2 > 0.0):
mag = (s_num / su2v2)
else:
raise SingularMatrixError('Singular matrix: suspected colinear points.')
if (det < 0):
sthetax = ((- mag) * stheta)
cthetay = ((- mag) * ctheta)
else:
sthetax = (mag * stheta)
cthetay = (mag * ctheta)
cthetax = (mag * ctheta)
sthetay = (mag * stheta)
sdet = np.sign(det)
xshift = ((xm - (um * cthetax)) - ((sdet * vm) * sthetax))
yshift = ((ym + ((sdet * um) * sthetay)) - (vm * cthetay))
p = np.array([cthetax, sthetay, xshift], dtype=np.longdouble)
q = np.array([(- sthetax), cthetay, yshift], dtype=np.longdouble)
fit = _build_fit(p, q, fitgeom='rscale')
resids = ((xy - np.dot(uv, fit['matrix_ld'].T)) - fit['shift_ld'])
fit['resids'] = resids.astype(np.double)
_compute_stat(fit, residuals=resids, weights=w)
return fit | 2,982,510,986,755,505,700 | Fits (non-iteratively and without sigma-clipping) a displacement,
rotation and scale transformations between input lists of positions
``xy`` and ``uv``. When weights are provided, a weighted fit is performed.
Parameter descriptions and return values are identical to those
in `iter_linear_fit`, except returned ``fit`` dictionary does not contain
the following keys irrelevant to this function: ``'center'``,
``'fitmask'``, and ``'eff_nclip'``. | tweakwcs/linearfit.py | fit_rscale | jhunkeler/tweakwcs | python | def fit_rscale(xy, uv, wxy=None, wuv=None):
" Fits (non-iteratively and without sigma-clipping) a displacement,\n rotation and scale transformations between input lists of positions\n ``xy`` and ``uv``. When weights are provided, a weighted fit is performed.\n Parameter descriptions and return values are identical to those\n in `iter_linear_fit`, except returned ``fit`` dictionary does not contain\n the following keys irrelevant to this function: ``'center'``,\n ``'fitmask'``, and ``'eff_nclip'``.\n\n "
if (len(xy) < 2):
raise NotEnoughPointsError('At least two points are required to find shifts, rotation, and scale.')
x = np.array(xy[:, 0], dtype=np.longdouble)
y = np.array(xy[:, 1], dtype=np.longdouble)
u = np.array(uv[:, 0], dtype=np.longdouble)
v = np.array(uv[:, 1], dtype=np.longdouble)
if ((wxy is None) and (wuv is None)):
w = None
xm = np.mean(x)
ym = np.mean(y)
um = np.mean(u)
vm = np.mean(v)
x -= xm
y -= ym
u -= um
v -= vm
su2 = np.dot(u, u)
sv2 = np.dot(v, v)
sxv = np.dot(x, v)
syu = np.dot(y, u)
sxu = np.dot(x, u)
syv = np.dot(y, v)
su2v2 = (su2 + sv2)
else:
if (wxy is None):
w = np.array(wuv, dtype=np.longdouble)
elif (wuv is None):
w = np.array(wxy, dtype=np.longdouble)
else:
wuv = np.array(wuv, dtype=np.longdouble)
wxy = np.array(wxy, dtype=np.longdouble)
m = np.logical_and((wuv > 0), (wxy > 0))
w = np.zeros_like(wuv)
w[m] = ((wxy[m] * wuv[m]) / (wxy[m] + wuv[m]))
if np.any((w < 0.0)):
raise ValueError('Invalid weights: weights must be non-negative.')
if (np.sum((w > 0)) < 2):
raise ValueError("Not enough valid data for 'rscale' fit: too many weights are zero!")
w /= np.sum(w, dtype=np.longdouble)
xm = np.dot(w, x)
ym = np.dot(w, y)
um = np.dot(w, u)
vm = np.dot(w, v)
x -= xm
y -= ym
u -= um
v -= vm
su2 = np.dot(w, (u ** 2))
sv2 = np.dot(w, (v ** 2))
sxv = np.dot(w, (x * v))
syu = np.dot(w, (y * u))
sxu = np.dot(w, (x * u))
syv = np.dot(w, (y * v))
su2v2 = (su2 + sv2)
det = ((sxu * syv) - (sxv * syu))
if (det < 0):
rot_num = (sxv + syu)
rot_denom = (sxu - syv)
else:
rot_num = (sxv - syu)
rot_denom = (sxu + syv)
if (rot_num == rot_denom):
theta = 0.0
else:
theta = np.rad2deg(np.arctan2(rot_num, rot_denom))
if (theta < 0):
theta += 360.0
ctheta = np.cos(np.deg2rad(theta))
stheta = np.sin(np.deg2rad(theta))
s_num = ((rot_denom * ctheta) + (rot_num * stheta))
if (su2v2 > 0.0):
mag = (s_num / su2v2)
else:
raise SingularMatrixError('Singular matrix: suspected colinear points.')
if (det < 0):
sthetax = ((- mag) * stheta)
cthetay = ((- mag) * ctheta)
else:
sthetax = (mag * stheta)
cthetay = (mag * ctheta)
cthetax = (mag * ctheta)
sthetay = (mag * stheta)
sdet = np.sign(det)
xshift = ((xm - (um * cthetax)) - ((sdet * vm) * sthetax))
yshift = ((ym + ((sdet * um) * sthetay)) - (vm * cthetay))
p = np.array([cthetax, sthetay, xshift], dtype=np.longdouble)
q = np.array([(- sthetax), cthetay, yshift], dtype=np.longdouble)
fit = _build_fit(p, q, fitgeom='rscale')
resids = ((xy - np.dot(uv, fit['matrix_ld'].T)) - fit['shift_ld'])
fit['resids'] = resids.astype(np.double)
_compute_stat(fit, residuals=resids, weights=w)
return fit |
def fit_general(xy, uv, wxy=None, wuv=None):
" Fits (non-iteratively and without sigma-clipping) a displacement,\n rotation, scale, and skew transformations (i.e., the full ``2x2``\n transformation matrix) between input lists of positions\n ``xy`` and ``uv``. When weights are provided, a weighted fit is performed.\n Parameter descriptions and return values are identical to those\n in `iter_linear_fit`, except returned ``fit`` dictionary does not contain\n the following keys irrelevant to this function: ``'center'``,\n ``'fitmask'``, and ``'eff_nclip'``.\n\n "
if (len(xy) < 3):
raise NotEnoughPointsError('At least three points are required to find 6-parameter linear affine transformations.')
x = np.array(xy[:, 0], dtype=np.longdouble)
y = np.array(xy[:, 1], dtype=np.longdouble)
u = np.array(uv[:, 0], dtype=np.longdouble)
v = np.array(uv[:, 1], dtype=np.longdouble)
if ((wxy is None) and (wuv is None)):
w = None
sw = float(x.size)
sx = x.sum()
sy = y.sum()
su = u.sum()
sv = v.sum()
sxu = np.dot(x, u)
syu = np.dot(y, u)
sxv = np.dot(x, v)
syv = np.dot(y, v)
suu = np.dot(u, u)
svv = np.dot(v, v)
suv = np.dot(u, v)
else:
if (wxy is None):
w = np.array(wuv, dtype=np.longdouble)
elif (wuv is None):
w = np.array(wxy, dtype=np.longdouble)
else:
wuv = np.array(wuv, dtype=np.longdouble)
wxy = np.array(wxy, dtype=np.longdouble)
m = np.logical_and((wuv > 0), (wxy > 0))
w = np.zeros_like(wuv)
w[m] = ((wxy[m] * wuv[m]) / (wxy[m] + wuv[m]))
if np.any((w < 0.0)):
raise ValueError('Invalid weights: weights must be non-negative.')
if (np.sum((w > 0)) < 3):
raise ValueError("Not enough valid data for 'general' fit: too many weights are zero!")
sw = np.sum(w, dtype=np.longdouble)
sx = np.dot(w, x)
sy = np.dot(w, y)
su = np.dot(w, u)
sv = np.dot(w, v)
sxu = np.dot(w, (x * u))
syu = np.dot(w, (y * u))
sxv = np.dot(w, (x * v))
syv = np.dot(w, (y * v))
suu = np.dot(w, (u * u))
svv = np.dot(w, (v * v))
suv = np.dot(w, (u * v))
m = np.array([[su, sv, sw], [suu, suv, su], [suv, svv, sv]], dtype=np.longdouble)
a = np.array([sx, sxu, sxv], dtype=np.longdouble)
b = np.array([sy, syu, syv], dtype=np.longdouble)
try:
inv_m = inv(m)
except np.linalg.LinAlgError:
raise SingularMatrixError('Singular matrix: suspected colinear points.')
p = np.dot(inv_m, a)
q = np.dot(inv_m, b)
if (not (np.all(np.isfinite(p)) and np.all(np.isfinite(q)))):
raise SingularMatrixError('Singular matrix: suspected colinear points.')
fit = _build_fit(p, q, 'general')
resids = ((xy - np.dot(uv, fit['matrix_ld'].T)) - fit['shift_ld'])
fit['resids'] = resids.astype(np.double)
_compute_stat(fit, residuals=resids, weights=w)
return fit | 3,775,803,470,868,652,500 | Fits (non-iteratively and without sigma-clipping) a displacement,
rotation, scale, and skew transformations (i.e., the full ``2x2``
transformation matrix) between input lists of positions
``xy`` and ``uv``. When weights are provided, a weighted fit is performed.
Parameter descriptions and return values are identical to those
in `iter_linear_fit`, except returned ``fit`` dictionary does not contain
the following keys irrelevant to this function: ``'center'``,
``'fitmask'``, and ``'eff_nclip'``. | tweakwcs/linearfit.py | fit_general | jhunkeler/tweakwcs | python | def fit_general(xy, uv, wxy=None, wuv=None):
" Fits (non-iteratively and without sigma-clipping) a displacement,\n rotation, scale, and skew transformations (i.e., the full ``2x2``\n transformation matrix) between input lists of positions\n ``xy`` and ``uv``. When weights are provided, a weighted fit is performed.\n Parameter descriptions and return values are identical to those\n in `iter_linear_fit`, except returned ``fit`` dictionary does not contain\n the following keys irrelevant to this function: ``'center'``,\n ``'fitmask'``, and ``'eff_nclip'``.\n\n "
if (len(xy) < 3):
raise NotEnoughPointsError('At least three points are required to find 6-parameter linear affine transformations.')
x = np.array(xy[:, 0], dtype=np.longdouble)
y = np.array(xy[:, 1], dtype=np.longdouble)
u = np.array(uv[:, 0], dtype=np.longdouble)
v = np.array(uv[:, 1], dtype=np.longdouble)
if ((wxy is None) and (wuv is None)):
w = None
sw = float(x.size)
sx = x.sum()
sy = y.sum()
su = u.sum()
sv = v.sum()
sxu = np.dot(x, u)
syu = np.dot(y, u)
sxv = np.dot(x, v)
syv = np.dot(y, v)
suu = np.dot(u, u)
svv = np.dot(v, v)
suv = np.dot(u, v)
else:
if (wxy is None):
w = np.array(wuv, dtype=np.longdouble)
elif (wuv is None):
w = np.array(wxy, dtype=np.longdouble)
else:
wuv = np.array(wuv, dtype=np.longdouble)
wxy = np.array(wxy, dtype=np.longdouble)
m = np.logical_and((wuv > 0), (wxy > 0))
w = np.zeros_like(wuv)
w[m] = ((wxy[m] * wuv[m]) / (wxy[m] + wuv[m]))
if np.any((w < 0.0)):
raise ValueError('Invalid weights: weights must be non-negative.')
if (np.sum((w > 0)) < 3):
raise ValueError("Not enough valid data for 'general' fit: too many weights are zero!")
sw = np.sum(w, dtype=np.longdouble)
sx = np.dot(w, x)
sy = np.dot(w, y)
su = np.dot(w, u)
sv = np.dot(w, v)
sxu = np.dot(w, (x * u))
syu = np.dot(w, (y * u))
sxv = np.dot(w, (x * v))
syv = np.dot(w, (y * v))
suu = np.dot(w, (u * u))
svv = np.dot(w, (v * v))
suv = np.dot(w, (u * v))
m = np.array([[su, sv, sw], [suu, suv, su], [suv, svv, sv]], dtype=np.longdouble)
a = np.array([sx, sxu, sxv], dtype=np.longdouble)
b = np.array([sy, syu, syv], dtype=np.longdouble)
try:
inv_m = inv(m)
except np.linalg.LinAlgError:
raise SingularMatrixError('Singular matrix: suspected colinear points.')
p = np.dot(inv_m, a)
q = np.dot(inv_m, b)
if (not (np.all(np.isfinite(p)) and np.all(np.isfinite(q)))):
raise SingularMatrixError('Singular matrix: suspected colinear points.')
fit = _build_fit(p, q, 'general')
resids = ((xy - np.dot(uv, fit['matrix_ld'].T)) - fit['shift_ld'])
fit['resids'] = resids.astype(np.double)
_compute_stat(fit, residuals=resids, weights=w)
return fit |
def build_fit_matrix(rot, scale=1):
'\n Create an affine transformation matrix (2x2) from the provided rotation\n angle(s) and scale(s):\n\n .. math::\n\n M = \\begin{bmatrix}\n s_x \\cos(\\theta_x) & s_y \\sin(\\theta_y) \\\\\n -s_x \\sin(\\theta_x) & s_y \\cos(\\theta_y)\n \\end{bmatrix}\n\n Parameters\n ----------\n rot: tuple, float, optional\n Rotation angle in degrees. Two values (one for each axis) can be\n provided as a tuple.\n\n scale: tuple, float, optional\n Scale of the liniar transformation. Two values (one for each axis)\n can be provided as a tuple.\n\n Returns\n -------\n matrix: numpy.ndarray\n A 2x2 `numpy.ndarray` containing coefficients of a liniear\n transformation.\n\n '
if hasattr(rot, '__iter__'):
(rx, ry) = map(np.deg2rad, rot)
else:
rx = ry = np.deg2rad(float(rot))
if hasattr(scale, '__iter__'):
(sx, sy) = scale
else:
sx = sy = float(scale)
matrix = np.array([[(sx * np.cos(rx)), (sy * np.sin(ry))], [((- sx) * np.sin(rx)), (sy * np.cos(ry))]])
return matrix | -4,852,843,185,402,100,000 | Create an affine transformation matrix (2x2) from the provided rotation
angle(s) and scale(s):
.. math::
M = \begin{bmatrix}
s_x \cos(\theta_x) & s_y \sin(\theta_y) \\
-s_x \sin(\theta_x) & s_y \cos(\theta_y)
\end{bmatrix}
Parameters
----------
rot: tuple, float, optional
Rotation angle in degrees. Two values (one for each axis) can be
provided as a tuple.
scale: tuple, float, optional
Scale of the liniar transformation. Two values (one for each axis)
can be provided as a tuple.
Returns
-------
matrix: numpy.ndarray
A 2x2 `numpy.ndarray` containing coefficients of a liniear
transformation. | tweakwcs/linearfit.py | build_fit_matrix | jhunkeler/tweakwcs | python | def build_fit_matrix(rot, scale=1):
'\n Create an affine transformation matrix (2x2) from the provided rotation\n angle(s) and scale(s):\n\n .. math::\n\n M = \\begin{bmatrix}\n s_x \\cos(\\theta_x) & s_y \\sin(\\theta_y) \\\\\n -s_x \\sin(\\theta_x) & s_y \\cos(\\theta_y)\n \\end{bmatrix}\n\n Parameters\n ----------\n rot: tuple, float, optional\n Rotation angle in degrees. Two values (one for each axis) can be\n provided as a tuple.\n\n scale: tuple, float, optional\n Scale of the liniar transformation. Two values (one for each axis)\n can be provided as a tuple.\n\n Returns\n -------\n matrix: numpy.ndarray\n A 2x2 `numpy.ndarray` containing coefficients of a liniear\n transformation.\n\n '
if hasattr(rot, '__iter__'):
(rx, ry) = map(np.deg2rad, rot)
else:
rx = ry = np.deg2rad(float(rot))
if hasattr(scale, '__iter__'):
(sx, sy) = scale
else:
sx = sy = float(scale)
matrix = np.array([[(sx * np.cos(rx)), (sy * np.sin(ry))], [((- sx) * np.sin(rx)), (sy * np.cos(ry))]])
return matrix |
@pytest.fixture
async def trio_kivy_app(request, nursery, _app_release_list, _app_release) -> AsyncUnitApp:
'Fixture yielding a :class:`~pytest_kivy.app.AsyncUnitApp` using\n explicitly trio as backend for the async library.\n\n pytest-trio and trio must be installed, and ``trio_mode = true`` must be\n set in pytest.ini.\n '
(cls, kwargs, app_cls, app_list) = _get_request_config(request, _app_release_list, _app_release)
async with cls(nursery=nursery, async_lib='trio', **kwargs) as app:
if (app_list is not None):
app_list.append((weakref.ref(app), weakref.ref(request)))
if (app_cls is not None):
(await app(app_cls))
app.raise_startup_exception()
(yield app)
(await app.wait_stop_app()) | -5,318,611,033,535,439,000 | Fixture yielding a :class:`~pytest_kivy.app.AsyncUnitApp` using
explicitly trio as backend for the async library.
pytest-trio and trio must be installed, and ``trio_mode = true`` must be
set in pytest.ini. | pytest_kivy/plugin.py | trio_kivy_app | matham/pytest-kivy | python | @pytest.fixture
async def trio_kivy_app(request, nursery, _app_release_list, _app_release) -> AsyncUnitApp:
'Fixture yielding a :class:`~pytest_kivy.app.AsyncUnitApp` using\n explicitly trio as backend for the async library.\n\n pytest-trio and trio must be installed, and ``trio_mode = true`` must be\n set in pytest.ini.\n '
(cls, kwargs, app_cls, app_list) = _get_request_config(request, _app_release_list, _app_release)
async with cls(nursery=nursery, async_lib='trio', **kwargs) as app:
if (app_list is not None):
app_list.append((weakref.ref(app), weakref.ref(request)))
if (app_cls is not None):
(await app(app_cls))
app.raise_startup_exception()
(yield app)
(await app.wait_stop_app()) |
@pytest.fixture
async def asyncio_kivy_app(request, event_loop, _app_release_list, _app_release) -> AsyncUnitApp:
'Fixture yielding a :class:`~pytest_kivy.app.AsyncUnitApp` using\n explicitly asyncio as backend for the async library.\n\n pytest-asyncio must be installed.\n '
(cls, kwargs, app_cls, app_list) = _get_request_config(request, _app_release_list, _app_release)
async with cls(event_loop=event_loop, async_lib='asyncio', **kwargs) as app:
if (app_list is not None):
app_list.append((weakref.ref(app), weakref.ref(request)))
if (app_cls is not None):
(await app(app_cls))
app.raise_startup_exception()
(yield app)
(await app.wait_stop_app()) | -1,731,140,748,102,840,000 | Fixture yielding a :class:`~pytest_kivy.app.AsyncUnitApp` using
explicitly asyncio as backend for the async library.
pytest-asyncio must be installed. | pytest_kivy/plugin.py | asyncio_kivy_app | matham/pytest-kivy | python | @pytest.fixture
async def asyncio_kivy_app(request, event_loop, _app_release_list, _app_release) -> AsyncUnitApp:
'Fixture yielding a :class:`~pytest_kivy.app.AsyncUnitApp` using\n explicitly asyncio as backend for the async library.\n\n pytest-asyncio must be installed.\n '
(cls, kwargs, app_cls, app_list) = _get_request_config(request, _app_release_list, _app_release)
async with cls(event_loop=event_loop, async_lib='asyncio', **kwargs) as app:
if (app_list is not None):
app_list.append((weakref.ref(app), weakref.ref(request)))
if (app_cls is not None):
(await app(app_cls))
app.raise_startup_exception()
(yield app)
(await app.wait_stop_app()) |
@pytest.fixture
async def async_kivy_app(request, _app_release_list, _app_release, _nursery, _event_loop) -> AsyncUnitApp:
'Fixture yielding a :class:`~pytest_kivy.app.AsyncUnitApp` using\n trio or asyncio as backend for the async library, depending on\n KIVY_EVENTLOOP.\n\n If using trio, pytest-trio and trio must be installed, and\n ``trio_mode = true`` must be set in pytest.ini. If using asyncio,\n pytest-asyncio must be installed.\n '
(cls, kwargs, app_cls, app_list) = _get_request_config(request, _app_release_list, _app_release)
async with cls(nursery=_nursery, event_loop=_event_loop, async_lib=_async_lib, **kwargs) as app:
if (app_list is not None):
app_list.append((weakref.ref(app), weakref.ref(request)))
if (app_cls is not None):
(await app(app_cls))
app.raise_startup_exception()
(yield app)
(await app.wait_stop_app()) | -737,944,087,339,065,900 | Fixture yielding a :class:`~pytest_kivy.app.AsyncUnitApp` using
trio or asyncio as backend for the async library, depending on
KIVY_EVENTLOOP.
If using trio, pytest-trio and trio must be installed, and
``trio_mode = true`` must be set in pytest.ini. If using asyncio,
pytest-asyncio must be installed. | pytest_kivy/plugin.py | async_kivy_app | matham/pytest-kivy | python | @pytest.fixture
async def async_kivy_app(request, _app_release_list, _app_release, _nursery, _event_loop) -> AsyncUnitApp:
'Fixture yielding a :class:`~pytest_kivy.app.AsyncUnitApp` using\n trio or asyncio as backend for the async library, depending on\n KIVY_EVENTLOOP.\n\n If using trio, pytest-trio and trio must be installed, and\n ``trio_mode = true`` must be set in pytest.ini. If using asyncio,\n pytest-asyncio must be installed.\n '
(cls, kwargs, app_cls, app_list) = _get_request_config(request, _app_release_list, _app_release)
async with cls(nursery=_nursery, event_loop=_event_loop, async_lib=_async_lib, **kwargs) as app:
if (app_list is not None):
app_list.append((weakref.ref(app), weakref.ref(request)))
if (app_cls is not None):
(await app(app_cls))
app.raise_startup_exception()
(yield app)
(await app.wait_stop_app()) |
def test_purge_old_states(hass, hass_recorder):
'Test deleting old states.'
hass = hass_recorder()
_add_test_states(hass)
with session_scope(hass=hass) as session:
states = session.query(States)
assert (states.count() == 6)
finished = purge_old_data(hass.data[DATA_INSTANCE], 4, repack=False)
assert (not finished)
assert (states.count() == 4)
finished = purge_old_data(hass.data[DATA_INSTANCE], 4, repack=False)
assert (not finished)
assert (states.count() == 2)
finished = purge_old_data(hass.data[DATA_INSTANCE], 4, repack=False)
assert finished
assert (states.count() == 2) | 3,161,758,145,078,438,000 | Test deleting old states. | tests/components/recorder/test_purge.py | test_purge_old_states | AdmiralStipe/core | python | def test_purge_old_states(hass, hass_recorder):
hass = hass_recorder()
_add_test_states(hass)
with session_scope(hass=hass) as session:
states = session.query(States)
assert (states.count() == 6)
finished = purge_old_data(hass.data[DATA_INSTANCE], 4, repack=False)
assert (not finished)
assert (states.count() == 4)
finished = purge_old_data(hass.data[DATA_INSTANCE], 4, repack=False)
assert (not finished)
assert (states.count() == 2)
finished = purge_old_data(hass.data[DATA_INSTANCE], 4, repack=False)
assert finished
assert (states.count() == 2) |
def test_purge_old_events(hass, hass_recorder):
'Test deleting old events.'
hass = hass_recorder()
_add_test_events(hass)
with session_scope(hass=hass) as session:
events = session.query(Events).filter(Events.event_type.like('EVENT_TEST%'))
assert (events.count() == 6)
finished = purge_old_data(hass.data[DATA_INSTANCE], 4, repack=False)
assert (not finished)
assert (events.count() == 4)
finished = purge_old_data(hass.data[DATA_INSTANCE], 4, repack=False)
assert (not finished)
assert (events.count() == 2)
finished = purge_old_data(hass.data[DATA_INSTANCE], 4, repack=False)
assert finished
assert (events.count() == 2) | 3,234,367,589,813,852,700 | Test deleting old events. | tests/components/recorder/test_purge.py | test_purge_old_events | AdmiralStipe/core | python | def test_purge_old_events(hass, hass_recorder):
hass = hass_recorder()
_add_test_events(hass)
with session_scope(hass=hass) as session:
events = session.query(Events).filter(Events.event_type.like('EVENT_TEST%'))
assert (events.count() == 6)
finished = purge_old_data(hass.data[DATA_INSTANCE], 4, repack=False)
assert (not finished)
assert (events.count() == 4)
finished = purge_old_data(hass.data[DATA_INSTANCE], 4, repack=False)
assert (not finished)
assert (events.count() == 2)
finished = purge_old_data(hass.data[DATA_INSTANCE], 4, repack=False)
assert finished
assert (events.count() == 2) |
def test_purge_old_recorder_runs(hass, hass_recorder):
'Test deleting old recorder runs keeps current run.'
hass = hass_recorder()
_add_test_recorder_runs(hass)
with session_scope(hass=hass) as session:
recorder_runs = session.query(RecorderRuns)
assert (recorder_runs.count() == 7)
finished = purge_old_data(hass.data[DATA_INSTANCE], 0, repack=False)
assert finished
assert (recorder_runs.count() == 1) | -3,228,106,912,315,216,000 | Test deleting old recorder runs keeps current run. | tests/components/recorder/test_purge.py | test_purge_old_recorder_runs | AdmiralStipe/core | python | def test_purge_old_recorder_runs(hass, hass_recorder):
hass = hass_recorder()
_add_test_recorder_runs(hass)
with session_scope(hass=hass) as session:
recorder_runs = session.query(RecorderRuns)
assert (recorder_runs.count() == 7)
finished = purge_old_data(hass.data[DATA_INSTANCE], 0, repack=False)
assert finished
assert (recorder_runs.count() == 1) |
def test_purge_method(hass, hass_recorder):
'Test purge method.'
hass = hass_recorder()
service_data = {'keep_days': 4}
_add_test_events(hass)
_add_test_states(hass)
_add_test_recorder_runs(hass)
with session_scope(hass=hass) as session:
states = session.query(States)
assert (states.count() == 6)
events = session.query(Events).filter(Events.event_type.like('EVENT_TEST%'))
assert (events.count() == 6)
recorder_runs = session.query(RecorderRuns)
assert (recorder_runs.count() == 7)
hass.data[DATA_INSTANCE].block_till_done()
wait_recording_done(hass)
hass.services.call('recorder', 'purge')
hass.block_till_done()
hass.data[DATA_INSTANCE].block_till_done()
wait_recording_done(hass)
assert (states.count() == 4)
assert (events.count() == 4)
hass.services.call('recorder', 'purge', service_data=service_data)
hass.block_till_done()
hass.data[DATA_INSTANCE].block_till_done()
wait_recording_done(hass)
assert (states.count() == 2)
assert (events.count() == 2)
assert (recorder_runs.count() == 3)
assert (not ('EVENT_TEST_PURGE' in (event.event_type for event in events.all())))
with patch('homeassistant.components.recorder.purge._LOGGER') as mock_logger:
service_data['repack'] = True
hass.services.call('recorder', 'purge', service_data=service_data)
hass.block_till_done()
hass.data[DATA_INSTANCE].block_till_done()
wait_recording_done(hass)
assert (mock_logger.debug.mock_calls[5][1][0] == 'Vacuuming SQL DB to free space') | 7,501,323,893,733,130,000 | Test purge method. | tests/components/recorder/test_purge.py | test_purge_method | AdmiralStipe/core | python | def test_purge_method(hass, hass_recorder):
hass = hass_recorder()
service_data = {'keep_days': 4}
_add_test_events(hass)
_add_test_states(hass)
_add_test_recorder_runs(hass)
with session_scope(hass=hass) as session:
states = session.query(States)
assert (states.count() == 6)
events = session.query(Events).filter(Events.event_type.like('EVENT_TEST%'))
assert (events.count() == 6)
recorder_runs = session.query(RecorderRuns)
assert (recorder_runs.count() == 7)
hass.data[DATA_INSTANCE].block_till_done()
wait_recording_done(hass)
hass.services.call('recorder', 'purge')
hass.block_till_done()
hass.data[DATA_INSTANCE].block_till_done()
wait_recording_done(hass)
assert (states.count() == 4)
assert (events.count() == 4)
hass.services.call('recorder', 'purge', service_data=service_data)
hass.block_till_done()
hass.data[DATA_INSTANCE].block_till_done()
wait_recording_done(hass)
assert (states.count() == 2)
assert (events.count() == 2)
assert (recorder_runs.count() == 3)
assert (not ('EVENT_TEST_PURGE' in (event.event_type for event in events.all())))
with patch('homeassistant.components.recorder.purge._LOGGER') as mock_logger:
service_data['repack'] = True
hass.services.call('recorder', 'purge', service_data=service_data)
hass.block_till_done()
hass.data[DATA_INSTANCE].block_till_done()
wait_recording_done(hass)
assert (mock_logger.debug.mock_calls[5][1][0] == 'Vacuuming SQL DB to free space') |
def _add_test_states(hass):
'Add multiple states to the db for testing.'
now = datetime.now()
five_days_ago = (now - timedelta(days=5))
eleven_days_ago = (now - timedelta(days=11))
attributes = {'test_attr': 5, 'test_attr_10': 'nice'}
hass.block_till_done()
hass.data[DATA_INSTANCE].block_till_done()
wait_recording_done(hass)
with recorder.session_scope(hass=hass) as session:
for event_id in range(6):
if (event_id < 2):
timestamp = eleven_days_ago
state = 'autopurgeme'
elif (event_id < 4):
timestamp = five_days_ago
state = 'purgeme'
else:
timestamp = now
state = 'dontpurgeme'
session.add(States(entity_id='test.recorder2', domain='sensor', state=state, attributes=json.dumps(attributes), last_changed=timestamp, last_updated=timestamp, created=timestamp, event_id=(event_id + 1000))) | 6,691,055,191,001,606,000 | Add multiple states to the db for testing. | tests/components/recorder/test_purge.py | _add_test_states | AdmiralStipe/core | python | def _add_test_states(hass):
now = datetime.now()
five_days_ago = (now - timedelta(days=5))
eleven_days_ago = (now - timedelta(days=11))
attributes = {'test_attr': 5, 'test_attr_10': 'nice'}
hass.block_till_done()
hass.data[DATA_INSTANCE].block_till_done()
wait_recording_done(hass)
with recorder.session_scope(hass=hass) as session:
for event_id in range(6):
if (event_id < 2):
timestamp = eleven_days_ago
state = 'autopurgeme'
elif (event_id < 4):
timestamp = five_days_ago
state = 'purgeme'
else:
timestamp = now
state = 'dontpurgeme'
session.add(States(entity_id='test.recorder2', domain='sensor', state=state, attributes=json.dumps(attributes), last_changed=timestamp, last_updated=timestamp, created=timestamp, event_id=(event_id + 1000))) |
def _add_test_events(hass):
'Add a few events for testing.'
now = datetime.now()
five_days_ago = (now - timedelta(days=5))
eleven_days_ago = (now - timedelta(days=11))
event_data = {'test_attr': 5, 'test_attr_10': 'nice'}
hass.block_till_done()
hass.data[DATA_INSTANCE].block_till_done()
wait_recording_done(hass)
with recorder.session_scope(hass=hass) as session:
for event_id in range(6):
if (event_id < 2):
timestamp = eleven_days_ago
event_type = 'EVENT_TEST_AUTOPURGE'
elif (event_id < 4):
timestamp = five_days_ago
event_type = 'EVENT_TEST_PURGE'
else:
timestamp = now
event_type = 'EVENT_TEST'
session.add(Events(event_type=event_type, event_data=json.dumps(event_data), origin='LOCAL', created=timestamp, time_fired=timestamp)) | -3,786,387,227,581,622,300 | Add a few events for testing. | tests/components/recorder/test_purge.py | _add_test_events | AdmiralStipe/core | python | def _add_test_events(hass):
now = datetime.now()
five_days_ago = (now - timedelta(days=5))
eleven_days_ago = (now - timedelta(days=11))
event_data = {'test_attr': 5, 'test_attr_10': 'nice'}
hass.block_till_done()
hass.data[DATA_INSTANCE].block_till_done()
wait_recording_done(hass)
with recorder.session_scope(hass=hass) as session:
for event_id in range(6):
if (event_id < 2):
timestamp = eleven_days_ago
event_type = 'EVENT_TEST_AUTOPURGE'
elif (event_id < 4):
timestamp = five_days_ago
event_type = 'EVENT_TEST_PURGE'
else:
timestamp = now
event_type = 'EVENT_TEST'
session.add(Events(event_type=event_type, event_data=json.dumps(event_data), origin='LOCAL', created=timestamp, time_fired=timestamp)) |
def _add_test_recorder_runs(hass):
'Add a few recorder_runs for testing.'
now = datetime.now()
five_days_ago = (now - timedelta(days=5))
eleven_days_ago = (now - timedelta(days=11))
hass.block_till_done()
hass.data[DATA_INSTANCE].block_till_done()
wait_recording_done(hass)
with recorder.session_scope(hass=hass) as session:
for rec_id in range(6):
if (rec_id < 2):
timestamp = eleven_days_ago
elif (rec_id < 4):
timestamp = five_days_ago
else:
timestamp = now
session.add(RecorderRuns(start=timestamp, created=dt_util.utcnow(), end=(timestamp + timedelta(days=1)))) | -535,643,562,059,519,400 | Add a few recorder_runs for testing. | tests/components/recorder/test_purge.py | _add_test_recorder_runs | AdmiralStipe/core | python | def _add_test_recorder_runs(hass):
now = datetime.now()
five_days_ago = (now - timedelta(days=5))
eleven_days_ago = (now - timedelta(days=11))
hass.block_till_done()
hass.data[DATA_INSTANCE].block_till_done()
wait_recording_done(hass)
with recorder.session_scope(hass=hass) as session:
for rec_id in range(6):
if (rec_id < 2):
timestamp = eleven_days_ago
elif (rec_id < 4):
timestamp = five_days_ago
else:
timestamp = now
session.add(RecorderRuns(start=timestamp, created=dt_util.utcnow(), end=(timestamp + timedelta(days=1)))) |
@callback
@bind_hass
def entity_sources(hass: HomeAssistant) -> Dict[(str, Dict[(str, str)])]:
'Get the entity sources.'
return hass.data.get(DATA_ENTITY_SOURCE, {}) | -6,209,629,769,332,989,000 | Get the entity sources. | homeassistant/helpers/entity.py | entity_sources | Leviosa-Shades/core | python | @callback
@bind_hass
def entity_sources(hass: HomeAssistant) -> Dict[(str, Dict[(str, str)])]:
return hass.data.get(DATA_ENTITY_SOURCE, {}) |
def generate_entity_id(entity_id_format: str, name: Optional[str], current_ids: Optional[List[str]]=None, hass: Optional[HomeAssistant]=None) -> str:
'Generate a unique entity ID based on given entity IDs or used IDs.'
return async_generate_entity_id(entity_id_format, name, current_ids, hass) | 3,168,408,939,775,173,600 | Generate a unique entity ID based on given entity IDs or used IDs. | homeassistant/helpers/entity.py | generate_entity_id | Leviosa-Shades/core | python | def generate_entity_id(entity_id_format: str, name: Optional[str], current_ids: Optional[List[str]]=None, hass: Optional[HomeAssistant]=None) -> str:
return async_generate_entity_id(entity_id_format, name, current_ids, hass) |
@callback
def async_generate_entity_id(entity_id_format: str, name: Optional[str], current_ids: Optional[Iterable[str]]=None, hass: Optional[HomeAssistant]=None) -> str:
'Generate a unique entity ID based on given entity IDs or used IDs.'
name = (name or DEVICE_DEFAULT_NAME).lower()
preferred_string = entity_id_format.format(slugify(name))
if (current_ids is not None):
return ensure_unique_string(preferred_string, current_ids)
if (hass is None):
raise ValueError('Missing required parameter current_ids or hass')
test_string = preferred_string
tries = 1
while (not hass.states.async_available(test_string)):
tries += 1
test_string = f'{preferred_string}_{tries}'
return test_string | 5,117,351,041,053,576,000 | Generate a unique entity ID based on given entity IDs or used IDs. | homeassistant/helpers/entity.py | async_generate_entity_id | Leviosa-Shades/core | python | @callback
def async_generate_entity_id(entity_id_format: str, name: Optional[str], current_ids: Optional[Iterable[str]]=None, hass: Optional[HomeAssistant]=None) -> str:
name = (name or DEVICE_DEFAULT_NAME).lower()
preferred_string = entity_id_format.format(slugify(name))
if (current_ids is not None):
return ensure_unique_string(preferred_string, current_ids)
if (hass is None):
raise ValueError('Missing required parameter current_ids or hass')
test_string = preferred_string
tries = 1
while (not hass.states.async_available(test_string)):
tries += 1
test_string = f'{preferred_string}_{tries}'
return test_string |
@property
def should_poll(self) -> bool:
'Return True if entity has to be polled for state.\n\n False if entity pushes its state to HA.\n '
return True | 8,328,211,760,097,568,000 | Return True if entity has to be polled for state.
False if entity pushes its state to HA. | homeassistant/helpers/entity.py | should_poll | Leviosa-Shades/core | python | @property
def should_poll(self) -> bool:
'Return True if entity has to be polled for state.\n\n False if entity pushes its state to HA.\n '
return True |
@property
def unique_id(self) -> Optional[str]:
'Return a unique ID.'
return None | 2,538,883,729,920,680,000 | Return a unique ID. | homeassistant/helpers/entity.py | unique_id | Leviosa-Shades/core | python | @property
def unique_id(self) -> Optional[str]:
return None |
@property
def name(self) -> Optional[str]:
'Return the name of the entity.'
return None | 5,905,829,013,751,394,000 | Return the name of the entity. | homeassistant/helpers/entity.py | name | Leviosa-Shades/core | python | @property
def name(self) -> Optional[str]:
return None |
@property
def state(self) -> StateType:
'Return the state of the entity.'
return STATE_UNKNOWN | 1,356,358,700,227,505,700 | Return the state of the entity. | homeassistant/helpers/entity.py | state | Leviosa-Shades/core | python | @property
def state(self) -> StateType:
return STATE_UNKNOWN |
@property
def capability_attributes(self) -> Optional[Dict[(str, Any)]]:
'Return the capability attributes.\n\n Attributes that explain the capabilities of an entity.\n\n Implemented by component base class. Convention for attribute names\n is lowercase snake_case.\n '
return None | 4,158,045,754,724,678,700 | Return the capability attributes.
Attributes that explain the capabilities of an entity.
Implemented by component base class. Convention for attribute names
is lowercase snake_case. | homeassistant/helpers/entity.py | capability_attributes | Leviosa-Shades/core | python | @property
def capability_attributes(self) -> Optional[Dict[(str, Any)]]:
'Return the capability attributes.\n\n Attributes that explain the capabilities of an entity.\n\n Implemented by component base class. Convention for attribute names\n is lowercase snake_case.\n '
return None |
@property
def state_attributes(self) -> Optional[Dict[(str, Any)]]:
'Return the state attributes.\n\n Implemented by component base class, should not be extended by integrations.\n Convention for attribute names is lowercase snake_case.\n '
return None | 7,571,243,939,475,639,000 | Return the state attributes.
Implemented by component base class, should not be extended by integrations.
Convention for attribute names is lowercase snake_case. | homeassistant/helpers/entity.py | state_attributes | Leviosa-Shades/core | python | @property
def state_attributes(self) -> Optional[Dict[(str, Any)]]:
'Return the state attributes.\n\n Implemented by component base class, should not be extended by integrations.\n Convention for attribute names is lowercase snake_case.\n '
return None |
@property
def device_state_attributes(self) -> Optional[Dict[(str, Any)]]:
'Return entity specific state attributes.\n\n This method is deprecated, platform classes should implement\n extra_state_attributes instead.\n '
return None | -3,700,846,581,224,470,000 | Return entity specific state attributes.
This method is deprecated, platform classes should implement
extra_state_attributes instead. | homeassistant/helpers/entity.py | device_state_attributes | Leviosa-Shades/core | python | @property
def device_state_attributes(self) -> Optional[Dict[(str, Any)]]:
'Return entity specific state attributes.\n\n This method is deprecated, platform classes should implement\n extra_state_attributes instead.\n '
return None |
@property
def extra_state_attributes(self) -> Optional[Dict[(str, Any)]]:
'Return entity specific state attributes.\n\n Implemented by platform classes. Convention for attribute names\n is lowercase snake_case.\n '
return None | 4,480,478,286,833,381,000 | Return entity specific state attributes.
Implemented by platform classes. Convention for attribute names
is lowercase snake_case. | homeassistant/helpers/entity.py | extra_state_attributes | Leviosa-Shades/core | python | @property
def extra_state_attributes(self) -> Optional[Dict[(str, Any)]]:
'Return entity specific state attributes.\n\n Implemented by platform classes. Convention for attribute names\n is lowercase snake_case.\n '
return None |
@property
def device_info(self) -> Optional[Dict[(str, Any)]]:
'Return device specific attributes.\n\n Implemented by platform classes.\n '
return None | 7,117,282,649,591,217,000 | Return device specific attributes.
Implemented by platform classes. | homeassistant/helpers/entity.py | device_info | Leviosa-Shades/core | python | @property
def device_info(self) -> Optional[Dict[(str, Any)]]:
'Return device specific attributes.\n\n Implemented by platform classes.\n '
return None |
@property
def device_class(self) -> Optional[str]:
'Return the class of this device, from component DEVICE_CLASSES.'
return None | -3,089,613,303,586,104,000 | Return the class of this device, from component DEVICE_CLASSES. | homeassistant/helpers/entity.py | device_class | Leviosa-Shades/core | python | @property
def device_class(self) -> Optional[str]:
return None |
@property
def unit_of_measurement(self) -> Optional[str]:
'Return the unit of measurement of this entity, if any.'
return None | 4,082,615,635,480,496,000 | Return the unit of measurement of this entity, if any. | homeassistant/helpers/entity.py | unit_of_measurement | Leviosa-Shades/core | python | @property
def unit_of_measurement(self) -> Optional[str]:
return None |
@property
def icon(self) -> Optional[str]:
'Return the icon to use in the frontend, if any.'
return None | -5,121,040,458,521,612,000 | Return the icon to use in the frontend, if any. | homeassistant/helpers/entity.py | icon | Leviosa-Shades/core | python | @property
def icon(self) -> Optional[str]:
return None |
@property
def entity_picture(self) -> Optional[str]:
'Return the entity picture to use in the frontend, if any.'
return None | 1,998,631,624,207,249,400 | Return the entity picture to use in the frontend, if any. | homeassistant/helpers/entity.py | entity_picture | Leviosa-Shades/core | python | @property
def entity_picture(self) -> Optional[str]:
return None |
@property
def available(self) -> bool:
'Return True if entity is available.'
return True | -3,548,180,598,612,628,500 | Return True if entity is available. | homeassistant/helpers/entity.py | available | Leviosa-Shades/core | python | @property
def available(self) -> bool:
return True |
@property
def assumed_state(self) -> bool:
'Return True if unable to access real state of the entity.'
return False | 1,269,755,479,481,557,800 | Return True if unable to access real state of the entity. | homeassistant/helpers/entity.py | assumed_state | Leviosa-Shades/core | python | @property
def assumed_state(self) -> bool:
return False |
@property
def force_update(self) -> bool:
'Return True if state updates should be forced.\n\n If True, a state change will be triggered anytime the state property is\n updated, not just when the value changes.\n '
return False | -1,228,016,239,530,733,600 | Return True if state updates should be forced.
If True, a state change will be triggered anytime the state property is
updated, not just when the value changes. | homeassistant/helpers/entity.py | force_update | Leviosa-Shades/core | python | @property
def force_update(self) -> bool:
'Return True if state updates should be forced.\n\n If True, a state change will be triggered anytime the state property is\n updated, not just when the value changes.\n '
return False |
@property
def supported_features(self) -> Optional[int]:
'Flag supported features.'
return None | -6,673,123,116,929,117,000 | Flag supported features. | homeassistant/helpers/entity.py | supported_features | Leviosa-Shades/core | python | @property
def supported_features(self) -> Optional[int]:
return None |
@property
def context_recent_time(self) -> timedelta:
'Time that a context is considered recent.'
return timedelta(seconds=5) | 4,102,250,335,939,575,300 | Time that a context is considered recent. | homeassistant/helpers/entity.py | context_recent_time | Leviosa-Shades/core | python | @property
def context_recent_time(self) -> timedelta:
return timedelta(seconds=5) |
@property
def entity_registry_enabled_default(self) -> bool:
'Return if the entity should be enabled when first added to the entity registry.'
return True | -6,825,246,018,270,339,000 | Return if the entity should be enabled when first added to the entity registry. | homeassistant/helpers/entity.py | entity_registry_enabled_default | Leviosa-Shades/core | python | @property
def entity_registry_enabled_default(self) -> bool:
return True |
@property
def enabled(self) -> bool:
'Return if the entity is enabled in the entity registry.\n\n If an entity is not part of the registry, it cannot be disabled\n and will therefore always be enabled.\n '
return ((self.registry_entry is None) or (not self.registry_entry.disabled)) | -4,212,866,319,016,292,000 | Return if the entity is enabled in the entity registry.
If an entity is not part of the registry, it cannot be disabled
and will therefore always be enabled. | homeassistant/helpers/entity.py | enabled | Leviosa-Shades/core | python | @property
def enabled(self) -> bool:
'Return if the entity is enabled in the entity registry.\n\n If an entity is not part of the registry, it cannot be disabled\n and will therefore always be enabled.\n '
return ((self.registry_entry is None) or (not self.registry_entry.disabled)) |
@callback
def async_set_context(self, context: Context) -> None:
'Set the context the entity currently operates under.'
self._context = context
self._context_set = dt_util.utcnow() | 7,498,294,261,140,403,000 | Set the context the entity currently operates under. | homeassistant/helpers/entity.py | async_set_context | Leviosa-Shades/core | python | @callback
def async_set_context(self, context: Context) -> None:
self._context = context
self._context_set = dt_util.utcnow() |
async def async_update_ha_state(self, force_refresh: bool=False) -> None:
'Update Home Assistant with current state of entity.\n\n If force_refresh == True will update entity before setting state.\n\n This method must be run in the event loop.\n '
if (self.hass is None):
raise RuntimeError(f'Attribute hass is None for {self}')
if (self.entity_id is None):
raise NoEntitySpecifiedError(f'No entity id specified for entity {self.name}')
if force_refresh:
try:
(await self.async_device_update())
except Exception:
_LOGGER.exception('Update for %s fails', self.entity_id)
return
self._async_write_ha_state() | -2,629,200,371,007,674,000 | Update Home Assistant with current state of entity.
If force_refresh == True will update entity before setting state.
This method must be run in the event loop. | homeassistant/helpers/entity.py | async_update_ha_state | Leviosa-Shades/core | python | async def async_update_ha_state(self, force_refresh: bool=False) -> None:
'Update Home Assistant with current state of entity.\n\n If force_refresh == True will update entity before setting state.\n\n This method must be run in the event loop.\n '
if (self.hass is None):
raise RuntimeError(f'Attribute hass is None for {self}')
if (self.entity_id is None):
raise NoEntitySpecifiedError(f'No entity id specified for entity {self.name}')
if force_refresh:
try:
(await self.async_device_update())
except Exception:
_LOGGER.exception('Update for %s fails', self.entity_id)
return
self._async_write_ha_state() |
@callback
def async_write_ha_state(self) -> None:
'Write the state to the state machine.'
if (self.hass is None):
raise RuntimeError(f'Attribute hass is None for {self}')
if (self.entity_id is None):
raise NoEntitySpecifiedError(f'No entity id specified for entity {self.name}')
self._async_write_ha_state() | -7,814,871,863,252,089,000 | Write the state to the state machine. | homeassistant/helpers/entity.py | async_write_ha_state | Leviosa-Shades/core | python | @callback
def async_write_ha_state(self) -> None:
if (self.hass is None):
raise RuntimeError(f'Attribute hass is None for {self}')
if (self.entity_id is None):
raise NoEntitySpecifiedError(f'No entity id specified for entity {self.name}')
self._async_write_ha_state() |
@callback
def _async_write_ha_state(self) -> None:
'Write the state to the state machine.'
if (self.registry_entry and self.registry_entry.disabled_by):
if (not self._disabled_reported):
self._disabled_reported = True
assert (self.platform is not None)
_LOGGER.warning('Entity %s is incorrectly being triggered for updates while it is disabled. This is a bug in the %s integration', self.entity_id, self.platform.platform_name)
return
start = timer()
attr = self.capability_attributes
attr = (dict(attr) if attr else {})
if (not self.available):
state = STATE_UNAVAILABLE
else:
sstate = self.state
state = (STATE_UNKNOWN if (sstate is None) else str(sstate))
attr.update((self.state_attributes or {}))
extra_state_attributes = self.extra_state_attributes
if (extra_state_attributes is None):
extra_state_attributes = self.device_state_attributes
attr.update((extra_state_attributes or {}))
unit_of_measurement = self.unit_of_measurement
if (unit_of_measurement is not None):
attr[ATTR_UNIT_OF_MEASUREMENT] = unit_of_measurement
entry = self.registry_entry
name = ((entry and entry.name) or self.name)
if (name is not None):
attr[ATTR_FRIENDLY_NAME] = name
icon = ((entry and entry.icon) or self.icon)
if (icon is not None):
attr[ATTR_ICON] = icon
entity_picture = self.entity_picture
if (entity_picture is not None):
attr[ATTR_ENTITY_PICTURE] = entity_picture
assumed_state = self.assumed_state
if assumed_state:
attr[ATTR_ASSUMED_STATE] = assumed_state
supported_features = self.supported_features
if (supported_features is not None):
attr[ATTR_SUPPORTED_FEATURES] = supported_features
device_class = self.device_class
if (device_class is not None):
attr[ATTR_DEVICE_CLASS] = str(device_class)
end = timer()
if (((end - start) > 0.4) and (not self._slow_reported)):
self._slow_reported = True
extra = ''
if ('custom_components' in type(self).__module__):
extra = 'Please report it to the custom component author.'
else:
extra = 'Please create a bug report at https://github.com/home-assistant/core/issues?q=is%3Aopen+is%3Aissue'
if self.platform:
extra += f'+label%3A%22integration%3A+{self.platform.platform_name}%22'
_LOGGER.warning('Updating state for %s (%s) took %.3f seconds. %s', self.entity_id, type(self), (end - start), extra)
if (DATA_CUSTOMIZE in self.hass.data):
attr.update(self.hass.data[DATA_CUSTOMIZE].get(self.entity_id))
try:
unit_of_measure = attr.get(ATTR_UNIT_OF_MEASUREMENT)
units = self.hass.config.units
if ((unit_of_measure in (TEMP_CELSIUS, TEMP_FAHRENHEIT)) and (unit_of_measure != units.temperature_unit)):
prec = (((len(state) - state.index('.')) - 1) if ('.' in state) else 0)
temp = units.temperature(float(state), unit_of_measure)
state = str((round(temp) if (prec == 0) else round(temp, prec)))
attr[ATTR_UNIT_OF_MEASUREMENT] = units.temperature_unit
except ValueError:
pass
if ((self._context_set is not None) and ((dt_util.utcnow() - self._context_set) > self.context_recent_time)):
self._context = None
self._context_set = None
self.hass.states.async_set(self.entity_id, state, attr, self.force_update, self._context) | 5,703,493,031,829,491,000 | Write the state to the state machine. | homeassistant/helpers/entity.py | _async_write_ha_state | Leviosa-Shades/core | python | @callback
def _async_write_ha_state(self) -> None:
if (self.registry_entry and self.registry_entry.disabled_by):
if (not self._disabled_reported):
self._disabled_reported = True
assert (self.platform is not None)
_LOGGER.warning('Entity %s is incorrectly being triggered for updates while it is disabled. This is a bug in the %s integration', self.entity_id, self.platform.platform_name)
return
start = timer()
attr = self.capability_attributes
attr = (dict(attr) if attr else {})
if (not self.available):
state = STATE_UNAVAILABLE
else:
sstate = self.state
state = (STATE_UNKNOWN if (sstate is None) else str(sstate))
attr.update((self.state_attributes or {}))
extra_state_attributes = self.extra_state_attributes
if (extra_state_attributes is None):
extra_state_attributes = self.device_state_attributes
attr.update((extra_state_attributes or {}))
unit_of_measurement = self.unit_of_measurement
if (unit_of_measurement is not None):
attr[ATTR_UNIT_OF_MEASUREMENT] = unit_of_measurement
entry = self.registry_entry
name = ((entry and entry.name) or self.name)
if (name is not None):
attr[ATTR_FRIENDLY_NAME] = name
icon = ((entry and entry.icon) or self.icon)
if (icon is not None):
attr[ATTR_ICON] = icon
entity_picture = self.entity_picture
if (entity_picture is not None):
attr[ATTR_ENTITY_PICTURE] = entity_picture
assumed_state = self.assumed_state
if assumed_state:
attr[ATTR_ASSUMED_STATE] = assumed_state
supported_features = self.supported_features
if (supported_features is not None):
attr[ATTR_SUPPORTED_FEATURES] = supported_features
device_class = self.device_class
if (device_class is not None):
attr[ATTR_DEVICE_CLASS] = str(device_class)
end = timer()
if (((end - start) > 0.4) and (not self._slow_reported)):
self._slow_reported = True
extra =
if ('custom_components' in type(self).__module__):
extra = 'Please report it to the custom component author.'
else:
extra = 'Please create a bug report at https://github.com/home-assistant/core/issues?q=is%3Aopen+is%3Aissue'
if self.platform:
extra += f'+label%3A%22integration%3A+{self.platform.platform_name}%22'
_LOGGER.warning('Updating state for %s (%s) took %.3f seconds. %s', self.entity_id, type(self), (end - start), extra)
if (DATA_CUSTOMIZE in self.hass.data):
attr.update(self.hass.data[DATA_CUSTOMIZE].get(self.entity_id))
try:
unit_of_measure = attr.get(ATTR_UNIT_OF_MEASUREMENT)
units = self.hass.config.units
if ((unit_of_measure in (TEMP_CELSIUS, TEMP_FAHRENHEIT)) and (unit_of_measure != units.temperature_unit)):
prec = (((len(state) - state.index('.')) - 1) if ('.' in state) else 0)
temp = units.temperature(float(state), unit_of_measure)
state = str((round(temp) if (prec == 0) else round(temp, prec)))
attr[ATTR_UNIT_OF_MEASUREMENT] = units.temperature_unit
except ValueError:
pass
if ((self._context_set is not None) and ((dt_util.utcnow() - self._context_set) > self.context_recent_time)):
self._context = None
self._context_set = None
self.hass.states.async_set(self.entity_id, state, attr, self.force_update, self._context) |
def schedule_update_ha_state(self, force_refresh: bool=False) -> None:
'Schedule an update ha state change task.\n\n Scheduling the update avoids executor deadlocks.\n\n Entity state and attributes are read when the update ha state change\n task is executed.\n If state is changed more than once before the ha state change task has\n been executed, the intermediate state transitions will be missed.\n '
self.hass.add_job(self.async_update_ha_state(force_refresh)) | 4,930,532,841,608,269,000 | Schedule an update ha state change task.
Scheduling the update avoids executor deadlocks.
Entity state and attributes are read when the update ha state change
task is executed.
If state is changed more than once before the ha state change task has
been executed, the intermediate state transitions will be missed. | homeassistant/helpers/entity.py | schedule_update_ha_state | Leviosa-Shades/core | python | def schedule_update_ha_state(self, force_refresh: bool=False) -> None:
'Schedule an update ha state change task.\n\n Scheduling the update avoids executor deadlocks.\n\n Entity state and attributes are read when the update ha state change\n task is executed.\n If state is changed more than once before the ha state change task has\n been executed, the intermediate state transitions will be missed.\n '
self.hass.add_job(self.async_update_ha_state(force_refresh)) |
@callback
def async_schedule_update_ha_state(self, force_refresh: bool=False) -> None:
'Schedule an update ha state change task.\n\n This method must be run in the event loop.\n Scheduling the update avoids executor deadlocks.\n\n Entity state and attributes are read when the update ha state change\n task is executed.\n If state is changed more than once before the ha state change task has\n been executed, the intermediate state transitions will be missed.\n '
if force_refresh:
self.hass.async_create_task(self.async_update_ha_state(force_refresh))
else:
self.async_write_ha_state() | 221,238,759,961,931,260 | Schedule an update ha state change task.
This method must be run in the event loop.
Scheduling the update avoids executor deadlocks.
Entity state and attributes are read when the update ha state change
task is executed.
If state is changed more than once before the ha state change task has
been executed, the intermediate state transitions will be missed. | homeassistant/helpers/entity.py | async_schedule_update_ha_state | Leviosa-Shades/core | python | @callback
def async_schedule_update_ha_state(self, force_refresh: bool=False) -> None:
'Schedule an update ha state change task.\n\n This method must be run in the event loop.\n Scheduling the update avoids executor deadlocks.\n\n Entity state and attributes are read when the update ha state change\n task is executed.\n If state is changed more than once before the ha state change task has\n been executed, the intermediate state transitions will be missed.\n '
if force_refresh:
self.hass.async_create_task(self.async_update_ha_state(force_refresh))
else:
self.async_write_ha_state() |
async def async_device_update(self, warning: bool=True) -> None:
"Process 'update' or 'async_update' from entity.\n\n This method is a coroutine.\n "
if self._update_staged:
return
self._update_staged = True
if self.parallel_updates:
(await self.parallel_updates.acquire())
try:
if hasattr(self, 'async_update'):
task = self.hass.async_create_task(self.async_update())
elif hasattr(self, 'update'):
task = self.hass.async_add_executor_job(self.update)
else:
return
if (not warning):
(await task)
return
(finished, _) = (await asyncio.wait([task], timeout=SLOW_UPDATE_WARNING))
for done in finished:
exc = done.exception()
if exc:
raise exc
return
_LOGGER.warning('Update of %s is taking over %s seconds', self.entity_id, SLOW_UPDATE_WARNING)
(await task)
finally:
self._update_staged = False
if self.parallel_updates:
self.parallel_updates.release() | -3,065,611,561,549,544,400 | Process 'update' or 'async_update' from entity.
This method is a coroutine. | homeassistant/helpers/entity.py | async_device_update | Leviosa-Shades/core | python | async def async_device_update(self, warning: bool=True) -> None:
"Process 'update' or 'async_update' from entity.\n\n This method is a coroutine.\n "
if self._update_staged:
return
self._update_staged = True
if self.parallel_updates:
(await self.parallel_updates.acquire())
try:
if hasattr(self, 'async_update'):
task = self.hass.async_create_task(self.async_update())
elif hasattr(self, 'update'):
task = self.hass.async_add_executor_job(self.update)
else:
return
if (not warning):
(await task)
return
(finished, _) = (await asyncio.wait([task], timeout=SLOW_UPDATE_WARNING))
for done in finished:
exc = done.exception()
if exc:
raise exc
return
_LOGGER.warning('Update of %s is taking over %s seconds', self.entity_id, SLOW_UPDATE_WARNING)
(await task)
finally:
self._update_staged = False
if self.parallel_updates:
self.parallel_updates.release() |
@callback
def async_on_remove(self, func: CALLBACK_TYPE) -> None:
'Add a function to call when entity removed.'
if (self._on_remove is None):
self._on_remove = []
self._on_remove.append(func) | 2,298,680,603,316,999,700 | Add a function to call when entity removed. | homeassistant/helpers/entity.py | async_on_remove | Leviosa-Shades/core | python | @callback
def async_on_remove(self, func: CALLBACK_TYPE) -> None:
if (self._on_remove is None):
self._on_remove = []
self._on_remove.append(func) |
async def async_removed_from_registry(self) -> None:
'Run when entity has been removed from entity registry.\n\n To be extended by integrations.\n ' | -4,028,870,512,133,826,600 | Run when entity has been removed from entity registry.
To be extended by integrations. | homeassistant/helpers/entity.py | async_removed_from_registry | Leviosa-Shades/core | python | async def async_removed_from_registry(self) -> None:
'Run when entity has been removed from entity registry.\n\n To be extended by integrations.\n ' |
@callback
def add_to_platform_start(self, hass: HomeAssistant, platform: EntityPlatform, parallel_updates: Optional[asyncio.Semaphore]) -> None:
'Start adding an entity to a platform.'
if self._added:
raise HomeAssistantError(f'Entity {self.entity_id} cannot be added a second time to an entity platform')
self.hass = hass
self.platform = platform
self.parallel_updates = parallel_updates
self._added = True | -1,761,300,229,261,884,400 | Start adding an entity to a platform. | homeassistant/helpers/entity.py | add_to_platform_start | Leviosa-Shades/core | python | @callback
def add_to_platform_start(self, hass: HomeAssistant, platform: EntityPlatform, parallel_updates: Optional[asyncio.Semaphore]) -> None:
if self._added:
raise HomeAssistantError(f'Entity {self.entity_id} cannot be added a second time to an entity platform')
self.hass = hass
self.platform = platform
self.parallel_updates = parallel_updates
self._added = True |
@callback
def add_to_platform_abort(self) -> None:
'Abort adding an entity to a platform.'
self.hass = None
self.platform = None
self.parallel_updates = None
self._added = False | -2,633,042,765,756,663,300 | Abort adding an entity to a platform. | homeassistant/helpers/entity.py | add_to_platform_abort | Leviosa-Shades/core | python | @callback
def add_to_platform_abort(self) -> None:
self.hass = None
self.platform = None
self.parallel_updates = None
self._added = False |
async def add_to_platform_finish(self) -> None:
'Finish adding an entity to a platform.'
(await self.async_internal_added_to_hass())
(await self.async_added_to_hass())
self.async_write_ha_state() | -2,320,288,375,294,386,000 | Finish adding an entity to a platform. | homeassistant/helpers/entity.py | add_to_platform_finish | Leviosa-Shades/core | python | async def add_to_platform_finish(self) -> None:
(await self.async_internal_added_to_hass())
(await self.async_added_to_hass())
self.async_write_ha_state() |
async def async_remove(self, *, force_remove: bool=False) -> None:
"Remove entity from Home Assistant.\n\n If the entity has a non disabled entry in the entity registry,\n the entity's state will be set to unavailable, in the same way\n as when the entity registry is loaded.\n\n If the entity doesn't have a non disabled entry in the entity registry,\n or if force_remove=True, its state will be removed.\n "
if (self.platform and (not self._added)):
raise HomeAssistantError(f'Entity {self.entity_id} async_remove called twice')
self._added = False
if (self._on_remove is not None):
while self._on_remove:
self._on_remove.pop()()
(await self.async_internal_will_remove_from_hass())
(await self.async_will_remove_from_hass())
if ((not force_remove) and self.registry_entry and (not self.registry_entry.disabled)):
self.registry_entry.write_unavailable_state(self.hass)
else:
self.hass.states.async_remove(self.entity_id, context=self._context) | 149,070,520,219,460,640 | Remove entity from Home Assistant.
If the entity has a non disabled entry in the entity registry,
the entity's state will be set to unavailable, in the same way
as when the entity registry is loaded.
If the entity doesn't have a non disabled entry in the entity registry,
or if force_remove=True, its state will be removed. | homeassistant/helpers/entity.py | async_remove | Leviosa-Shades/core | python | async def async_remove(self, *, force_remove: bool=False) -> None:
"Remove entity from Home Assistant.\n\n If the entity has a non disabled entry in the entity registry,\n the entity's state will be set to unavailable, in the same way\n as when the entity registry is loaded.\n\n If the entity doesn't have a non disabled entry in the entity registry,\n or if force_remove=True, its state will be removed.\n "
if (self.platform and (not self._added)):
raise HomeAssistantError(f'Entity {self.entity_id} async_remove called twice')
self._added = False
if (self._on_remove is not None):
while self._on_remove:
self._on_remove.pop()()
(await self.async_internal_will_remove_from_hass())
(await self.async_will_remove_from_hass())
if ((not force_remove) and self.registry_entry and (not self.registry_entry.disabled)):
self.registry_entry.write_unavailable_state(self.hass)
else:
self.hass.states.async_remove(self.entity_id, context=self._context) |
async def async_added_to_hass(self) -> None:
'Run when entity about to be added to hass.\n\n To be extended by integrations.\n ' | -5,585,018,881,033,491,000 | Run when entity about to be added to hass.
To be extended by integrations. | homeassistant/helpers/entity.py | async_added_to_hass | Leviosa-Shades/core | python | async def async_added_to_hass(self) -> None:
'Run when entity about to be added to hass.\n\n To be extended by integrations.\n ' |
async def async_will_remove_from_hass(self) -> None:
'Run when entity will be removed from hass.\n\n To be extended by integrations.\n ' | -8,817,618,303,532,068,000 | Run when entity will be removed from hass.
To be extended by integrations. | homeassistant/helpers/entity.py | async_will_remove_from_hass | Leviosa-Shades/core | python | async def async_will_remove_from_hass(self) -> None:
'Run when entity will be removed from hass.\n\n To be extended by integrations.\n ' |
async def async_internal_added_to_hass(self) -> None:
'Run when entity about to be added to hass.\n\n Not to be extended by integrations.\n '
if self.platform:
info = {'domain': self.platform.platform_name}
if self.platform.config_entry:
info['source'] = SOURCE_CONFIG_ENTRY
info['config_entry'] = self.platform.config_entry.entry_id
else:
info['source'] = SOURCE_PLATFORM_CONFIG
self.hass.data.setdefault(DATA_ENTITY_SOURCE, {})[self.entity_id] = info
if (self.registry_entry is not None):
assert (not self.registry_entry.disabled_by), f"Entity {self.entity_id} is being added while it's disabled"
self.async_on_remove(async_track_entity_registry_updated_event(self.hass, self.entity_id, self._async_registry_updated)) | 7,854,938,287,459,399,000 | Run when entity about to be added to hass.
Not to be extended by integrations. | homeassistant/helpers/entity.py | async_internal_added_to_hass | Leviosa-Shades/core | python | async def async_internal_added_to_hass(self) -> None:
'Run when entity about to be added to hass.\n\n Not to be extended by integrations.\n '
if self.platform:
info = {'domain': self.platform.platform_name}
if self.platform.config_entry:
info['source'] = SOURCE_CONFIG_ENTRY
info['config_entry'] = self.platform.config_entry.entry_id
else:
info['source'] = SOURCE_PLATFORM_CONFIG
self.hass.data.setdefault(DATA_ENTITY_SOURCE, {})[self.entity_id] = info
if (self.registry_entry is not None):
assert (not self.registry_entry.disabled_by), f"Entity {self.entity_id} is being added while it's disabled"
self.async_on_remove(async_track_entity_registry_updated_event(self.hass, self.entity_id, self._async_registry_updated)) |
async def async_internal_will_remove_from_hass(self) -> None:
'Run when entity will be removed from hass.\n\n Not to be extended by integrations.\n '
if self.platform:
self.hass.data[DATA_ENTITY_SOURCE].pop(self.entity_id) | 1,536,591,258,824,101,000 | Run when entity will be removed from hass.
Not to be extended by integrations. | homeassistant/helpers/entity.py | async_internal_will_remove_from_hass | Leviosa-Shades/core | python | async def async_internal_will_remove_from_hass(self) -> None:
'Run when entity will be removed from hass.\n\n Not to be extended by integrations.\n '
if self.platform:
self.hass.data[DATA_ENTITY_SOURCE].pop(self.entity_id) |
async def _async_registry_updated(self, event: Event) -> None:
'Handle entity registry update.'
data = event.data
if (data['action'] == 'remove'):
(await self.async_removed_from_registry())
self.registry_entry = None
(await self.async_remove())
if (data['action'] != 'update'):
return
ent_reg = (await self.hass.helpers.entity_registry.async_get_registry())
old = self.registry_entry
self.registry_entry = ent_reg.async_get(data['entity_id'])
assert (self.registry_entry is not None)
if self.registry_entry.disabled:
(await self.async_remove())
return
assert (old is not None)
if (self.registry_entry.entity_id == old.entity_id):
self.async_write_ha_state()
return
(await self.async_remove(force_remove=True))
assert (self.platform is not None)
self.entity_id = self.registry_entry.entity_id
(await self.platform.async_add_entities([self])) | 8,834,064,360,167,315,000 | Handle entity registry update. | homeassistant/helpers/entity.py | _async_registry_updated | Leviosa-Shades/core | python | async def _async_registry_updated(self, event: Event) -> None:
data = event.data
if (data['action'] == 'remove'):
(await self.async_removed_from_registry())
self.registry_entry = None
(await self.async_remove())
if (data['action'] != 'update'):
return
ent_reg = (await self.hass.helpers.entity_registry.async_get_registry())
old = self.registry_entry
self.registry_entry = ent_reg.async_get(data['entity_id'])
assert (self.registry_entry is not None)
if self.registry_entry.disabled:
(await self.async_remove())
return
assert (old is not None)
if (self.registry_entry.entity_id == old.entity_id):
self.async_write_ha_state()
return
(await self.async_remove(force_remove=True))
assert (self.platform is not None)
self.entity_id = self.registry_entry.entity_id
(await self.platform.async_add_entities([self])) |
def __eq__(self, other: Any) -> bool:
'Return the comparison.'
if (not isinstance(other, self.__class__)):
return False
if ((self.unique_id is None) or (other.unique_id is None)):
return False
if ((self.platform is not None) or (other.platform is not None)):
if ((self.platform is None) or (other.platform is None)):
return False
if (self.platform.platform != other.platform.platform):
return False
return (self.unique_id == other.unique_id) | -4,800,598,507,128,546,000 | Return the comparison. | homeassistant/helpers/entity.py | __eq__ | Leviosa-Shades/core | python | def __eq__(self, other: Any) -> bool:
if (not isinstance(other, self.__class__)):
return False
if ((self.unique_id is None) or (other.unique_id is None)):
return False
if ((self.platform is not None) or (other.platform is not None)):
if ((self.platform is None) or (other.platform is None)):
return False
if (self.platform.platform != other.platform.platform):
return False
return (self.unique_id == other.unique_id) |
def __repr__(self) -> str:
'Return the representation.'
return f'<Entity {self.name}: {self.state}>' | 5,428,986,823,689,221,000 | Return the representation. | homeassistant/helpers/entity.py | __repr__ | Leviosa-Shades/core | python | def __repr__(self) -> str:
return f'<Entity {self.name}: {self.state}>' |
async def async_request_call(self, coro: Awaitable) -> None:
'Process request batched.'
if self.parallel_updates:
(await self.parallel_updates.acquire())
try:
(await coro)
finally:
if self.parallel_updates:
self.parallel_updates.release() | 3,011,644,997,447,190,500 | Process request batched. | homeassistant/helpers/entity.py | async_request_call | Leviosa-Shades/core | python | async def async_request_call(self, coro: Awaitable) -> None:
if self.parallel_updates:
(await self.parallel_updates.acquire())
try:
(await coro)
finally:
if self.parallel_updates:
self.parallel_updates.release() |
@property
def state(self) -> str:
'Return the state.'
return (STATE_ON if self.is_on else STATE_OFF) | -3,434,155,257,435,733,000 | Return the state. | homeassistant/helpers/entity.py | state | Leviosa-Shades/core | python | @property
def state(self) -> str:
return (STATE_ON if self.is_on else STATE_OFF) |
@property
def is_on(self) -> bool:
'Return True if entity is on.'
raise NotImplementedError() | 8,799,682,652,869,394,000 | Return True if entity is on. | homeassistant/helpers/entity.py | is_on | Leviosa-Shades/core | python | @property
def is_on(self) -> bool:
raise NotImplementedError() |
def turn_on(self, **kwargs: Any) -> None:
'Turn the entity on.'
raise NotImplementedError() | 2,322,256,188,001,028,600 | Turn the entity on. | homeassistant/helpers/entity.py | turn_on | Leviosa-Shades/core | python | def turn_on(self, **kwargs: Any) -> None:
raise NotImplementedError() |
async def async_turn_on(self, **kwargs: Any) -> None:
'Turn the entity on.'
(await self.hass.async_add_executor_job(ft.partial(self.turn_on, **kwargs))) | -8,243,134,644,201,260,000 | Turn the entity on. | homeassistant/helpers/entity.py | async_turn_on | Leviosa-Shades/core | python | async def async_turn_on(self, **kwargs: Any) -> None:
(await self.hass.async_add_executor_job(ft.partial(self.turn_on, **kwargs))) |
def turn_off(self, **kwargs: Any) -> None:
'Turn the entity off.'
raise NotImplementedError() | -3,799,708,282,002,590,700 | Turn the entity off. | homeassistant/helpers/entity.py | turn_off | Leviosa-Shades/core | python | def turn_off(self, **kwargs: Any) -> None:
raise NotImplementedError() |
async def async_turn_off(self, **kwargs: Any) -> None:
'Turn the entity off.'
(await self.hass.async_add_executor_job(ft.partial(self.turn_off, **kwargs))) | -6,121,593,494,274,528,000 | Turn the entity off. | homeassistant/helpers/entity.py | async_turn_off | Leviosa-Shades/core | python | async def async_turn_off(self, **kwargs: Any) -> None:
(await self.hass.async_add_executor_job(ft.partial(self.turn_off, **kwargs))) |
def toggle(self, **kwargs: Any) -> None:
'Toggle the entity.'
if self.is_on:
self.turn_off(**kwargs)
else:
self.turn_on(**kwargs) | -6,398,084,867,639,560,000 | Toggle the entity. | homeassistant/helpers/entity.py | toggle | Leviosa-Shades/core | python | def toggle(self, **kwargs: Any) -> None:
if self.is_on:
self.turn_off(**kwargs)
else:
self.turn_on(**kwargs) |
async def async_toggle(self, **kwargs: Any) -> None:
'Toggle the entity.'
if self.is_on:
(await self.async_turn_off(**kwargs))
else:
(await self.async_turn_on(**kwargs)) | 6,141,564,757,956,466,000 | Toggle the entity. | homeassistant/helpers/entity.py | async_toggle | Leviosa-Shades/core | python | async def async_toggle(self, **kwargs: Any) -> None:
if self.is_on:
(await self.async_turn_off(**kwargs))
else:
(await self.async_turn_on(**kwargs)) |
def get_image_size(fname):
'Determine the image type of fhandle and return its size.\n from draco'
with open(fname, 'rb') as fhandle:
head = fhandle.read(24)
if (len(head) != 24):
return
if (imghdr.what(fname) == 'png'):
check = struct.unpack('>i', head[4:8])[0]
if (check != 218765834):
return
(width, height) = struct.unpack('>ii', head[16:24])
elif (imghdr.what(fname) == 'gif'):
(width, height) = struct.unpack('<HH', head[6:10])
elif (imghdr.what(fname) == 'jpeg'):
try:
fhandle.seek(0)
size = 2
ftype = 0
while (not (192 <= ftype <= 207)):
fhandle.seek(size, 1)
byte = fhandle.read(1)
while (ord(byte) == 255):
byte = fhandle.read(1)
ftype = ord(byte)
size = (struct.unpack('>H', fhandle.read(2))[0] - 2)
fhandle.seek(1, 1)
(height, width) = struct.unpack('>HH', fhandle.read(4))
except Exception:
return
else:
return
return (width, height) | 3,716,537,104,232,310,000 | Determine the image type of fhandle and return its size.
from draco | mdpdfbook/mdpdf/image.py | get_image_size | geraked/mdpdfbook | python | def get_image_size(fname):
'Determine the image type of fhandle and return its size.\n from draco'
with open(fname, 'rb') as fhandle:
head = fhandle.read(24)
if (len(head) != 24):
return
if (imghdr.what(fname) == 'png'):
check = struct.unpack('>i', head[4:8])[0]
if (check != 218765834):
return
(width, height) = struct.unpack('>ii', head[16:24])
elif (imghdr.what(fname) == 'gif'):
(width, height) = struct.unpack('<HH', head[6:10])
elif (imghdr.what(fname) == 'jpeg'):
try:
fhandle.seek(0)
size = 2
ftype = 0
while (not (192 <= ftype <= 207)):
fhandle.seek(size, 1)
byte = fhandle.read(1)
while (ord(byte) == 255):
byte = fhandle.read(1)
ftype = ord(byte)
size = (struct.unpack('>H', fhandle.read(2))[0] - 2)
fhandle.seek(1, 1)
(height, width) = struct.unpack('>HH', fhandle.read(4))
except Exception:
return
else:
return
return (width, height) |
@property
def path_url(self):
'Build the path URL to use.'
url = []
p = urlsplit(self.url)
path = p.path
if (not path):
path = '/'
url.append(path)
query = p.query
if query:
url.append('?')
url.append(query)
return ''.join(url) | 1,763,220,040,231,475,700 | Build the path URL to use. | src/oci/_vendor/requests/models.py | path_url | LaudateCorpus1/oci-python-sdk | python | @property
def path_url(self):
url = []
p = urlsplit(self.url)
path = p.path
if (not path):
path = '/'
url.append(path)
query = p.query
if query:
url.append('?')
url.append(query)
return .join(url) |
@staticmethod
def _encode_params(data):
'Encode parameters in a piece of data.\n\n Will successfully encode parameters when passed as a dict or a list of\n 2-tuples. Order is retained if data is a list of 2-tuples but arbitrary\n if parameters are supplied as a dict.\n '
if isinstance(data, (str, bytes)):
return data
elif hasattr(data, 'read'):
return data
elif hasattr(data, '__iter__'):
result = []
for (k, vs) in to_key_val_list(data):
if (isinstance(vs, basestring) or (not hasattr(vs, '__iter__'))):
vs = [vs]
for v in vs:
if (v is not None):
result.append(((k.encode('utf-8') if isinstance(k, str) else k), (v.encode('utf-8') if isinstance(v, str) else v)))
return urlencode(result, doseq=True)
else:
return data | -8,298,462,985,702,277,000 | Encode parameters in a piece of data.
Will successfully encode parameters when passed as a dict or a list of
2-tuples. Order is retained if data is a list of 2-tuples but arbitrary
if parameters are supplied as a dict. | src/oci/_vendor/requests/models.py | _encode_params | LaudateCorpus1/oci-python-sdk | python | @staticmethod
def _encode_params(data):
'Encode parameters in a piece of data.\n\n Will successfully encode parameters when passed as a dict or a list of\n 2-tuples. Order is retained if data is a list of 2-tuples but arbitrary\n if parameters are supplied as a dict.\n '
if isinstance(data, (str, bytes)):
return data
elif hasattr(data, 'read'):
return data
elif hasattr(data, '__iter__'):
result = []
for (k, vs) in to_key_val_list(data):
if (isinstance(vs, basestring) or (not hasattr(vs, '__iter__'))):
vs = [vs]
for v in vs:
if (v is not None):
result.append(((k.encode('utf-8') if isinstance(k, str) else k), (v.encode('utf-8') if isinstance(v, str) else v)))
return urlencode(result, doseq=True)
else:
return data |
@staticmethod
def _encode_files(files, data):
'Build the body for a multipart/form-data request.\n\n Will successfully encode files when passed as a dict or a list of\n tuples. Order is retained if data is a list of tuples but arbitrary\n if parameters are supplied as a dict.\n The tuples may be 2-tuples (filename, fileobj), 3-tuples (filename, fileobj, contentype)\n or 4-tuples (filename, fileobj, contentype, custom_headers).\n '
if (not files):
raise ValueError('Files must be provided.')
elif isinstance(data, basestring):
raise ValueError('Data must not be a string.')
new_fields = []
fields = to_key_val_list((data or {}))
files = to_key_val_list((files or {}))
for (field, val) in fields:
if (isinstance(val, basestring) or (not hasattr(val, '__iter__'))):
val = [val]
for v in val:
if (v is not None):
if (not isinstance(v, bytes)):
v = str(v)
new_fields.append(((field.decode('utf-8') if isinstance(field, bytes) else field), (v.encode('utf-8') if isinstance(v, str) else v)))
for (k, v) in files:
ft = None
fh = None
if isinstance(v, (tuple, list)):
if (len(v) == 2):
(fn, fp) = v
elif (len(v) == 3):
(fn, fp, ft) = v
else:
(fn, fp, ft, fh) = v
else:
fn = (guess_filename(v) or k)
fp = v
if isinstance(fp, (str, bytes, bytearray)):
fdata = fp
elif hasattr(fp, 'read'):
fdata = fp.read()
elif (fp is None):
continue
else:
fdata = fp
rf = RequestField(name=k, data=fdata, filename=fn, headers=fh)
rf.make_multipart(content_type=ft)
new_fields.append(rf)
(body, content_type) = encode_multipart_formdata(new_fields)
return (body, content_type) | -5,009,398,987,837,660,000 | Build the body for a multipart/form-data request.
Will successfully encode files when passed as a dict or a list of
tuples. Order is retained if data is a list of tuples but arbitrary
if parameters are supplied as a dict.
The tuples may be 2-tuples (filename, fileobj), 3-tuples (filename, fileobj, contentype)
or 4-tuples (filename, fileobj, contentype, custom_headers). | src/oci/_vendor/requests/models.py | _encode_files | LaudateCorpus1/oci-python-sdk | python | @staticmethod
def _encode_files(files, data):
'Build the body for a multipart/form-data request.\n\n Will successfully encode files when passed as a dict or a list of\n tuples. Order is retained if data is a list of tuples but arbitrary\n if parameters are supplied as a dict.\n The tuples may be 2-tuples (filename, fileobj), 3-tuples (filename, fileobj, contentype)\n or 4-tuples (filename, fileobj, contentype, custom_headers).\n '
if (not files):
raise ValueError('Files must be provided.')
elif isinstance(data, basestring):
raise ValueError('Data must not be a string.')
new_fields = []
fields = to_key_val_list((data or {}))
files = to_key_val_list((files or {}))
for (field, val) in fields:
if (isinstance(val, basestring) or (not hasattr(val, '__iter__'))):
val = [val]
for v in val:
if (v is not None):
if (not isinstance(v, bytes)):
v = str(v)
new_fields.append(((field.decode('utf-8') if isinstance(field, bytes) else field), (v.encode('utf-8') if isinstance(v, str) else v)))
for (k, v) in files:
ft = None
fh = None
if isinstance(v, (tuple, list)):
if (len(v) == 2):
(fn, fp) = v
elif (len(v) == 3):
(fn, fp, ft) = v
else:
(fn, fp, ft, fh) = v
else:
fn = (guess_filename(v) or k)
fp = v
if isinstance(fp, (str, bytes, bytearray)):
fdata = fp
elif hasattr(fp, 'read'):
fdata = fp.read()
elif (fp is None):
continue
else:
fdata = fp
rf = RequestField(name=k, data=fdata, filename=fn, headers=fh)
rf.make_multipart(content_type=ft)
new_fields.append(rf)
(body, content_type) = encode_multipart_formdata(new_fields)
return (body, content_type) |
def register_hook(self, event, hook):
'Properly register a hook.'
if (event not in self.hooks):
raise ValueError(('Unsupported event specified, with event name "%s"' % event))
if isinstance(hook, Callable):
self.hooks[event].append(hook)
elif hasattr(hook, '__iter__'):
self.hooks[event].extend((h for h in hook if isinstance(h, Callable))) | -4,681,226,212,105,443,000 | Properly register a hook. | src/oci/_vendor/requests/models.py | register_hook | LaudateCorpus1/oci-python-sdk | python | def register_hook(self, event, hook):
if (event not in self.hooks):
raise ValueError(('Unsupported event specified, with event name "%s"' % event))
if isinstance(hook, Callable):
self.hooks[event].append(hook)
elif hasattr(hook, '__iter__'):
self.hooks[event].extend((h for h in hook if isinstance(h, Callable))) |
def deregister_hook(self, event, hook):
'Deregister a previously registered hook.\n Returns True if the hook existed, False if not.\n '
try:
self.hooks[event].remove(hook)
return True
except ValueError:
return False | 7,969,727,305,059,734,000 | Deregister a previously registered hook.
Returns True if the hook existed, False if not. | src/oci/_vendor/requests/models.py | deregister_hook | LaudateCorpus1/oci-python-sdk | python | def deregister_hook(self, event, hook):
'Deregister a previously registered hook.\n Returns True if the hook existed, False if not.\n '
try:
self.hooks[event].remove(hook)
return True
except ValueError:
return False |
def prepare(self):
'Constructs a :class:`PreparedRequest <PreparedRequest>` for transmission and returns it.'
p = PreparedRequest()
p.prepare(method=self.method, url=self.url, headers=self.headers, files=self.files, data=self.data, json=self.json, params=self.params, auth=self.auth, cookies=self.cookies, hooks=self.hooks)
return p | -3,505,980,801,294,424,600 | Constructs a :class:`PreparedRequest <PreparedRequest>` for transmission and returns it. | src/oci/_vendor/requests/models.py | prepare | LaudateCorpus1/oci-python-sdk | python | def prepare(self):
p = PreparedRequest()
p.prepare(method=self.method, url=self.url, headers=self.headers, files=self.files, data=self.data, json=self.json, params=self.params, auth=self.auth, cookies=self.cookies, hooks=self.hooks)
return p |
def prepare(self, method=None, url=None, headers=None, files=None, data=None, params=None, auth=None, cookies=None, hooks=None, json=None):
'Prepares the entire request with the given parameters.'
self.prepare_method(method)
self.prepare_url(url, params)
self.prepare_headers(headers)
self.prepare_cookies(cookies)
self.prepare_body(data, files, json)
self.prepare_auth(auth, url)
self.prepare_hooks(hooks) | -8,748,719,649,105,786,000 | Prepares the entire request with the given parameters. | src/oci/_vendor/requests/models.py | prepare | LaudateCorpus1/oci-python-sdk | python | def prepare(self, method=None, url=None, headers=None, files=None, data=None, params=None, auth=None, cookies=None, hooks=None, json=None):
self.prepare_method(method)
self.prepare_url(url, params)
self.prepare_headers(headers)
self.prepare_cookies(cookies)
self.prepare_body(data, files, json)
self.prepare_auth(auth, url)
self.prepare_hooks(hooks) |
def prepare_method(self, method):
'Prepares the given HTTP method.'
self.method = method
if (self.method is not None):
self.method = to_native_string(self.method.upper()) | -8,811,068,185,766,678,000 | Prepares the given HTTP method. | src/oci/_vendor/requests/models.py | prepare_method | LaudateCorpus1/oci-python-sdk | python | def prepare_method(self, method):
self.method = method
if (self.method is not None):
self.method = to_native_string(self.method.upper()) |
def prepare_url(self, url, params):
'Prepares the given HTTP URL.'
if isinstance(url, bytes):
url = url.decode('utf8')
else:
url = (unicode(url) if is_py2 else str(url))
url = url.lstrip()
if ((':' in url) and (not url.lower().startswith('http'))):
self.url = url
return
try:
(scheme, auth, host, port, path, query, fragment) = parse_url(url)
except LocationParseError as e:
raise InvalidURL(*e.args)
if (not scheme):
error = 'Invalid URL {0!r}: No schema supplied. Perhaps you meant http://{0}?'
error = error.format(to_native_string(url, 'utf8'))
raise MissingSchema(error)
if (not host):
raise InvalidURL(('Invalid URL %r: No host supplied' % url))
if (not unicode_is_ascii(host)):
try:
host = self._get_idna_encoded_host(host)
except UnicodeError:
raise InvalidURL('URL has an invalid label.')
elif host.startswith(u'*'):
raise InvalidURL('URL has an invalid label.')
netloc = (auth or '')
if netloc:
netloc += '@'
netloc += host
if port:
netloc += (':' + str(port))
if (not path):
path = '/'
if is_py2:
if isinstance(scheme, str):
scheme = scheme.encode('utf-8')
if isinstance(netloc, str):
netloc = netloc.encode('utf-8')
if isinstance(path, str):
path = path.encode('utf-8')
if isinstance(query, str):
query = query.encode('utf-8')
if isinstance(fragment, str):
fragment = fragment.encode('utf-8')
if isinstance(params, (str, bytes)):
params = to_native_string(params)
enc_params = self._encode_params(params)
if enc_params:
if query:
query = ('%s&%s' % (query, enc_params))
else:
query = enc_params
url = requote_uri(urlunparse([scheme, netloc, path, None, query, fragment]))
self.url = url | -5,598,319,010,347,869,000 | Prepares the given HTTP URL. | src/oci/_vendor/requests/models.py | prepare_url | LaudateCorpus1/oci-python-sdk | python | def prepare_url(self, url, params):
if isinstance(url, bytes):
url = url.decode('utf8')
else:
url = (unicode(url) if is_py2 else str(url))
url = url.lstrip()
if ((':' in url) and (not url.lower().startswith('http'))):
self.url = url
return
try:
(scheme, auth, host, port, path, query, fragment) = parse_url(url)
except LocationParseError as e:
raise InvalidURL(*e.args)
if (not scheme):
error = 'Invalid URL {0!r}: No schema supplied. Perhaps you meant http://{0}?'
error = error.format(to_native_string(url, 'utf8'))
raise MissingSchema(error)
if (not host):
raise InvalidURL(('Invalid URL %r: No host supplied' % url))
if (not unicode_is_ascii(host)):
try:
host = self._get_idna_encoded_host(host)
except UnicodeError:
raise InvalidURL('URL has an invalid label.')
elif host.startswith(u'*'):
raise InvalidURL('URL has an invalid label.')
netloc = (auth or )
if netloc:
netloc += '@'
netloc += host
if port:
netloc += (':' + str(port))
if (not path):
path = '/'
if is_py2:
if isinstance(scheme, str):
scheme = scheme.encode('utf-8')
if isinstance(netloc, str):
netloc = netloc.encode('utf-8')
if isinstance(path, str):
path = path.encode('utf-8')
if isinstance(query, str):
query = query.encode('utf-8')
if isinstance(fragment, str):
fragment = fragment.encode('utf-8')
if isinstance(params, (str, bytes)):
params = to_native_string(params)
enc_params = self._encode_params(params)
if enc_params:
if query:
query = ('%s&%s' % (query, enc_params))
else:
query = enc_params
url = requote_uri(urlunparse([scheme, netloc, path, None, query, fragment]))
self.url = url |
def prepare_headers(self, headers):
'Prepares the given HTTP headers.'
self.headers = CaseInsensitiveDict()
if headers:
for header in headers.items():
check_header_validity(header)
(name, value) = header
self.headers[to_native_string(name)] = value | 5,526,637,669,010,412,000 | Prepares the given HTTP headers. | src/oci/_vendor/requests/models.py | prepare_headers | LaudateCorpus1/oci-python-sdk | python | def prepare_headers(self, headers):
self.headers = CaseInsensitiveDict()
if headers:
for header in headers.items():
check_header_validity(header)
(name, value) = header
self.headers[to_native_string(name)] = value |
def prepare_body(self, data, files, json=None):
'Prepares the given HTTP body data.'
body = None
content_type = None
if ((not data) and (json is not None)):
content_type = 'application/json'
body = complexjson.dumps(json)
if (not isinstance(body, bytes)):
body = body.encode('utf-8')
is_stream = all([hasattr(data, '__iter__'), (not isinstance(data, (basestring, list, tuple, Mapping)))])
if is_stream:
try:
length = super_len(data)
except (TypeError, AttributeError, UnsupportedOperation):
length = None
body = data
if (getattr(body, 'tell', None) is not None):
try:
self._body_position = body.tell()
except (IOError, OSError):
self._body_position = object()
if files:
raise NotImplementedError('Streamed bodies and files are mutually exclusive.')
if length:
self.headers['Content-Length'] = builtin_str(length)
else:
self.headers['Transfer-Encoding'] = 'chunked'
else:
if files:
(body, content_type) = self._encode_files(files, data)
elif data:
body = self._encode_params(data)
if (isinstance(data, basestring) or hasattr(data, 'read')):
content_type = None
else:
content_type = 'application/x-www-form-urlencoded'
self.prepare_content_length(body)
if (content_type and ('content-type' not in self.headers)):
self.headers['Content-Type'] = content_type
self.body = body | 2,758,324,703,423,574,000 | Prepares the given HTTP body data. | src/oci/_vendor/requests/models.py | prepare_body | LaudateCorpus1/oci-python-sdk | python | def prepare_body(self, data, files, json=None):
body = None
content_type = None
if ((not data) and (json is not None)):
content_type = 'application/json'
body = complexjson.dumps(json)
if (not isinstance(body, bytes)):
body = body.encode('utf-8')
is_stream = all([hasattr(data, '__iter__'), (not isinstance(data, (basestring, list, tuple, Mapping)))])
if is_stream:
try:
length = super_len(data)
except (TypeError, AttributeError, UnsupportedOperation):
length = None
body = data
if (getattr(body, 'tell', None) is not None):
try:
self._body_position = body.tell()
except (IOError, OSError):
self._body_position = object()
if files:
raise NotImplementedError('Streamed bodies and files are mutually exclusive.')
if length:
self.headers['Content-Length'] = builtin_str(length)
else:
self.headers['Transfer-Encoding'] = 'chunked'
else:
if files:
(body, content_type) = self._encode_files(files, data)
elif data:
body = self._encode_params(data)
if (isinstance(data, basestring) or hasattr(data, 'read')):
content_type = None
else:
content_type = 'application/x-www-form-urlencoded'
self.prepare_content_length(body)
if (content_type and ('content-type' not in self.headers)):
self.headers['Content-Type'] = content_type
self.body = body |
def prepare_content_length(self, body):
'Prepare Content-Length header based on request method and body'
if (body is not None):
length = super_len(body)
if length:
self.headers['Content-Length'] = builtin_str(length)
elif ((self.method not in ('GET', 'HEAD')) and (self.headers.get('Content-Length') is None)):
self.headers['Content-Length'] = '0' | -1,581,070,182,644,072,400 | Prepare Content-Length header based on request method and body | src/oci/_vendor/requests/models.py | prepare_content_length | LaudateCorpus1/oci-python-sdk | python | def prepare_content_length(self, body):
if (body is not None):
length = super_len(body)
if length:
self.headers['Content-Length'] = builtin_str(length)
elif ((self.method not in ('GET', 'HEAD')) and (self.headers.get('Content-Length') is None)):
self.headers['Content-Length'] = '0' |
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