body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
def get_dataframe(self, squeeze=True):
'\n Cast recarrays for stress periods into single\n dataframe containing all stress periods.\n\n Parameters\n ----------\n squeeze : bool\n Reduce number of columns in dataframe to only include\n stress periods where a v... | 6,324,942,353,991,596,000 | Cast recarrays for stress periods into single
dataframe containing all stress periods.
Parameters
----------
squeeze : bool
Reduce number of columns in dataframe to only include
stress periods where a variable changes.
Returns
-------
df : dataframe
Dataframe of shape nrow = ncells, ncol = nvar x nper. If... | flopy/utils/util_list.py | get_dataframe | aleaf/flopy | python | def get_dataframe(self, squeeze=True):
'\n Cast recarrays for stress periods into single\n dataframe containing all stress periods.\n\n Parameters\n ----------\n squeeze : bool\n Reduce number of columns in dataframe to only include\n stress periods where a v... |
def get_indices(self):
'\n a helper function for plotting - get all unique indices\n '
names = self.dtype.names
lnames = []
[lnames.append(name.lower()) for name in names]
if (('k' not in lnames) or ('j' not in lnames)):
raise NotImplementedError('MfList.get_indices require... | 3,431,313,341,761,410,600 | a helper function for plotting - get all unique indices | flopy/utils/util_list.py | get_indices | aleaf/flopy | python | def get_indices(self):
'\n \n '
names = self.dtype.names
lnames = []
[lnames.append(name.lower()) for name in names]
if (('k' not in lnames) or ('j' not in lnames)):
raise NotImplementedError('MfList.get_indices requires kij')
kpers = list(self.data.keys())
kpers.so... |
def plot(self, key=None, names=None, kper=0, filename_base=None, file_extension=None, mflay=None, **kwargs):
"\n Plot stress period boundary condition (MfList) data for a specified\n stress period\n\n Parameters\n ----------\n key : str\n MfList dictionary key. (default... | 7,490,638,837,416,212,000 | Plot stress period boundary condition (MfList) data for a specified
stress period
Parameters
----------
key : str
MfList dictionary key. (default is None)
names : list
List of names for figure titles. (default is None)
kper : int
MODFLOW zero-based stress period number to return. (default is zero)
filename... | flopy/utils/util_list.py | plot | aleaf/flopy | python | def plot(self, key=None, names=None, kper=0, filename_base=None, file_extension=None, mflay=None, **kwargs):
"\n Plot stress period boundary condition (MfList) data for a specified\n stress period\n\n Parameters\n ----------\n key : str\n MfList dictionary key. (default... |
def to_shapefile(self, filename, kper=None):
"\n Export stress period boundary condition (MfList) data for a specified\n stress period\n\n Parameters\n ----------\n filename : str\n Shapefile name to write\n kper : int\n MODFLOW zero-based stress perio... | -2,751,994,616,249,143,000 | Export stress period boundary condition (MfList) data for a specified
stress period
Parameters
----------
filename : str
Shapefile name to write
kper : int
MODFLOW zero-based stress period number to return. (default is None)
Returns
----------
None
See Also
--------
Notes
-----
Examples
--------
>>> import... | flopy/utils/util_list.py | to_shapefile | aleaf/flopy | python | def to_shapefile(self, filename, kper=None):
"\n Export stress period boundary condition (MfList) data for a specified\n stress period\n\n Parameters\n ----------\n filename : str\n Shapefile name to write\n kper : int\n MODFLOW zero-based stress perio... |
def to_array(self, kper=0, mask=False):
"\n Convert stress period boundary condition (MfList) data for a\n specified stress period to a 3-D numpy array\n\n Parameters\n ----------\n kper : int\n MODFLOW zero-based stress period number to return. (default is zero)\n ... | 49,518,715,375,102,890 | Convert stress period boundary condition (MfList) data for a
specified stress period to a 3-D numpy array
Parameters
----------
kper : int
MODFLOW zero-based stress period number to return. (default is zero)
mask : boolean
return array with np.NaN instead of zero
Returns
----------
out : dict of numpy.ndarrays... | flopy/utils/util_list.py | to_array | aleaf/flopy | python | def to_array(self, kper=0, mask=False):
"\n Convert stress period boundary condition (MfList) data for a\n specified stress period to a 3-D numpy array\n\n Parameters\n ----------\n kper : int\n MODFLOW zero-based stress period number to return. (default is zero)\n ... |
@classmethod
def from_4d(cls, model, pak_name, m4ds):
'construct an MfList instance from a dict of\n (attribute_name,masked 4D ndarray\n Parameters\n ----------\n model : mbase derived type\n pak_name : str package name (e.g GHB)\n m4ds : {attribute name:4d mask... | 7,955,611,825,442,068,000 | construct an MfList instance from a dict of
(attribute_name,masked 4D ndarray
Parameters
----------
model : mbase derived type
pak_name : str package name (e.g GHB)
m4ds : {attribute name:4d masked numpy.ndarray}
Returns
-------
MfList instance | flopy/utils/util_list.py | from_4d | aleaf/flopy | python | @classmethod
def from_4d(cls, model, pak_name, m4ds):
'construct an MfList instance from a dict of\n (attribute_name,masked 4D ndarray\n Parameters\n ----------\n model : mbase derived type\n pak_name : str package name (e.g GHB)\n m4ds : {attribute name:4d mask... |
@staticmethod
def masked4D_arrays_to_stress_period_data(dtype, m4ds):
' convert a dictionary of 4-dim masked arrays to\n a stress_period_data style dict of recarray\n Parameters\n ----------\n dtype : numpy dtype\n\n m4ds : dict {name:masked numpy 4-dim ndarray}\n ... | 1,632,529,002,862,806,500 | convert a dictionary of 4-dim masked arrays to
a stress_period_data style dict of recarray
Parameters
----------
dtype : numpy dtype
m4ds : dict {name:masked numpy 4-dim ndarray}
Returns
-------
dict {kper:recarray} | flopy/utils/util_list.py | masked4D_arrays_to_stress_period_data | aleaf/flopy | python | @staticmethod
def masked4D_arrays_to_stress_period_data(dtype, m4ds):
' convert a dictionary of 4-dim masked arrays to\n a stress_period_data style dict of recarray\n Parameters\n ----------\n dtype : numpy dtype\n\n m4ds : dict {name:masked numpy 4-dim ndarray}\n ... |
@main_app.route('/api')
def swagger():
'\n Responds with the OpenAPI specification for this application.\n '
return jsonify(spec.to_dict()) | -3,434,190,599,110,189,000 | Responds with the OpenAPI specification for this application. | flask_service/views.py | swagger | mwprog/atomist-flask-microservice | python | @main_app.route('/api')
def swagger():
'\n \n '
return jsonify(spec.to_dict()) |
@main_app.route('/health')
def health():
"\n Responds with the current's service health.\n\n Could be used by the liveness probe of a Kubernetes cluster for instance.\n "
return '' | -8,312,535,159,261,387,000 | Responds with the current's service health.
Could be used by the liveness probe of a Kubernetes cluster for instance. | flask_service/views.py | health | mwprog/atomist-flask-microservice | python | @main_app.route('/health')
def health():
"\n Responds with the current's service health.\n\n Could be used by the liveness probe of a Kubernetes cluster for instance.\n "
return |
@main_app.route('/status')
def status():
"\n Responds with the current's service status.\n\n Could be used by the readiness probe of a Kubernetes cluster.\n "
return '' | -83,078,368,568,048,130 | Responds with the current's service status.
Could be used by the readiness probe of a Kubernetes cluster. | flask_service/views.py | status | mwprog/atomist-flask-microservice | python | @main_app.route('/status')
def status():
"\n Responds with the current's service status.\n\n Could be used by the readiness probe of a Kubernetes cluster.\n "
return |
def get_covariance(self):
'Compute data covariance with the generative model.\n\n ``cov = components_.T * S**2 * components_ + sigma2 * eye(n_features)``\n where S**2 contains the explained variances, and sigma2 contains the\n noise variances.\n\n Returns\n -------\n cov : ... | 6,392,993,480,560,589,000 | Compute data covariance with the generative model.
``cov = components_.T * S**2 * components_ + sigma2 * eye(n_features)``
where S**2 contains the explained variances, and sigma2 contains the
noise variances.
Returns
-------
cov : array of shape=(n_features, n_features)
Estimated covariance of data. | sklearn/decomposition/_base.py | get_covariance | 40104/Scikit-Learn | python | def get_covariance(self):
'Compute data covariance with the generative model.\n\n ``cov = components_.T * S**2 * components_ + sigma2 * eye(n_features)``\n where S**2 contains the explained variances, and sigma2 contains the\n noise variances.\n\n Returns\n -------\n cov : ... |
def get_precision(self):
'Compute data precision matrix with the generative model.\n\n Equals the inverse of the covariance but computed with\n the matrix inversion lemma for efficiency.\n\n Returns\n -------\n precision : array, shape=(n_features, n_features)\n Estimat... | 316,780,617,108,224,700 | Compute data precision matrix with the generative model.
Equals the inverse of the covariance but computed with
the matrix inversion lemma for efficiency.
Returns
-------
precision : array, shape=(n_features, n_features)
Estimated precision of data. | sklearn/decomposition/_base.py | get_precision | 40104/Scikit-Learn | python | def get_precision(self):
'Compute data precision matrix with the generative model.\n\n Equals the inverse of the covariance but computed with\n the matrix inversion lemma for efficiency.\n\n Returns\n -------\n precision : array, shape=(n_features, n_features)\n Estimat... |
@abstractmethod
def fit(self, X, y=None):
'Placeholder for fit. Subclasses should implement this method!\n\n Fit the model with X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training data, where `n_samples` is the number of samples and\n ... | -3,515,658,082,423,659,500 | Placeholder for fit. Subclasses should implement this method!
Fit the model with X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples and
`n_features` is the number of features.
Returns
-------
self : object
Returns the instanc... | sklearn/decomposition/_base.py | fit | 40104/Scikit-Learn | python | @abstractmethod
def fit(self, X, y=None):
'Placeholder for fit. Subclasses should implement this method!\n\n Fit the model with X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training data, where `n_samples` is the number of samples and\n ... |
def transform(self, X):
'Apply dimensionality reduction to X.\n\n X is projected on the first principal components previously extracted\n from a training set.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n New data, where `n_samples` is ... | 6,729,435,418,467,268,000 | Apply dimensionality reduction to X.
X is projected on the first principal components previously extracted
from a training set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
New data, where `n_samples` is the number of samples
and `n_features` is the number of features.
Returns
------... | sklearn/decomposition/_base.py | transform | 40104/Scikit-Learn | python | def transform(self, X):
'Apply dimensionality reduction to X.\n\n X is projected on the first principal components previously extracted\n from a training set.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n New data, where `n_samples` is ... |
def inverse_transform(self, X):
'Transform data back to its original space.\n\n In other words, return an input `X_original` whose transform would be X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_components)\n New data, where `n_samples` is the number ... | -4,156,797,171,178,644,500 | Transform data back to its original space.
In other words, return an input `X_original` whose transform would be X.
Parameters
----------
X : array-like of shape (n_samples, n_components)
New data, where `n_samples` is the number of samples
and `n_components` is the number of components.
Returns
-------
X_or... | sklearn/decomposition/_base.py | inverse_transform | 40104/Scikit-Learn | python | def inverse_transform(self, X):
'Transform data back to its original space.\n\n In other words, return an input `X_original` whose transform would be X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_components)\n New data, where `n_samples` is the number ... |
@property
def _n_features_out(self):
'Number of transformed output features.'
return self.components_.shape[0] | -7,760,010,724,038,969,000 | Number of transformed output features. | sklearn/decomposition/_base.py | _n_features_out | 40104/Scikit-Learn | python | @property
def _n_features_out(self):
return self.components_.shape[0] |
async def broadcast_avatar_position(room_channel_name, channel_name, json_data):
"\n Sends the new avatar's position to the users of the room.\n "
type = json_data['type']
payload = json_data['payload']
position = payload['position']
animate = payload['animate']
participant = (await sync_t... | -7,486,539,441,214,262,000 | Sends the new avatar's position to the users of the room. | server/websockets/consumers/world/broadcasts/avatar.py | broadcast_avatar_position | Shadowsych/html5-msoy | python | async def broadcast_avatar_position(room_channel_name, channel_name, json_data):
"\n \n "
type = json_data['type']
payload = json_data['payload']
position = payload['position']
animate = payload['animate']
participant = (await sync_to_async(get_participant)(room_channel_name, channel_name)... |
async def broadcast_avatar_state(room_channel_name, channel_name, json_data):
"\n Sends the new avatar's state to the users of the room.\n "
type = json_data['type']
payload = json_data['payload']
state = payload['value']
participant = (await sync_to_async(get_participant)(room_channel_name, c... | -5,717,356,674,393,249,000 | Sends the new avatar's state to the users of the room. | server/websockets/consumers/world/broadcasts/avatar.py | broadcast_avatar_state | Shadowsych/html5-msoy | python | async def broadcast_avatar_state(room_channel_name, channel_name, json_data):
"\n \n "
type = json_data['type']
payload = json_data['payload']
state = payload['value']
participant = (await sync_to_async(get_participant)(room_channel_name, channel_name))
participant_id = (await sync_to_asyn... |
def height(root):
'\n DFS\n\n v = Vertices\n e = Edges\n d = Depth\n\n Time complexity: O(v + e)\n Space complexity: O(d)\n '
if root:
return (1 + max(height(root.left), height(root.right)))
else:
return (- 1) | -1,252,895,384,553,899,300 | DFS
v = Vertices
e = Edges
d = Depth
Time complexity: O(v + e)
Space complexity: O(d) | HackerRank/Data Structures/Trees/height-of-a-binary-tree.py | height | danielfsousa/algorithms-solutions | python | def height(root):
'\n DFS\n\n v = Vertices\n e = Edges\n d = Depth\n\n Time complexity: O(v + e)\n Space complexity: O(d)\n '
if root:
return (1 + max(height(root.left), height(root.right)))
else:
return (- 1) |
def set_random_seed(seed: Optional[int]=None) -> None:
'Set random seed for random, numpy, and pytorch.\n\n Args:\n seed: The random seed, defaults to `None` which select it randomly.\n '
max_value = np.iinfo(np.uint32).max
min_value = np.iinfo(np.uint32).min
try:
seed = int(seed)
... | -7,442,857,920,851,555,000 | Set random seed for random, numpy, and pytorch.
Args:
seed: The random seed, defaults to `None` which select it randomly. | src/emmental/utils/seed.py | set_random_seed | KeAWang/emmental | python | def set_random_seed(seed: Optional[int]=None) -> None:
'Set random seed for random, numpy, and pytorch.\n\n Args:\n seed: The random seed, defaults to `None` which select it randomly.\n '
max_value = np.iinfo(np.uint32).max
min_value = np.iinfo(np.uint32).min
try:
seed = int(seed)
... |
def setUp(self):
'Setup'
super(TestName, self).setUp()
self.collection.register(Name())
self.success_templates = ['fixtures/templates/good/outputs/name.yaml'] | 3,146,337,064,199,645,700 | Setup | test/rules/outputs/test_name.py | setUp | SanderKnape/cfn-python-lint | python | def setUp(self):
super(TestName, self).setUp()
self.collection.register(Name())
self.success_templates = ['fixtures/templates/good/outputs/name.yaml'] |
def test_file_positive(self):
'Test Positive'
self.helper_file_positive() | -1,556,978,985,838,885,400 | Test Positive | test/rules/outputs/test_name.py | test_file_positive | SanderKnape/cfn-python-lint | python | def test_file_positive(self):
self.helper_file_positive() |
def test_file_negative(self):
'Test failure'
self.helper_file_negative('fixtures/templates/bad/outputs/name.yaml', 1) | -4,366,943,575,606,948,000 | Test failure | test/rules/outputs/test_name.py | test_file_negative | SanderKnape/cfn-python-lint | python | def test_file_negative(self):
self.helper_file_negative('fixtures/templates/bad/outputs/name.yaml', 1) |
def _infer_state_dtype(explicit_dtype, state):
"Infer the dtype of an RNN state.\n\n Args:\n explicit_dtype: explicitly declared dtype or None.\n state: RNN's hidden state. Must be a Tensor or a nested iterable containing\n Tensors.\n\n Returns:\n dtype: inferred dtype of hidden state.\n\n Raises:\... | -5,892,994,636,942,259,000 | Infer the dtype of an RNN state.
Args:
explicit_dtype: explicitly declared dtype or None.
state: RNN's hidden state. Must be a Tensor or a nested iterable containing
Tensors.
Returns:
dtype: inferred dtype of hidden state.
Raises:
ValueError: if `state` has heterogeneous dtypes or is empty. | tensorflow/python/ops/rnn.py | _infer_state_dtype | gameover27/hiptensorflow | python | def _infer_state_dtype(explicit_dtype, state):
"Infer the dtype of an RNN state.\n\n Args:\n explicit_dtype: explicitly declared dtype or None.\n state: RNN's hidden state. Must be a Tensor or a nested iterable containing\n Tensors.\n\n Returns:\n dtype: inferred dtype of hidden state.\n\n Raises:\... |
def _on_device(fn, device):
"Build the subgraph defined by lambda `fn` on `device` if it's not None."
if device:
with ops.device(device):
return fn()
else:
return fn() | -2,863,435,495,451,946,000 | Build the subgraph defined by lambda `fn` on `device` if it's not None. | tensorflow/python/ops/rnn.py | _on_device | gameover27/hiptensorflow | python | def _on_device(fn, device):
if device:
with ops.device(device):
return fn()
else:
return fn() |
def _rnn_step(time, sequence_length, min_sequence_length, max_sequence_length, zero_output, state, call_cell, state_size, skip_conditionals=False):
"Calculate one step of a dynamic RNN minibatch.\n\n Returns an (output, state) pair conditioned on the sequence_lengths.\n When skip_conditionals=False, the pseudocod... | -261,439,537,545,024,900 | Calculate one step of a dynamic RNN minibatch.
Returns an (output, state) pair conditioned on the sequence_lengths.
When skip_conditionals=False, the pseudocode is something like:
if t >= max_sequence_length:
return (zero_output, state)
if t < min_sequence_length:
return call_cell()
# Selectively output zeros or... | tensorflow/python/ops/rnn.py | _rnn_step | gameover27/hiptensorflow | python | def _rnn_step(time, sequence_length, min_sequence_length, max_sequence_length, zero_output, state, call_cell, state_size, skip_conditionals=False):
"Calculate one step of a dynamic RNN minibatch.\n\n Returns an (output, state) pair conditioned on the sequence_lengths.\n When skip_conditionals=False, the pseudocod... |
def _reverse_seq(input_seq, lengths):
'Reverse a list of Tensors up to specified lengths.\n\n Args:\n input_seq: Sequence of seq_len tensors of dimension (batch_size, n_features)\n or nested tuples of tensors.\n lengths: A `Tensor` of dimension batch_size, containing lengths for each\n ... | 9,143,443,463,951,144,000 | Reverse a list of Tensors up to specified lengths.
Args:
input_seq: Sequence of seq_len tensors of dimension (batch_size, n_features)
or nested tuples of tensors.
lengths: A `Tensor` of dimension batch_size, containing lengths for each
sequence in the batch. If "None" is specified, simp... | tensorflow/python/ops/rnn.py | _reverse_seq | gameover27/hiptensorflow | python | def _reverse_seq(input_seq, lengths):
'Reverse a list of Tensors up to specified lengths.\n\n Args:\n input_seq: Sequence of seq_len tensors of dimension (batch_size, n_features)\n or nested tuples of tensors.\n lengths: A `Tensor` of dimension batch_size, containing lengths for each\n ... |
def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, initial_state_fw=None, initial_state_bw=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None):
'Creates a dynamic version of bidirectional recurrent neural network.\n\n Similar to the unidirectional... | -1,378,400,897,695,843,300 | Creates a dynamic version of bidirectional recurrent neural network.
Similar to the unidirectional case above (rnn) but takes input and builds
independent forward and backward RNNs. The input_size of forward and
backward cell must match. The initial state for both directions is zero by
default (but can be set optional... | tensorflow/python/ops/rnn.py | bidirectional_dynamic_rnn | gameover27/hiptensorflow | python | def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, initial_state_fw=None, initial_state_bw=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None):
'Creates a dynamic version of bidirectional recurrent neural network.\n\n Similar to the unidirectional... |
def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None):
'Creates a recurrent neural network specified by RNNCell `cell`.\n\n This function is functionally identical to the function `rnn` above, but\n performs f... | 1,271,462,727,045,380,000 | Creates a recurrent neural network specified by RNNCell `cell`.
This function is functionally identical to the function `rnn` above, but
performs fully dynamic unrolling of `inputs`.
Unlike `rnn`, the input `inputs` is not a Python list of `Tensors`, one for
each frame. Instead, `inputs` may be a single `Tensor` whe... | tensorflow/python/ops/rnn.py | dynamic_rnn | gameover27/hiptensorflow | python | def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None):
'Creates a recurrent neural network specified by RNNCell `cell`.\n\n This function is functionally identical to the function `rnn` above, but\n performs f... |
def _dynamic_rnn_loop(cell, inputs, initial_state, parallel_iterations, swap_memory, sequence_length=None, dtype=None):
'Internal implementation of Dynamic RNN.\n\n Args:\n cell: An instance of RNNCell.\n inputs: A `Tensor` of shape [time, batch_size, input_size], or a nested\n tuple of such elements.\n... | -1,497,298,739,967,970,000 | Internal implementation of Dynamic RNN.
Args:
cell: An instance of RNNCell.
inputs: A `Tensor` of shape [time, batch_size, input_size], or a nested
tuple of such elements.
initial_state: A `Tensor` of shape `[batch_size, state_size]`, or if
`cell.state_size` is a tuple, then this should be a tuple of
... | tensorflow/python/ops/rnn.py | _dynamic_rnn_loop | gameover27/hiptensorflow | python | def _dynamic_rnn_loop(cell, inputs, initial_state, parallel_iterations, swap_memory, sequence_length=None, dtype=None):
'Internal implementation of Dynamic RNN.\n\n Args:\n cell: An instance of RNNCell.\n inputs: A `Tensor` of shape [time, batch_size, input_size], or a nested\n tuple of such elements.\n... |
def raw_rnn(cell, loop_fn, parallel_iterations=None, swap_memory=False, scope=None):
'Creates an `RNN` specified by RNNCell `cell` and loop function `loop_fn`.\n\n **NOTE: This method is still in testing, and the API may change.**\n\n This function is a more primitive version of `dynamic_rnn` that provides\n mor... | -1,963,316,333,818,678,500 | Creates an `RNN` specified by RNNCell `cell` and loop function `loop_fn`.
**NOTE: This method is still in testing, and the API may change.**
This function is a more primitive version of `dynamic_rnn` that provides
more direct access to the inputs each iteration. It also provides more
control over when to start and f... | tensorflow/python/ops/rnn.py | raw_rnn | gameover27/hiptensorflow | python | def raw_rnn(cell, loop_fn, parallel_iterations=None, swap_memory=False, scope=None):
'Creates an `RNN` specified by RNNCell `cell` and loop function `loop_fn`.\n\n **NOTE: This method is still in testing, and the API may change.**\n\n This function is a more primitive version of `dynamic_rnn` that provides\n mor... |
def _maybe_copy_some_through():
'Run RNN step. Pass through either no or some past state.'
(new_output, new_state) = call_cell()
nest.assert_same_structure(state, new_state)
flat_new_state = nest.flatten(new_state)
flat_new_output = nest.flatten(new_output)
return control_flow_ops.cond((time < ... | -2,520,382,574,250,251,000 | Run RNN step. Pass through either no or some past state. | tensorflow/python/ops/rnn.py | _maybe_copy_some_through | gameover27/hiptensorflow | python | def _maybe_copy_some_through():
(new_output, new_state) = call_cell()
nest.assert_same_structure(state, new_state)
flat_new_state = nest.flatten(new_state)
flat_new_output = nest.flatten(new_output)
return control_flow_ops.cond((time < min_sequence_length), (lambda : (flat_new_output + flat_new... |
def _time_step(time, output_ta_t, state):
'Take a time step of the dynamic RNN.\n\n Args:\n time: int32 scalar Tensor.\n output_ta_t: List of `TensorArray`s that represent the output.\n state: nested tuple of vector tensors that represent the state.\n\n Returns:\n The tuple (time + 1, outp... | -4,486,669,498,042,699,000 | Take a time step of the dynamic RNN.
Args:
time: int32 scalar Tensor.
output_ta_t: List of `TensorArray`s that represent the output.
state: nested tuple of vector tensors that represent the state.
Returns:
The tuple (time + 1, output_ta_t with updated flow, new_state). | tensorflow/python/ops/rnn.py | _time_step | gameover27/hiptensorflow | python | def _time_step(time, output_ta_t, state):
'Take a time step of the dynamic RNN.\n\n Args:\n time: int32 scalar Tensor.\n output_ta_t: List of `TensorArray`s that represent the output.\n state: nested tuple of vector tensors that represent the state.\n\n Returns:\n The tuple (time + 1, outp... |
def body(time, elements_finished, current_input, emit_ta, state, loop_state):
'Internal while loop body for raw_rnn.\n\n Args:\n time: time scalar.\n elements_finished: batch-size vector.\n current_input: possibly nested tuple of input tensors.\n emit_ta: possibly nested tuple of ou... | 1,539,779,915,144,872,400 | Internal while loop body for raw_rnn.
Args:
time: time scalar.
elements_finished: batch-size vector.
current_input: possibly nested tuple of input tensors.
emit_ta: possibly nested tuple of output TensorArrays.
state: possibly nested tuple of state tensors.
loop_state: possibly nested tuple of loop state t... | tensorflow/python/ops/rnn.py | body | gameover27/hiptensorflow | python | def body(time, elements_finished, current_input, emit_ta, state, loop_state):
'Internal while loop body for raw_rnn.\n\n Args:\n time: time scalar.\n elements_finished: batch-size vector.\n current_input: possibly nested tuple of input tensors.\n emit_ta: possibly nested tuple of ou... |
def _copy_some_through(current, candidate):
'Copy some tensors through via array_ops.where.'
current_flat = nest.flatten(current)
candidate_flat = nest.flatten(candidate)
result_flat = [_on_device((lambda : array_ops.where(elements_finished, current_i, candidate_i)), device=candidate_i.op.device) for (c... | 4,694,326,597,159,489,000 | Copy some tensors through via array_ops.where. | tensorflow/python/ops/rnn.py | _copy_some_through | gameover27/hiptensorflow | python | def _copy_some_through(current, candidate):
current_flat = nest.flatten(current)
candidate_flat = nest.flatten(candidate)
result_flat = [_on_device((lambda : array_ops.where(elements_finished, current_i, candidate_i)), device=candidate_i.op.device) for (current_i, candidate_i) in zip(current_flat, cand... |
def extract_app(self, arch):
'\n\t\tReturn an `App` object from this TAB. You must specify the desired\n\t\tMCU architecture so the correct binary can be retrieved.\n\t\t'
binary_tarinfo = self.tab.getmember('{}.bin'.format(arch))
binary = self.tab.extractfile(binary_tarinfo).read()
tbfh = TBFHeader(bin... | -8,530,720,299,191,283,000 | Return an `App` object from this TAB. You must specify the desired
MCU architecture so the correct binary can be retrieved. | tockloader/tab.py | extract_app | torfmaster/tockloader | python | def extract_app(self, arch):
'\n\t\tReturn an `App` object from this TAB. You must specify the desired\n\t\tMCU architecture so the correct binary can be retrieved.\n\t\t'
binary_tarinfo = self.tab.getmember('{}.bin'.format(arch))
binary = self.tab.extractfile(binary_tarinfo).read()
tbfh = TBFHeader(bin... |
def is_compatible_with_board(self, board):
'\n\t\tCheck if the Tock app is compatible with a particular Tock board.\n\t\t'
metadata = self.parse_metadata()
if (metadata['tab-version'] == 1):
return (('only-for-boards' not in metadata) or (board in metadata['only-for-boards']) or (metadata['only-for-... | -8,559,294,918,589,570,000 | Check if the Tock app is compatible with a particular Tock board. | tockloader/tab.py | is_compatible_with_board | torfmaster/tockloader | python | def is_compatible_with_board(self, board):
'\n\t\t\n\t\t'
metadata = self.parse_metadata()
if (metadata['tab-version'] == 1):
return (('only-for-boards' not in metadata) or (board in metadata['only-for-boards']) or (metadata['only-for-boards'] == ))
else:
raise TockLoaderException('Unabl... |
def parse_metadata(self):
'\n\t\tOpen and parse the included metadata file in the TAB.\n\t\t'
metadata_tarinfo = self.tab.getmember('metadata.toml')
metadata_str = self.tab.extractfile(metadata_tarinfo).read().decode('utf-8')
return pytoml.loads(metadata_str) | -3,623,499,767,396,370,000 | Open and parse the included metadata file in the TAB. | tockloader/tab.py | parse_metadata | torfmaster/tockloader | python | def parse_metadata(self):
'\n\t\t\n\t\t'
metadata_tarinfo = self.tab.getmember('metadata.toml')
metadata_str = self.tab.extractfile(metadata_tarinfo).read().decode('utf-8')
return pytoml.loads(metadata_str) |
def get_supported_architectures(self):
'\n\t\tReturn a list of architectures that this TAB has compiled binaries for.\n\t\t'
contained_files = self.tab.getnames()
return [i[:(- 4)] for i in contained_files if (i[(- 4):] == '.bin')] | -3,800,365,372,095,071,000 | Return a list of architectures that this TAB has compiled binaries for. | tockloader/tab.py | get_supported_architectures | torfmaster/tockloader | python | def get_supported_architectures(self):
'\n\t\t\n\t\t'
contained_files = self.tab.getnames()
return [i[:(- 4)] for i in contained_files if (i[(- 4):] == '.bin')] |
def get_tbf_header(self):
'\n\t\tReturn a TBFHeader object with the TBF header from the app in the TAB.\n\t\tTBF headers are not architecture specific, so we pull from a random\n\t\tbinary if there are multiple architectures supported.\n\t\t'
for f in self.tab.getnames():
if (f[(- 4):] == '.bin'):
... | -604,436,729,304,759,000 | Return a TBFHeader object with the TBF header from the app in the TAB.
TBF headers are not architecture specific, so we pull from a random
binary if there are multiple architectures supported. | tockloader/tab.py | get_tbf_header | torfmaster/tockloader | python | def get_tbf_header(self):
'\n\t\tReturn a TBFHeader object with the TBF header from the app in the TAB.\n\t\tTBF headers are not architecture specific, so we pull from a random\n\t\tbinary if there are multiple architectures supported.\n\t\t'
for f in self.tab.getnames():
if (f[(- 4):] == '.bin'):
... |
def beta_create_ImageAnnotator_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None):
'The Beta API is deprecated for 0.15.0 and later.\n\n It is recommended to use the GA API (classes and functions in this\n file not marked beta) for all further purposes. This function was\n... | -7,068,467,836,664,310,000 | The Beta API is deprecated for 0.15.0 and later.
It is recommended to use the GA API (classes and functions in this
file not marked beta) for all further purposes. This function was
generated only to ease transition from grpcio<0.15.0 to grpcio>=0.15.0 | vision/google/cloud/vision_v1p1beta1/proto/image_annotator_pb2.py | beta_create_ImageAnnotator_server | Alexander-Minyushkin/google-cloud-python | python | def beta_create_ImageAnnotator_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None):
'The Beta API is deprecated for 0.15.0 and later.\n\n It is recommended to use the GA API (classes and functions in this\n file not marked beta) for all further purposes. This function was\n... |
def beta_create_ImageAnnotator_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None):
'The Beta API is deprecated for 0.15.0 and later.\n\n It is recommended to use the GA API (classes and functions in this\n file not marked beta) for all further purposes. This function was\n gener... | 2,554,020,353,311,455,000 | The Beta API is deprecated for 0.15.0 and later.
It is recommended to use the GA API (classes and functions in this
file not marked beta) for all further purposes. This function was
generated only to ease transition from grpcio<0.15.0 to grpcio>=0.15.0 | vision/google/cloud/vision_v1p1beta1/proto/image_annotator_pb2.py | beta_create_ImageAnnotator_stub | Alexander-Minyushkin/google-cloud-python | python | def beta_create_ImageAnnotator_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None):
'The Beta API is deprecated for 0.15.0 and later.\n\n It is recommended to use the GA API (classes and functions in this\n file not marked beta) for all further purposes. This function was\n gener... |
def __init__(self, channel):
'Constructor.\n\n Args:\n channel: A grpc.Channel.\n '
self.BatchAnnotateImages = channel.unary_unary('/google.cloud.vision.v1p1beta1.ImageAnnotator/BatchAnnotateImages', request_serializer=BatchAnnotateImagesRequest.SerializeToString, response_deserializer=BatchAnn... | 7,415,371,588,454,651,000 | Constructor.
Args:
channel: A grpc.Channel. | vision/google/cloud/vision_v1p1beta1/proto/image_annotator_pb2.py | __init__ | Alexander-Minyushkin/google-cloud-python | python | def __init__(self, channel):
'Constructor.\n\n Args:\n channel: A grpc.Channel.\n '
self.BatchAnnotateImages = channel.unary_unary('/google.cloud.vision.v1p1beta1.ImageAnnotator/BatchAnnotateImages', request_serializer=BatchAnnotateImagesRequest.SerializeToString, response_deserializer=BatchAnn... |
def BatchAnnotateImages(self, request, context):
'Run image detection and annotation for a batch of images.\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') | 3,685,372,308,543,791,600 | Run image detection and annotation for a batch of images. | vision/google/cloud/vision_v1p1beta1/proto/image_annotator_pb2.py | BatchAnnotateImages | Alexander-Minyushkin/google-cloud-python | python | def BatchAnnotateImages(self, request, context):
'\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') |
def BatchAnnotateImages(self, request, context):
'Run image detection and annotation for a batch of images.\n '
context.code(beta_interfaces.StatusCode.UNIMPLEMENTED) | 823,033,015,200,478,100 | Run image detection and annotation for a batch of images. | vision/google/cloud/vision_v1p1beta1/proto/image_annotator_pb2.py | BatchAnnotateImages | Alexander-Minyushkin/google-cloud-python | python | def BatchAnnotateImages(self, request, context):
'\n '
context.code(beta_interfaces.StatusCode.UNIMPLEMENTED) |
def BatchAnnotateImages(self, request, timeout, metadata=None, with_call=False, protocol_options=None):
'Run image detection and annotation for a batch of images.\n '
raise NotImplementedError() | -2,228,279,714,628,205,600 | Run image detection and annotation for a batch of images. | vision/google/cloud/vision_v1p1beta1/proto/image_annotator_pb2.py | BatchAnnotateImages | Alexander-Minyushkin/google-cloud-python | python | def BatchAnnotateImages(self, request, timeout, metadata=None, with_call=False, protocol_options=None):
'\n '
raise NotImplementedError() |
@property
def V_max(self):
'[float] The designed maximum liquid volume, not accounting for increased volume due to aeration, in m^3.'
return self._V_max | -3,506,608,882,236,248,600 | [float] The designed maximum liquid volume, not accounting for increased volume due to aeration, in m^3. | qsdsan/sanunits/_suspended_growth_bioreactor.py | V_max | QSD-for-WaSH/sanitation | python | @property
def V_max(self):
return self._V_max |
@property
def aeration(self):
'[:class:`Process` or float or NoneType] Aeration model.'
return self._aeration | -4,594,160,389,946,629,000 | [:class:`Process` or float or NoneType] Aeration model. | qsdsan/sanunits/_suspended_growth_bioreactor.py | aeration | QSD-for-WaSH/sanitation | python | @property
def aeration(self):
return self._aeration |
@property
def suspended_growth_model(self):
'[:class:`CompiledProcesses` or NoneType] Suspended growth model.'
return self._model | -1,955,942,594,984,201,000 | [:class:`CompiledProcesses` or NoneType] Suspended growth model. | qsdsan/sanunits/_suspended_growth_bioreactor.py | suspended_growth_model | QSD-for-WaSH/sanitation | python | @property
def suspended_growth_model(self):
return self._model |
@property
def DO_ID(self):
'[str] The `Component` ID for dissolved oxygen used in the suspended growth model and the aeration model.'
return self._DO_ID | -6,349,589,402,538,691,000 | [str] The `Component` ID for dissolved oxygen used in the suspended growth model and the aeration model. | qsdsan/sanunits/_suspended_growth_bioreactor.py | DO_ID | QSD-for-WaSH/sanitation | python | @property
def DO_ID(self):
return self._DO_ID |
@property
def split(self):
'[numpy.1darray or NoneType] The volumetric split of outs.'
return self._split | 2,645,588,217,702,961,000 | [numpy.1darray or NoneType] The volumetric split of outs. | qsdsan/sanunits/_suspended_growth_bioreactor.py | split | QSD-for-WaSH/sanitation | python | @property
def split(self):
return self._split |
@property
def state(self):
'The state of the CSTR, including component concentrations [mg/L] and flow rate [m^3/d].'
if (self._state is None):
return None
else:
return dict(zip((list(self.components.IDs) + ['Q']), self._state)) | -5,897,520,368,430,410,000 | The state of the CSTR, including component concentrations [mg/L] and flow rate [m^3/d]. | qsdsan/sanunits/_suspended_growth_bioreactor.py | state | QSD-for-WaSH/sanitation | python | @property
def state(self):
if (self._state is None):
return None
else:
return dict(zip((list(self.components.IDs) + ['Q']), self._state)) |
def set_init_conc(self, **kwargs):
'set the initial concentrations [mg/L] of the CSTR.'
Cs = np.zeros(len(self.components))
cmpx = self.components.index
for (k, v) in kwargs.items():
Cs[cmpx(k)] = v
self._concs = Cs | 2,782,257,569,388,788,700 | set the initial concentrations [mg/L] of the CSTR. | qsdsan/sanunits/_suspended_growth_bioreactor.py | set_init_conc | QSD-for-WaSH/sanitation | python | def set_init_conc(self, **kwargs):
Cs = np.zeros(len(self.components))
cmpx = self.components.index
for (k, v) in kwargs.items():
Cs[cmpx(k)] = v
self._concs = Cs |
def _run(self):
'Only to converge volumetric flows.'
mixed = self._mixed
mixed.mix_from(self.ins)
Q = mixed.F_vol
if (self.split is None):
self.outs[0].copy_like(mixed)
else:
for (ws, spl) in zip(self._outs, self.split):
ws.copy_like(mixed)
ws.set_total_fl... | 8,490,941,481,928,547,000 | Only to converge volumetric flows. | qsdsan/sanunits/_suspended_growth_bioreactor.py | _run | QSD-for-WaSH/sanitation | python | def _run(self):
mixed = self._mixed
mixed.mix_from(self.ins)
Q = mixed.F_vol
if (self.split is None):
self.outs[0].copy_like(mixed)
else:
for (ws, spl) in zip(self._outs, self.split):
ws.copy_like(mixed)
ws.set_total_flow((Q * spl), 'm3/hr') |
def docstring_values(values, indent=8):
'\n Formats a dictionary of values for inclusion in a docstring.\n '
return ('\n' + (' ' * indent)).join((("* ``'%s'``" % k) for (k, v) in sorted(values.items(), key=itemgetter(1)))) | 8,566,033,594,727,782,000 | Formats a dictionary of values for inclusion in a docstring. | picamera/camera.py | docstring_values | RobertLucian/picamera | python | def docstring_values(values, indent=8):
'\n \n '
return ('\n' + (' ' * indent)).join((("* ``'%s'``" % k) for (k, v) in sorted(values.items(), key=itemgetter(1)))) |
def _check_camera_open(self):
'\n Raise an exception if the camera is already closed, or if the camera\n has encountered a fatal error.\n '
(exc, self._camera_exception) = (self._camera_exception, None)
if exc:
raise exc
if self.closed:
raise PiCameraClosed('Camera i... | -7,220,415,521,090,356,000 | Raise an exception if the camera is already closed, or if the camera
has encountered a fatal error. | picamera/camera.py | _check_camera_open | RobertLucian/picamera | python | def _check_camera_open(self):
'\n Raise an exception if the camera is already closed, or if the camera\n has encountered a fatal error.\n '
(exc, self._camera_exception) = (self._camera_exception, None)
if exc:
raise exc
if self.closed:
raise PiCameraClosed('Camera i... |
def _check_recording_stopped(self):
'\n Raise an exception if the camera is currently recording.\n '
if self.recording:
raise PiCameraRuntimeError('Recording is currently running') | -6,894,281,195,436,221,000 | Raise an exception if the camera is currently recording. | picamera/camera.py | _check_recording_stopped | RobertLucian/picamera | python | def _check_recording_stopped(self):
'\n \n '
if self.recording:
raise PiCameraRuntimeError('Recording is currently running') |
def _get_ports(self, from_video_port, splitter_port):
"\n Determine the camera and output ports for given capture options.\n\n See :ref:`camera_hardware` for more information on picamera's usage of\n camera, splitter, and encoder ports. The general idea here is that the\n capture (still)... | 5,066,261,328,255,325,000 | Determine the camera and output ports for given capture options.
See :ref:`camera_hardware` for more information on picamera's usage of
camera, splitter, and encoder ports. The general idea here is that the
capture (still) port operates on its own, while the video port is
always connected to a splitter component, so r... | picamera/camera.py | _get_ports | RobertLucian/picamera | python | def _get_ports(self, from_video_port, splitter_port):
"\n Determine the camera and output ports for given capture options.\n\n See :ref:`camera_hardware` for more information on picamera's usage of\n camera, splitter, and encoder ports. The general idea here is that the\n capture (still)... |
def _get_output_format(self, output):
'\n Given an output object, attempt to determine the requested format.\n\n We attempt to determine the filename of the *output* object and derive\n a MIME type from the extension. If *output* has no filename, an error\n is raised.\n '
if i... | -5,423,043,728,053,178,000 | Given an output object, attempt to determine the requested format.
We attempt to determine the filename of the *output* object and derive
a MIME type from the extension. If *output* has no filename, an error
is raised. | picamera/camera.py | _get_output_format | RobertLucian/picamera | python | def _get_output_format(self, output):
'\n Given an output object, attempt to determine the requested format.\n\n We attempt to determine the filename of the *output* object and derive\n a MIME type from the extension. If *output* has no filename, an error\n is raised.\n '
if i... |
def _get_image_format(self, output, format=None):
'\n Given an output object and an optional format, attempt to determine the\n requested image format.\n\n This method is used by all capture methods to determine the requested\n output format. If *format* is specified as a MIME-type the "... | -5,771,226,095,761,120,000 | Given an output object and an optional format, attempt to determine the
requested image format.
This method is used by all capture methods to determine the requested
output format. If *format* is specified as a MIME-type the "image/"
prefix is stripped. If *format* is not specified, then
:meth:`_get_output_format` wil... | picamera/camera.py | _get_image_format | RobertLucian/picamera | python | def _get_image_format(self, output, format=None):
'\n Given an output object and an optional format, attempt to determine the\n requested image format.\n\n This method is used by all capture methods to determine the requested\n output format. If *format* is specified as a MIME-type the "... |
def _get_video_format(self, output, format=None):
'\n Given an output object and an optional format, attempt to determine the\n requested video format.\n\n This method is used by all recording methods to determine the requested\n output format. If *format* is specified as a MIME-type the... | 2,566,541,926,593,424,400 | Given an output object and an optional format, attempt to determine the
requested video format.
This method is used by all recording methods to determine the requested
output format. If *format* is specified as a MIME-type the "video/" or
"application/" prefix will be stripped. If *format* is not specified,
then :meth... | picamera/camera.py | _get_video_format | RobertLucian/picamera | python | def _get_video_format(self, output, format=None):
'\n Given an output object and an optional format, attempt to determine the\n requested video format.\n\n This method is used by all recording methods to determine the requested\n output format. If *format* is specified as a MIME-type the... |
def _get_image_encoder(self, camera_port, output_port, format, resize, **options):
"\n Construct an image encoder for the requested parameters.\n\n This method is called by :meth:`capture` and :meth:`capture_continuous`\n to construct an image encoder. The *camera_port* parameter gives the\n ... | -7,457,254,361,250,503,000 | Construct an image encoder for the requested parameters.
This method is called by :meth:`capture` and :meth:`capture_continuous`
to construct an image encoder. The *camera_port* parameter gives the
MMAL camera port that should be enabled for capture by the encoder. The
*output_port* parameter gives the MMAL port that ... | picamera/camera.py | _get_image_encoder | RobertLucian/picamera | python | def _get_image_encoder(self, camera_port, output_port, format, resize, **options):
"\n Construct an image encoder for the requested parameters.\n\n This method is called by :meth:`capture` and :meth:`capture_continuous`\n to construct an image encoder. The *camera_port* parameter gives the\n ... |
def _get_images_encoder(self, camera_port, output_port, format, resize, **options):
'\n Construct a multi-image encoder for the requested parameters.\n\n This method is largely equivalent to :meth:`_get_image_encoder` with\n the exception that the encoder returned should expect to be passed an\... | -3,635,200,426,073,873,400 | Construct a multi-image encoder for the requested parameters.
This method is largely equivalent to :meth:`_get_image_encoder` with
the exception that the encoder returned should expect to be passed an
iterable of outputs to its :meth:`~PiEncoder.start` method, rather than
a single output object. This method is called ... | picamera/camera.py | _get_images_encoder | RobertLucian/picamera | python | def _get_images_encoder(self, camera_port, output_port, format, resize, **options):
'\n Construct a multi-image encoder for the requested parameters.\n\n This method is largely equivalent to :meth:`_get_image_encoder` with\n the exception that the encoder returned should expect to be passed an\... |
def _get_video_encoder(self, camera_port, output_port, format, resize, **options):
"\n Construct a video encoder for the requested parameters.\n\n This method is called by :meth:`start_recording` and\n :meth:`record_sequence` to construct a video encoder. The\n *camera_port* parameter g... | 6,569,485,216,671,746,000 | Construct a video encoder for the requested parameters.
This method is called by :meth:`start_recording` and
:meth:`record_sequence` to construct a video encoder. The
*camera_port* parameter gives the MMAL camera port that should be
enabled for capture by the encoder. The *output_port* parameter gives
the MMAL port t... | picamera/camera.py | _get_video_encoder | RobertLucian/picamera | python | def _get_video_encoder(self, camera_port, output_port, format, resize, **options):
"\n Construct a video encoder for the requested parameters.\n\n This method is called by :meth:`start_recording` and\n :meth:`record_sequence` to construct a video encoder. The\n *camera_port* parameter g... |
def close(self):
'\n Finalizes the state of the camera.\n\n After successfully constructing a :class:`PiCamera` object, you should\n ensure you call the :meth:`close` method once you are finished with the\n camera (e.g. in the ``finally`` section of a ``try..finally`` block).\n Th... | -1,952,082,261,474,689,300 | Finalizes the state of the camera.
After successfully constructing a :class:`PiCamera` object, you should
ensure you call the :meth:`close` method once you are finished with the
camera (e.g. in the ``finally`` section of a ``try..finally`` block).
This method stops all recording and preview activities and releases all... | picamera/camera.py | close | RobertLucian/picamera | python | def close(self):
'\n Finalizes the state of the camera.\n\n After successfully constructing a :class:`PiCamera` object, you should\n ensure you call the :meth:`close` method once you are finished with the\n camera (e.g. in the ``finally`` section of a ``try..finally`` block).\n Th... |
def start_preview(self, **options):
'\n Displays the preview overlay.\n\n This method starts a camera preview as an overlay on the Pi\'s primary\n display (HDMI or composite). A :class:`PiRenderer` instance (more\n specifically, a :class:`PiPreviewRenderer`) is constructed with the\n ... | 5,153,514,928,868,468,000 | Displays the preview overlay.
This method starts a camera preview as an overlay on the Pi's primary
display (HDMI or composite). A :class:`PiRenderer` instance (more
specifically, a :class:`PiPreviewRenderer`) is constructed with the
keyword arguments captured in *options*, and is returned from the
method (this instan... | picamera/camera.py | start_preview | RobertLucian/picamera | python | def start_preview(self, **options):
'\n Displays the preview overlay.\n\n This method starts a camera preview as an overlay on the Pi\'s primary\n display (HDMI or composite). A :class:`PiRenderer` instance (more\n specifically, a :class:`PiPreviewRenderer`) is constructed with the\n ... |
def stop_preview(self):
'\n Hides the preview overlay.\n\n If :meth:`start_preview` has previously been called, this method shuts\n down the preview display which generally results in the underlying\n display becoming visible again. If a preview is not currently running,\n no exce... | 3,200,172,221,825,403,400 | Hides the preview overlay.
If :meth:`start_preview` has previously been called, this method shuts
down the preview display which generally results in the underlying
display becoming visible again. If a preview is not currently running,
no exception is raised - the method will simply do nothing. | picamera/camera.py | stop_preview | RobertLucian/picamera | python | def stop_preview(self):
'\n Hides the preview overlay.\n\n If :meth:`start_preview` has previously been called, this method shuts\n down the preview display which generally results in the underlying\n display becoming visible again. If a preview is not currently running,\n no exce... |
def add_overlay(self, source, size=None, format=None, **options):
'\n Adds a static overlay to the preview output.\n\n This method creates a new static overlay using the same rendering\n mechanism as the preview. Overlays will appear on the Pi\'s video\n output, but will not appear in ca... | -4,768,258,809,790,032,000 | Adds a static overlay to the preview output.
This method creates a new static overlay using the same rendering
mechanism as the preview. Overlays will appear on the Pi's video
output, but will not appear in captures or video recordings. Multiple
overlays can exist; each call to :meth:`add_overlay` returns a new
:class... | picamera/camera.py | add_overlay | RobertLucian/picamera | python | def add_overlay(self, source, size=None, format=None, **options):
'\n Adds a static overlay to the preview output.\n\n This method creates a new static overlay using the same rendering\n mechanism as the preview. Overlays will appear on the Pi\'s video\n output, but will not appear in ca... |
def remove_overlay(self, overlay):
'\n Removes a static overlay from the preview output.\n\n This method removes an overlay which was previously created by\n :meth:`add_overlay`. The *overlay* parameter specifies the\n :class:`PiRenderer` instance that was returned by :meth:`add_overlay`... | -4,147,339,455,242,650,600 | Removes a static overlay from the preview output.
This method removes an overlay which was previously created by
:meth:`add_overlay`. The *overlay* parameter specifies the
:class:`PiRenderer` instance that was returned by :meth:`add_overlay`.
.. versionadded:: 1.8 | picamera/camera.py | remove_overlay | RobertLucian/picamera | python | def remove_overlay(self, overlay):
'\n Removes a static overlay from the preview output.\n\n This method removes an overlay which was previously created by\n :meth:`add_overlay`. The *overlay* parameter specifies the\n :class:`PiRenderer` instance that was returned by :meth:`add_overlay`... |
def start_recording(self, output, format=None, resize=None, splitter_port=1, **options):
'\n Start recording video from the camera, storing it in *output*.\n\n If *output* is a string, it will be treated as a filename for a new\n file which the video will be written to. If *output* is not a str... | -7,157,117,653,464,527,000 | Start recording video from the camera, storing it in *output*.
If *output* is a string, it will be treated as a filename for a new
file which the video will be written to. If *output* is not a string,
but is an object with a ``write`` method, it is assumed to be a
file-like object and the video data is appended to it ... | picamera/camera.py | start_recording | RobertLucian/picamera | python | def start_recording(self, output, format=None, resize=None, splitter_port=1, **options):
'\n Start recording video from the camera, storing it in *output*.\n\n If *output* is a string, it will be treated as a filename for a new\n file which the video will be written to. If *output* is not a str... |
def split_recording(self, output, splitter_port=1, **options):
'\n Continue the recording in the specified output; close existing output.\n\n When called, the video encoder will wait for the next appropriate\n split point (an inline SPS header), then will cease writing to the\n current o... | 3,772,228,076,396,274,000 | Continue the recording in the specified output; close existing output.
When called, the video encoder will wait for the next appropriate
split point (an inline SPS header), then will cease writing to the
current output (and close it, if it was specified as a filename), and
continue writing to the newly specified *outp... | picamera/camera.py | split_recording | RobertLucian/picamera | python | def split_recording(self, output, splitter_port=1, **options):
'\n Continue the recording in the specified output; close existing output.\n\n When called, the video encoder will wait for the next appropriate\n split point (an inline SPS header), then will cease writing to the\n current o... |
def request_key_frame(self, splitter_port=1):
"\n Request the encoder generate a key-frame as soon as possible.\n\n When called, the video encoder running on the specified *splitter_port*\n will attempt to produce a key-frame (full-image frame) as soon as\n possible. The *splitter_port* ... | -5,464,494,046,034,014,000 | Request the encoder generate a key-frame as soon as possible.
When called, the video encoder running on the specified *splitter_port*
will attempt to produce a key-frame (full-image frame) as soon as
possible. The *splitter_port* defaults to ``1``. Valid values are
between ``0`` and ``3`` inclusive.
.. note::
Th... | picamera/camera.py | request_key_frame | RobertLucian/picamera | python | def request_key_frame(self, splitter_port=1):
"\n Request the encoder generate a key-frame as soon as possible.\n\n When called, the video encoder running on the specified *splitter_port*\n will attempt to produce a key-frame (full-image frame) as soon as\n possible. The *splitter_port* ... |
def wait_recording(self, timeout=0, splitter_port=1):
'\n Wait on the video encoder for timeout seconds.\n\n It is recommended that this method is called while recording to check\n for exceptions. If an error occurs during recording (for example out of\n disk space) the recording will st... | 5,793,073,833,200,230,000 | Wait on the video encoder for timeout seconds.
It is recommended that this method is called while recording to check
for exceptions. If an error occurs during recording (for example out of
disk space) the recording will stop, but an exception will only be
raised when the :meth:`wait_recording` or :meth:`stop_recording... | picamera/camera.py | wait_recording | RobertLucian/picamera | python | def wait_recording(self, timeout=0, splitter_port=1):
'\n Wait on the video encoder for timeout seconds.\n\n It is recommended that this method is called while recording to check\n for exceptions. If an error occurs during recording (for example out of\n disk space) the recording will st... |
def stop_recording(self, splitter_port=1):
'\n Stop recording video from the camera.\n\n After calling this method the video encoder will be shut down and\n output will stop being written to the file-like object specified with\n :meth:`start_recording`. If an error occurred during record... | 3,910,143,239,972,328,000 | Stop recording video from the camera.
After calling this method the video encoder will be shut down and
output will stop being written to the file-like object specified with
:meth:`start_recording`. If an error occurred during recording and
:meth:`wait_recording` has not been called since the error then this
method wi... | picamera/camera.py | stop_recording | RobertLucian/picamera | python | def stop_recording(self, splitter_port=1):
'\n Stop recording video from the camera.\n\n After calling this method the video encoder will be shut down and\n output will stop being written to the file-like object specified with\n :meth:`start_recording`. If an error occurred during record... |
def record_sequence(self, outputs, format='h264', resize=None, splitter_port=1, **options):
"\n Record a sequence of video clips from the camera.\n\n This method accepts a sequence or iterator of *outputs* each of which\n must either be a string specifying a filename for output, or a\n f... | 7,498,983,051,958,845,000 | Record a sequence of video clips from the camera.
This method accepts a sequence or iterator of *outputs* each of which
must either be a string specifying a filename for output, or a
file-like object with a ``write`` method.
The method acts as an iterator itself, yielding each item of the
sequence in turn. In this wa... | picamera/camera.py | record_sequence | RobertLucian/picamera | python | def record_sequence(self, outputs, format='h264', resize=None, splitter_port=1, **options):
"\n Record a sequence of video clips from the camera.\n\n This method accepts a sequence or iterator of *outputs* each of which\n must either be a string specifying a filename for output, or a\n f... |
def capture(self, output, format=None, use_video_port=False, resize=None, splitter_port=0, bayer=False, **options):
'\n Capture an image from the camera, storing it in *output*.\n\n If *output* is a string, it will be treated as a filename for a new\n file which the image will be written to. If... | -4,667,808,940,049,474,000 | Capture an image from the camera, storing it in *output*.
If *output* is a string, it will be treated as a filename for a new
file which the image will be written to. If *output* is not a string,
but is an object with a ``write`` method, it is assumed to be a
file-like object and the image data is appended to it (the
... | picamera/camera.py | capture | RobertLucian/picamera | python | def capture(self, output, format=None, use_video_port=False, resize=None, splitter_port=0, bayer=False, **options):
'\n Capture an image from the camera, storing it in *output*.\n\n If *output* is a string, it will be treated as a filename for a new\n file which the image will be written to. If... |
def capture_sequence(self, outputs, format='jpeg', use_video_port=False, resize=None, splitter_port=0, burst=False, bayer=False, **options):
"\n Capture a sequence of consecutive images from the camera.\n\n This method accepts a sequence or iterator of *outputs* each of which\n must either be a... | 8,720,782,316,086,409,000 | Capture a sequence of consecutive images from the camera.
This method accepts a sequence or iterator of *outputs* each of which
must either be a string specifying a filename for output, or a
file-like object with a ``write`` method, or a writeable buffer object.
For each item in the sequence or iterator of outputs, th... | picamera/camera.py | capture_sequence | RobertLucian/picamera | python | def capture_sequence(self, outputs, format='jpeg', use_video_port=False, resize=None, splitter_port=0, burst=False, bayer=False, **options):
"\n Capture a sequence of consecutive images from the camera.\n\n This method accepts a sequence or iterator of *outputs* each of which\n must either be a... |
def capture_continuous(self, output, format=None, use_video_port=False, resize=None, splitter_port=0, burst=False, bayer=False, **options):
"\n Capture images continuously from the camera as an infinite iterator.\n\n This method returns an infinite iterator of images captured\n continuously fro... | -7,093,969,075,329,482,000 | Capture images continuously from the camera as an infinite iterator.
This method returns an infinite iterator of images captured
continuously from the camera. If *output* is a string, each captured
image is stored in a file named after *output* after substitution of
two values with the :meth:`~str.format` method. Thos... | picamera/camera.py | capture_continuous | RobertLucian/picamera | python | def capture_continuous(self, output, format=None, use_video_port=False, resize=None, splitter_port=0, burst=False, bayer=False, **options):
"\n Capture images continuously from the camera as an infinite iterator.\n\n This method returns an infinite iterator of images captured\n continuously fro... |
@property
def closed(self):
'\n Returns ``True`` if the :meth:`close` method has been called.\n '
return (not self._camera) | 3,284,690,800,250,654,000 | Returns ``True`` if the :meth:`close` method has been called. | picamera/camera.py | closed | RobertLucian/picamera | python | @property
def closed(self):
'\n \n '
return (not self._camera) |
@property
def recording(self):
'\n Returns ``True`` if the :meth:`start_recording` method has been called,\n and no :meth:`stop_recording` call has been made yet.\n '
return any(((isinstance(e, PiVideoEncoder) and e.active) for e in self._encoders.values())) | -7,456,034,436,530,100,000 | Returns ``True`` if the :meth:`start_recording` method has been called,
and no :meth:`stop_recording` call has been made yet. | picamera/camera.py | recording | RobertLucian/picamera | python | @property
def recording(self):
'\n Returns ``True`` if the :meth:`start_recording` method has been called,\n and no :meth:`stop_recording` call has been made yet.\n '
return any(((isinstance(e, PiVideoEncoder) and e.active) for e in self._encoders.values())) |
@property
def previewing(self):
'\n Returns ``True`` if the :meth:`start_preview` method has been called,\n and no :meth:`stop_preview` call has been made yet.\n\n .. deprecated:: 1.8\n Test whether :attr:`preview` is ``None`` instead.\n '
warnings.warn(PiCameraDeprecated(... | 1,968,703,755,299,582,700 | Returns ``True`` if the :meth:`start_preview` method has been called,
and no :meth:`stop_preview` call has been made yet.
.. deprecated:: 1.8
Test whether :attr:`preview` is ``None`` instead. | picamera/camera.py | previewing | RobertLucian/picamera | python | @property
def previewing(self):
'\n Returns ``True`` if the :meth:`start_preview` method has been called,\n and no :meth:`stop_preview` call has been made yet.\n\n .. deprecated:: 1.8\n Test whether :attr:`preview` is ``None`` instead.\n '
warnings.warn(PiCameraDeprecated(... |
@property
def revision(self):
"\n Returns a string representing the revision of the Pi's camera module.\n At the time of writing, the string returned is 'ov5647' for the V1\n module, and 'imx219' for the V2 module.\n "
return self._revision | -4,425,374,092,435,434,000 | Returns a string representing the revision of the Pi's camera module.
At the time of writing, the string returned is 'ov5647' for the V1
module, and 'imx219' for the V2 module. | picamera/camera.py | revision | RobertLucian/picamera | python | @property
def revision(self):
"\n Returns a string representing the revision of the Pi's camera module.\n At the time of writing, the string returned is 'ov5647' for the V1\n module, and 'imx219' for the V2 module.\n "
return self._revision |
@property
def exif_tags(self):
"\n Holds a mapping of the Exif tags to apply to captured images.\n\n .. note::\n\n Please note that Exif tagging is only supported with the ``jpeg``\n format.\n\n By default several Exif tags are automatically applied to any images\n ... | -2,373,883,298,329,916,400 | Holds a mapping of the Exif tags to apply to captured images.
.. note::
Please note that Exif tagging is only supported with the ``jpeg``
format.
By default several Exif tags are automatically applied to any images
taken with the :meth:`capture` method: ``IFD0.Make`` (which is set to
``RaspberryPi``), ``IFD0... | picamera/camera.py | exif_tags | RobertLucian/picamera | python | @property
def exif_tags(self):
"\n Holds a mapping of the Exif tags to apply to captured images.\n\n .. note::\n\n Please note that Exif tagging is only supported with the ``jpeg``\n format.\n\n By default several Exif tags are automatically applied to any images\n ... |
def _disable_camera(self):
'\n An internal method for disabling the camera, e.g. for re-configuration.\n This disables the splitter and preview connections (if they exist).\n '
self._splitter.connection.disable()
self._preview.renderer.connection.disable()
self._camera.disable() | 3,535,478,066,715,093,000 | An internal method for disabling the camera, e.g. for re-configuration.
This disables the splitter and preview connections (if they exist). | picamera/camera.py | _disable_camera | RobertLucian/picamera | python | def _disable_camera(self):
'\n An internal method for disabling the camera, e.g. for re-configuration.\n This disables the splitter and preview connections (if they exist).\n '
self._splitter.connection.disable()
self._preview.renderer.connection.disable()
self._camera.disable() |
def _enable_camera(self):
'\n An internal method for enabling the camera after re-configuration.\n This ensures the splitter configuration is consistent, then re-enables\n the camera along with the splitter and preview connections.\n '
self._camera.enable()
self._preview.renderer... | 1,428,951,581,791,775,700 | An internal method for enabling the camera after re-configuration.
This ensures the splitter configuration is consistent, then re-enables
the camera along with the splitter and preview connections. | picamera/camera.py | _enable_camera | RobertLucian/picamera | python | def _enable_camera(self):
'\n An internal method for enabling the camera after re-configuration.\n This ensures the splitter configuration is consistent, then re-enables\n the camera along with the splitter and preview connections.\n '
self._camera.enable()
self._preview.renderer... |
def _configure_splitter(self):
'\n Ensures all splitter output ports have a sensible format (I420) and\n buffer sizes.\n\n This method is used to ensure the splitter configuration is sane,\n typically after :meth:`_configure_camera` is called.\n '
self._splitter.inputs[0].copy... | -1,595,100,311,936,897,000 | Ensures all splitter output ports have a sensible format (I420) and
buffer sizes.
This method is used to ensure the splitter configuration is sane,
typically after :meth:`_configure_camera` is called. | picamera/camera.py | _configure_splitter | RobertLucian/picamera | python | def _configure_splitter(self):
'\n Ensures all splitter output ports have a sensible format (I420) and\n buffer sizes.\n\n This method is used to ensure the splitter configuration is sane,\n typically after :meth:`_configure_camera` is called.\n '
self._splitter.inputs[0].copy... |
def _configure_camera(self, sensor_mode, framerate, resolution, clock_mode, old_sensor_mode=0):
"\n An internal method for setting a new camera mode, framerate,\n resolution, and/or clock_mode.\n\n This method is used by the setters of the :attr:`resolution`,\n :attr:`framerate`, and :at... | 863,730,291,302,044,500 | An internal method for setting a new camera mode, framerate,
resolution, and/or clock_mode.
This method is used by the setters of the :attr:`resolution`,
:attr:`framerate`, and :attr:`sensor_mode` properties. It assumes the
camera is currently disabled. The *old_mode* and *new_mode* arguments
are required to ensure co... | picamera/camera.py | _configure_camera | RobertLucian/picamera | python | def _configure_camera(self, sensor_mode, framerate, resolution, clock_mode, old_sensor_mode=0):
"\n An internal method for setting a new camera mode, framerate,\n resolution, and/or clock_mode.\n\n This method is used by the setters of the :attr:`resolution`,\n :attr:`framerate`, and :at... |
@logical_and.register('Number', 'Number')
def _logical_and_scala(x, y):
'\n Return logical and operation result of x and y.\n\n Args:\n x(Number): Number.\n y(Number): Number.\n\n Returns:\n bool, Return logical and operation result of x and y.\n '
return F.bool_and(x.__bool__(), y.... | -7,286,355,318,795,096,000 | Return logical and operation result of x and y.
Args:
x(Number): Number.
y(Number): Number.
Returns:
bool, Return logical and operation result of x and y. | mindspore/ops/composite/multitype_ops/logical_and_impl.py | _logical_and_scala | Gavin-Hoang/mindspore | python | @logical_and.register('Number', 'Number')
def _logical_and_scala(x, y):
'\n Return logical and operation result of x and y.\n\n Args:\n x(Number): Number.\n y(Number): Number.\n\n Returns:\n bool, Return logical and operation result of x and y.\n '
return F.bool_and(x.__bool__(), y.... |
@logical_and.register('Tensor', 'Tensor')
def _logical_and_tensor(x, y):
'\n Return logical and operation result of x and y.\n\n Args:\n x(Tensor): Tensor.\n y(Tensor): Tensor.\n\n Returns:\n Tensor, Return logical and operation result of x and y.\n '
return F.logical_and(x, y) | 4,841,098,281,168,699,000 | Return logical and operation result of x and y.
Args:
x(Tensor): Tensor.
y(Tensor): Tensor.
Returns:
Tensor, Return logical and operation result of x and y. | mindspore/ops/composite/multitype_ops/logical_and_impl.py | _logical_and_tensor | Gavin-Hoang/mindspore | python | @logical_and.register('Tensor', 'Tensor')
def _logical_and_tensor(x, y):
'\n Return logical and operation result of x and y.\n\n Args:\n x(Tensor): Tensor.\n y(Tensor): Tensor.\n\n Returns:\n Tensor, Return logical and operation result of x and y.\n '
return F.logical_and(x, y) |
def test_create_file(self):
'Test the creation of a simple XlsxWriter file.'
workbook = Workbook(self.got_filename)
worksheet = workbook.add_worksheet()
chart = workbook.add_chart({'type': 'stock'})
date_format = workbook.add_format({'num_format': 14})
chart.axis_ids = [45740032, 45747200]
d... | -3,457,937,152,193,595,000 | Test the creation of a simple XlsxWriter file. | xlsxwriter/test/comparison/test_chart_data_labels17.py | test_create_file | hugovk/XlsxWriter | python | def test_create_file(self):
workbook = Workbook(self.got_filename)
worksheet = workbook.add_worksheet()
chart = workbook.add_chart({'type': 'stock'})
date_format = workbook.add_format({'num_format': 14})
chart.axis_ids = [45740032, 45747200]
data = [[39083, 39084, 39085, 39086, 39087], [27.... |
def get_key(node):
'Generate a fresh key on node\n\n Returns a named tuple of privkey, pubkey and all address and scripts.'
addr = node.getnewaddress()
pubkey = node.getaddressinfo(addr)['pubkey']
return Key(privkey=node.dumpprivkey(addr), pubkey=pubkey, p2pkh_script=key_to_p2pkh_script(pubkey).hex()... | -563,035,140,004,875,460 | Generate a fresh key on node
Returns a named tuple of privkey, pubkey and all address and scripts. | test/functional/test_framework/wallet_util.py | get_key | ludirium/ludirium | python | def get_key(node):
'Generate a fresh key on node\n\n Returns a named tuple of privkey, pubkey and all address and scripts.'
addr = node.getnewaddress()
pubkey = node.getaddressinfo(addr)['pubkey']
return Key(privkey=node.dumpprivkey(addr), pubkey=pubkey, p2pkh_script=key_to_p2pkh_script(pubkey).hex()... |
def get_generate_key():
'Generate a fresh key\n\n Returns a named tuple of privkey, pubkey and all address and scripts.'
eckey = ECKey()
eckey.generate()
privkey = bytes_to_wif(eckey.get_bytes())
pubkey = eckey.get_pubkey().get_bytes().hex()
return Key(privkey=privkey, pubkey=pubkey, p2pkh_sc... | 787,008,133,391,978,900 | Generate a fresh key
Returns a named tuple of privkey, pubkey and all address and scripts. | test/functional/test_framework/wallet_util.py | get_generate_key | ludirium/ludirium | python | def get_generate_key():
'Generate a fresh key\n\n Returns a named tuple of privkey, pubkey and all address and scripts.'
eckey = ECKey()
eckey.generate()
privkey = bytes_to_wif(eckey.get_bytes())
pubkey = eckey.get_pubkey().get_bytes().hex()
return Key(privkey=privkey, pubkey=pubkey, p2pkh_sc... |
def get_multisig(node):
'Generate a fresh 2-of-3 multisig on node\n\n Returns a named tuple of privkeys, pubkeys and all address and scripts.'
addrs = []
pubkeys = []
for _ in range(3):
addr = node.getaddressinfo(node.getnewaddress())
addrs.append(addr['address'])
pubkeys.appe... | -5,366,923,151,664,502,000 | Generate a fresh 2-of-3 multisig on node
Returns a named tuple of privkeys, pubkeys and all address and scripts. | test/functional/test_framework/wallet_util.py | get_multisig | ludirium/ludirium | python | def get_multisig(node):
'Generate a fresh 2-of-3 multisig on node\n\n Returns a named tuple of privkeys, pubkeys and all address and scripts.'
addrs = []
pubkeys = []
for _ in range(3):
addr = node.getaddressinfo(node.getnewaddress())
addrs.append(addr['address'])
pubkeys.appe... |
def test_address(node, address, **kwargs):
'Get address info for `address` and test whether the returned values are as expected.'
addr_info = node.getaddressinfo(address)
for (key, value) in kwargs.items():
if (value is None):
if (key in addr_info.keys()):
raise Assertion... | 2,198,220,858,924,984,800 | Get address info for `address` and test whether the returned values are as expected. | test/functional/test_framework/wallet_util.py | test_address | ludirium/ludirium | python | def test_address(node, address, **kwargs):
addr_info = node.getaddressinfo(address)
for (key, value) in kwargs.items():
if (value is None):
if (key in addr_info.keys()):
raise AssertionError('key {} unexpectedly returned in getaddressinfo.'.format(key))
elif (add... |
def read_global_config(path: Text) -> Dict[(Text, Any)]:
'Read global Rasa configuration.\n\n Args:\n path: Path to the configuration\n Returns:\n The global configuration\n '
try:
return rasa.shared.utils.io.read_config_file(path)
except Exception:
return {} | 8,840,443,311,206,454,000 | Read global Rasa configuration.
Args:
path: Path to the configuration
Returns:
The global configuration | rasa/utils/common.py | read_global_config | karen-white/rasa | python | def read_global_config(path: Text) -> Dict[(Text, Any)]:
'Read global Rasa configuration.\n\n Args:\n path: Path to the configuration\n Returns:\n The global configuration\n '
try:
return rasa.shared.utils.io.read_config_file(path)
except Exception:
return {} |
def set_log_level(log_level: Optional[int]=None):
"Set log level of Rasa and Tensorflow either to the provided log level or\n to the log level specified in the environment variable 'LOG_LEVEL'. If none is set\n a default log level will be used."
if (not log_level):
log_level = os.environ.get(ENV_L... | 7,298,357,630,811,053,000 | Set log level of Rasa and Tensorflow either to the provided log level or
to the log level specified in the environment variable 'LOG_LEVEL'. If none is set
a default log level will be used. | rasa/utils/common.py | set_log_level | karen-white/rasa | python | def set_log_level(log_level: Optional[int]=None):
"Set log level of Rasa and Tensorflow either to the provided log level or\n to the log level specified in the environment variable 'LOG_LEVEL'. If none is set\n a default log level will be used."
if (not log_level):
log_level = os.environ.get(ENV_L... |
def update_tensorflow_log_level() -> None:
"Set the log level of Tensorflow to the log level specified in the environment\n variable 'LOG_LEVEL_LIBRARIES'."
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
log_level = os.environ.get(ENV_LOG_LEVEL_LIBRARIES, DEFAULT_LOG_LEVEL_LIBRARIES... | -6,638,952,293,186,640,000 | Set the log level of Tensorflow to the log level specified in the environment
variable 'LOG_LEVEL_LIBRARIES'. | rasa/utils/common.py | update_tensorflow_log_level | karen-white/rasa | python | def update_tensorflow_log_level() -> None:
"Set the log level of Tensorflow to the log level specified in the environment\n variable 'LOG_LEVEL_LIBRARIES'."
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
log_level = os.environ.get(ENV_LOG_LEVEL_LIBRARIES, DEFAULT_LOG_LEVEL_LIBRARIES... |
def update_sanic_log_level(log_file: Optional[Text]=None):
"Set the log level of sanic loggers to the log level specified in the environment\n variable 'LOG_LEVEL_LIBRARIES'."
from sanic.log import logger, error_logger, access_logger
log_level = os.environ.get(ENV_LOG_LEVEL_LIBRARIES, DEFAULT_LOG_LEVEL_L... | -5,810,610,133,777,619,000 | Set the log level of sanic loggers to the log level specified in the environment
variable 'LOG_LEVEL_LIBRARIES'. | rasa/utils/common.py | update_sanic_log_level | karen-white/rasa | python | def update_sanic_log_level(log_file: Optional[Text]=None):
"Set the log level of sanic loggers to the log level specified in the environment\n variable 'LOG_LEVEL_LIBRARIES'."
from sanic.log import logger, error_logger, access_logger
log_level = os.environ.get(ENV_LOG_LEVEL_LIBRARIES, DEFAULT_LOG_LEVEL_L... |
def update_asyncio_log_level() -> None:
"Set the log level of asyncio to the log level specified in the environment\n variable 'LOG_LEVEL_LIBRARIES'."
log_level = os.environ.get(ENV_LOG_LEVEL_LIBRARIES, DEFAULT_LOG_LEVEL_LIBRARIES)
logging.getLogger('asyncio').setLevel(log_level) | -7,843,690,206,043,967,000 | Set the log level of asyncio to the log level specified in the environment
variable 'LOG_LEVEL_LIBRARIES'. | rasa/utils/common.py | update_asyncio_log_level | karen-white/rasa | python | def update_asyncio_log_level() -> None:
"Set the log level of asyncio to the log level specified in the environment\n variable 'LOG_LEVEL_LIBRARIES'."
log_level = os.environ.get(ENV_LOG_LEVEL_LIBRARIES, DEFAULT_LOG_LEVEL_LIBRARIES)
logging.getLogger('asyncio').setLevel(log_level) |
def set_log_and_warnings_filters() -> None:
'\n Set log filters on the root logger, and duplicate filters for warnings.\n\n Filters only propagate on handlers, not loggers.\n '
for handler in logging.getLogger().handlers:
handler.addFilter(RepeatedLogFilter())
warnings.filterwarnings('once'... | 5,511,566,338,182,023,000 | Set log filters on the root logger, and duplicate filters for warnings.
Filters only propagate on handlers, not loggers. | rasa/utils/common.py | set_log_and_warnings_filters | karen-white/rasa | python | def set_log_and_warnings_filters() -> None:
'\n Set log filters on the root logger, and duplicate filters for warnings.\n\n Filters only propagate on handlers, not loggers.\n '
for handler in logging.getLogger().handlers:
handler.addFilter(RepeatedLogFilter())
warnings.filterwarnings('once'... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.