body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1
value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
cfc2436b02bdd2f072445ee72fc0feeeb27c5bc6d29f606946ee74e4ebd7cc42 | def GetDelegateKeyByValidator(self, request, context):
'Missing associated documentation comment in .proto file.'
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') | Missing associated documentation comment in .proto file. | pyinjective/proto/injective/peggy/v1/query_pb2_grpc.py | GetDelegateKeyByValidator | CtheSky/sdk-python | 10 | python | def GetDelegateKeyByValidator(self, request, context):
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') | def GetDelegateKeyByValidator(self, request, context):
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')<|docstring|>Missing associated documentation comment in .proto file.<|endoftext|> |
e0ed6935a13e7a9c1ca53030dffc4f0c923358cf8cdc917a0e79482ce1f0b25b | def GetDelegateKeyByEth(self, request, context):
'Missing associated documentation comment in .proto file.'
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') | Missing associated documentation comment in .proto file. | pyinjective/proto/injective/peggy/v1/query_pb2_grpc.py | GetDelegateKeyByEth | CtheSky/sdk-python | 10 | python | def GetDelegateKeyByEth(self, request, context):
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') | def GetDelegateKeyByEth(self, request, context):
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')<|docstring|>Missing associated documentation comment in .proto file.<|endoftext|> |
8ab6f3fe14c650dbc572a469aa94efbd56cc1a630d46c517567e43aa7bf347d6 | def GetDelegateKeyByOrchestrator(self, request, context):
'Missing associated documentation comment in .proto file.'
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') | Missing associated documentation comment in .proto file. | pyinjective/proto/injective/peggy/v1/query_pb2_grpc.py | GetDelegateKeyByOrchestrator | CtheSky/sdk-python | 10 | python | def GetDelegateKeyByOrchestrator(self, request, context):
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') | def GetDelegateKeyByOrchestrator(self, request, context):
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')<|docstring|>Missing associated documentation comment in .proto file.<|endoftext|> |
5b7fca8171782e39d9eb82f5eac101eb4cf63d8fc5801b11f739318fc6a91c45 | def PeggyModuleState(self, request, context):
"Retrieves the entire peggy module's state\n "
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') | Retrieves the entire peggy module's state | pyinjective/proto/injective/peggy/v1/query_pb2_grpc.py | PeggyModuleState | CtheSky/sdk-python | 10 | python | def PeggyModuleState(self, request, context):
"\n "
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') | def PeggyModuleState(self, request, context):
"\n "
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')<|docstring|>Retrieves the entire peggy module's state<|endoftext|> |
fe91de6aa905fbb61f1eee8cc7c0f0463cb9ede2574d6498264577068c34a1a7 | def soft_to_hard_permutation(inputs):
'Returns permutation matrices by solving a matching problem.\n\n Solves linear sum assignment to convert doubly-stochastic matrices to\n permutation matrices. It uses scipy.optimize.linear_sum_assignment to solve\n the optimization problem max_P sum_i,j M_i,j P_i,j with P a ... | Returns permutation matrices by solving a matching problem.
Solves linear sum assignment to convert doubly-stochastic matrices to
permutation matrices. It uses scipy.optimize.linear_sum_assignment to solve
the optimization problem max_P sum_i,j M_i,j P_i,j with P a permutation
matrix. Notice the negative sign; the rea... | tensor2tensor/layers/reversible_layers.py | soft_to_hard_permutation | ekuznetsov139/tensor2tensor | 34 | python | def soft_to_hard_permutation(inputs):
'Returns permutation matrices by solving a matching problem.\n\n Solves linear sum assignment to convert doubly-stochastic matrices to\n permutation matrices. It uses scipy.optimize.linear_sum_assignment to solve\n the optimization problem max_P sum_i,j M_i,j P_i,j with P a ... | def soft_to_hard_permutation(inputs):
'Returns permutation matrices by solving a matching problem.\n\n Solves linear sum assignment to convert doubly-stochastic matrices to\n permutation matrices. It uses scipy.optimize.linear_sum_assignment to solve\n the optimization problem max_P sum_i,j M_i,j P_i,j with P a ... |
702632db5cd8b9939e3df496497870dd78116e8b1c55e182ccfd1378032ba077 | def one_hot_argmax(inputs, temperature, axis=(- 1)):
'Returns one-hot of argmax with backward pass set to softmax-temperature.'
vocab_size = inputs.shape[(- 1)]
hard = tf.one_hot(tf.argmax(inputs, axis=axis), depth=vocab_size, axis=axis, dtype=inputs.dtype)
soft = tf.nn.softmax((inputs / temperature), a... | Returns one-hot of argmax with backward pass set to softmax-temperature. | tensor2tensor/layers/reversible_layers.py | one_hot_argmax | ekuznetsov139/tensor2tensor | 34 | python | def one_hot_argmax(inputs, temperature, axis=(- 1)):
vocab_size = inputs.shape[(- 1)]
hard = tf.one_hot(tf.argmax(inputs, axis=axis), depth=vocab_size, axis=axis, dtype=inputs.dtype)
soft = tf.nn.softmax((inputs / temperature), axis=axis)
outputs = (soft + tf.stop_gradient((hard - soft)))
retur... | def one_hot_argmax(inputs, temperature, axis=(- 1)):
vocab_size = inputs.shape[(- 1)]
hard = tf.one_hot(tf.argmax(inputs, axis=axis), depth=vocab_size, axis=axis, dtype=inputs.dtype)
soft = tf.nn.softmax((inputs / temperature), axis=axis)
outputs = (soft + tf.stop_gradient((hard - soft)))
retur... |
8f67c178211114d69b3830915e7ba1759587fa2367c4b88529c65dc917fb117e | def one_hot_add(inputs, shift):
'Performs (inputs + shift) % vocab_size in the one-hot space.\n\n Args:\n inputs: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor.\n shift: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor specifying how much to ... | Performs (inputs + shift) % vocab_size in the one-hot space.
Args:
inputs: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot
Tensor.
shift: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot
Tensor specifying how much to shift the corresponding one-hot vector in
inputs.... | tensor2tensor/layers/reversible_layers.py | one_hot_add | ekuznetsov139/tensor2tensor | 34 | python | def one_hot_add(inputs, shift):
'Performs (inputs + shift) % vocab_size in the one-hot space.\n\n Args:\n inputs: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor.\n shift: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor specifying how much to ... | def one_hot_add(inputs, shift):
'Performs (inputs + shift) % vocab_size in the one-hot space.\n\n Args:\n inputs: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor.\n shift: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor specifying how much to ... |
64cb9955e04883123070b53db3ab21504b656eae6153b25f1a465359ee281f9e | def one_hot_minus(inputs, shift):
'Performs (inputs - shift) % vocab_size in the one-hot space.\n\n Args:\n inputs: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor.\n shift: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor specifying how much t... | Performs (inputs - shift) % vocab_size in the one-hot space.
Args:
inputs: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot
Tensor.
shift: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot
Tensor specifying how much to shift the corresponding one-hot vector in
inputs.... | tensor2tensor/layers/reversible_layers.py | one_hot_minus | ekuznetsov139/tensor2tensor | 34 | python | def one_hot_minus(inputs, shift):
'Performs (inputs - shift) % vocab_size in the one-hot space.\n\n Args:\n inputs: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor.\n shift: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor specifying how much t... | def one_hot_minus(inputs, shift):
'Performs (inputs - shift) % vocab_size in the one-hot space.\n\n Args:\n inputs: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor.\n shift: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor specifying how much t... |
8632db3d36f1ae5000fec3e1af51ca457c38525599b48ad9edfbbe5a46733fd5 | def one_hot_multiply(inputs, scale):
'Performs (inputs * scale) % vocab_size in the one-hot space.\n\n Args:\n inputs: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor.\n scale: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor specifying how muc... | Performs (inputs * scale) % vocab_size in the one-hot space.
Args:
inputs: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot
Tensor.
scale: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot
Tensor specifying how much to scale the corresponding one-hot vector in
inputs.... | tensor2tensor/layers/reversible_layers.py | one_hot_multiply | ekuznetsov139/tensor2tensor | 34 | python | def one_hot_multiply(inputs, scale):
'Performs (inputs * scale) % vocab_size in the one-hot space.\n\n Args:\n inputs: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor.\n scale: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor specifying how muc... | def one_hot_multiply(inputs, scale):
'Performs (inputs * scale) % vocab_size in the one-hot space.\n\n Args:\n inputs: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor.\n scale: Tensor of shape `[..., vocab_size]`. Typically a soft/hard one-hot\n Tensor specifying how muc... |
aaaeccc39bd9b7d15b9262225fd5a4948104811d7fccd1f84588bd83dc63277a | def py_multiplicative_inverse(a, n):
'Multiplicative inverse of a modulo n (in Python).\n\n Implements extended Euclidean algorithm.\n\n Args:\n a: int-like np.ndarray.\n n: int.\n\n Returns:\n Multiplicative inverse as an int32 np.ndarray with same shape as a.\n '
batched_a = np.asarray(a, dtype=n... | Multiplicative inverse of a modulo n (in Python).
Implements extended Euclidean algorithm.
Args:
a: int-like np.ndarray.
n: int.
Returns:
Multiplicative inverse as an int32 np.ndarray with same shape as a. | tensor2tensor/layers/reversible_layers.py | py_multiplicative_inverse | ekuznetsov139/tensor2tensor | 34 | python | def py_multiplicative_inverse(a, n):
'Multiplicative inverse of a modulo n (in Python).\n\n Implements extended Euclidean algorithm.\n\n Args:\n a: int-like np.ndarray.\n n: int.\n\n Returns:\n Multiplicative inverse as an int32 np.ndarray with same shape as a.\n '
batched_a = np.asarray(a, dtype=n... | def py_multiplicative_inverse(a, n):
'Multiplicative inverse of a modulo n (in Python).\n\n Implements extended Euclidean algorithm.\n\n Args:\n a: int-like np.ndarray.\n n: int.\n\n Returns:\n Multiplicative inverse as an int32 np.ndarray with same shape as a.\n '
batched_a = np.asarray(a, dtype=n... |
694919ee64ec0b1f6072e15bfb4dc83be4747064ffa2567fa9c29edae4c3b330 | def multiplicative_inverse(a, n):
'Multiplicative inverse of a modulo n.\n\n Args:\n a: Tensor of shape [..., vocab_size]. It denotes an integer in the one-hot\n space.\n n: int Tensor of shape [...].\n\n Returns:\n Tensor of same shape and dtype as a.\n '
a = tf.convert_to_tensor(a)
n = tf... | Multiplicative inverse of a modulo n.
Args:
a: Tensor of shape [..., vocab_size]. It denotes an integer in the one-hot
space.
n: int Tensor of shape [...].
Returns:
Tensor of same shape and dtype as a. | tensor2tensor/layers/reversible_layers.py | multiplicative_inverse | ekuznetsov139/tensor2tensor | 34 | python | def multiplicative_inverse(a, n):
'Multiplicative inverse of a modulo n.\n\n Args:\n a: Tensor of shape [..., vocab_size]. It denotes an integer in the one-hot\n space.\n n: int Tensor of shape [...].\n\n Returns:\n Tensor of same shape and dtype as a.\n '
a = tf.convert_to_tensor(a)
n = tf... | def multiplicative_inverse(a, n):
'Multiplicative inverse of a modulo n.\n\n Args:\n a: Tensor of shape [..., vocab_size]. It denotes an integer in the one-hot\n space.\n n: int Tensor of shape [...].\n\n Returns:\n Tensor of same shape and dtype as a.\n '
a = tf.convert_to_tensor(a)
n = tf... |
4f7c06862e75a6f4891cfd777729b123cfa300537cd6a1c9ce19712e56a7df61 | def create_degrees(input_dim, hidden_dims, input_order='left-to-right', hidden_order='left-to-right'):
"Returns a list of degree vectors, one for each input and hidden layer.\n\n A unit with degree d can only receive input from units with degree < d. Output\n units always have the same degree as their associated ... | Returns a list of degree vectors, one for each input and hidden layer.
A unit with degree d can only receive input from units with degree < d. Output
units always have the same degree as their associated input unit.
Args:
input_dim: Number of inputs.
hidden_dims: list with the number of hidden units per layer. It... | tensor2tensor/layers/reversible_layers.py | create_degrees | ekuznetsov139/tensor2tensor | 34 | python | def create_degrees(input_dim, hidden_dims, input_order='left-to-right', hidden_order='left-to-right'):
"Returns a list of degree vectors, one for each input and hidden layer.\n\n A unit with degree d can only receive input from units with degree < d. Output\n units always have the same degree as their associated ... | def create_degrees(input_dim, hidden_dims, input_order='left-to-right', hidden_order='left-to-right'):
"Returns a list of degree vectors, one for each input and hidden layer.\n\n A unit with degree d can only receive input from units with degree < d. Output\n units always have the same degree as their associated ... |
4086085419d0b0671f3335fc62cee345ce97bdd6088a8049b63e201c02b26638 | def create_masks(input_dim, hidden_dims, input_order='left-to-right', hidden_order='left-to-right'):
"Returns a list of binary mask matrices respecting autoregressive ordering.\n\n Args:\n input_dim: Number of inputs.\n hidden_dims: list with the number of hidden units per layer. It does not\n include t... | Returns a list of binary mask matrices respecting autoregressive ordering.
Args:
input_dim: Number of inputs.
hidden_dims: list with the number of hidden units per layer. It does not
include the output layer; those number of units will always be set to
input_dim downstream. Each hidden unit size must be at... | tensor2tensor/layers/reversible_layers.py | create_masks | ekuznetsov139/tensor2tensor | 34 | python | def create_masks(input_dim, hidden_dims, input_order='left-to-right', hidden_order='left-to-right'):
"Returns a list of binary mask matrices respecting autoregressive ordering.\n\n Args:\n input_dim: Number of inputs.\n hidden_dims: list with the number of hidden units per layer. It does not\n include t... | def create_masks(input_dim, hidden_dims, input_order='left-to-right', hidden_order='left-to-right'):
"Returns a list of binary mask matrices respecting autoregressive ordering.\n\n Args:\n input_dim: Number of inputs.\n hidden_dims: list with the number of hidden units per layer. It does not\n include t... |
fa7c7b7e6bf7283c548717c9e7bba98d72d6f81a02338c5fe02b9d7836e6a4ac | def sinkhorn(inputs, n_iters=20):
'Performs incomplete Sinkhorn normalization to inputs.\n\n By a theorem by Sinkhorn and Knopp [1], a sufficiently well-behaved matrix\n with positive entries can be turned into a doubly-stochastic matrix\n (i.e. its rows and columns add up to one) via the succesive row and colu... | Performs incomplete Sinkhorn normalization to inputs.
By a theorem by Sinkhorn and Knopp [1], a sufficiently well-behaved matrix
with positive entries can be turned into a doubly-stochastic matrix
(i.e. its rows and columns add up to one) via the succesive row and column
normalization.
-To ensure positivity, the effe... | tensor2tensor/layers/reversible_layers.py | sinkhorn | ekuznetsov139/tensor2tensor | 34 | python | def sinkhorn(inputs, n_iters=20):
'Performs incomplete Sinkhorn normalization to inputs.\n\n By a theorem by Sinkhorn and Knopp [1], a sufficiently well-behaved matrix\n with positive entries can be turned into a doubly-stochastic matrix\n (i.e. its rows and columns add up to one) via the succesive row and colu... | def sinkhorn(inputs, n_iters=20):
'Performs incomplete Sinkhorn normalization to inputs.\n\n By a theorem by Sinkhorn and Knopp [1], a sufficiently well-behaved matrix\n with positive entries can be turned into a doubly-stochastic matrix\n (i.e. its rows and columns add up to one) via the succesive row and colu... |
fd1449bd89b5577880d1195d43e44df7a046bf965ea9872bcdcbe047d246935a | @ed.interceptable
def TransformedRandomVariable(random_variable, reversible_layer, name=None, sample_shape=(), value=None):
'Random variable for f(x), where x ~ p(x) and f is reversible.'
return ed.RandomVariable(distribution=TransformedDistribution(random_variable.distribution, reversible_layer, name=name), sa... | Random variable for f(x), where x ~ p(x) and f is reversible. | tensor2tensor/layers/reversible_layers.py | TransformedRandomVariable | ekuznetsov139/tensor2tensor | 34 | python | @ed.interceptable
def TransformedRandomVariable(random_variable, reversible_layer, name=None, sample_shape=(), value=None):
return ed.RandomVariable(distribution=TransformedDistribution(random_variable.distribution, reversible_layer, name=name), sample_shape=sample_shape, value=value) | @ed.interceptable
def TransformedRandomVariable(random_variable, reversible_layer, name=None, sample_shape=(), value=None):
return ed.RandomVariable(distribution=TransformedDistribution(random_variable.distribution, reversible_layer, name=name), sample_shape=sample_shape, value=value)<|docstring|>Random variab... |
e7851fcafb6eebcc67dd98a7b44a114eb3967f7d7543ecb430bbf16ee9c3b012 | def __init__(self, layer, temperature, **kwargs):
'Constructs flow.\n\n Args:\n layer: Two-headed masked network taking the inputs and returning a\n real-valued Tensor of shape `[..., length, 2*vocab_size]`.\n Alternatively, `layer` may return a Tensor of shape\n `[..., length, vocab_si... | Constructs flow.
Args:
layer: Two-headed masked network taking the inputs and returning a
real-valued Tensor of shape `[..., length, 2*vocab_size]`.
Alternatively, `layer` may return a Tensor of shape
`[..., length, vocab_size]` to be used as the location transform; the
scale transform will be hard-c... | tensor2tensor/layers/reversible_layers.py | __init__ | ekuznetsov139/tensor2tensor | 34 | python | def __init__(self, layer, temperature, **kwargs):
'Constructs flow.\n\n Args:\n layer: Two-headed masked network taking the inputs and returning a\n real-valued Tensor of shape `[..., length, 2*vocab_size]`.\n Alternatively, `layer` may return a Tensor of shape\n `[..., length, vocab_si... | def __init__(self, layer, temperature, **kwargs):
'Constructs flow.\n\n Args:\n layer: Two-headed masked network taking the inputs and returning a\n real-valued Tensor of shape `[..., length, 2*vocab_size]`.\n Alternatively, `layer` may return a Tensor of shape\n `[..., length, vocab_si... |
f71cfe696cf2862b3b9cdcc9e9edcb110f0ca21f1d011a03abe4a7dfd3f814f9 | def call(self, inputs, **kwargs):
'Forward pass for left-to-right autoregressive generation.'
inputs = tf.convert_to_tensor(inputs)
length = inputs.shape[(- 2)].value
if (length is None):
raise NotImplementedError('length dimension must be known.')
outputs = self._initial_call(inputs[(..., 0... | Forward pass for left-to-right autoregressive generation. | tensor2tensor/layers/reversible_layers.py | call | ekuznetsov139/tensor2tensor | 34 | python | def call(self, inputs, **kwargs):
inputs = tf.convert_to_tensor(inputs)
length = inputs.shape[(- 2)].value
if (length is None):
raise NotImplementedError('length dimension must be known.')
outputs = self._initial_call(inputs[(..., 0, :)], length, **kwargs)
for t in range(1, length):
... | def call(self, inputs, **kwargs):
inputs = tf.convert_to_tensor(inputs)
length = inputs.shape[(- 2)].value
if (length is None):
raise NotImplementedError('length dimension must be known.')
outputs = self._initial_call(inputs[(..., 0, :)], length, **kwargs)
for t in range(1, length):
... |
1721f7524a0acf0c33d2424ed8250f3f9836bd07cddcbd8cf2e357ed6721b1dd | def _initial_call(self, new_inputs, length, **kwargs):
'Returns Tensor of shape [..., 1, vocab_size].\n\n Args:\n new_inputs: Tensor of shape [..., vocab_size], the new input to generate\n its output.\n length: Length of final desired sequence.\n **kwargs: Optional keyword arguments to laye... | Returns Tensor of shape [..., 1, vocab_size].
Args:
new_inputs: Tensor of shape [..., vocab_size], the new input to generate
its output.
length: Length of final desired sequence.
**kwargs: Optional keyword arguments to layer. | tensor2tensor/layers/reversible_layers.py | _initial_call | ekuznetsov139/tensor2tensor | 34 | python | def _initial_call(self, new_inputs, length, **kwargs):
'Returns Tensor of shape [..., 1, vocab_size].\n\n Args:\n new_inputs: Tensor of shape [..., vocab_size], the new input to generate\n its output.\n length: Length of final desired sequence.\n **kwargs: Optional keyword arguments to laye... | def _initial_call(self, new_inputs, length, **kwargs):
'Returns Tensor of shape [..., 1, vocab_size].\n\n Args:\n new_inputs: Tensor of shape [..., vocab_size], the new input to generate\n its output.\n length: Length of final desired sequence.\n **kwargs: Optional keyword arguments to laye... |
0da26e7536ca7922cb05f26767ba321f3982e50276f1f1af96b673794e9a497a | def _per_timestep_call(self, current_outputs, new_inputs, length, timestep, **kwargs):
'Returns Tensor of shape [..., timestep+1, vocab_size].\n\n Args:\n current_outputs: Tensor of shape [..., timestep, vocab_size], the so-far\n generated sequence Tensor.\n new_inputs: Tensor of shape [..., voc... | Returns Tensor of shape [..., timestep+1, vocab_size].
Args:
current_outputs: Tensor of shape [..., timestep, vocab_size], the so-far
generated sequence Tensor.
new_inputs: Tensor of shape [..., vocab_size], the new input to generate
its output given current_outputs.
length: Length of final desired seque... | tensor2tensor/layers/reversible_layers.py | _per_timestep_call | ekuznetsov139/tensor2tensor | 34 | python | def _per_timestep_call(self, current_outputs, new_inputs, length, timestep, **kwargs):
'Returns Tensor of shape [..., timestep+1, vocab_size].\n\n Args:\n current_outputs: Tensor of shape [..., timestep, vocab_size], the so-far\n generated sequence Tensor.\n new_inputs: Tensor of shape [..., voc... | def _per_timestep_call(self, current_outputs, new_inputs, length, timestep, **kwargs):
'Returns Tensor of shape [..., timestep+1, vocab_size].\n\n Args:\n current_outputs: Tensor of shape [..., timestep, vocab_size], the so-far\n generated sequence Tensor.\n new_inputs: Tensor of shape [..., voc... |
f297fc2513fe378005f560dcf0bbdde88d2e375142f906125c677317dcefff29 | def reverse(self, inputs, **kwargs):
'Reverse pass returning the inverse autoregressive transformation.'
if (not self.built):
self._maybe_build(inputs)
net = self.layer(inputs, **kwargs)
if (net.shape[(- 1)] == (2 * self.vocab_size)):
(loc, scale) = tf.split(net, 2, axis=(- 1))
s... | Reverse pass returning the inverse autoregressive transformation. | tensor2tensor/layers/reversible_layers.py | reverse | ekuznetsov139/tensor2tensor | 34 | python | def reverse(self, inputs, **kwargs):
if (not self.built):
self._maybe_build(inputs)
net = self.layer(inputs, **kwargs)
if (net.shape[(- 1)] == (2 * self.vocab_size)):
(loc, scale) = tf.split(net, 2, axis=(- 1))
scale = tf.cast(one_hot_argmax(scale, self.temperature), inputs.dtyp... | def reverse(self, inputs, **kwargs):
if (not self.built):
self._maybe_build(inputs)
net = self.layer(inputs, **kwargs)
if (net.shape[(- 1)] == (2 * self.vocab_size)):
(loc, scale) = tf.split(net, 2, axis=(- 1))
scale = tf.cast(one_hot_argmax(scale, self.temperature), inputs.dtyp... |
7660a621b23610fe3d6b0f79bc852ab3eb84e22745232b6296622d13fbe9cfd0 | def __init__(self, layer, mask, temperature, **kwargs):
'Constructs flow.\n\n Args:\n layer: Two-headed masked network taking the inputs and returning a\n real-valued Tensor of shape `[..., length, 2*vocab_size]`.\n Alternatively, `layer` may return a Tensor of shape\n `[..., length, vo... | Constructs flow.
Args:
layer: Two-headed masked network taking the inputs and returning a
real-valued Tensor of shape `[..., length, 2*vocab_size]`.
Alternatively, `layer` may return a Tensor of shape
`[..., length, vocab_size]` to be used as the location transform; the
scale transform will be hard-c... | tensor2tensor/layers/reversible_layers.py | __init__ | ekuznetsov139/tensor2tensor | 34 | python | def __init__(self, layer, mask, temperature, **kwargs):
'Constructs flow.\n\n Args:\n layer: Two-headed masked network taking the inputs and returning a\n real-valued Tensor of shape `[..., length, 2*vocab_size]`.\n Alternatively, `layer` may return a Tensor of shape\n `[..., length, vo... | def __init__(self, layer, mask, temperature, **kwargs):
'Constructs flow.\n\n Args:\n layer: Two-headed masked network taking the inputs and returning a\n real-valued Tensor of shape `[..., length, 2*vocab_size]`.\n Alternatively, `layer` may return a Tensor of shape\n `[..., length, vo... |
a6fd67a6f6cf6b12877b5c21912a3863a00eb43f544e5a01e7fad60e87118a6e | def call(self, inputs, **kwargs):
'Forward pass for bipartite generation.'
inputs = tf.convert_to_tensor(inputs)
batch_ndims = (inputs.shape.ndims - 2)
mask = tf.reshape(tf.cast(self.mask, inputs.dtype), (([1] * batch_ndims) + [(- 1), 1]))
masked_inputs = (mask * inputs)
net = self.layer(masked_... | Forward pass for bipartite generation. | tensor2tensor/layers/reversible_layers.py | call | ekuznetsov139/tensor2tensor | 34 | python | def call(self, inputs, **kwargs):
inputs = tf.convert_to_tensor(inputs)
batch_ndims = (inputs.shape.ndims - 2)
mask = tf.reshape(tf.cast(self.mask, inputs.dtype), (([1] * batch_ndims) + [(- 1), 1]))
masked_inputs = (mask * inputs)
net = self.layer(masked_inputs, **kwargs)
if (net.shape[(- 1... | def call(self, inputs, **kwargs):
inputs = tf.convert_to_tensor(inputs)
batch_ndims = (inputs.shape.ndims - 2)
mask = tf.reshape(tf.cast(self.mask, inputs.dtype), (([1] * batch_ndims) + [(- 1), 1]))
masked_inputs = (mask * inputs)
net = self.layer(masked_inputs, **kwargs)
if (net.shape[(- 1... |
a8b957d5a91288fc1a39e051d879fbcbd4efd6a1a0d201a71ae4d5492c077515 | def reverse(self, inputs, **kwargs):
'Reverse pass for the inverse bipartite transformation.'
if (not self.built):
self._maybe_build(inputs)
inputs = tf.convert_to_tensor(inputs)
batch_ndims = (inputs.shape.ndims - 2)
mask = tf.reshape(tf.cast(self.mask, inputs.dtype), (([1] * batch_ndims) +... | Reverse pass for the inverse bipartite transformation. | tensor2tensor/layers/reversible_layers.py | reverse | ekuznetsov139/tensor2tensor | 34 | python | def reverse(self, inputs, **kwargs):
if (not self.built):
self._maybe_build(inputs)
inputs = tf.convert_to_tensor(inputs)
batch_ndims = (inputs.shape.ndims - 2)
mask = tf.reshape(tf.cast(self.mask, inputs.dtype), (([1] * batch_ndims) + [(- 1), 1]))
masked_inputs = (mask * inputs)
ne... | def reverse(self, inputs, **kwargs):
if (not self.built):
self._maybe_build(inputs)
inputs = tf.convert_to_tensor(inputs)
batch_ndims = (inputs.shape.ndims - 2)
mask = tf.reshape(tf.cast(self.mask, inputs.dtype), (([1] * batch_ndims) + [(- 1), 1]))
masked_inputs = (mask * inputs)
ne... |
6672473612a4f4efe6904d0b0def3c43cf85754f3825d6ee82cc8d5a5e4412fa | def __init__(self, layer, temperature, **kwargs):
'Constructs flow.\n\n Args:\n layer: Masked network taking inputs with shape `[..., length, vocab_size]`\n and returning a real-valued Tensor of shape\n `[..., length, vocab_size ** 2]`. Sinkhorn iterations are applied to\n each `layer` ... | Constructs flow.
Args:
layer: Masked network taking inputs with shape `[..., length, vocab_size]`
and returning a real-valued Tensor of shape
`[..., length, vocab_size ** 2]`. Sinkhorn iterations are applied to
each `layer` output to produce permutation matrices.
temperature: Positive value determining... | tensor2tensor/layers/reversible_layers.py | __init__ | ekuznetsov139/tensor2tensor | 34 | python | def __init__(self, layer, temperature, **kwargs):
'Constructs flow.\n\n Args:\n layer: Masked network taking inputs with shape `[..., length, vocab_size]`\n and returning a real-valued Tensor of shape\n `[..., length, vocab_size ** 2]`. Sinkhorn iterations are applied to\n each `layer` ... | def __init__(self, layer, temperature, **kwargs):
'Constructs flow.\n\n Args:\n layer: Masked network taking inputs with shape `[..., length, vocab_size]`\n and returning a real-valued Tensor of shape\n `[..., length, vocab_size ** 2]`. Sinkhorn iterations are applied to\n each `layer` ... |
f71cfe696cf2862b3b9cdcc9e9edcb110f0ca21f1d011a03abe4a7dfd3f814f9 | def call(self, inputs, **kwargs):
'Forward pass for left-to-right autoregressive generation.'
inputs = tf.convert_to_tensor(inputs)
length = inputs.shape[(- 2)].value
if (length is None):
raise NotImplementedError('length dimension must be known.')
outputs = self._initial_call(inputs[(..., 0... | Forward pass for left-to-right autoregressive generation. | tensor2tensor/layers/reversible_layers.py | call | ekuznetsov139/tensor2tensor | 34 | python | def call(self, inputs, **kwargs):
inputs = tf.convert_to_tensor(inputs)
length = inputs.shape[(- 2)].value
if (length is None):
raise NotImplementedError('length dimension must be known.')
outputs = self._initial_call(inputs[(..., 0, :)], length, **kwargs)
for t in range(1, length):
... | def call(self, inputs, **kwargs):
inputs = tf.convert_to_tensor(inputs)
length = inputs.shape[(- 2)].value
if (length is None):
raise NotImplementedError('length dimension must be known.')
outputs = self._initial_call(inputs[(..., 0, :)], length, **kwargs)
for t in range(1, length):
... |
b3c16c689efcc329352314fdd87be4ccfb7a2e4f8fd8a06d4b9d91f44409052c | def _initial_call(self, new_inputs, length, **kwargs):
'Returns Tensor of shape [..., 1, vocab_size].\n\n Args:\n new_inputs: Tensor of shape [..., vocab_size], the new input to generate\n its output.\n length: Length of final desired sequence.\n **kwargs: Optional keyword arguments to laye... | Returns Tensor of shape [..., 1, vocab_size].
Args:
new_inputs: Tensor of shape [..., vocab_size], the new input to generate
its output.
length: Length of final desired sequence.
**kwargs: Optional keyword arguments to layer. | tensor2tensor/layers/reversible_layers.py | _initial_call | ekuznetsov139/tensor2tensor | 34 | python | def _initial_call(self, new_inputs, length, **kwargs):
'Returns Tensor of shape [..., 1, vocab_size].\n\n Args:\n new_inputs: Tensor of shape [..., vocab_size], the new input to generate\n its output.\n length: Length of final desired sequence.\n **kwargs: Optional keyword arguments to laye... | def _initial_call(self, new_inputs, length, **kwargs):
'Returns Tensor of shape [..., 1, vocab_size].\n\n Args:\n new_inputs: Tensor of shape [..., vocab_size], the new input to generate\n its output.\n length: Length of final desired sequence.\n **kwargs: Optional keyword arguments to laye... |
73c78507f841b6dc7ebb2d547fdc41dc67a0ecd120a16ed6180afc8bb14d1392 | def _per_timestep_call(self, current_outputs, new_inputs, length, timestep, **kwargs):
'Returns Tensor of shape [..., timestep+1, vocab_size].\n\n Args:\n current_outputs: Tensor of shape [..., timestep, vocab_size], the so-far\n generated sequence Tensor.\n new_inputs: Tensor of shape [..., voc... | Returns Tensor of shape [..., timestep+1, vocab_size].
Args:
current_outputs: Tensor of shape [..., timestep, vocab_size], the so-far
generated sequence Tensor.
new_inputs: Tensor of shape [..., vocab_size], the new input to generate
its output given current_outputs.
length: Length of final desired seque... | tensor2tensor/layers/reversible_layers.py | _per_timestep_call | ekuznetsov139/tensor2tensor | 34 | python | def _per_timestep_call(self, current_outputs, new_inputs, length, timestep, **kwargs):
'Returns Tensor of shape [..., timestep+1, vocab_size].\n\n Args:\n current_outputs: Tensor of shape [..., timestep, vocab_size], the so-far\n generated sequence Tensor.\n new_inputs: Tensor of shape [..., voc... | def _per_timestep_call(self, current_outputs, new_inputs, length, timestep, **kwargs):
'Returns Tensor of shape [..., timestep+1, vocab_size].\n\n Args:\n current_outputs: Tensor of shape [..., timestep, vocab_size], the so-far\n generated sequence Tensor.\n new_inputs: Tensor of shape [..., voc... |
cd526d7ddc49114bb7ef501eabb207f598588ddb6338dc16743fe9a001aee8dc | def reverse(self, inputs, **kwargs):
'Reverse pass returning the inverse autoregressive transformation.'
if (not self.built):
self._maybe_build(inputs)
logits = self.layer(inputs, **kwargs)
logits = tf.reshape(logits, logits.shape[:(- 1)].concatenate([self.vocab_size, self.vocab_size]))
soft... | Reverse pass returning the inverse autoregressive transformation. | tensor2tensor/layers/reversible_layers.py | reverse | ekuznetsov139/tensor2tensor | 34 | python | def reverse(self, inputs, **kwargs):
if (not self.built):
self._maybe_build(inputs)
logits = self.layer(inputs, **kwargs)
logits = tf.reshape(logits, logits.shape[:(- 1)].concatenate([self.vocab_size, self.vocab_size]))
soft = sinkhorn((logits / self.temperature), n_iters=20)
hard = sof... | def reverse(self, inputs, **kwargs):
if (not self.built):
self._maybe_build(inputs)
logits = self.layer(inputs, **kwargs)
logits = tf.reshape(logits, logits.shape[:(- 1)].concatenate([self.vocab_size, self.vocab_size]))
soft = sinkhorn((logits / self.temperature), n_iters=20)
hard = sof... |
752d604aa42f42b613106f21de04e1cbb0c245f9be349b78baf05aa27f4d507c | def log_det_jacobian(self, inputs):
'Returns log det | dx / dy | = num_events * sum log | scale |.'
del inputs
num_events = tf.reduce_prod(tf.shape(inputs)[1:(- 1)])
log_det_jacobian = (num_events * tf.reduce_sum(self.log_scale))
return log_det_jacobian | Returns log det | dx / dy | = num_events * sum log | scale |. | tensor2tensor/layers/reversible_layers.py | log_det_jacobian | ekuznetsov139/tensor2tensor | 34 | python | def log_det_jacobian(self, inputs):
del inputs
num_events = tf.reduce_prod(tf.shape(inputs)[1:(- 1)])
log_det_jacobian = (num_events * tf.reduce_sum(self.log_scale))
return log_det_jacobian | def log_det_jacobian(self, inputs):
del inputs
num_events = tf.reduce_prod(tf.shape(inputs)[1:(- 1)])
log_det_jacobian = (num_events * tf.reduce_sum(self.log_scale))
return log_det_jacobian<|docstring|>Returns log det | dx / dy | = num_events * sum log | scale |.<|endoftext|> |
1cf0ee0ca571a7d2df41f152b6eb9f2c27fc44b1c2c4366510c585aa4b1e35cc | def __init__(self, units, hidden_dims, input_order='left-to-right', hidden_order='left-to-right', activation=None, use_bias=True, **kwargs):
"Constructs network.\n\n Args:\n units: Positive integer, dimensionality of the output space.\n hidden_dims: list with the number of hidden units per layer. It do... | Constructs network.
Args:
units: Positive integer, dimensionality of the output space.
hidden_dims: list with the number of hidden units per layer. It does not
include the output layer; those number of units will always be set to
the input dimension multiplied by `num_heads`. Each hidden unit size
must... | tensor2tensor/layers/reversible_layers.py | __init__ | ekuznetsov139/tensor2tensor | 34 | python | def __init__(self, units, hidden_dims, input_order='left-to-right', hidden_order='left-to-right', activation=None, use_bias=True, **kwargs):
"Constructs network.\n\n Args:\n units: Positive integer, dimensionality of the output space.\n hidden_dims: list with the number of hidden units per layer. It do... | def __init__(self, units, hidden_dims, input_order='left-to-right', hidden_order='left-to-right', activation=None, use_bias=True, **kwargs):
"Constructs network.\n\n Args:\n units: Positive integer, dimensionality of the output space.\n hidden_dims: list with the number of hidden units per layer. It do... |
3f41fa566f02150a445e0b3ea0b09d7b2f62dce6c186e1d1a6ed084792ed47c5 | def __init__(self, base, reversible_layer, name=None):
'Constructs a transformed distribution.\n\n Args:\n base: Base distribution.\n reversible_layer: Callable with methods `reverse` and `log_det_jacobian`.\n name: Name for scoping operations in the class.\n '
self.base = base
self.rev... | Constructs a transformed distribution.
Args:
base: Base distribution.
reversible_layer: Callable with methods `reverse` and `log_det_jacobian`.
name: Name for scoping operations in the class. | tensor2tensor/layers/reversible_layers.py | __init__ | ekuznetsov139/tensor2tensor | 34 | python | def __init__(self, base, reversible_layer, name=None):
'Constructs a transformed distribution.\n\n Args:\n base: Base distribution.\n reversible_layer: Callable with methods `reverse` and `log_det_jacobian`.\n name: Name for scoping operations in the class.\n '
self.base = base
self.rev... | def __init__(self, base, reversible_layer, name=None):
'Constructs a transformed distribution.\n\n Args:\n base: Base distribution.\n reversible_layer: Callable with methods `reverse` and `log_det_jacobian`.\n name: Name for scoping operations in the class.\n '
self.base = base
self.rev... |
75aee68793e5e09eb594f9e355ea6df9d78ffc44349219a51088af2a78302462 | def trimesh_remesh(mesh, target, kmax=100, tol=0.1, divergence=0.01, verbose=False, allow_boundary_split=False, allow_boundary_swap=False, allow_boundary_collapse=False, smooth=True, fixed=None, callback=None, callback_args=None):
"Remesh until all edges have a specified target length.\n\n Parameters\n ------... | Remesh until all edges have a specified target length.
Parameters
----------
mesh : Mesh
A triangle mesh.
target : float
The target length for the mesh edges.
kmax : int, optional [100]
The number of iterations.
tol : float, optional [0.1]
Length deviation tolerance.
divergence : float, optional [0.01]... | src/compas/datastructures/mesh/remesh/remesh.py | trimesh_remesh | elidim/compas | 1 | python | def trimesh_remesh(mesh, target, kmax=100, tol=0.1, divergence=0.01, verbose=False, allow_boundary_split=False, allow_boundary_swap=False, allow_boundary_collapse=False, smooth=True, fixed=None, callback=None, callback_args=None):
"Remesh until all edges have a specified target length.\n\n Parameters\n ------... | def trimesh_remesh(mesh, target, kmax=100, tol=0.1, divergence=0.01, verbose=False, allow_boundary_split=False, allow_boundary_swap=False, allow_boundary_collapse=False, smooth=True, fixed=None, callback=None, callback_args=None):
"Remesh until all edges have a specified target length.\n\n Parameters\n ------... |
d6d0c6a1c80a0c2c287aa74a90c8232fdbdb7ee623817acc8636ef2ed9c2d61d | def test_context_stack(mocker):
'Check that the context stack is pushed and popped correctly.'
assert_that(midnite.get_device()).is_equal_to(torch.device('cpu'))
mock_device = mocker.Mock(spec=torch.device)
mocker.patch('torch.device', return_value=mock_device)
with midnite.device('cuda:0'):
... | Check that the context stack is pushed and popped correctly. | tests/unit/context_test.py | test_context_stack | christina-aigner/midnite | 0 | python | def test_context_stack(mocker):
assert_that(midnite.get_device()).is_equal_to(torch.device('cpu'))
mock_device = mocker.Mock(spec=torch.device)
mocker.patch('torch.device', return_value=mock_device)
with midnite.device('cuda:0'):
torch.device.assert_called_once_with('cuda:0')
torch.... | def test_context_stack(mocker):
assert_that(midnite.get_device()).is_equal_to(torch.device('cpu'))
mock_device = mocker.Mock(spec=torch.device)
mocker.patch('torch.device', return_value=mock_device)
with midnite.device('cuda:0'):
torch.device.assert_called_once_with('cuda:0')
torch.... |
69e7c3390a540568df2e8c5fef74f2a10234c5837f91e7b56b84f6a003edc780 | def test_context_fail_early():
'Checks that creating a context with invalid device immediately fails'
with pytest.raises(RuntimeError):
with midnite.device('does not exist'):
pass | Checks that creating a context with invalid device immediately fails | tests/unit/context_test.py | test_context_fail_early | christina-aigner/midnite | 0 | python | def test_context_fail_early():
with pytest.raises(RuntimeError):
with midnite.device('does not exist'):
pass | def test_context_fail_early():
with pytest.raises(RuntimeError):
with midnite.device('does not exist'):
pass<|docstring|>Checks that creating a context with invalid device immediately fails<|endoftext|> |
5d372b07e88c87eee33b62b65db39e782524fddcfac4dfe3f6c5f119f9979fad | def transform_landmarks(data, angle, scale, translation, center):
'\n Landmark transform\n '
(translation, center) = validate_transform_params(angle, scale, translation, center)
data_copy = data.copy()
angle = np.deg2rad((- angle))
rotation_matrix = np.array([[np.cos(angle), (- np.sin(angle))]... | Landmark transform | utils.py | transform_landmarks | kivancyuksel/cfpd | 4 | python | def transform_landmarks(data, angle, scale, translation, center):
'\n \n '
(translation, center) = validate_transform_params(angle, scale, translation, center)
data_copy = data.copy()
angle = np.deg2rad((- angle))
rotation_matrix = np.array([[np.cos(angle), (- np.sin(angle))], [np.sin(angle), ... | def transform_landmarks(data, angle, scale, translation, center):
'\n \n '
(translation, center) = validate_transform_params(angle, scale, translation, center)
data_copy = data.copy()
angle = np.deg2rad((- angle))
rotation_matrix = np.array([[np.cos(angle), (- np.sin(angle))], [np.sin(angle), ... |
29eb79f46050730e66fe496e87133a89ef7a8f21e1e45d0f072fbdedaaa0ad96 | def transform_affine(data, angle, scale, translation, center):
'\n 2D affine transformation\n '
(translation, center) = validate_transform_params(angle, scale, translation, center)
angle = np.deg2rad((angle + 90))
data_copy = data.copy()
translation_matrix = np.array([[1, 0, translation[0]], [... | 2D affine transformation | utils.py | transform_affine | kivancyuksel/cfpd | 4 | python | def transform_affine(data, angle, scale, translation, center):
'\n \n '
(translation, center) = validate_transform_params(angle, scale, translation, center)
angle = np.deg2rad((angle + 90))
data_copy = data.copy()
translation_matrix = np.array([[1, 0, translation[0]], [0, 1, translation[1]]])
... | def transform_affine(data, angle, scale, translation, center):
'\n \n '
(translation, center) = validate_transform_params(angle, scale, translation, center)
angle = np.deg2rad((angle + 90))
data_copy = data.copy()
translation_matrix = np.array([[1, 0, translation[0]], [0, 1, translation[1]]])
... |
2e60243310cdd495ce6d4417435e6f4487882677de6f1465012127b23ed434fa | def load_pts_file(path):
' Load .pts file'
landmarks = np.genfromtxt(path, skip_header=3, skip_footer=1)
return landmarks | Load .pts file | utils.py | load_pts_file | kivancyuksel/cfpd | 4 | python | def load_pts_file(path):
' '
landmarks = np.genfromtxt(path, skip_header=3, skip_footer=1)
return landmarks | def load_pts_file(path):
' '
landmarks = np.genfromtxt(path, skip_header=3, skip_footer=1)
return landmarks<|docstring|>Load .pts file<|endoftext|> |
81b61e3dfd29543d26f678c677e4fd744379f399821c02194f216d21946ee8c4 | def save_landmarks_as_pts_file(landmarks, path):
'Save landmark coordinates as .pts file'
landmarks_pts = 'version: 1\nn_points: 68\n{\n'
for pts in landmarks:
landmarks_pts += (((str(pts[0]) + ' ') + str(pts[1])) + '\n')
landmarks_pts += '}'
with open(path, 'w') as file_:
print(land... | Save landmark coordinates as .pts file | utils.py | save_landmarks_as_pts_file | kivancyuksel/cfpd | 4 | python | def save_landmarks_as_pts_file(landmarks, path):
landmarks_pts = 'version: 1\nn_points: 68\n{\n'
for pts in landmarks:
landmarks_pts += (((str(pts[0]) + ' ') + str(pts[1])) + '\n')
landmarks_pts += '}'
with open(path, 'w') as file_:
print(landmarks_pts, file=file_) | def save_landmarks_as_pts_file(landmarks, path):
landmarks_pts = 'version: 1\nn_points: 68\n{\n'
for pts in landmarks:
landmarks_pts += (((str(pts[0]) + ' ') + str(pts[1])) + '\n')
landmarks_pts += '}'
with open(path, 'w') as file_:
print(landmarks_pts, file=file_)<|docstring|>Save ... |
8f23b0e4a4455be0cb8d86a73839cfa7f6987245cf3239027b35e9885054069f | def mirror_landmarks(landmarks, img_shape):
'Mirror landmarks carefully'
landmarks_copy = landmarks.copy()
face_parts_to_indices = get_face_parts_to_indices()
indices = face_parts_to_indices
landmarks_copy[(:, 0)] = (img_shape[1] - landmarks_copy[(:, 0)])
left_eye_to_right_eye = np.concatenate((... | Mirror landmarks carefully | utils.py | mirror_landmarks | kivancyuksel/cfpd | 4 | python | def mirror_landmarks(landmarks, img_shape):
landmarks_copy = landmarks.copy()
face_parts_to_indices = get_face_parts_to_indices()
indices = face_parts_to_indices
landmarks_copy[(:, 0)] = (img_shape[1] - landmarks_copy[(:, 0)])
left_eye_to_right_eye = np.concatenate((indices['left_eye'], indices... | def mirror_landmarks(landmarks, img_shape):
landmarks_copy = landmarks.copy()
face_parts_to_indices = get_face_parts_to_indices()
indices = face_parts_to_indices
landmarks_copy[(:, 0)] = (img_shape[1] - landmarks_copy[(:, 0)])
left_eye_to_right_eye = np.concatenate((indices['left_eye'], indices... |
952d2c77faf771031264daa676b20fae0679c0f13f38a4a9d9bb8fd1a2b45a87 | def analyse(self):
'\n read from the messages queue, and generate:\n 1. Counter for From field\n 2. Counter for Time field (by hour)\n '
with concurrent.futures.ThreadPoolExecutor() as executor:
progress = Spinner(f'{helpers.loader_icn} Loading messages ')
event = Eve... | read from the messages queue, and generate:
1. Counter for From field
2. Counter for Time field (by hour) | src/metrics.py | analyse | 0xbsec/gmail_analyzer | 34 | python | def analyse(self):
'\n read from the messages queue, and generate:\n 1. Counter for From field\n 2. Counter for Time field (by hour)\n '
with concurrent.futures.ThreadPoolExecutor() as executor:
progress = Spinner(f'{helpers.loader_icn} Loading messages ')
event = Eve... | def analyse(self):
'\n read from the messages queue, and generate:\n 1. Counter for From field\n 2. Counter for Time field (by hour)\n '
with concurrent.futures.ThreadPoolExecutor() as executor:
progress = Spinner(f'{helpers.loader_icn} Loading messages ')
event = Eve... |
90f07e8732787b3a679974151c61fd63df37347a6826e75e33ac58ebfa8a7abf | def download_demo_dataset(data, workspace, load_demo_proc_dict):
"下载样例工程\n\n Args:\n data为dict, key包括\n 'prj_type' 样例类型(ProjectType)\n "
if isinstance(data['prj_type'], str):
prj_type = ProjectType(prj_type_list.index(data['prj_type']))
else:
prj_type = Pr... | 下载样例工程
Args:
data为dict, key包括
'prj_type' 样例类型(ProjectType) | paddlex_restful/restful/demo.py | download_demo_dataset | jercheng/PaddleX | 3,655 | python | def download_demo_dataset(data, workspace, load_demo_proc_dict):
"下载样例工程\n\n Args:\n data为dict, key包括\n 'prj_type' 样例类型(ProjectType)\n "
if isinstance(data['prj_type'], str):
prj_type = ProjectType(prj_type_list.index(data['prj_type']))
else:
prj_type = Pr... | def download_demo_dataset(data, workspace, load_demo_proc_dict):
"下载样例工程\n\n Args:\n data为dict, key包括\n 'prj_type' 样例类型(ProjectType)\n "
if isinstance(data['prj_type'], str):
prj_type = ProjectType(prj_type_list.index(data['prj_type']))
else:
prj_type = Pr... |
54589c0dacadedbb4375a5622953a8e7056a47956879f6baf64daa89c66a68b6 | def load_demo_project(data, workspace, monitored_processes, load_demo_proj_data_dict, load_demo_proc_dict):
"导入样例工程\n\n Args:\n data为dict, key包括\n 'prj_type' 样例类型(ProjectType)\n "
if isinstance(data['prj_type'], str):
prj_type = ProjectType(prj_type_list.index(data['prj_type']))
... | 导入样例工程
Args:
data为dict, key包括
'prj_type' 样例类型(ProjectType) | paddlex_restful/restful/demo.py | load_demo_project | jercheng/PaddleX | 3,655 | python | def load_demo_project(data, workspace, monitored_processes, load_demo_proj_data_dict, load_demo_proc_dict):
"导入样例工程\n\n Args:\n data为dict, key包括\n 'prj_type' 样例类型(ProjectType)\n "
if isinstance(data['prj_type'], str):
prj_type = ProjectType(prj_type_list.index(data['prj_type']))
... | def load_demo_project(data, workspace, monitored_processes, load_demo_proj_data_dict, load_demo_proc_dict):
"导入样例工程\n\n Args:\n data为dict, key包括\n 'prj_type' 样例类型(ProjectType)\n "
if isinstance(data['prj_type'], str):
prj_type = ProjectType(prj_type_list.index(data['prj_type']))
... |
e5adbc747a82481525df4d8d47c4f03da25b7b744983a54f490f8dea61072ad8 | def get_download_demo_progress(data, workspace):
"查询样例工程的下载进度\n\n Args:\n data为dict, key包括\n 'prj_type' 样例类型(ProjectType)\n "
if isinstance(data['prj_type'], str):
target_path = osp.join(workspace.path, 'demo_datasets', data['prj_type'])
else:
prj_type = ProjectType(data[... | 查询样例工程的下载进度
Args:
data为dict, key包括
'prj_type' 样例类型(ProjectType) | paddlex_restful/restful/demo.py | get_download_demo_progress | jercheng/PaddleX | 3,655 | python | def get_download_demo_progress(data, workspace):
"查询样例工程的下载进度\n\n Args:\n data为dict, key包括\n 'prj_type' 样例类型(ProjectType)\n "
if isinstance(data['prj_type'], str):
target_path = osp.join(workspace.path, 'demo_datasets', data['prj_type'])
else:
prj_type = ProjectType(data[... | def get_download_demo_progress(data, workspace):
"查询样例工程的下载进度\n\n Args:\n data为dict, key包括\n 'prj_type' 样例类型(ProjectType)\n "
if isinstance(data['prj_type'], str):
target_path = osp.join(workspace.path, 'demo_datasets', data['prj_type'])
else:
prj_type = ProjectType(data[... |
cb2f524905338f829322a1534f7d63c34e961366b258b2b0cf614a7952a23cfb | def stop_import_demo(data, workspace, load_demo_proc_dict, load_demo_proj_data_dict):
"停止样例工程的导入进度\n\n Args:\n request(comm.Request): 其中request.params为dict, key包括\n 'prj_type' 样例类型(ProjectType)\n "
if isinstance(data['prj_type'], str):
prj_type = ProjectType(prj_type_list.index(data[... | 停止样例工程的导入进度
Args:
request(comm.Request): 其中request.params为dict, key包括
'prj_type' 样例类型(ProjectType) | paddlex_restful/restful/demo.py | stop_import_demo | jercheng/PaddleX | 3,655 | python | def stop_import_demo(data, workspace, load_demo_proc_dict, load_demo_proj_data_dict):
"停止样例工程的导入进度\n\n Args:\n request(comm.Request): 其中request.params为dict, key包括\n 'prj_type' 样例类型(ProjectType)\n "
if isinstance(data['prj_type'], str):
prj_type = ProjectType(prj_type_list.index(data[... | def stop_import_demo(data, workspace, load_demo_proc_dict, load_demo_proj_data_dict):
"停止样例工程的导入进度\n\n Args:\n request(comm.Request): 其中request.params为dict, key包括\n 'prj_type' 样例类型(ProjectType)\n "
if isinstance(data['prj_type'], str):
prj_type = ProjectType(prj_type_list.index(data[... |
9f28db3a0c249a154f2c85cf86fda663b043bcf2b57a1d5bda5ee221a79dac3d | def create_app(config_name='dev'):
'\n Create App\n :param config_name:\n :return:\n '
app_api = app
load_config(app_api, config_name)
return app_api | Create App
:param config_name:
:return: | app/__init__.py | create_app | fabidick22/inject-sec-to-devops | 7 | python | def create_app(config_name='dev'):
'\n Create App\n :param config_name:\n :return:\n '
app_api = app
load_config(app_api, config_name)
return app_api | def create_app(config_name='dev'):
'\n Create App\n :param config_name:\n :return:\n '
app_api = app
load_config(app_api, config_name)
return app_api<|docstring|>Create App
:param config_name:
:return:<|endoftext|> |
faf48ada32a0f7abe036c600f2fee606424cbbb0b091fd22cb5a6f67200a9928 | def load_config(app_config: Flask, config_mode) -> None:
"\n Load the application's config\n :param (flask.app.Flask) app_config: The application instance Flask that'll be running\n :param config_mode: Config mode (Allowed: [dev, stg, prod])\n :return:\n "
if (config_mode in config_values):
... | Load the application's config
:param (flask.app.Flask) app_config: The application instance Flask that'll be running
:param config_mode: Config mode (Allowed: [dev, stg, prod])
:return: | app/__init__.py | load_config | fabidick22/inject-sec-to-devops | 7 | python | def load_config(app_config: Flask, config_mode) -> None:
"\n Load the application's config\n :param (flask.app.Flask) app_config: The application instance Flask that'll be running\n :param config_mode: Config mode (Allowed: [dev, stg, prod])\n :return:\n "
if (config_mode in config_values):
... | def load_config(app_config: Flask, config_mode) -> None:
"\n Load the application's config\n :param (flask.app.Flask) app_config: The application instance Flask that'll be running\n :param config_mode: Config mode (Allowed: [dev, stg, prod])\n :return:\n "
if (config_mode in config_values):
... |
f7d848efd90e2ce56b550e909b30b6b79226bcfabc5526758997122c1c6ecf89 | def __init__(__self__, *, address1: str, city: str, country: str, postal_code: str, state: str, address2: Optional[str]=None):
'\n Address information for domain registration.\n :param str address1: First line of an Address.\n :param str city: The city for the address.\n :param str count... | Address information for domain registration.
:param str address1: First line of an Address.
:param str city: The city for the address.
:param str country: The country for the address.
:param str postal_code: The postal code for the address.
:param str state: The state or province for the address.
:param str address2: T... | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | __init__ | pulumi-bot/pulumi-azure-native | 31 | python | def __init__(__self__, *, address1: str, city: str, country: str, postal_code: str, state: str, address2: Optional[str]=None):
'\n Address information for domain registration.\n :param str address1: First line of an Address.\n :param str city: The city for the address.\n :param str count... | def __init__(__self__, *, address1: str, city: str, country: str, postal_code: str, state: str, address2: Optional[str]=None):
'\n Address information for domain registration.\n :param str address1: First line of an Address.\n :param str city: The city for the address.\n :param str count... |
b80116b4739a876d32099389e95709b065650b4e6275233de11343a680c75ee3 | @property
@pulumi.getter
def address1(self) -> str:
'\n First line of an Address.\n '
return pulumi.get(self, 'address1') | First line of an Address. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | address1 | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter
def address1(self) -> str:
'\n \n '
return pulumi.get(self, 'address1') | @property
@pulumi.getter
def address1(self) -> str:
'\n \n '
return pulumi.get(self, 'address1')<|docstring|>First line of an Address.<|endoftext|> |
1303f015b8f3c70b6c3fda899c2b01880e5d02281e7be9848812e6f535142ac0 | @property
@pulumi.getter
def city(self) -> str:
'\n The city for the address.\n '
return pulumi.get(self, 'city') | The city for the address. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | city | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter
def city(self) -> str:
'\n \n '
return pulumi.get(self, 'city') | @property
@pulumi.getter
def city(self) -> str:
'\n \n '
return pulumi.get(self, 'city')<|docstring|>The city for the address.<|endoftext|> |
12f5d317a925dafc36f774cdfa8b9ae45a3951dcf4b23f7c13aaf54e951ba613 | @property
@pulumi.getter
def country(self) -> str:
'\n The country for the address.\n '
return pulumi.get(self, 'country') | The country for the address. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | country | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter
def country(self) -> str:
'\n \n '
return pulumi.get(self, 'country') | @property
@pulumi.getter
def country(self) -> str:
'\n \n '
return pulumi.get(self, 'country')<|docstring|>The country for the address.<|endoftext|> |
ce7a3b066afe70a1507d956ac742d08c7f8cc7a887f4b7e05b6cba0cf69b0acc | @property
@pulumi.getter(name='postalCode')
def postal_code(self) -> str:
'\n The postal code for the address.\n '
return pulumi.get(self, 'postal_code') | The postal code for the address. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | postal_code | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='postalCode')
def postal_code(self) -> str:
'\n \n '
return pulumi.get(self, 'postal_code') | @property
@pulumi.getter(name='postalCode')
def postal_code(self) -> str:
'\n \n '
return pulumi.get(self, 'postal_code')<|docstring|>The postal code for the address.<|endoftext|> |
fcfbc0a95c561e37d1bcc7a3b6f9f48183ad5e5c993da726c2928a6b266e99b6 | @property
@pulumi.getter
def state(self) -> str:
'\n The state or province for the address.\n '
return pulumi.get(self, 'state') | The state or province for the address. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | state | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter
def state(self) -> str:
'\n \n '
return pulumi.get(self, 'state') | @property
@pulumi.getter
def state(self) -> str:
'\n \n '
return pulumi.get(self, 'state')<|docstring|>The state or province for the address.<|endoftext|> |
f7e1a697f9c8753d6df7425d3bb85f22e7b2e377187453802d081e5b78b0f0ca | @property
@pulumi.getter
def address2(self) -> Optional[str]:
'\n The second line of the Address. Optional.\n '
return pulumi.get(self, 'address2') | The second line of the Address. Optional. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | address2 | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter
def address2(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'address2') | @property
@pulumi.getter
def address2(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'address2')<|docstring|>The second line of the Address. Optional.<|endoftext|> |
b3ffc38b49609ce02f5dfb8ab76c6ca74f118a8de9db1c2ef9209880988f3991 | def __init__(__self__, *, email: str, name_first: str, name_last: str, phone: str, address_mailing: Optional['outputs.AddressResponse']=None, fax: Optional[str]=None, job_title: Optional[str]=None, name_middle: Optional[str]=None, organization: Optional[str]=None):
"\n Contact information for domain registra... | Contact information for domain registration. If 'Domain Privacy' option is not selected then the contact information is made publicly available through the Whois
directories as per ICANN requirements.
:param str email: Email address.
:param str name_first: First name.
:param str name_last: Last name.
:param str phone:... | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | __init__ | pulumi-bot/pulumi-azure-native | 31 | python | def __init__(__self__, *, email: str, name_first: str, name_last: str, phone: str, address_mailing: Optional['outputs.AddressResponse']=None, fax: Optional[str]=None, job_title: Optional[str]=None, name_middle: Optional[str]=None, organization: Optional[str]=None):
"\n Contact information for domain registra... | def __init__(__self__, *, email: str, name_first: str, name_last: str, phone: str, address_mailing: Optional['outputs.AddressResponse']=None, fax: Optional[str]=None, job_title: Optional[str]=None, name_middle: Optional[str]=None, organization: Optional[str]=None):
"\n Contact information for domain registra... |
286de140f732e30fb694fc1697de4a72a30b6561e3c3fd7c163aa83d74db3d61 | @property
@pulumi.getter
def email(self) -> str:
'\n Email address.\n '
return pulumi.get(self, 'email') | Email address. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | email | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter
def email(self) -> str:
'\n \n '
return pulumi.get(self, 'email') | @property
@pulumi.getter
def email(self) -> str:
'\n \n '
return pulumi.get(self, 'email')<|docstring|>Email address.<|endoftext|> |
bb7ab498741aaabfbe8f7b57f05abf51dc2d843098748bb11d0c6e3b207cc268 | @property
@pulumi.getter(name='nameFirst')
def name_first(self) -> str:
'\n First name.\n '
return pulumi.get(self, 'name_first') | First name. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | name_first | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='nameFirst')
def name_first(self) -> str:
'\n \n '
return pulumi.get(self, 'name_first') | @property
@pulumi.getter(name='nameFirst')
def name_first(self) -> str:
'\n \n '
return pulumi.get(self, 'name_first')<|docstring|>First name.<|endoftext|> |
b418b8f5eb3b43aee2611939ece416523b9fa445acba3262e35256d8385e6620 | @property
@pulumi.getter(name='nameLast')
def name_last(self) -> str:
'\n Last name.\n '
return pulumi.get(self, 'name_last') | Last name. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | name_last | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='nameLast')
def name_last(self) -> str:
'\n \n '
return pulumi.get(self, 'name_last') | @property
@pulumi.getter(name='nameLast')
def name_last(self) -> str:
'\n \n '
return pulumi.get(self, 'name_last')<|docstring|>Last name.<|endoftext|> |
9a3d92516cbdbc1272dd4e5ac8ed0935f71e35b69f06a2bc39de9b7ab4082629 | @property
@pulumi.getter
def phone(self) -> str:
'\n Phone number.\n '
return pulumi.get(self, 'phone') | Phone number. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | phone | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter
def phone(self) -> str:
'\n \n '
return pulumi.get(self, 'phone') | @property
@pulumi.getter
def phone(self) -> str:
'\n \n '
return pulumi.get(self, 'phone')<|docstring|>Phone number.<|endoftext|> |
9d43e257dc6ec5aefd5dab405704edfa749829e9c51c63b96920e81b40cd9501 | @property
@pulumi.getter(name='addressMailing')
def address_mailing(self) -> Optional['outputs.AddressResponse']:
'\n Mailing address.\n '
return pulumi.get(self, 'address_mailing') | Mailing address. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | address_mailing | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='addressMailing')
def address_mailing(self) -> Optional['outputs.AddressResponse']:
'\n \n '
return pulumi.get(self, 'address_mailing') | @property
@pulumi.getter(name='addressMailing')
def address_mailing(self) -> Optional['outputs.AddressResponse']:
'\n \n '
return pulumi.get(self, 'address_mailing')<|docstring|>Mailing address.<|endoftext|> |
9bc97d5b619938854775fb2c4d0fcde7736b62f69560779b593b050f399f0988 | @property
@pulumi.getter
def fax(self) -> Optional[str]:
'\n Fax number.\n '
return pulumi.get(self, 'fax') | Fax number. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | fax | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter
def fax(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'fax') | @property
@pulumi.getter
def fax(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'fax')<|docstring|>Fax number.<|endoftext|> |
c399bb346e5d4bb7c7940273bbca1daec7f820980f44d1c3ca227774b63ba24b | @property
@pulumi.getter(name='jobTitle')
def job_title(self) -> Optional[str]:
'\n Job title.\n '
return pulumi.get(self, 'job_title') | Job title. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | job_title | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='jobTitle')
def job_title(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'job_title') | @property
@pulumi.getter(name='jobTitle')
def job_title(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'job_title')<|docstring|>Job title.<|endoftext|> |
2e622f4d784f2cd48d45b416e43ddba9b588a6bf4992d94ea64374be7bb6ab9b | @property
@pulumi.getter(name='nameMiddle')
def name_middle(self) -> Optional[str]:
'\n Middle name.\n '
return pulumi.get(self, 'name_middle') | Middle name. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | name_middle | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='nameMiddle')
def name_middle(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'name_middle') | @property
@pulumi.getter(name='nameMiddle')
def name_middle(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'name_middle')<|docstring|>Middle name.<|endoftext|> |
71a46bfe11a50b2e85811a020500f20e21ea02ca4a9aef1fd03e85e8eaa90829 | @property
@pulumi.getter
def organization(self) -> Optional[str]:
'\n Organization contact belongs to.\n '
return pulumi.get(self, 'organization') | Organization contact belongs to. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | organization | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter
def organization(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'organization') | @property
@pulumi.getter
def organization(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'organization')<|docstring|>Organization contact belongs to.<|endoftext|> |
45dcf6bbbc214b23a7583eb7a3cd01f6f11545fd52d4deb98a78719aad3eeabd | def __init__(__self__, *, agreed_at: Optional[str]=None, agreed_by: Optional[str]=None, agreement_keys: Optional[Sequence[str]]=None):
'\n Domain purchase consent object, representing acceptance of applicable legal agreements.\n :param str agreed_at: Timestamp when the agreements were accepted.\n ... | Domain purchase consent object, representing acceptance of applicable legal agreements.
:param str agreed_at: Timestamp when the agreements were accepted.
:param str agreed_by: Client IP address.
:param Sequence[str] agreement_keys: List of applicable legal agreement keys. This list can be retrieved using ListLegalAgre... | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | __init__ | pulumi-bot/pulumi-azure-native | 31 | python | def __init__(__self__, *, agreed_at: Optional[str]=None, agreed_by: Optional[str]=None, agreement_keys: Optional[Sequence[str]]=None):
'\n Domain purchase consent object, representing acceptance of applicable legal agreements.\n :param str agreed_at: Timestamp when the agreements were accepted.\n ... | def __init__(__self__, *, agreed_at: Optional[str]=None, agreed_by: Optional[str]=None, agreement_keys: Optional[Sequence[str]]=None):
'\n Domain purchase consent object, representing acceptance of applicable legal agreements.\n :param str agreed_at: Timestamp when the agreements were accepted.\n ... |
9ce456cdf8e7c38c704f532cc41383f96cb0a10e509275bb4beddb8317b94a55 | @property
@pulumi.getter(name='agreedAt')
def agreed_at(self) -> Optional[str]:
'\n Timestamp when the agreements were accepted.\n '
return pulumi.get(self, 'agreed_at') | Timestamp when the agreements were accepted. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | agreed_at | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='agreedAt')
def agreed_at(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'agreed_at') | @property
@pulumi.getter(name='agreedAt')
def agreed_at(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'agreed_at')<|docstring|>Timestamp when the agreements were accepted.<|endoftext|> |
9bca656aa5c55bc93c273d53c7d2a9e6813121f2749625ec9a54547edeb5016a | @property
@pulumi.getter(name='agreedBy')
def agreed_by(self) -> Optional[str]:
'\n Client IP address.\n '
return pulumi.get(self, 'agreed_by') | Client IP address. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | agreed_by | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='agreedBy')
def agreed_by(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'agreed_by') | @property
@pulumi.getter(name='agreedBy')
def agreed_by(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'agreed_by')<|docstring|>Client IP address.<|endoftext|> |
67d49115887c5cafb0cfbb13f422ad2893d886d2893f26cf55d62836f099d65f | @property
@pulumi.getter(name='agreementKeys')
def agreement_keys(self) -> Optional[Sequence[str]]:
'\n List of applicable legal agreement keys. This list can be retrieved using ListLegalAgreements API under <code>TopLevelDomain</code> resource.\n '
return pulumi.get(self, 'agreement_keys') | List of applicable legal agreement keys. This list can be retrieved using ListLegalAgreements API under <code>TopLevelDomain</code> resource. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | agreement_keys | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='agreementKeys')
def agreement_keys(self) -> Optional[Sequence[str]]:
'\n \n '
return pulumi.get(self, 'agreement_keys') | @property
@pulumi.getter(name='agreementKeys')
def agreement_keys(self) -> Optional[Sequence[str]]:
'\n \n '
return pulumi.get(self, 'agreement_keys')<|docstring|>List of applicable legal agreement keys. This list can be retrieved using ListLegalAgreements API under <code>TopLevelDomain</code> res... |
7527b8c2f1de0f285a550e3990a0d49e4188a8f64340514adfca8f69ccf0a90a | def __init__(__self__, *, azure_resource_name: Optional[str]=None, azure_resource_type: Optional[str]=None, custom_host_name_dns_record_type: Optional[str]=None, host_name_type: Optional[str]=None, name: Optional[str]=None, site_names: Optional[Sequence[str]]=None):
'\n Details of a hostname derived from a d... | Details of a hostname derived from a domain.
:param str azure_resource_name: Name of the Azure resource the hostname is assigned to. If it is assigned to a Traffic Manager then it will be the Traffic Manager name otherwise it will be the app name.
:param str azure_resource_type: Type of the Azure resource the hostname ... | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | __init__ | pulumi-bot/pulumi-azure-native | 31 | python | def __init__(__self__, *, azure_resource_name: Optional[str]=None, azure_resource_type: Optional[str]=None, custom_host_name_dns_record_type: Optional[str]=None, host_name_type: Optional[str]=None, name: Optional[str]=None, site_names: Optional[Sequence[str]]=None):
'\n Details of a hostname derived from a d... | def __init__(__self__, *, azure_resource_name: Optional[str]=None, azure_resource_type: Optional[str]=None, custom_host_name_dns_record_type: Optional[str]=None, host_name_type: Optional[str]=None, name: Optional[str]=None, site_names: Optional[Sequence[str]]=None):
'\n Details of a hostname derived from a d... |
f7ec255e558c6be26c1bcb749026787bc7dbd0f4f5b1ab8e8ba9258e522933b9 | @property
@pulumi.getter(name='azureResourceName')
def azure_resource_name(self) -> Optional[str]:
'\n Name of the Azure resource the hostname is assigned to. If it is assigned to a Traffic Manager then it will be the Traffic Manager name otherwise it will be the app name.\n '
return pulumi.get(se... | Name of the Azure resource the hostname is assigned to. If it is assigned to a Traffic Manager then it will be the Traffic Manager name otherwise it will be the app name. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | azure_resource_name | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='azureResourceName')
def azure_resource_name(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'azure_resource_name') | @property
@pulumi.getter(name='azureResourceName')
def azure_resource_name(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'azure_resource_name')<|docstring|>Name of the Azure resource the hostname is assigned to. If it is assigned to a Traffic Manager then it will be the Traffic Manager ... |
757552508f740d833e14746ea50430829aeb0e723a1d06853619eab5b2612d25 | @property
@pulumi.getter(name='azureResourceType')
def azure_resource_type(self) -> Optional[str]:
'\n Type of the Azure resource the hostname is assigned to.\n '
return pulumi.get(self, 'azure_resource_type') | Type of the Azure resource the hostname is assigned to. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | azure_resource_type | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='azureResourceType')
def azure_resource_type(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'azure_resource_type') | @property
@pulumi.getter(name='azureResourceType')
def azure_resource_type(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'azure_resource_type')<|docstring|>Type of the Azure resource the hostname is assigned to.<|endoftext|> |
d205244e6d2eafac0423d16f5f1faaf1c7ae5c59207273a22edcef8fa47c6aaa | @property
@pulumi.getter(name='customHostNameDnsRecordType')
def custom_host_name_dns_record_type(self) -> Optional[str]:
'\n Type of the DNS record.\n '
return pulumi.get(self, 'custom_host_name_dns_record_type') | Type of the DNS record. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | custom_host_name_dns_record_type | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='customHostNameDnsRecordType')
def custom_host_name_dns_record_type(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'custom_host_name_dns_record_type') | @property
@pulumi.getter(name='customHostNameDnsRecordType')
def custom_host_name_dns_record_type(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'custom_host_name_dns_record_type')<|docstring|>Type of the DNS record.<|endoftext|> |
2b9c684b8faa0d6c40aefd938209aabb1b7b419384cdbe9e7c7243aed244a643 | @property
@pulumi.getter(name='hostNameType')
def host_name_type(self) -> Optional[str]:
'\n Type of the hostname.\n '
return pulumi.get(self, 'host_name_type') | Type of the hostname. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | host_name_type | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='hostNameType')
def host_name_type(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'host_name_type') | @property
@pulumi.getter(name='hostNameType')
def host_name_type(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'host_name_type')<|docstring|>Type of the hostname.<|endoftext|> |
9ef39563671c8063dc2b19aaa4d32a036637c89abfa06a09535f2e020c3640dc | @property
@pulumi.getter
def name(self) -> Optional[str]:
'\n Name of the hostname.\n '
return pulumi.get(self, 'name') | Name of the hostname. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | name | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter
def name(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'name') | @property
@pulumi.getter
def name(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'name')<|docstring|>Name of the hostname.<|endoftext|> |
8d907c17651110a450968518f1e4c0a9954d3b7ecc139df8d8080ec540253e19 | @property
@pulumi.getter(name='siteNames')
def site_names(self) -> Optional[Sequence[str]]:
'\n List of apps the hostname is assigned to. This list will have more than one app only if the hostname is pointing to a Traffic Manager.\n '
return pulumi.get(self, 'site_names') | List of apps the hostname is assigned to. This list will have more than one app only if the hostname is pointing to a Traffic Manager. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | site_names | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='siteNames')
def site_names(self) -> Optional[Sequence[str]]:
'\n \n '
return pulumi.get(self, 'site_names') | @property
@pulumi.getter(name='siteNames')
def site_names(self) -> Optional[Sequence[str]]:
'\n \n '
return pulumi.get(self, 'site_names')<|docstring|>List of apps the hostname is assigned to. This list will have more than one app only if the hostname is pointing to a Traffic Manager.<|endoftext|> |
aa70371be71a464e4f2205ed5c7317a360c85b458735923a328a21d6e7b8a224 | def __init__(__self__, *, name: Optional[str]=None):
'\n Identifies an object.\n :param str name: Name of the object.\n '
if (name is not None):
pulumi.set(__self__, 'name', name) | Identifies an object.
:param str name: Name of the object. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | __init__ | pulumi-bot/pulumi-azure-native | 31 | python | def __init__(__self__, *, name: Optional[str]=None):
'\n Identifies an object.\n :param str name: Name of the object.\n '
if (name is not None):
pulumi.set(__self__, 'name', name) | def __init__(__self__, *, name: Optional[str]=None):
'\n Identifies an object.\n :param str name: Name of the object.\n '
if (name is not None):
pulumi.set(__self__, 'name', name)<|docstring|>Identifies an object.
:param str name: Name of the object.<|endoftext|> |
82b802a8aeeee52fff6f8b378547364efa8e09ef5856925a9b12582e65ffd241 | @property
@pulumi.getter
def name(self) -> Optional[str]:
'\n Name of the object.\n '
return pulumi.get(self, 'name') | Name of the object. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | name | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter
def name(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'name') | @property
@pulumi.getter
def name(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'name')<|docstring|>Name of the object.<|endoftext|> |
94a47dd884fe9dbfb062ed4959ef31b1a7e1fc0220e8a27d9efd95f6d6356469 | def __init__(__self__, *, created_at: Optional[str]=None, created_by: Optional[str]=None, created_by_type: Optional[str]=None, last_modified_at: Optional[str]=None, last_modified_by: Optional[str]=None, last_modified_by_type: Optional[str]=None):
'\n Metadata pertaining to creation and last modification of t... | Metadata pertaining to creation and last modification of the resource.
:param str created_at: The timestamp of resource creation (UTC).
:param str created_by: The identity that created the resource.
:param str created_by_type: The type of identity that created the resource.
:param str last_modified_at: The timestamp of... | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | __init__ | pulumi-bot/pulumi-azure-native | 31 | python | def __init__(__self__, *, created_at: Optional[str]=None, created_by: Optional[str]=None, created_by_type: Optional[str]=None, last_modified_at: Optional[str]=None, last_modified_by: Optional[str]=None, last_modified_by_type: Optional[str]=None):
'\n Metadata pertaining to creation and last modification of t... | def __init__(__self__, *, created_at: Optional[str]=None, created_by: Optional[str]=None, created_by_type: Optional[str]=None, last_modified_at: Optional[str]=None, last_modified_by: Optional[str]=None, last_modified_by_type: Optional[str]=None):
'\n Metadata pertaining to creation and last modification of t... |
e5327ee2800d99a9cbbe7bde47291f1c647496707002eb4902d7c8ff27b968b9 | @property
@pulumi.getter(name='createdAt')
def created_at(self) -> Optional[str]:
'\n The timestamp of resource creation (UTC).\n '
return pulumi.get(self, 'created_at') | The timestamp of resource creation (UTC). | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | created_at | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='createdAt')
def created_at(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'created_at') | @property
@pulumi.getter(name='createdAt')
def created_at(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'created_at')<|docstring|>The timestamp of resource creation (UTC).<|endoftext|> |
527e076fe3b36844fa1ee613514acf4c691ec5c00052af683a0b926eb513d2c2 | @property
@pulumi.getter(name='createdBy')
def created_by(self) -> Optional[str]:
'\n The identity that created the resource.\n '
return pulumi.get(self, 'created_by') | The identity that created the resource. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | created_by | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='createdBy')
def created_by(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'created_by') | @property
@pulumi.getter(name='createdBy')
def created_by(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'created_by')<|docstring|>The identity that created the resource.<|endoftext|> |
0f880a1086365a1d74c0afc8c7b9e2470f33a14563baf2cc83163212ca968133 | @property
@pulumi.getter(name='createdByType')
def created_by_type(self) -> Optional[str]:
'\n The type of identity that created the resource.\n '
return pulumi.get(self, 'created_by_type') | The type of identity that created the resource. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | created_by_type | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='createdByType')
def created_by_type(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'created_by_type') | @property
@pulumi.getter(name='createdByType')
def created_by_type(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'created_by_type')<|docstring|>The type of identity that created the resource.<|endoftext|> |
2cf8fd52c412a625c6606d643fe4b2b32be7b731ea5a1595989d697d9752f472 | @property
@pulumi.getter(name='lastModifiedAt')
def last_modified_at(self) -> Optional[str]:
'\n The timestamp of resource last modification (UTC)\n '
return pulumi.get(self, 'last_modified_at') | The timestamp of resource last modification (UTC) | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | last_modified_at | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='lastModifiedAt')
def last_modified_at(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'last_modified_at') | @property
@pulumi.getter(name='lastModifiedAt')
def last_modified_at(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'last_modified_at')<|docstring|>The timestamp of resource last modification (UTC)<|endoftext|> |
c4b983166ec5bdf701fb701b8072f42e1a255e45f32c6bff555384b2622f549c | @property
@pulumi.getter(name='lastModifiedBy')
def last_modified_by(self) -> Optional[str]:
'\n The identity that last modified the resource.\n '
return pulumi.get(self, 'last_modified_by') | The identity that last modified the resource. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | last_modified_by | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='lastModifiedBy')
def last_modified_by(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'last_modified_by') | @property
@pulumi.getter(name='lastModifiedBy')
def last_modified_by(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'last_modified_by')<|docstring|>The identity that last modified the resource.<|endoftext|> |
d45bdc60142433bffd319e8c803988017a9bab94338ab594bcfe68246757766f | @property
@pulumi.getter(name='lastModifiedByType')
def last_modified_by_type(self) -> Optional[str]:
'\n The type of identity that last modified the resource.\n '
return pulumi.get(self, 'last_modified_by_type') | The type of identity that last modified the resource. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | last_modified_by_type | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='lastModifiedByType')
def last_modified_by_type(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'last_modified_by_type') | @property
@pulumi.getter(name='lastModifiedByType')
def last_modified_by_type(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'last_modified_by_type')<|docstring|>The type of identity that last modified the resource.<|endoftext|> |
ad16cbc0a4a852cb81e5583f34d2df44ad45c2d3091f07726ceff936c25d4674 | def __init__(__self__, *, agreement_key: str, content: str, title: str, url: Optional[str]=None):
'\n Legal agreement for a top level domain.\n :param str agreement_key: Unique identifier for the agreement.\n :param str content: Agreement details.\n :param str title: Agreement title.\n ... | Legal agreement for a top level domain.
:param str agreement_key: Unique identifier for the agreement.
:param str content: Agreement details.
:param str title: Agreement title.
:param str url: URL where a copy of the agreement details is hosted. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | __init__ | pulumi-bot/pulumi-azure-native | 31 | python | def __init__(__self__, *, agreement_key: str, content: str, title: str, url: Optional[str]=None):
'\n Legal agreement for a top level domain.\n :param str agreement_key: Unique identifier for the agreement.\n :param str content: Agreement details.\n :param str title: Agreement title.\n ... | def __init__(__self__, *, agreement_key: str, content: str, title: str, url: Optional[str]=None):
'\n Legal agreement for a top level domain.\n :param str agreement_key: Unique identifier for the agreement.\n :param str content: Agreement details.\n :param str title: Agreement title.\n ... |
cece0778b86da345e6e0db95b65af2806e001e3956d7b04afcd92beaa70e57a7 | @property
@pulumi.getter(name='agreementKey')
def agreement_key(self) -> str:
'\n Unique identifier for the agreement.\n '
return pulumi.get(self, 'agreement_key') | Unique identifier for the agreement. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | agreement_key | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter(name='agreementKey')
def agreement_key(self) -> str:
'\n \n '
return pulumi.get(self, 'agreement_key') | @property
@pulumi.getter(name='agreementKey')
def agreement_key(self) -> str:
'\n \n '
return pulumi.get(self, 'agreement_key')<|docstring|>Unique identifier for the agreement.<|endoftext|> |
3ccb740d090820f5e69af44f44a66a00daf81dfd7be1e023dfb44f5d115f08d1 | @property
@pulumi.getter
def content(self) -> str:
'\n Agreement details.\n '
return pulumi.get(self, 'content') | Agreement details. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | content | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter
def content(self) -> str:
'\n \n '
return pulumi.get(self, 'content') | @property
@pulumi.getter
def content(self) -> str:
'\n \n '
return pulumi.get(self, 'content')<|docstring|>Agreement details.<|endoftext|> |
d9a09d324862d3d8c7b9b8dfe87d6b5b594ba12b3f40641e1682ab51b5881d88 | @property
@pulumi.getter
def title(self) -> str:
'\n Agreement title.\n '
return pulumi.get(self, 'title') | Agreement title. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | title | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter
def title(self) -> str:
'\n \n '
return pulumi.get(self, 'title') | @property
@pulumi.getter
def title(self) -> str:
'\n \n '
return pulumi.get(self, 'title')<|docstring|>Agreement title.<|endoftext|> |
89bdc2edacb472071f40826b7ac888c9c490d47cfb472ba1a67e9193f01bc064 | @property
@pulumi.getter
def url(self) -> Optional[str]:
'\n URL where a copy of the agreement details is hosted.\n '
return pulumi.get(self, 'url') | URL where a copy of the agreement details is hosted. | sdk/python/pulumi_azure_native/domainregistration/v20201001/outputs.py | url | pulumi-bot/pulumi-azure-native | 31 | python | @property
@pulumi.getter
def url(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'url') | @property
@pulumi.getter
def url(self) -> Optional[str]:
'\n \n '
return pulumi.get(self, 'url')<|docstring|>URL where a copy of the agreement details is hosted.<|endoftext|> |
d40b826db8d91d5f881a1368b97abf7af9603ca9ff483f706cb06a643dd17d70 | def exceedingCapacityRemoval(candidate, n, data):
'\n Given a solution(candidate) and a nurse\n this function removes any assignment w[n][h] whenever\n capacity is exceeded at some hour h and\n the resulting schedule is valid\n '
for h in range(data['hours']):
if ((candida... | Given a solution(candidate) and a nurse
this function removes any assignment w[n][h] whenever
capacity is exceeded at some hour h and
the resulting schedule is valid | Metaheuristics/GRASP/LocalSearch2.py | exceedingCapacityRemoval | presmerats/Nurse-Scheduling-LP-and-Heuristics | 1 | python | def exceedingCapacityRemoval(candidate, n, data):
'\n Given a solution(candidate) and a nurse\n this function removes any assignment w[n][h] whenever\n capacity is exceeded at some hour h and\n the resulting schedule is valid\n '
for h in range(data['hours']):
if ((candida... | def exceedingCapacityRemoval(candidate, n, data):
'\n Given a solution(candidate) and a nurse\n this function removes any assignment w[n][h] whenever\n capacity is exceeded at some hour h and\n the resulting schedule is valid\n '
for h in range(data['hours']):
if ((candida... |
6aa72af8ad656109443b4c134f21906eee202d7bfc8c4c5aa0cc60c516fbe80d | def buildNewSol(neighbor, data):
'\n given a recently build solution (neighbor)\n this function computes:\n the cost,\n the exceeding capacity,\n the pending assignments\n the total assigned hours\n '
totalw = 0
sumz = 0
columnarSum = [((- 1) ... | given a recently build solution (neighbor)
this function computes:
the cost,
the exceeding capacity,
the pending assignments
the total assigned hours | Metaheuristics/GRASP/LocalSearch2.py | buildNewSol | presmerats/Nurse-Scheduling-LP-and-Heuristics | 1 | python | def buildNewSol(neighbor, data):
'\n given a recently build solution (neighbor)\n this function computes:\n the cost,\n the exceeding capacity,\n the pending assignments\n the total assigned hours\n '
totalw = 0
sumz = 0
columnarSum = [((- 1) ... | def buildNewSol(neighbor, data):
'\n given a recently build solution (neighbor)\n this function computes:\n the cost,\n the exceeding capacity,\n the pending assignments\n the total assigned hours\n '
totalw = 0
sumz = 0
columnarSum = [((- 1) ... |
f100e0766aa601f899dbf1796910b60051ec638837cec95dfac4bd92a2a5d8f8 | def findCandidate(solution, data, n):
' \n this function returns a new solution that include:\n - 0 hours assigned to nurse n\n - all the hours that where assigned to nurse n\n are assigned to other nurses\n - the solution is valid (constraints)\n '
s = c... | this function returns a new solution that include:
- 0 hours assigned to nurse n
- all the hours that where assigned to nurse n
are assigned to other nurses
- the solution is valid (constraints) | Metaheuristics/GRASP/LocalSearch2.py | findCandidate | presmerats/Nurse-Scheduling-LP-and-Heuristics | 1 | python | def findCandidate(solution, data, n):
' \n this function returns a new solution that include:\n - 0 hours assigned to nurse n\n - all the hours that where assigned to nurse n\n are assigned to other nurses\n - the solution is valid (constraints)\n '
s = c... | def findCandidate(solution, data, n):
' \n this function returns a new solution that include:\n - 0 hours assigned to nurse n\n - all the hours that where assigned to nurse n\n are assigned to other nurses\n - the solution is valid (constraints)\n '
s = c... |
fffc7cab6d4352071ccacfc9e65fd34e787da132051ffcd99072307c7741c22a | def nursesAtHourH(solution, h):
'\n this function computes the total nurse assignments\n at hour h\n '
sumN = 0
for w in solution['w']:
sumN += w[h]
return sumN | this function computes the total nurse assignments
at hour h | Metaheuristics/GRASP/LocalSearch2.py | nursesAtHourH | presmerats/Nurse-Scheduling-LP-and-Heuristics | 1 | python | def nursesAtHourH(solution, h):
'\n this function computes the total nurse assignments\n at hour h\n '
sumN = 0
for w in solution['w']:
sumN += w[h]
return sumN | def nursesAtHourH(solution, h):
'\n this function computes the total nurse assignments\n at hour h\n '
sumN = 0
for w in solution['w']:
sumN += w[h]
return sumN<|docstring|>this function computes the total nurse assignments
at hour h<|endoftext|> |
1672a98df8a1652354b976c7956b70f05e8314e7c2e2c0b9ed624dfbfcff0911 | def exceedingNurseHours(solution, data):
'\n this function computes and stores the\n exceeding capacity for each hour\n\n '
solution['exceeding'] = ([0] * len(solution['w'][0]))
for h in range(data['hours']):
solution['exceeding'][h] = (nursesAtHourH(solution, h) - data['demand'][h])
if... | this function computes and stores the
exceeding capacity for each hour | Metaheuristics/GRASP/LocalSearch2.py | exceedingNurseHours | presmerats/Nurse-Scheduling-LP-and-Heuristics | 1 | python | def exceedingNurseHours(solution, data):
'\n this function computes and stores the\n exceeding capacity for each hour\n\n '
solution['exceeding'] = ([0] * len(solution['w'][0]))
for h in range(data['hours']):
solution['exceeding'][h] = (nursesAtHourH(solution, h) - data['demand'][h])
if... | def exceedingNurseHours(solution, data):
'\n this function computes and stores the\n exceeding capacity for each hour\n\n '
solution['exceeding'] = ([0] * len(solution['w'][0]))
for h in range(data['hours']):
solution['exceeding'][h] = (nursesAtHourH(solution, h) - data['demand'][h])
if... |
1436dd53ce77947f890b37856df543ccd3270d71fb9e53dbc8485c35ec2cd777 | def run(script_args: List) -> int:
'Run the actual script.'
from sqlalchemy import create_engine
from sqlalchemy import func
from sqlalchemy.orm import sessionmaker
from influxdb import InfluxDBClient
from homeassistant.components.recorder import models
from homeassistant.helpers import stat... | Run the actual script. | homeassistant/scripts/influxdb_import.py | run | spacesuitdiver/home-assistant | 37 | python | def run(script_args: List) -> int:
from sqlalchemy import create_engine
from sqlalchemy import func
from sqlalchemy.orm import sessionmaker
from influxdb import InfluxDBClient
from homeassistant.components.recorder import models
from homeassistant.helpers import state as state_helper
fr... | def run(script_args: List) -> int:
from sqlalchemy import create_engine
from sqlalchemy import func
from sqlalchemy.orm import sessionmaker
from influxdb import InfluxDBClient
from homeassistant.components.recorder import models
from homeassistant.helpers import state as state_helper
fr... |
b028f4a31004762096e7abc225477afefcf4ec6dcab0a8925b5cc4303cd9f8c8 | def print_progress(iteration: int, total: int, prefix: str='', suffix: str='', decimals: int=2, bar_length: int=68) -> None:
'Print progress bar.\n\n Call in a loop to create terminal progress bar\n @params:\n iteration - Required : current iteration (Int)\n total - Required : total it... | Print progress bar.
Call in a loop to create terminal progress bar
@params:
iteration - Required : current iteration (Int)
total - Required : total iterations (Int)
prefix - Optional : prefix string (Str)
suffix - Optional : suffix string (Str)
decimals - Optional : number... | homeassistant/scripts/influxdb_import.py | print_progress | spacesuitdiver/home-assistant | 37 | python | def print_progress(iteration: int, total: int, prefix: str=, suffix: str=, decimals: int=2, bar_length: int=68) -> None:
'Print progress bar.\n\n Call in a loop to create terminal progress bar\n @params:\n iteration - Required : current iteration (Int)\n total - Required : total iterat... | def print_progress(iteration: int, total: int, prefix: str=, suffix: str=, decimals: int=2, bar_length: int=68) -> None:
'Print progress bar.\n\n Call in a loop to create terminal progress bar\n @params:\n iteration - Required : current iteration (Int)\n total - Required : total iterat... |
3a0be941a762723e5137831baf0f09cf487aa3d237184ad6e3aeb69c0ea8f7a1 | def kepclean(infile, outfile=None, zero=True, overwrite=False, verbose=False, logfile='kepclean.log'):
'\n Remove NaN values from a kepler light curve or TPF fits file. If passed a TPF\n only cadences where the postage stamp is ALL NaN values will be removed.\n\n Parameters\n ----------\n infile: str... | Remove NaN values from a kepler light curve or TPF fits file. If passed a TPF
only cadences where the postage stamp is ALL NaN values will be removed.
Parameters
----------
infile: str
The name of a MAST standard format FITS file containing a Kepler light
curve within the first data extension.
outfile: str
... | pyke/kepclean.py | kepclean | christinahedges/PyKE | 0 | python | def kepclean(infile, outfile=None, zero=True, overwrite=False, verbose=False, logfile='kepclean.log'):
'\n Remove NaN values from a kepler light curve or TPF fits file. If passed a TPF\n only cadences where the postage stamp is ALL NaN values will be removed.\n\n Parameters\n ----------\n infile: str... | def kepclean(infile, outfile=None, zero=True, overwrite=False, verbose=False, logfile='kepclean.log'):
'\n Remove NaN values from a kepler light curve or TPF fits file. If passed a TPF\n only cadences where the postage stamp is ALL NaN values will be removed.\n\n Parameters\n ----------\n infile: str... |
9ed6d775a82ca0f636691a7e7aabdba4834df3062789f19dabb2b44757c14e41 | @property
def sids(self):
"\n This seems to be used to pre-fetch assets.\n I don't think that we need this for live-trading.\n Leaving the list empty.\n "
all_sids = []
for exchange_name in self.exchanges:
exchange = self.exchanges[exchange_name]
exchange.init()
... | This seems to be used to pre-fetch assets.
I don't think that we need this for live-trading.
Leaving the list empty. | catalyst/exchange/exchange_asset_finder.py | sids | korigod/catalyst | 6 | python | @property
def sids(self):
"\n This seems to be used to pre-fetch assets.\n I don't think that we need this for live-trading.\n Leaving the list empty.\n "
all_sids = []
for exchange_name in self.exchanges:
exchange = self.exchanges[exchange_name]
exchange.init()
... | @property
def sids(self):
"\n This seems to be used to pre-fetch assets.\n I don't think that we need this for live-trading.\n Leaving the list empty.\n "
all_sids = []
for exchange_name in self.exchanges:
exchange = self.exchanges[exchange_name]
exchange.init()
... |
86bf7158617c5bf442b81559a7124b658c6d02ce37c119d2064f48e462ec9044 | def retrieve_asset(self, sid, default_none=False):
'\n Retrieve the first Asset found for a given sid.\n '
asset = None
for exchange_name in self.exchanges:
if (asset is not None):
break
exchange = self.exchanges[exchange_name]
assets = [a for a in exchange.... | Retrieve the first Asset found for a given sid. | catalyst/exchange/exchange_asset_finder.py | retrieve_asset | korigod/catalyst | 6 | python | def retrieve_asset(self, sid, default_none=False):
'\n \n '
asset = None
for exchange_name in self.exchanges:
if (asset is not None):
break
exchange = self.exchanges[exchange_name]
assets = [a for a in exchange.assets if (a.sid == sid)]
if assets:
... | def retrieve_asset(self, sid, default_none=False):
'\n \n '
asset = None
for exchange_name in self.exchanges:
if (asset is not None):
break
exchange = self.exchanges[exchange_name]
assets = [a for a in exchange.assets if (a.sid == sid)]
if assets:
... |
90f66d1ef33ef30de480464135f376012590e9fc92e24cd92de75d592cd03ce4 | def retrieve_all(self, sids, default_none=False):
'\n Retrieve all assets in `sids`.\n\n Parameters\n ----------\n sids : iterable of int\n Assets to retrieve.\n default_none : bool\n If True, return None for failed lookups.\n If False, raise `Sids... | Retrieve all assets in `sids`.
Parameters
----------
sids : iterable of int
Assets to retrieve.
default_none : bool
If True, return None for failed lookups.
If False, raise `SidsNotFound`.
Returns
-------
assets : list[Asset or None]
A list of the same length as `sids` containing Assets (or Nones)
... | catalyst/exchange/exchange_asset_finder.py | retrieve_all | korigod/catalyst | 6 | python | def retrieve_all(self, sids, default_none=False):
'\n Retrieve all assets in `sids`.\n\n Parameters\n ----------\n sids : iterable of int\n Assets to retrieve.\n default_none : bool\n If True, return None for failed lookups.\n If False, raise `Sids... | def retrieve_all(self, sids, default_none=False):
'\n Retrieve all assets in `sids`.\n\n Parameters\n ----------\n sids : iterable of int\n Assets to retrieve.\n default_none : bool\n If True, return None for failed lookups.\n If False, raise `Sids... |
f0afbf7019711dce51fc6eb1bb07423b5c30c29f534412c43efe007fa37bc3fa | def lookup_symbol(self, symbol, exchange, data_frequency=None, as_of_date=None, fuzzy=False):
'Lookup an asset by symbol.\n\n Parameters\n ----------\n symbol : str\n The ticker symbol to resolve.\n as_of_date : datetime or None\n Look up the last owner of this symb... | Lookup an asset by symbol.
Parameters
----------
symbol : str
The ticker symbol to resolve.
as_of_date : datetime or None
Look up the last owner of this symbol as of this datetime.
If ``as_of_date`` is None, then this can only resolve the equity
if exactly one equity has ever owned the ticker.
fuzzy : ... | catalyst/exchange/exchange_asset_finder.py | lookup_symbol | korigod/catalyst | 6 | python | def lookup_symbol(self, symbol, exchange, data_frequency=None, as_of_date=None, fuzzy=False):
'Lookup an asset by symbol.\n\n Parameters\n ----------\n symbol : str\n The ticker symbol to resolve.\n as_of_date : datetime or None\n Look up the last owner of this symb... | def lookup_symbol(self, symbol, exchange, data_frequency=None, as_of_date=None, fuzzy=False):
'Lookup an asset by symbol.\n\n Parameters\n ----------\n symbol : str\n The ticker symbol to resolve.\n as_of_date : datetime or None\n Look up the last owner of this symb... |
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