xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

a3e2a0ac0503f5dc07ca496db5ccde98867c070b

93

py

Python

dlc/pose-tensorflow/net_factory.py

JaneliaSciComp/delectable

a8a1eb23b96f83c332d4b14593e0ae209bb062b2

[ "BSD-3-Clause" ]

null

dlc/pose-tensorflow/net_factory.py

JaneliaSciComp/delectable

a8a1eb23b96f83c332d4b14593e0ae209bb062b2

[ "BSD-3-Clause" ]

1

2020-03-09T07:32:01.000Z

2020-03-09T17:43:00.000Z

dlc/pose-tensorflow/nnet/net_factory.py

JaneliaSciComp/delectable

a8a1eb23b96f83c332d4b14593e0ae209bb062b2

[ "BSD-3-Clause" ]

1

2020-06-16T04:12:58.000Z

from nnet.pose_net import PoseNet def pose_net(cfg): cls = PoseNet return cls(cfg)

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4

430402d4455b92d16eb05a544b90fc89eded6d1a

274

py

Python

kedro/framework/cli/__init__.py

hfwittmann/kedro

b0d4fcd8f19b49a7916d78fd09daeb6209a7b6c6

[ "Apache-2.0" ]

1

2021-11-25T12:33:13.000Z

kedro/framework/cli/__init__.py

MerelTheisenQB/kedro

1eaa2e0fa5d80f96e18ea60b9f3d6e6efc161827

[ "Apache-2.0" ]

null

kedro/framework/cli/__init__.py

MerelTheisenQB/kedro

1eaa2e0fa5d80f96e18ea60b9f3d6e6efc161827

[ "Apache-2.0" ]

null

"""``kedro.framework.cli`` implements commands available from Kedro's CLI. """ from .cli import get_project_context, main from .utils import command_with_verbosity, load_entry_points __all__ = ["get_project_context", "main", "command_with_verbosity", "load_entry_points"]

34.25

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0.094891

274

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null

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1

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1

0

4

43078238db5cfaba6f3c4b017569cdaf0cf2dff0

303

py

Python

cvxlinreg/abs_value.py

alexshtf/inc_prox_pt

a826c7179a528757399e661c5619a68dad254711

[ "MIT" ]

null

cvxlinreg/abs_value.py

alexshtf/inc_prox_pt

a826c7179a528757399e661c5619a68dad254711

[ "MIT" ]

null

cvxlinreg/abs_value.py

alexshtf/inc_prox_pt

a826c7179a528757399e661c5619a68dad254711

[ "MIT" ]

null

import math class AbsValue: def eval(self, z): return abs(z) def conjugate_has_compact_domain(self): return True def domain(self): return (-1, 1) def conjugate(self, s): if -1 <= s <= 1: return 0 else: return math.inf

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null

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1

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4

431052d72c290d057f695aebfccff80b6ecec3c2

32,126

py

Python

torchdrug/layers/conv.py

wconnell/torchdrug

a710097cb4ad4c48e0de0d18fbb77ef0e806cdc8

[ "Apache-2.0" ]

772

2021-08-10T05:03:46.000Z

2022-03-31T12:48:31.000Z

torchdrug/layers/conv.py

wconnell/torchdrug

a710097cb4ad4c48e0de0d18fbb77ef0e806cdc8

[ "Apache-2.0" ]

77

2021-08-12T16:19:15.000Z

2022-03-30T14:32:14.000Z

torchdrug/layers/conv.py

wconnell/torchdrug

a710097cb4ad4c48e0de0d18fbb77ef0e806cdc8

[ "Apache-2.0" ]

90

2021-08-11T16:27:13.000Z

2022-03-28T11:41:53.000Z

import functools import torch from torch import nn from torch.nn import functional as F from torch.utils import checkpoint from torch_scatter import scatter_mean, scatter_add, scatter_max from torchdrug import data, layers, utils from torchdrug.layers import functional class MessagePassingBase(nn.Module): """ Base module for message passing. Any custom message passing module should be derived from this class. """ gradient_checkpoint = False def message(self, graph, input): """ Compute edge messages for the graph. Parameters: graph (Graph): graph(s) input (Tensor): node representations of shape :math:`(|V|, ...)` Returns: Tensor: edge messages of shape :math:`(|E|, ...)` """ raise NotImplementedError def aggregate(self, graph, message): """ Aggregate edge messages to nodes. Parameters: graph (Graph): graph(s) message (Tensor): edge messages of shape :math:`(|E|, ...)` Returns: Tensor: node updates of shape :math:`(|V|, ...)` """ raise NotImplementedError def message_and_aggregate(self, graph, input): """ Fused computation of message and aggregation over the graph. This may provide better time or memory complexity than separate calls of :meth:`message <MessagePassingBase.message>` and :meth:`aggregate <MessagePassingBase.aggregate>`. Parameters: graph (Graph): graph(s) input (Tensor): node representations of shape :math:`(|V|, ...)` Returns: Tensor: node updates of shape :math:`(|V|, ...)` """ message = self.message(graph, input) update = self.aggregate(graph, message) return update def _message_and_aggregate(self, *tensors): graph = data.Graph.from_tensors(tensors[:-1]) input = tensors[-1] update = self.message_and_aggregate(graph, input) return update def combine(self, input, update): """ Combine node input and node update. Parameters: input (Tensor): node representations of shape :math:`(|V|, ...)` update (Tensor): node updates of shape :math:`(|V|, ...)` """ raise NotImplementedError def forward(self, graph, input): """ Perform message passing over the graph(s). Parameters: graph (Graph): graph(s) input (Tensor): node representations of shape :math:`(|V|, ...)` """ if self.gradient_checkpoint: update = checkpoint.checkpoint(self._message_and_aggregate, *graph.to_tensors(), input) else: update = self.message_and_aggregate(graph, input) output = self.combine(input, update) return output class GraphConv(MessagePassingBase): """ Graph convolution operator from `Semi-Supervised Classification with Graph Convolutional Networks`_. .. _Semi-Supervised Classification with Graph Convolutional Networks: https://arxiv.org/pdf/1609.02907.pdf Parameters: input_dim (int): input dimension output_dim (int): output dimension edge_input_dim (int, optional): dimension of edge features batch_norm (bool, optional): apply batch normalization on nodes or not activation (str or function, optional): activation function """ def __init__(self, input_dim, output_dim, edge_input_dim=None, batch_norm=False, activation="relu"): super(GraphConv, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.edge_input_dim = edge_input_dim if batch_norm: self.batch_norm = nn.BatchNorm1d(output_dim) else: self.batch_norm = None if isinstance(activation, str): self.activation = getattr(F, activation) else: self.activation = activation self.linear = nn.Linear(input_dim, output_dim) if edge_input_dim: self.edge_linear = nn.Linear(edge_input_dim, input_dim) else: self.edge_linear = None def message(self, graph, input): # add self loop node_in = torch.cat([graph.edge_list[:, 0], torch.arange(graph.num_node, device=graph.device)]) degree_in = graph.degree_in.unsqueeze(-1) + 1 message = input[node_in] if self.edge_linear: edge_input = self.edge_linear(graph.edge_feature.float()) edge_input = torch.cat([edge_input, torch.zeros(graph.num_node, self.input_dim, device=graph.device)]) message += edge_input message /= degree_in[node_in].sqrt() return message def aggregate(self, graph, message): # add self loop node_out = torch.cat([graph.edge_list[:, 1], torch.arange(graph.num_node, device=graph.device)]) edge_weight = torch.cat([graph.edge_weight, torch.ones(graph.num_node, device=graph.device)]) edge_weight = edge_weight.unsqueeze(-1) degree_out = graph.degree_out.unsqueeze(-1) + 1 update = scatter_add(message * edge_weight, node_out, dim=0, dim_size=graph.num_node) update = update / degree_out.sqrt() return update def message_and_aggregate(self, graph, input): node_in, node_out = graph.edge_list.t()[:2] node_in = torch.cat([node_in, torch.arange(graph.num_node, device=graph.device)]) node_out = torch.cat([node_out, torch.arange(graph.num_node, device=graph.device)]) edge_weight = torch.cat([graph.edge_weight, torch.ones(graph.num_node, device=graph.device)]) degree_in = graph.degree_in + 1 degree_out = graph.degree_out + 1 edge_weight = edge_weight / (degree_in[node_in] * degree_out[node_out]).sqrt() adjacency = utils.sparse_coo_tensor(torch.stack([node_in, node_out]), edge_weight, (graph.num_node, graph.num_node)) update = torch.sparse.mm(adjacency.t(), input) if self.edge_linear: edge_input = graph.edge_feature.float() if self.edge_linear.in_features > self.edge_linear.out_features: edge_input = self.edge_linear(edge_input) edge_weight = edge_weight.unsqueeze(-1) edge_update = scatter_add(edge_input * edge_weight, graph.edge_list[:, 1], dim=0, dim_size=graph.num_node) if self.edge_linear.in_features <= self.edge_linear.out_features: edge_update = self.edge_linear(edge_update) update += edge_update return update def combine(self, input, update): output = self.linear(update) if self.batch_norm: output = self.batch_norm(output) if self.activation: output = self.activation(output) return output class GraphAttentionConv(MessagePassingBase): """ Graph attentional convolution operator from `Graph Attention Networks`_. .. _Graph Attention Networks: https://arxiv.org/pdf/1710.10903.pdf Parameters: input_dim (int): input dimension output_dim (int): output dimension edge_input_dim (int, optional): dimension of edge features num_head (int, optional): number of attention heads negative_slope (float, optional): negative slope of leaky relu activation batch_norm (bool, optional): apply batch normalization on nodes or not activation (str or function, optional): activation function """ eps = 1e-10 def __init__(self, input_dim, output_dim, edge_input_dim=None, num_head=1, negative_slope=0.2, concat=True, batch_norm=False, activation="relu"): super(GraphAttentionConv, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.edge_input_dim = edge_input_dim self.num_head = num_head self.concat = concat self.leaky_relu = functools.partial(F.leaky_relu, negative_slope=negative_slope) if batch_norm: self.batch_norm = nn.BatchNorm1d(output_dim) else: self.batch_norm = None if isinstance(activation, str): self.activation = getattr(F, activation) else: self.activation = activation if output_dim % num_head != 0: raise ValueError("Expect output_dim to be a multiplier of num_head, but found `%d` and `%d`" % (output_dim, num_head)) self.linear = nn.Linear(input_dim, output_dim) if edge_input_dim: self.edge_linear = nn.Linear(edge_input_dim, output_dim) else: self.edge_linear = None self.query = nn.Parameter(torch.zeros(num_head, output_dim * 2 // num_head)) nn.init.kaiming_uniform_(self.query, negative_slope, mode="fan_in") def message(self, graph, input): # add self loop node_in = torch.cat([graph.edge_list[:, 0], torch.arange(graph.num_node, device=graph.device)]) node_out = torch.cat([graph.edge_list[:, 1], torch.arange(graph.num_node, device=graph.device)]) edge_weight = torch.cat([graph.edge_weight, torch.ones(graph.num_node, device=graph.device)]) edge_weight = edge_weight.unsqueeze(-1) hidden = self.linear(input) key = torch.stack([hidden[node_in], hidden[node_out]], dim=-1) if self.edge_linear: edge_input = self.edge_linear(graph.edge_feature.float()) edge_input = torch.cat([edge_input, torch.zeros(graph.num_node, self.output_dim, device=graph.device)]) key += edge_input.unsqueeze(-1) key = key.view(-1, *self.query.shape) weight = torch.einsum("hd, nhd -> nh", self.query, key) weight = self.leaky_relu(weight) weight = weight - scatter_max(weight, node_out, dim=0, dim_size=graph.num_node)[0][node_out] attention = weight.exp() * edge_weight # why mean? because with mean we have normalized message scale across different node degrees normalizer = scatter_mean(attention, node_out, dim=0, dim_size=graph.num_node)[node_out] attention = attention / (normalizer + self.eps) value = hidden[node_in].view(-1, self.num_head, self.query.shape[-1] // 2) attention = attention.unsqueeze(-1).expand_as(value) message = (attention * value).flatten(1) return message def aggregate(self, graph, message): # add self loop node_out = torch.cat([graph.edge_list[:, 1], torch.arange(graph.num_node, device=graph.device)]) update = scatter_mean(message, node_out, dim=0, dim_size=graph.num_node) return update def combine(self, input, update): output = update if self.batch_norm: output = self.batch_norm(output) if self.activation: output = self.activation(output) return output class GraphIsomorphismConv(MessagePassingBase): """ Graph isomorphism convolution operator from `How Powerful are Graph Neural Networks?`_ .. _How Powerful are Graph Neural Networks?: https://arxiv.org/pdf/1810.00826.pdf Parameters: input_dim (int): input dimension output_dim (int): output dimension edge_input_dim (int, optional): dimension of edge features hidden_dims (list of int, optional): hidden dimensions eps (float, optional): initial epsilon learn_eps (bool, optional): learn epsilon or not batch_norm (bool, optional): apply batch normalization on nodes or not activation (str or function, optional): activation function """ def __init__(self, input_dim, output_dim, edge_input_dim=None, hidden_dims=None, eps=0, learn_eps=False, batch_norm=False, activation="relu"): super(GraphIsomorphismConv, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.edge_input_dim = edge_input_dim eps = torch.tensor([eps], dtype=torch.float32) if learn_eps: self.eps = nn.Parameter(eps) else: self.register_buffer("eps", eps) if batch_norm: self.batch_norm = nn.BatchNorm1d(output_dim) else: self.batch_norm = None if isinstance(activation, str): self.activation = getattr(F, activation) else: self.activation = activation if hidden_dims is None: hidden_dims = [] self.mlp = layers.MLP(input_dim, list(hidden_dims) + [output_dim], activation) if edge_input_dim: self.edge_linear = nn.Linear(edge_input_dim, input_dim) else: self.edge_linear = None def message(self, graph, input): node_in = graph.edge_list[:, 0] message = input[node_in] if self.edge_linear: message += self.edge_linear(graph.edge_feature.float()) return message def aggregate(self, graph, message): node_out = graph.edge_list[:, 1] edge_weight = graph.edge_weight.unsqueeze(-1) update = scatter_add(message * edge_weight, node_out, dim=0, dim_size=graph.num_node) return update def message_and_aggregate(self, graph, input): adjacency = utils.sparse_coo_tensor(graph.edge_list.t()[:2], graph.edge_weight, (graph.num_node, graph.num_node)) update = torch.sparse.mm(adjacency.t(), input) if self.edge_linear: edge_input = graph.edge_feature.float() edge_weight = graph.edge_weight.unsqueeze(-1) if self.edge_linear.in_features > self.edge_linear.out_features: edge_input = self.edge_linear(edge_input) edge_update = scatter_add(edge_input * edge_weight, graph.edge_list[:, 1], dim=0, dim_size=graph.num_node) if self.edge_linear.in_features <= self.edge_linear.out_features: edge_update = self.edge_linear(edge_update) update += edge_update return update def combine(self, input, update): output = self.mlp((1 + self.eps) * input + update) if self.batch_norm: output = self.batch_norm(output) if self.activation: output = self.activation(output) return output class RelationalGraphConv(MessagePassingBase): """ Relational graph convolution operator from `Modeling Relational Data with Graph Convolutional Networks`_. .. _Modeling Relational Data with Graph Convolutional Networks: https://arxiv.org/pdf/1703.06103.pdf Parameters: input_dim (int): input dimension output_dim (int): output dimension num_relation (int): number of relations edge_input_dim (int, optional): dimension of edge features batch_norm (bool, optional): apply batch normalization on nodes or not activation (str or function, optional): activation function """ eps = 1e-10 def __init__(self, input_dim, output_dim, num_relation, edge_input_dim=None, batch_norm=False, activation="relu"): super(RelationalGraphConv, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.num_relation = num_relation self.edge_input_dim = edge_input_dim if batch_norm: self.batch_norm = nn.BatchNorm1d(output_dim) else: self.batch_norm = None if isinstance(activation, str): self.activation = getattr(F, activation) else: self.activation = activation self.self_loop = nn.Linear(input_dim, output_dim) self.linear = nn.Linear(num_relation * input_dim, output_dim) if edge_input_dim: self.edge_linear = nn.Linear(edge_input_dim, input_dim) else: self.edge_linear = None def message(self, graph, input): node_in = graph.edge_list[:, 0] message = input[node_in] if self.edge_linear: message += self.edge_linear(graph.edge_feature.float()) return message def aggregate(self, graph, message): assert graph.num_relation == self.num_relation node_out = graph.edge_list[:, 1] * self.num_relation + graph.edge_list[:, 2] edge_weight = graph.edge_weight.unsqueeze(-1) update = scatter_add(message * edge_weight, node_out, dim=0, dim_size=graph.num_node * self.num_relation) / \ (scatter_add(edge_weight, node_out, dim=0, dim_size=graph.num_node * self.num_relation) + self.eps) return update.view(graph.num_node, self.num_relation * self.input_dim) def message_and_aggregate(self, graph, input): assert graph.num_relation == self.num_relation node_in, node_out, relation = graph.edge_list.t() node_out = node_out * self.num_relation + relation degree_out = scatter_add(graph.edge_weight, node_out, dim_size=graph.num_node * graph.num_relation) edge_weight = graph.edge_weight / degree_out[node_out] adjacency = utils.sparse_coo_tensor(torch.stack([node_in, node_out]), edge_weight, (graph.num_node, graph.num_node * graph.num_relation)) update = torch.sparse.mm(adjacency.t(), input) if self.edge_linear: edge_input = graph.edge_feature.float() if self.edge_linear.in_features > self.edge_linear.out_features: edge_input = self.edge_linear(edge_input) edge_weight = edge_weight.unsqueeze(-1) edge_update = scatter_add(edge_input * edge_weight, node_out, dim=0, dim_size=graph.num_node * graph.num_relation) if self.edge_linear.in_features <= self.edge_linear.out_features: edge_update = self.edge_linear(edge_update) update += edge_update return update.view(graph.num_node, self.num_relation * self.input_dim) def combine(self, input, update): output = self.linear(update) + self.self_loop(input) if self.batch_norm: output = self.batch_norm(output) if self.activation: output = self.activation(output) return output class NeuralFingerprintConv(MessagePassingBase): """ Graph neural network operator from `Convolutional Networks on Graphs for Learning Molecular Fingerprints`_. Note this operator doesn't include the sparsifying step of the original paper. .. _Convolutional Networks on Graphs for Learning Molecular Fingerprints: https://arxiv.org/pdf/1509.09292.pdf Parameters: input_dim (int): input dimension output_dim (int): output dimension edge_input_dim (int, optional): dimension of edge features batch_norm (bool, optional): apply batch normalization on nodes or not activation (str or function, optional): activation function """ def __init__(self, input_dim, output_dim, edge_input_dim=None, batch_norm=False, activation="relu"): super(NeuralFingerprintConv, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.edge_input_dim = edge_input_dim if batch_norm: self.batch_norm = nn.BatchNorm1d(output_dim) else: self.batch_norm = None if isinstance(activation, str): self.activation = getattr(F, activation) else: self.activation = activation self.linear = nn.Linear(input_dim, output_dim) if edge_input_dim: self.edge_linear = nn.Linear(edge_input_dim, input_dim) else: self.edge_linear = None def message(self, graph, input): node_in = graph.edge_list[:, 0] message = input[node_in] if self.edge_linear: message += self.edge_linear(graph.edge_feature.float()) return message def aggregate(self, graph, message): node_out = graph.edge_list[:, 1] edge_weight = graph.edge_weight.unsqueeze(-1) update = scatter_add(message * edge_weight, node_out, dim=0, dim_size=graph.num_node) return update def message_and_aggregate(self, graph, input): adjacency = utils.sparse_coo_tensor(graph.edge_list.t()[:2], graph.edge_weight, (graph.num_node, graph.num_node)) update = torch.sparse.mm(adjacency.t(), input) if self.edge_linear: edge_input = graph.edge_feature.float() edge_weight = graph.edge_weight.unsqueeze(-1) if self.edge_linear.in_features > self.edge_linear.out_features: edge_input = self.edge_linear(edge_input) edge_update = scatter_add(edge_input * edge_weight, graph.edge_list[:, 1], dim=0, dim_size=graph.num_node) if self.edge_linear.in_features <= self.edge_linear.out_features: edge_update = self.edge_linear(edge_update) update += edge_update return update def combine(self, input, update): output = self.linear(input + update) if self.batch_norm: output = self.batch_norm(output) if self.activation: output = self.activation(output) return output class ContinuousFilterConv(MessagePassingBase): """ Continuous filter operator from `SchNet: A continuous-filter convolutional neural network for modeling quantum interactions`_. .. _SchNet\: A continuous-filter convolutional neural network for modeling quantum interactions: https://arxiv.org/pdf/1706.08566.pdf Parameters: input_dim (int): input dimension output_dim (int): output dimension edge_input_dim (int, optional): dimension of edge features hidden_dim (int, optional): hidden dimension. By default, same as :attr:`output_dim` cutoff (float, optional): maximal scale for RBF kernels num_gaussian (int, optional): number of RBF kernels batch_norm (bool, optional): apply batch normalization on nodes or not activation (str or function, optional): activation function """ def __init__(self, input_dim, output_dim, edge_input_dim=None, hidden_dim=None, cutoff=5, num_gaussian=100, batch_norm=False, activation="shifted_softplus"): super(ContinuousFilterConv, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.edge_input_dim = edge_input_dim if hidden_dim is None: hidden_dim = output_dim self.hidden_dim = hidden_dim self.rbf = layers.RBF(stop=cutoff, num_kernel=num_gaussian) if batch_norm: self.batch_norm = nn.BatchNorm1d(output_dim) else: self.batch_norm = None if activation == "shifted_softplus": self.activation = functional.shifted_softplus elif isinstance(activation, str): self.activation = getattr(F, activation) else: self.activation = activation self.input_layer = nn.Linear(input_dim, hidden_dim) self.rbf_layer = nn.Linear(num_gaussian, hidden_dim) self.output_layer = nn.Linear(hidden_dim, output_dim) if edge_input_dim: self.edge_linear = nn.Linear(edge_input_dim, input_dim) else: self.edge_linear = None def message(self, graph, input): node_in, node_out = graph.edge_list.t()[:2] position = graph.node_position message = self.input_layer(input)[node_in] if self.edge_linear: message += self.edge_linear(graph.edge_feature.float()) weight = self.rbf_layer(self.rbf(position[node_in], position[node_out])) message *= weight return message def aggregate(self, graph, message): node_out = graph.edge_list[:, 1] edge_weight = graph.edge_weight.unsqueeze(-1) update = scatter_add(message * edge_weight, node_out, dim=0, dim_size=graph.num_node) return update def message_and_aggregate(self, graph, input): node_in, node_out = graph.edge_list.t()[:2] position = graph.node_position rbf_weight = self.rbf_layer(self.rbf(position[node_in], position[node_out])) indices = torch.stack([node_out, node_in, torch.arange(graph.num_edge, device=graph.device)]) adjacency = utils.sparse_coo_tensor(indices, graph.edge_weight, (graph.num_node, graph.num_node, graph.num_edge)) update = functional.generalized_rspmm(adjacency, rbf_weight, self.input_layer(input)) if self.edge_linear: edge_input = graph.edge_feature.float() if self.edge_linear.in_features > self.edge_linear.out_features: edge_input = self.edge_linear(edge_input) edge_weight = graph.edge_weight.unsqueeze(-1) * rbf_weight edge_update = scatter_add(edge_input * edge_weight, graph.edge_list[:, 1], dim=0, dim_size=graph.num_node) if self.edge_linear.in_features <= self.edge_linear.out_features: edge_update = self.edge_linear(edge_update) update += edge_update return update def combine(self, input, update): output = self.output_layer(update) if self.batch_norm: output = self.batch_norm(output) if self.activation: output = self.activation(output) return output class MessagePassing(MessagePassingBase): """ Message passing operator from `Neural Message Passing for Quantum Chemistry`_. This implements the edge network variant in the original paper. .. _Neural Message Passing for Quantum Chemistry: https://arxiv.org/pdf/1704.01212.pdf Parameters: input_dim (int): input dimension edge_input_dim (int): dimension of edge features hidden_dims (list of int, optional): hidden dims of edge network batch_norm (bool, optional): apply batch normalization on nodes or not activation (str or function, optional): activation function """ def __init__(self, input_dim, edge_input_dim, hidden_dims=None, batch_norm=False, activation="relu"): super(MessagePassing, self).__init__() self.input_dim = input_dim self.output_dim = input_dim self.edge_input_dim = edge_input_dim if hidden_dims is None: hidden_dims = [] if batch_norm: self.batch_norm = nn.BatchNorm1d(input_dim) else: self.batch_norm = None if isinstance(activation, str): self.activation = getattr(F, activation) else: self.activation = activation self.edge_mlp = layers.MLP(edge_input_dim, list(hidden_dims) + [input_dim * input_dim], activation) def message(self, graph, input): node_in = graph.edge_list[:, 0] transform = self.edge_mlp(graph.edge_feature.float()).view(-1, self.input_dim, self.input_dim) if graph.num_edge: message = torch.einsum("bed, bd -> be", transform, input[node_in]) else: message = torch.zeros(0, self.input_dim, device=graph.device) return message def aggregate(self, graph, message): node_out = graph.edge_list[:, 1] edge_weight = graph.edge_weight.unsqueeze(-1) update = scatter_add(message * edge_weight, node_out, dim=0, dim_size=graph.num_node) return update def combine(self, input, update): output = update if self.batch_norm: output = self.batch_norm(output) if self.activation: output = self.activation(output) return output class ChebyshevConv(MessagePassingBase): """ Chebyshev spectral graph convolution operator from `Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering`_. .. _Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering: https://arxiv.org/pdf/1606.09375.pdf Parameters: input_dim (int): input dimension output_dim (int): output dimension edge_input_dim (int, optional): dimension of edge features k (int, optional): number of Chebyshev polynomials. This also corresponds to the radius of the receptive field. hidden_dims (list of int, optional): hidden dims of edge network batch_norm (bool, optional): apply batch normalization on nodes or not activation (str or function, optional): activation function """ def __init__(self, input_dim, output_dim, edge_input_dim=None, k=1, batch_norm=False, activation="relu"): super(ChebyshevConv, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.k = k self.edge_input_dim = edge_input_dim if batch_norm: self.batch_norm = nn.BatchNorm1d(output_dim) else: self.batch_norm = None if isinstance(activation, str): self.activation = getattr(F, activation) else: self.activation = activation self.linear = nn.Linear((k + 1) * input_dim, output_dim) if edge_input_dim: self.edge_linear = nn.Linear(edge_input_dim, input_dim) else: self.edge_linear = None def message(self, graph, input): node_in = graph.edge_list[:, 0] degree_in = graph.degree_in.unsqueeze(-1) # because self-loop messages have a different scale, they are processed in combine() message = input[node_in] if self.edge_linear: message += self.edge_linear(graph.edge_feature.float()) message /= degree_in[node_in].sqrt() return message def aggregate(self, graph, message): node_out = graph.edge_list[:, 1] edge_weight = graph.edge_weight.unsqueeze(-1) degree_out = graph.degree_out.unsqueeze(-1) # because self-loop messages have a different scale, they are processed in combine() update = -scatter_add(message * edge_weight, node_out, dim=0, dim_size=graph.num_node) update = update / degree_out.sqrt() return update def message_and_aggregate(self, graph, input): node_in, node_out = graph.edge_list.t()[:2] edge_weight = -graph.edge_weight / (graph.degree_in[node_in] * graph.degree_out[node_out]).sqrt() adjacency = utils.sparse_coo_tensor(graph.edge_list.t()[:2], edge_weight, (graph.num_node, graph.num_node)) update = torch.sparse.mm(adjacency.t(), input) if self.edge_linear: edge_input = graph.edge_feature.float() if self.edge_linear.in_features > self.edge_linear.out_features: edge_input = self.edge_linear(edge_input) edge_weight = edge_weight.unsqueeze(-1) edge_update = scatter_add(edge_input * edge_weight, graph.edge_list[:, 1], dim=0, dim_size=graph.num_node) if self.edge_linear.in_features <= self.edge_linear.out_features: edge_update = self.edge_linear(edge_update) update += edge_update return update def forward(self, graph, input): # Chebyshev polynomial bases bases = [input] for i in range(self.k): x = super(ChebyshevConv, self).forward(graph, bases[-1]) if i > 0: x = 2 * x - bases[-2] bases.append(x) bases = torch.cat(bases, dim=-1) output = self.linear(bases) if self.batch_norm: x = self.batch_norm(output) if self.activation: output = self.activation(output) return output def combine(self, input, update): output = input + update return output

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121

0.640976

3,995

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1

0

1

0

1

0

null

0

4

433e9e9e80a4303c7e141d6834d195f78e013a25

3,899

py

Python

metalibm_core/core/advanced_operations.py

kalray/metalibm

e331ee4a1b3df9ebdf581453852ac019d7c1b6da

[ "MIT" ]

27

2018-03-12T16:49:36.000Z

2021-12-15T06:53:55.000Z

metalibm_core/core/advanced_operations.py

kalray/metalibm

e331ee4a1b3df9ebdf581453852ac019d7c1b6da

[ "MIT" ]

57

2018-03-12T16:49:56.000Z

2021-03-04T15:25:39.000Z

metalibm_core/core/advanced_operations.py

kalray/metalibm

e331ee4a1b3df9ebdf581453852ac019d7c1b6da

[ "MIT" ]

4

2018-03-12T15:40:22.000Z

2018-11-28T14:34:54.000Z

# -*- coding: utf-8 -*- ## @package advanced_operations # Metalibm Description Language advanced Operations ############################################################################### # This file is part of metalibm (https://github.com/kalray/metalibm) ############################################################################### # MIT License # # Copyright (c) 2018 Kalray # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. ############################################################################### ############################################################################### # This file is part of the new Metalibm tool # created: Aug 9th, 2017 # last-modified: Mar 8th, 2018 # # author(s): Nicolas Brunie (nbrunie@kalray.eu) ############################################################################### from metalibm_core.core.ml_operations import ( SpecifierOperation, empty_range, GeneralOperation, ML_ArithmeticOperation, ) class FixedPointPosition(ML_ArithmeticOperation): """ Dynamic FixedPointPosition evaluator node convert to a constant during code generation, once input format has been determined """ name = "FixedPointPosition" def range_function(self, ops, ops_interval_getter=lambda op: op.get_interval()): return None class FromMSBToLSB: """ offset is given from MSB downward. The node returns the index of position (MSB - offset) from LSB """ pass class FromLSBToLSB: """ offset is given from LSB upward. The node returns the position of index (LSB + offset) from LSB (i.e result = offset) """ pass class FromPointToLSB: """ The offset is given from point position upward. The node returns the position of index (point + offset) from LSB """ pass class FromPointToMSB: """ The offset is given from point position upward. The node returns the position of (point + offset) from MSB. The result is expected to be negative """ pass def __init__(self, op, position, align = FromLSBToLSB, **kwords): self.__class__.__base__.__init__(self, op, position, **kwords) self.align = align def get_align(self): return self.align def finish_copy(self, new_copy, copy_map = None): new_copy.align = self.align class PlaceHolder(GeneralOperation): """ This operation has an arbitrary arity. For all purpose it is equal to its first input (main_input) but carries on several inputs """ name = "PlaceHolder" def __init__(self, *args, **kw): PlaceHolder.__base__.__init__(self, *args, **kw) def get_main_input(self): return self.get_input(0) def get_precision(self): return self.get_main_input().get_precision()

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3,899

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3,899

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null

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1

0

1

0

1

0

4

4a32263dc33654dd970020bbf524b4d9f6cff4b0

600

py

Python

task_manager/shell.py

natszp/Tasker

263ba508b75bca939ad5879db0ccc2a1ce1c39a4

[ "MIT" ]

null

task_manager/shell.py

natszp/Tasker

263ba508b75bca939ad5879db0ccc2a1ce1c39a4

[ "MIT" ]

null

task_manager/shell.py

natszp/Tasker

263ba508b75bca939ad5879db0ccc2a1ce1c39a4

[ "MIT" ]

null

from manager.models import Task from datetime import datetime task1 = Task.objects.create(name="zrobic koalcje", description="pożywna i staropolskia", date_created=datetime.now(), importance=False) task2 = Task.objects.create(name="zrobic zakupy", description="warzywa w Lidlu i miesny", date_created=datetime.now(), importance=True) task3 = Task.objects.create(name="posprzatać kuchnie", description="wymienic zapach w zmywarce", date_created=datetime.now(), importance=False) task4 = Task.objects.create(name="odebrac poczte", description="na cito!", date_created=datetime.now(), importance=True)

75

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0.791667

79

600

5.962025

0.493671

0.093418

0.144374

0.178344

0.424628

0.309979

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1

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1

0

4

4a3b848bde2e2dc5e18626e7c84b91315bb787ee

351

py

Python

sst/tests/test_utils/logger_utils.py

Adamage/tutorials

b6600c052613909dbec378fea4a69deff46004dc

[ "MIT" ]

null

sst/tests/test_utils/logger_utils.py

Adamage/tutorials

b6600c052613909dbec378fea4a69deff46004dc

[ "MIT" ]

78

2021-09-20T11:48:08.000Z

2021-10-21T07:10:39.000Z

sst/tests/test_utils/logger_utils.py

Adamage/tutorials

b6600c052613909dbec378fea4a69deff46004dc

[ "MIT" ]

null

import logging def disable_logging(): logging.disable(logging.CRITICAL) def enable_logging(): logging.disable(logging.NOTSET) class MockHandler(logging.Handler): def __init__(self, stream): super().__init__() self.log_records = stream def emit(self, record): self.log_records.append(record.getMessage())

18.473684

52

0.692308

40

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5.775

0.5

0.181818

0.242424

0

0.193732

351

18

53

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0

1

0.363636

false

0

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0.545455

0

null

0

1

0

null

0

1

0

1

0

4

4a493da45c1fcd153742053cfc6c491cb42b91c8

31

py

Python

tests/__init__.py

toddrme2178/pandas_ext

6e5fd5aa3e567dec641014dfaee3c9f616f7e057

[ "MIT" ]

4

2018-10-04T19:59:28.000Z

2020-09-12T01:47:40.000Z

tests/__init__.py

toddrme2178/pandas_ext

6e5fd5aa3e567dec641014dfaee3c9f616f7e057

[ "MIT" ]

11

2019-01-09T17:32:24.000Z

2019-05-09T16:01:00.000Z

tests/__init__.py

toddrme2178/pandas_ext

6e5fd5aa3e567dec641014dfaee3c9f616f7e057

[ "MIT" ]

1

2019-12-03T21:16:26.000Z

"""Initialize test package."""

15.5

30

0.677419

3

31

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1

0

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31

1

31

0.75

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0

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null

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null

0

null

1

null

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0

null

1

0

null

0

1

0

null

0

1

0

4

4a4ca06fd216c39984feab2385ed1e02123c4370

574

py

Python

special_process/process_flume.py

linlife/Nagios

edd60b218ffcc4569a2d07fb42f4e2752fac3f15

[ "Apache-2.0" ]

null

special_process/process_flume.py

linlife/Nagios

edd60b218ffcc4569a2d07fb42f4e2752fac3f15

[ "Apache-2.0" ]

null

special_process/process_flume.py

linlife/Nagios

edd60b218ffcc4569a2d07fb42f4e2752fac3f15

[ "Apache-2.0" ]

null

#!/usr/bin/env python2.7 import os import sys str1='usr/lib/jvm/java-1.7.0/bin/java -Xmx1024m -cp /opt/app/logcollector/flume/conf:/opt/app/logcollector/flume/lib/*:/opt/app/logcollector/flume/plugins.d/kafka-sink/lib/*:/opt/app/logcollector/flume/plugins.d/kafka-sink/libext/* -Djava.library.path= org.apache.flume.node.Application' with os.popen('ps -ef | grep flume ') as f: dt=f.readlines() data=''.join(dt) if str1 in data: print 'Process flume is on working now !' sys.exit(0) else: print 'Critical Process flume is stoped !!!' sys.exit(2)

31.888889

287

0.702091

96

574

4.197917

0.59375

0.059553

0.17866

0.228288

0.2134

0

0.025948

0.127178

574

17

288

33.764706

0.778443

0.04007

0

0.083333

0.670909

0.441818

0

null

0

0.166667

null

0.166667

0

null

0

1

0

1

null

0

1

0

4

4a5972cfb6b36acac3107ef07a192ca15c0f57b1

8,001

py

Python

sdk/python/pulumi_aws/servicediscovery/_inputs.py

alexbowers/pulumi-aws

7dbdb03b1e4f7c0d51d5b5d17233ff4465c3eff5

[ "ECL-2.0", "Apache-2.0" ]

260

2018-06-18T14:57:00.000Z

2022-03-29T11:41:03.000Z

sdk/python/pulumi_aws/servicediscovery/_inputs.py

alexbowers/pulumi-aws

7dbdb03b1e4f7c0d51d5b5d17233ff4465c3eff5

[ "ECL-2.0", "Apache-2.0" ]

1,154

2018-06-19T20:38:20.000Z

2022-03-31T19:48:16.000Z

sdk/python/pulumi_aws/servicediscovery/_inputs.py

alexbowers/pulumi-aws

7dbdb03b1e4f7c0d51d5b5d17233ff4465c3eff5

[ "ECL-2.0", "Apache-2.0" ]

115

2018-06-28T03:20:27.000Z

2022-03-29T11:41:06.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities __all__ = [ 'ServiceDnsConfigArgs', 'ServiceDnsConfigDnsRecordArgs', 'ServiceHealthCheckConfigArgs', 'ServiceHealthCheckCustomConfigArgs', ] @pulumi.input_type class ServiceDnsConfigArgs: def __init__(__self__, *, dns_records: pulumi.Input[Sequence[pulumi.Input['ServiceDnsConfigDnsRecordArgs']]], namespace_id: pulumi.Input[str], routing_policy: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[Sequence[pulumi.Input['ServiceDnsConfigDnsRecordArgs']]] dns_records: An array that contains one DnsRecord object for each resource record set. :param pulumi.Input[str] namespace_id: The ID of the namespace to use for DNS configuration. :param pulumi.Input[str] routing_policy: The routing policy that you want to apply to all records that Route 53 creates when you register an instance and specify the service. Valid Values: MULTIVALUE, WEIGHTED """ pulumi.set(__self__, "dns_records", dns_records) pulumi.set(__self__, "namespace_id", namespace_id) if routing_policy is not None: pulumi.set(__self__, "routing_policy", routing_policy) @property @pulumi.getter(name="dnsRecords") def dns_records(self) -> pulumi.Input[Sequence[pulumi.Input['ServiceDnsConfigDnsRecordArgs']]]: """ An array that contains one DnsRecord object for each resource record set. """ return pulumi.get(self, "dns_records") @dns_records.setter def dns_records(self, value: pulumi.Input[Sequence[pulumi.Input['ServiceDnsConfigDnsRecordArgs']]]): pulumi.set(self, "dns_records", value) @property @pulumi.getter(name="namespaceId") def namespace_id(self) -> pulumi.Input[str]: """ The ID of the namespace to use for DNS configuration. """ return pulumi.get(self, "namespace_id") @namespace_id.setter def namespace_id(self, value: pulumi.Input[str]): pulumi.set(self, "namespace_id", value) @property @pulumi.getter(name="routingPolicy") def routing_policy(self) -> Optional[pulumi.Input[str]]: """ The routing policy that you want to apply to all records that Route 53 creates when you register an instance and specify the service. Valid Values: MULTIVALUE, WEIGHTED """ return pulumi.get(self, "routing_policy") @routing_policy.setter def routing_policy(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "routing_policy", value) @pulumi.input_type class ServiceDnsConfigDnsRecordArgs: def __init__(__self__, *, ttl: pulumi.Input[int], type: pulumi.Input[str]): """ :param pulumi.Input[int] ttl: The amount of time, in seconds, that you want DNS resolvers to cache the settings for this resource record set. :param pulumi.Input[str] type: The type of health check that you want to create, which indicates how Route 53 determines whether an endpoint is healthy. Valid Values: HTTP, HTTPS, TCP """ pulumi.set(__self__, "ttl", ttl) pulumi.set(__self__, "type", type) @property @pulumi.getter def ttl(self) -> pulumi.Input[int]: """ The amount of time, in seconds, that you want DNS resolvers to cache the settings for this resource record set. """ return pulumi.get(self, "ttl") @ttl.setter def ttl(self, value: pulumi.Input[int]): pulumi.set(self, "ttl", value) @property @pulumi.getter def type(self) -> pulumi.Input[str]: """ The type of health check that you want to create, which indicates how Route 53 determines whether an endpoint is healthy. Valid Values: HTTP, HTTPS, TCP """ return pulumi.get(self, "type") @type.setter def type(self, value: pulumi.Input[str]): pulumi.set(self, "type", value) @pulumi.input_type class ServiceHealthCheckConfigArgs: def __init__(__self__, *, failure_threshold: Optional[pulumi.Input[int]] = None, resource_path: Optional[pulumi.Input[str]] = None, type: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[int] failure_threshold: The number of 30-second intervals that you want service discovery to wait before it changes the health status of a service instance. Maximum value of 10. :param pulumi.Input[str] resource_path: The path that you want Route 53 to request when performing health checks. Route 53 automatically adds the DNS name for the service. If you don't specify a value, the default value is /. :param pulumi.Input[str] type: The type of health check that you want to create, which indicates how Route 53 determines whether an endpoint is healthy. Valid Values: HTTP, HTTPS, TCP """ if failure_threshold is not None: pulumi.set(__self__, "failure_threshold", failure_threshold) if resource_path is not None: pulumi.set(__self__, "resource_path", resource_path) if type is not None: pulumi.set(__self__, "type", type) @property @pulumi.getter(name="failureThreshold") def failure_threshold(self) -> Optional[pulumi.Input[int]]: """ The number of 30-second intervals that you want service discovery to wait before it changes the health status of a service instance. Maximum value of 10. """ return pulumi.get(self, "failure_threshold") @failure_threshold.setter def failure_threshold(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "failure_threshold", value) @property @pulumi.getter(name="resourcePath") def resource_path(self) -> Optional[pulumi.Input[str]]: """ The path that you want Route 53 to request when performing health checks. Route 53 automatically adds the DNS name for the service. If you don't specify a value, the default value is /. """ return pulumi.get(self, "resource_path") @resource_path.setter def resource_path(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "resource_path", value) @property @pulumi.getter def type(self) -> Optional[pulumi.Input[str]]: """ The type of health check that you want to create, which indicates how Route 53 determines whether an endpoint is healthy. Valid Values: HTTP, HTTPS, TCP """ return pulumi.get(self, "type") @type.setter def type(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "type", value) @pulumi.input_type class ServiceHealthCheckCustomConfigArgs: def __init__(__self__, *, failure_threshold: Optional[pulumi.Input[int]] = None): """ :param pulumi.Input[int] failure_threshold: The number of 30-second intervals that you want service discovery to wait before it changes the health status of a service instance. Maximum value of 10. """ if failure_threshold is not None: pulumi.set(__self__, "failure_threshold", failure_threshold) @property @pulumi.getter(name="failureThreshold") def failure_threshold(self) -> Optional[pulumi.Input[int]]: """ The number of 30-second intervals that you want service discovery to wait before it changes the health status of a service instance. Maximum value of 10. """ return pulumi.get(self, "failure_threshold") @failure_threshold.setter def failure_threshold(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "failure_threshold", value)

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4a7b0c76f22f7bd0ff38b3f5cc2d57868cd3ca54

159

py

Python

Image-Editor/src/Functions/Functions.py

TheCodingJungle/Python-Projects

eaec5b363b190fb013bcacbed48410c4b338fcc5

[ "MIT" ]

5

2021-02-08T13:53:16.000Z

2021-09-20T05:14:19.000Z

Image-Editor/src/Functions/Functions.py

TheCodingJungle/Python-Projects

eaec5b363b190fb013bcacbed48410c4b338fcc5

[ "MIT" ]

1

2021-07-29T20:00:34.000Z

Image-Editor/src/Functions/Functions.py

TheCodingJungle/Python-Projects

eaec5b363b190fb013bcacbed48410c4b338fcc5

[ "MIT" ]

1

2021-08-31T04:22:17.000Z

# This file helps in imorting the functions. from Crop import crop from Resize import resize from writeImage import writeImage from readImage import readImage

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py

Python

grortir/externals/__init__.py

wojtekPi/grortir

0ef8b495527a4f3861e5df5db756d0ee3ed4aa6f

[ "MIT" ]

null

grortir/externals/__init__.py

wojtekPi/grortir

0ef8b495527a4f3861e5df5db756d0ee3ed4aa6f

[ "MIT" ]

null

grortir/externals/__init__.py

wojtekPi/grortir

0ef8b495527a4f3861e5df5db756d0ee3ed4aa6f

[ "MIT" ]

null

"""Package contains modified external modules.""" # pylint: skip-file

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py

Python

tests/core/test_test.py

Racerinorbit/golem

b02c7acaed6e84ff565e34e8626e835ec451e2e4

[ "MIT" ]

null

tests/core/test_test.py

Racerinorbit/golem

b02c7acaed6e84ff565e34e8626e835ec451e2e4

[ "MIT" ]

null

tests/core/test_test.py

Racerinorbit/golem

b02c7acaed6e84ff565e34e8626e835ec451e2e4

[ "MIT" ]

null

import os import sys import pytest from golem.core import test as test_module, settings_manager from golem.core.project import Project from golem.core.test import Test SAMPLE_TEST_CONTENT = """ description = 'some description' tags = [] data = [{'a': 'b'}] pages = ['page1', 'page2'] def setup(data): page1.func1() def test(data): page2.func2('a', 'b') click(page2.elem1) def teardown(data): pass """ NEW_TEST_CONTENT = """ description = '' tags = [] pages = [] def setup(data): pass def test(data): pass def teardown(data): pass """ EMPTY_STEPS = {'setup': [], 'test': [], 'teardown': []} class TestCreateTest: def test_create_test(self, project_session, test_utils): _, project = project_session.activate() test_name = test_utils.random_string() errors = test_module.create_test(project, test_name) test = Test(project, test_name) assert test.exists assert errors == [] assert test.code == NEW_TEST_CONTENT def test_create_test_name_exists(self, project_session, test_utils): _, project = project_session.activate() test_name = test_utils.random_string() test_module.create_test(project, test_name) errors = test_module.create_test(project, test_name) assert errors == ['A test with that name already exists'] def test_create_test_invalid_name(self, project_session): _, project = project_session.activate() # invalid chars invalid_names = [ 'te-st', 'te st', 'te?st', 'test. .test' ] for name in invalid_names: errors = test_module.create_test(project, name) assert errors == ['Only letters, numbers and underscores are allowed'] # empty directory invalid_names = [ '.test', 'test..test', ] for name in invalid_names: errors = test_module.create_test(project, name) assert errors == ['Directory name cannot be empty'] # empty file name invalid_names = [ '', 'test.', ] for name in invalid_names: errors = test_module.create_test(project, name) assert errors == ['File name cannot be empty'] def test_create_test_into_folder(self, project_session, test_utils): _, project = project_session.activate() random_dir = test_utils.random_string() # to folder test_name = '{}.test001'.format(random_dir) errors = test_module.create_test(project, test_name) assert errors == [] # verify that each parent dir has __init__.py file init_path = os.path.join(Project(project).test_directory_path, random_dir, '__init__.py') assert test_name in Project(project).tests() assert os.path.isfile(init_path) # to sub-folder random_dir = test_utils.random_string() random_subdir = test_utils.random_string() test_name = '{}.{}.test001'.format(random_dir, random_subdir) errors = test_module.create_test(project, test_name) assert errors == [] assert test_name in Project(project).tests() # verify that each parent dir has __init__.py file init_path = os.path.join(Project(project).test_directory_path, random_dir, '__init__.py') assert os.path.isfile(init_path) init_path = os.path.join(Project(project).test_directory_path, random_dir, random_subdir, '__init__.py') assert os.path.isfile(init_path) class TestRenameTest: def test_rename_test(self, project_session, test_utils): _, project = project_session.activate() test_name = test_utils.create_random_test(project) new_test_name = test_utils.random_string() errors = test_module.rename_test(project, test_name, new_test_name) assert errors == [] tests = Project(project).tests() assert test_name not in tests assert new_test_name in tests def test_rename_test_in_folder(self, project_session, test_utils): _, project = project_session.activate() dir = test_utils.random_string() name = test_utils.random_string() test_name = '{}.{}'.format(dir, name) test_utils.create_test(project, test_name) # rename within same folder new_name = test_utils.random_string() new_test_name = '{}.{}'.format(dir, new_name) errors = test_module.rename_test(project, test_name, new_test_name) assert errors == [] tests = Project(project).tests() assert test_name not in tests assert new_test_name in tests # rename to another non existent folder test_name = new_test_name name = new_name new_dir = test_utils.random_string() new_test_name = '{}.{}'.format(new_dir, name) errors = test_module.rename_test(project, test_name, new_test_name) assert errors == [] tests = Project(project).tests() assert test_name not in tests assert new_test_name in tests def test_rename_test_invalid_name(self, project_session, test_utils): _, project = project_session.activate() test_name = test_utils.create_random_test(project) # invalid chars new_test_name = 'new-name' errors = test_module.rename_test(project, test_name, new_test_name) assert errors == ['Only letters, numbers and underscores are allowed'] tests = Project(project).tests() assert test_name in tests assert new_test_name not in tests # empty filename new_test_name = 'test.' errors = test_module.rename_test(project, test_name, new_test_name) assert errors == ['File name cannot be empty'] tests = Project(project).tests() assert test_name in tests assert new_test_name not in tests # empty directory new_test_name = 'test..test' errors = test_module.rename_test(project, test_name, new_test_name) assert errors == ['Directory name cannot be empty'] tests = Project(project).tests() assert test_name in tests assert new_test_name not in tests def test_rename_test_src_does_not_exist(self, project_session, test_utils): _, project = project_session.activate() test_name = test_utils.random_string() new_test_name = test_utils.random_string() errors = test_module.rename_test(project, test_name, new_test_name) assert errors == ['Test {} does not exist'.format(test_name)] assert new_test_name not in Project(project).tests() def test_rename_test_with_data_file(self, project_session, test_utils): """Assert when a test has a data file the data file is renamed as well""" _, project = project_session.activate() test_name = test_utils.create_random_test(project) new_test_name = test_utils.random_string() data_path = os.path.splitext(Test(project, test_name).path)[0] + '.csv' with open(data_path, 'w+') as f: f.write('') new_data_path = os.path.splitext(Test(project, new_test_name).path)[0] + '.csv' test_module.rename_test(project, test_name, new_test_name) assert not os.path.isfile(data_path) assert os.path.isfile(new_data_path) def test_rename_dest_exists(self, project_session, test_utils): _, project = project_session.activate() dir = test_utils.random_string() name_one = test_utils.random_string() test_one = '{}.{}'.format(dir, name_one) name_two = test_utils.random_string() test_two = '{}.{}'.format(dir, name_two) test_utils.create_test(project, test_one) test_utils.create_test(project, test_two) # rename test to existing test name errors = test_module.rename_test(project, test_one, test_two) assert errors == ['A file with that name already exists'] # rename test to same name errors = test_module.rename_test(project, test_one, test_one) assert errors == ['A file with that name already exists'] @pytest.mark.skipif("os.name != 'nt'") def test_rename_test_test_is_open(self, project_session, test_utils): """Try to rename a test while it is open""" _, project = project_session.activate() test_name = test_utils.create_random_test(project) new_test_name = test_utils.random_string() with open(Test(project, test_name).path) as f: errors = test_module.rename_test(project, test_name, new_test_name) assert errors == ['There was an error renaming file'] class TestDuplicateTest: def test_duplicate_test(self, project_session, test_utils): _, project = project_session.activate() # in root folder test_name = test_utils.create_random_test(project) new_test_name = test_utils.random_string() errors = test_module.duplicate_test(project, test_name, new_test_name) assert errors == [] tests = Project(project).tests() assert test_name in tests assert new_test_name in tests # in folder dir = test_utils.random_string() name = test_utils.random_string() test_name = '{}.{}'.format(dir, name) test_utils.create_test(project, test_name) new_name = test_utils.random_string() new_test_name = '{}.{}'.format(dir, new_name) errors = test_module.duplicate_test(project, test_name, new_test_name) assert errors == [] tests = Project(project).tests() assert test_name in tests assert new_test_name in tests def test_duplicate_test_same_name(self, project_session, test_utils): _, project = project_session.activate() test_name = test_utils.create_random_test(project) errors = test_module.duplicate_test(project, test_name, test_name) assert errors == ['New test name cannot be the same as the original'] def test_duplicate_test_dest_exists(self, project_session, test_utils): _, project = project_session.activate() test_one = test_utils.create_random_test(project) test_two = test_utils.create_random_test(project) errors = test_module.duplicate_test(project, test_one, test_two) assert errors == ['A test with that name already exists'] # to another folder test_one = test_utils.create_random_test(project) test_two = '{}.{}'.format(test_utils.random_string(), test_utils.random_string()) test_utils.create_test(project, test_two) errors = test_module.duplicate_test(project, test_one, test_two) assert errors == ['A test with that name already exists'] # to same name test_one = test_utils.create_random_test(project) test_utils.create_test(project, test_two) errors = test_module.duplicate_test(project, test_one, test_one) assert errors == ['New test name cannot be the same as the original'] def test_duplicate_test_invalid_name(self, project_session, test_utils): _, project = project_session.activate() test_name = test_utils.create_random_test(project) # invalid name new_test_name = 'new-name' errors = test_module.duplicate_test(project, test_name, new_test_name) assert errors == ['Only letters, numbers and underscores are allowed'] # empty name new_test_name = 'test.' errors = test_module.duplicate_test(project, test_name, new_test_name) assert errors == ['File name cannot be empty'] # empty directory new_test_name = 'test.' errors = test_module.duplicate_test(project, test_name, new_test_name) assert errors == ['File name cannot be empty'] def test_duplicate_test_with_data_file(self, project_session, test_utils): """Assert when a test has a data file the data file is duplicated as well""" _, project = project_session.activate() test_name = test_utils.create_random_test(project) new_test_name = test_utils.random_string() data_path = os.path.splitext(Test(project, test_name).path)[0] + '.csv' with open(data_path, 'w+') as f: f.write('') new_data_path = os.path.splitext(test_module.Test(project, new_test_name).path)[0] + '.csv' test_module.duplicate_test(project, test_name, new_test_name) assert os.path.isfile(data_path) assert os.path.isfile(new_data_path) class TestEditTest: def test_edit_test_data_infile(self, project_function, test_utils): _, project = project_function.activate() test_name = test_utils.create_random_test(project) description = 'description' pages = ['page1', 'page2'] test_steps = { 'setup': [ {'type': 'function-call', 'action': 'click', 'parameters': ['elem1']} ], 'test': [ {'type': 'function-call', 'action': 'send_keys', 'parameters': ['elem2', 'keys']} ], 'teardown': [] } data = [{ 'key': '\'value\'' }] settings_manager.save_project_settings(project, '{"test_data": "infile"}') test_module.edit_test(project, test_name, description, pages, test_steps, data, []) expected = ( '\n' 'description = \'description\'\n' '\n' 'tags = []\n' '\n' 'pages = [\'page1\',\n' ' \'page2\']\n' '\n' 'data = [\n' ' {\n' ' \'key\': \'value\',\n' ' },\n' ']\n' '\n\n' 'def setup(data):\n' ' click(elem1)\n' '\n\n' 'def test(data):\n' ' send_keys(elem2, keys)\n' '\n\n' 'def teardown(data):\n' ' pass\n') with open(Test(project, test_name).path) as f: assert f.read() == expected def test_edit_test_data_csv(self, project_function, test_utils): _, project = project_function.activate() test_name = test_utils.create_random_test(project) description = 'description' pages = [] test_steps = { 'setup': [], 'test': [ {'type': 'function-call', 'action': 'send_keys', 'parameters': ['elem2', 'keys']} ], 'teardown': [] } data = [{ 'key': '\'value\'' }] settings_manager.save_project_settings(project, '{"test_data": "csv"}') test_module.edit_test(project, test_name, description, pages, test_steps, data, []) expected = ( '\n' 'description = \'description\'\n' '\n' 'tags = []\n' '\n' 'pages = []\n' '\n\n' 'def setup(data):\n' ' pass\n' '\n\n' 'def test(data):\n' ' send_keys(elem2, keys)\n' '\n\n' 'def teardown(data):\n' ' pass\n') with open(Test(project, test_name).path) as f: assert f.read() == expected data_path = os.path.join(Project(project).test_directory_path, '{}.csv'.format(test_name)) expected = ('key\n' '\'value\'\n') with open(data_path) as f: assert f.read() == expected def test_edit_test_explicit_page_import(self, project_function, test_utils): _, project = project_function.activate() test_name = test_utils.create_random_test(project) pages = ['page1', 'module.page2'] settings_manager.save_project_settings(project, '{"implicit_page_import": false}') test_module.edit_test(project, test_name, description='', pages=pages, steps=EMPTY_STEPS, test_data=[], tags=[]) expected = ('from projects.{}.pages import page1\n' 'from projects.{}.pages.module import page2\n' '\n\n' 'description = \'\'\n' '\n' 'tags = []\n' '\n\n' 'def setup(data):\n' ' pass\n' '\n\n' 'def test(data):\n' ' pass\n' '\n\n' 'def teardown(data):\n' ' pass\n'.format(project, project)) with open(Test(project, test_name).path) as f: assert f.read() == expected def test_edit_test_explicit_action_import(self, project_function, test_utils): _, project = project_function.activate() test_name = test_utils.create_random_test(project) settings_manager.save_project_settings(project, '{"implicit_actions_import": false}') test_module.edit_test(project, test_name, description='', pages=[], steps=EMPTY_STEPS, test_data=[], tags=[]) expected = ('from golem import actions\n\n\n' 'description = \'\'\n\n' 'tags = []\n\n' 'pages = []\n\n\n' 'def setup(data):\n' ' pass\n\n\n' 'def test(data):\n' ' pass\n\n\n' 'def teardown(data):\n' ' pass\n') with open(Test(project, test_name).path) as f: assert f.read() == expected def test_edit_test_skip(self, project_session, test_utils): _, project = project_session.activate() test_name = test_utils.create_random_test(project) test_module.edit_test(project, test_name, description='', pages=[], steps=EMPTY_STEPS, test_data=[], tags=[], skip=True) path = Test(project, test_name).path expected = ('\n' 'description = \'\'\n\n' 'tags = []\n\n' 'pages = []\n\n' 'skip = True\n\n\n' 'def setup(data):\n' ' pass\n\n\n' 'def test(data):\n' ' pass\n\n\n' 'def teardown(data):\n' ' pass\n') with open(path) as f: assert f.read() == expected # skip is string test_module.edit_test(project, test_name, description='', pages=[], steps=EMPTY_STEPS, test_data=[], tags=[], skip='please skip this') path = Test(project, test_name).path expected = ('\n' 'description = \'\'\n\n' 'tags = []\n\n' 'pages = []\n\n' 'skip = \'please skip this\'\n\n\n' 'def setup(data):\n' ' pass\n\n\n' 'def test(data):\n' ' pass\n\n\n' 'def teardown(data):\n' ' pass\n') with open(path) as f: assert f.read() == expected class TestEditTestCode: def test_edit_test_code_csv_data(self, project_session, test_utils): _, project = project_session.activate() test_data = [{'key': "'value'"}] settings_manager.save_project_settings(project, '{"test_data": "csv"}') test_name = test_utils.create_random_test(project) test_module.edit_test_code(project, test_name, SAMPLE_TEST_CONTENT, test_data) path = test_module.Test(project, test_name).path with open(path) as f: assert f.read() == SAMPLE_TEST_CONTENT path = os.path.join(Project(project).test_directory_path, test_name + '.csv') expected = ('key\n' '\'value\'\n') with open(path) as f: assert f.read() == expected class TestDeleteTest: def test_delete_test(self, project_session, test_utils): _, project = project_session.activate() test_one = test_utils.random_string() test_two = '{}.{}'.format(test_utils.random_string(), test_utils.random_string()) test_utils.create_test(project, test_one) test_utils.create_test(project, test_two) errors_one = test_module.delete_test(project, test_one) errors_two = test_module.delete_test(project, test_two) assert errors_one == [] assert errors_two == [] assert not os.path.isfile(Test(project, test_one).path) assert not os.path.isfile(Test(project, test_two).path) def test_delete_test_not_exist(self, project_session): _, project = project_session.activate() errors = test_module.delete_test(project, 'not-exist') assert errors == ['Test not-exist does not exist'] def test_delete_test_with_data(self, project_session, test_utils): """"test that when a test is deleted the data files are deleted as well """ _, project = project_session.activate() test_name = test_utils.create_random_test(project) data_path = os.path.splitext(test_module.Test(project, test_name).path)[0] + '.csv' open(data_path, 'x').close() errors = test_module.delete_test(project, test_name) assert errors == [] assert not os.path.isfile(data_path) class TestTestExists: def test_test_exists(self, project_session, test_utils): _, project = project_session.activate() test_name = test_utils.create_random_test(project) assert Test(project, test_name).exists assert not Test(project, 'not_exists_test').exists class TestTestCode: def test_test_code(self, project_session, test_utils): _, project = project_session.activate() test_name = test_utils.create_random_test(project) test = Test(project, test_name) with open(test.path, 'w') as f: f.write(SAMPLE_TEST_CONTENT) assert test.code == SAMPLE_TEST_CONTENT class TestTestComponents: def test_test_components(self, project_session, test_utils): _, project = project_session.activate() test_name = test_utils.create_random_test(project) test = Test(project, test_name) with open(test.path, 'w') as f: f.write(SAMPLE_TEST_CONTENT) components = test.components assert components['description'] == 'some description' assert components['pages'] == ['page1', 'page2'] assert components['tags'] == [] assert components['skip'] is False assert components['steps']['setup'] == [{'code': 'page1.func1()', 'function_name': 'page1.func1', 'parameters': [], 'type': 'function-call'}] expected_test_steps = [{'code': "page2.func2('a', 'b')", 'function_name': 'page2.func2', 'parameters': ["'a'", "'b'"], 'type': 'function-call'}, {'code': 'click(page2.elem1)', 'function_name': 'click', 'parameters': ['page2.elem1'], 'type': 'function-call'}] assert components['steps']['test'] == expected_test_steps assert components['steps']['teardown'] == [] def test_test_components_empty_test(self, project_session, test_utils): _, project = project_session.activate() test_name = test_utils.create_random_test(project) test_content = Test(project, test_name).components assert test_content['description'] == '' assert test_content['pages'] == [] assert test_content['steps']['setup'] == [] assert test_content['steps']['test'] == [] assert test_content['steps']['teardown'] == [] def test_test_components_pages(self, project_session, test_utils): """components['pages'] contains the imported pages and the pages defined in the list """ testdir, project = project_session.activate() test_name = test_utils.create_random_test(project) test_utils.create_page(project, 'page1') test_utils.create_page(project, 'page2') test_utils.create_page(project, 'module.page3') sys.path.append(testdir) with open(Test(project, test_name).path, 'w') as f: test_content = ('from projects.{}.pages import page1, page2\n' 'from projects.{}.pages.module import page3\n' '\n' 'pages = ["page4", "module2.page5"]\n' '\n' 'def test(data):\n' ' pass\n'.format(project, project)) f.write(test_content) components = Test(project, test_name).components expected = ['page1', 'page2', 'module.page3', 'page4', 'module2.page5'] assert components['pages'].sort() == expected.sort() def test_test_components_skip(self, project_session, test_utils): _, project = project_session.activate() test_name = test_utils.create_random_test(project) # default / empty skip is False assert Test(project, test_name).components['skip'] is False # skip is True test_module.edit_test(project, test_name, description='', pages=[], steps=EMPTY_STEPS, test_data=[], tags=[], skip=True) assert Test(project, test_name).components['skip'] is True # skip is string test_module.edit_test(project, test_name, description='', pages=[], steps=EMPTY_STEPS, test_data=[], tags=[], skip='please skip') assert Test(project, test_name).components['skip'] == 'please skip'

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Python

samples/migrateADCGen1/mappers/__init__.py

daniel-dqsdatalabs/pyapacheatlas

7fbc0ae3b3c661db07a443306995d4c416a01e1a

[ "MIT" ]

104

2020-12-07T14:18:20.000Z

2022-03-16T12:11:21.000Z

samples/migrateADCGen1/mappers/__init__.py

daniel-dqsdatalabs/pyapacheatlas

7fbc0ae3b3c661db07a443306995d4c416a01e1a

[ "MIT" ]

98

2020-12-23T20:27:02.000Z

2022-03-10T15:44:43.000Z

samples/migrateADCGen1/mappers/__init__.py

daniel-dqsdatalabs/pyapacheatlas

7fbc0ae3b3c661db07a443306995d4c416a01e1a

[ "MIT" ]

47

2020-12-17T16:28:31.000Z

2022-02-22T03:12:19.000Z

from .assetmapper import AssetMapper from .assetfactory import AssetFactory from .sqlserver import SqlServerTableMapper

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43531c2f54d2dd69fe9cc867d839f38ee386c401

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py

Python

scripts/test_dist.py

xcgoner/gluon-exp

432a1aafc1466720b6169bb41caabb2a217b0797

[ "Apache-2.0" ]

null

scripts/test_dist.py

xcgoner/gluon-exp

432a1aafc1466720b6169bb41caabb2a217b0797

[ "Apache-2.0" ]

null

scripts/test_dist.py

xcgoner/gluon-exp

432a1aafc1466720b6169bb41caabb2a217b0797

[ "Apache-2.0" ]

null

import mxnet as mx from mxnet import nd, gluon, autograd

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py

Python

model-optimizer/mo/front/common/partial_infer/split_test.py

zhoub/dldt

e42c01cf6e1d3aefa55e2c5df91f1054daddc575

[ "Apache-2.0" ]

3

2020-02-09T23:25:37.000Z

2021-01-19T09:44:12.000Z

model-optimizer/mo/front/common/partial_infer/split_test.py

zhoub/dldt

e42c01cf6e1d3aefa55e2c5df91f1054daddc575

[ "Apache-2.0" ]

null

model-optimizer/mo/front/common/partial_infer/split_test.py

zhoub/dldt

e42c01cf6e1d3aefa55e2c5df91f1054daddc575

[ "Apache-2.0" ]

2

2020-04-18T16:24:39.000Z

2021-01-19T09:42:19.000Z

""" Copyright (c) 2018-2019 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import unittest import numpy as np from mo.front.common.partial_infer.split import tf_split_infer, tf_unpack_infer, tf_split_v_infer, split from mo.front.common.partial_infer.utils import int64_array from mo.graph.graph import Node from mo.utils.unittest.graph import build_graph, build_graph_with_edge_attrs class TestTFSplitInfer(unittest.TestCase): graph = None def setUp(self): self.graph = build_graph({'split_dim': {'value': None, 'kind': 'data'}, 'data_to_split': {'value': None, 'shape': None, 'kind': 'data'}, 'split_node': {'kind': 'op', 'op': 'Split', 'num_split': 3, 'axis': None}, 'out_data_1': {'value': None, 'shape': None, 'kind': 'data'}, 'out_data_2': {'value': None, 'shape': None, 'kind': 'data'}, 'out_data_3': {'value': None, 'shape': None, 'kind': 'data'}, }, [('split_dim', 'split_node'), ('data_to_split', 'split_node'), ('split_node', 'out_data_1'), ('split_node', 'out_data_2'), ('split_node', 'out_data_3'), ]) def test_tf_split_infer(self): split_node = Node(self.graph, 'split_node') self.graph.node['split_dim']['value'] = np.array(1) self.graph.node['data_to_split']['shape'] = int64_array([2, 12, 25, 30]) tf_split_infer(split_node) exp_shape = int64_array([2, 4, 25, 30]) for out_node in split_node.out_nodes().values(): self.assertTrue(np.all(exp_shape == out_node.shape)) self.assertEqual(1, split_node.input_port) def test_tf_split_infer_negative_index(self): split_node = Node(self.graph, 'split_node') self.graph.node['split_dim']['value'] = np.array(-3) self.graph.node['data_to_split']['shape'] = int64_array([2, 12, 25, 30]) tf_split_infer(split_node) exp_shape = int64_array([2, 4, 25, 30]) for out_node in split_node.out_nodes().values(): self.assertTrue(np.all(exp_shape == out_node.shape)) self.assertEqual(1, split_node.input_port) def test_tf_split_infer_unknown_index(self): split_node = Node(self.graph, 'split_node') self.graph.node['data_to_split']['shape'] = int64_array([2, 12, 25, 30]) tf_split_infer(split_node) for out_node in split_node.out_nodes().values(): self.assertIsNone(out_node.shape) def test_tf_split_infer_input_shape_is_None(self): split_node = Node(self.graph, 'split_node') self.graph.node['split_dim']['value'] = np.array(1) tf_split_infer(split_node) for out_node in split_node.out_nodes().values(): self.assertIsNone(out_node.shape) def test_tf_split_infer_wrong_num_split(self): split_node = Node(self.graph, 'split_node') self.graph.node['split_dim']['value'] = np.array(0) self.graph.node['data_to_split']['shape'] = int64_array([2, 12, 25, 30]) tf_split_infer(split_node) for out_node in split_node.out_nodes().values(): self.assertIsNone(out_node.shape) class TestTFSplitVInfer(unittest.TestCase): graph = None def setUp(self): self.graph = build_graph({'data_to_split': {'value': None, 'shape': None, 'kind': 'data'}, 'size_splits': {'value': [3, 5, 4], 'kind': 'data'}, 'split_dim': {'value': None, 'kind': 'data'}, 'split_node': {'kind': 'op', 'op': 'Split', 'axis': None}, 'out_data_1': {'value': None, 'shape': None, 'kind': 'data'}, 'out_data_2': {'value': None, 'shape': None, 'kind': 'data'}, 'out_data_3': {'value': None, 'shape': None, 'kind': 'data'}, }, [('data_to_split', 'split_node'), ('size_splits', 'split_node'), ('split_dim', 'split_node'), ('split_node', 'out_data_1'), ('split_node', 'out_data_2'), ('split_node', 'out_data_3'), ]) def test_tf_split_infer_three_inputs(self): split_node = Node(self.graph, 'split_node') self.graph.node['split_dim']['value'] = np.array(1) self.graph.node['data_to_split']['shape'] = int64_array([2, 12, 25, 30]) tf_split_v_infer(split_node) exp_shape = [int64_array([2, 3, 25, 30]), int64_array([2, 5, 25, 30]), int64_array([2, 4, 25, 30])] for ind, out_node in split_node.out_nodes().items(): self.assertTrue(np.all(exp_shape[ind] == out_node.shape)) def test_tf_split_infer_undef_size(self): split_node = Node(self.graph, 'split_node') self.graph.node['split_dim']['value'] = np.array(1) self.graph.node['data_to_split']['shape'] = int64_array([2, 12, 25, 30]) self.graph.node['size_splits']['value'] = np.array([3, 2, -1]) tf_split_v_infer(split_node) exp_shape = [int64_array([2, 3, 25, 30]), int64_array([2, 2, 25, 30]), int64_array([2, 7, 25, 30])] for ind, out_node in split_node.out_nodes().items(): self.assertTrue(np.all(exp_shape[ind] == out_node.shape)) class TestTFUnpack(unittest.TestCase): graph = None def setUp(self): self.graph = build_graph({'data_to_split': {'value': None, 'shape': None, 'kind': 'data'}, 'unpack': {'kind': 'op', 'op': 'Split', 'num_split': 3, 'axis': None}, 'out_data_1': {'value': None, 'shape': None, 'kind': 'data'}, 'out_data_2': {'value': None, 'shape': None, 'kind': 'data'}, 'out_data_3': {'value': None, 'shape': None, 'kind': 'data'}, 'out_data_4': {'value': None, 'shape': None, 'kind': 'data'}, }, [('data_to_split', 'unpack'), ('unpack', 'out_data_1'), ('unpack', 'out_data_2'), ('unpack', 'out_data_3'), ]) def test_tf_unpack_infer(self): unpack_node = Node(self.graph, 'unpack') self.graph.node['unpack']['axis'] = np.array(1) self.graph.node['data_to_split']['shape'] = int64_array([2, 3, 25, 30]) tf_unpack_infer(unpack_node) exp_shape = int64_array([2, 1, 25, 30]) for out_node in unpack_node.out_nodes().values(): self.assertTrue(np.all(exp_shape == out_node.shape)) def test_tf_unpack_infer_default_number_of_pieces(self): unpack_node = Node(self.graph, 'unpack') self.graph.node['unpack']['axis'] = np.array(1) self.graph.node['unpack']['num_split'] = None self.graph.node['data_to_split']['shape'] = int64_array([2, 3, 25, 30]) tf_unpack_infer(unpack_node) exp_shape = int64_array([2, 1, 25, 30]) for out_node in unpack_node.out_nodes().values(): self.assertTrue(np.all(exp_shape == out_node.shape)) def test_tf_unpack_infer_not_supported(self): # the case when the size of the dimension being unpacked is not equal to number of pieces is not supported unpack_node = Node(self.graph, 'unpack') self.graph.node['unpack']['axis'] = np.array(1) self.graph.node['data_to_split']['shape'] = int64_array([2, 6, 25, 30]) tf_unpack_infer(unpack_node) for out_node in unpack_node.out_nodes().values(): self.assertIsNone(out_node.shape) class TestSplitFunc(unittest.TestCase): graph = None def setUp(self): self.graph = build_graph_with_edge_attrs( {'data_to_split': {'value': None, 'shape': int64_array([2, 12, 25, 44]), 'kind': 'data'}, 'split_node': {'kind': 'op', 'op': 'Split', 'axis': None}, 'out_data_2': {'value': None, 'shape': None, 'kind': 'data'}, 'out_data_5': {'value': None, 'shape': None, 'kind': 'data'}, 'out_data_7': {'value': None, 'shape': None, 'kind': 'data'}, }, [('data_to_split', 'split_node', {'in': 0}), ('split_node', 'out_data_2', {'out': 2}), ('split_node', 'out_data_5', {'out': 5}), ('split_node', 'out_data_7', {'out': 7}), ]) def test_split_non_sequential_output_port(self): split(Node(self.graph, 'data_to_split'), Node(self.graph, 'split_node'), -1, [3, 2, 7, 5, 6, 4, 9, 8]) self.assertTrue(np.all(Node(self.graph, 'out_data_2').shape == [2, 12, 25, 7])) self.assertTrue(np.all(Node(self.graph, 'out_data_5').shape == [2, 12, 25, 4])) self.assertTrue(np.all(Node(self.graph, 'out_data_7').shape == [2, 12, 25, 8])) def test_split_value_infer_non_sequential_output_port(self): data_node = Node(self.graph, 'data_to_split') value = np.array(range(2 * 12 * 25 * 44)).reshape(data_node.shape) data_node.value = value.copy() split(data_node, Node(self.graph, 'split_node'), -1, [3, 2, 7, 5, 6, 4, 9, 8]) self.assertTrue(np.all(Node(self.graph, 'out_data_2').shape == [2, 12, 25, 7])) self.assertTrue(np.all(Node(self.graph, 'out_data_5').shape == [2, 12, 25, 4])) self.assertTrue(np.all(Node(self.graph, 'out_data_7').shape == [2, 12, 25, 8])) self.assertTrue(np.all(Node(self.graph, 'out_data_2').value == value[:, :, :, 5:12])) self.assertTrue(np.all(Node(self.graph, 'out_data_5').value == value[:, :, :, 23:27])) self.assertTrue(np.all(Node(self.graph, 'out_data_7').value == value[:, :, :, 36:]))

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43790ed6541807d42a3623ea81620f736075ba4f

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py

Python

uitwerkingen/1h-importeren.py

harcel/PyDataScienceIntroNL

8041b55203b941d8c0f189cdd19bdfd96420225c

[ "MIT" ]

null

uitwerkingen/1h-importeren.py

harcel/PyDataScienceIntroNL

8041b55203b941d8c0f189cdd19bdfd96420225c

[ "MIT" ]

null

uitwerkingen/1h-importeren.py

harcel/PyDataScienceIntroNL

8041b55203b941d8c0f189cdd19bdfd96420225c

[ "MIT" ]

null

import eigenlib as lib lib.geefmeinfo() # get the documentation (alt-tab werkt ook!) print(lib.plus3.__doc__) print() print(lib.plus3(8.)) print(lib.plus3(np.array([2., 109.]))) print(lib.plus3('stykje tekst'))

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226

py

Python

bbridge_sdk/entity/request/nlp_data.py

bbridge-team/bbridge-sdk-python

10a46b9ff87a91fdebc65f74cfb30a05a10bb641

[ "MIT" ]

null

bbridge_sdk/entity/request/nlp_data.py

bbridge-team/bbridge-sdk-python

10a46b9ff87a91fdebc65f74cfb30a05a10bb641

[ "MIT" ]

1

2017-03-16T06:39:38.000Z

2017-03-16T09:50:27.000Z

bbridge_sdk/entity/request/nlp_data.py

bbridge-team/bbridge-sdk-python

10a46b9ff87a91fdebc65f74cfb30a05a10bb641

[ "MIT" ]

1

2017-04-07T01:29:55.000Z

class NLPData(object): def __init__(self, sentences): """ :type sentences: list[str] """ self.__sentences = sentences @property def sentences(self): return self.__sentences

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43a65437ec6212e13e832d9123f0c69b28eecb73

3,516

py

Python

tests/test_config_validation.py

anderskaplan/pomosite

59bc8e670c9a765953bc0535cf17de82e6fdec65

[ "MIT" ]

null

tests/test_config_validation.py

anderskaplan/pomosite

59bc8e670c9a765953bc0535cf17de82e6fdec65

[ "MIT" ]

null

tests/test_config_validation.py

anderskaplan/pomosite

59bc8e670c9a765953bc0535cf17de82e6fdec65

[ "MIT" ]

null

import unittest from pathlib import Path import shutil from xml.etree import ElementTree from pomosite import generate, ConfigurationError content_path = str(Path(Path(__file__).parent, "data/test_templating")) output_dir = "temp/test_validation" class TestConfigValidation(unittest.TestCase): @classmethod def setUpClass(self): p = Path(output_dir) if p.exists(): print("removing " + output_dir) shutil.rmtree(output_dir) def test_should_fail_on_missing_leading_slash(self): site_config = { "item_config": { "P1": { "endpoint": "x/", "template": "page.html", }, }, "template_dir": content_path + "/templates", } with self.assertRaises(ConfigurationError): generate(site_config, output_dir) def test_should_not_accept_two_identical_page_endpoints(self): site_config = { "item_config": { "P1": { "endpoint": "/xyz", "template": "page.html", }, "P2": { "endpoint": "/xyz", "template": "page.html", }, }, "template_dir": content_path + "/templates", } with self.assertRaises(ConfigurationError): generate(site_config, output_dir) def test_should_not_accept_two_identical_static_endpoints(self): site_config = { "item_config": { "S1": { "endpoint": "/xyz", "source": Path(content_path, "templates/page.html"), }, "S2": { "endpoint": "/xyz", "source": Path(content_path, "templates/page.html"), }, }, "template_dir": content_path + "/templates", } with self.assertRaises(ConfigurationError): generate(site_config, output_dir) def test_should_not_accept_two_identical_page_and_static_endpoints(self): site_config = { "item_config": { "P1": { "endpoint": "/xyz", "template": "page.html", }, "S1": { "endpoint": "/xyz", "source": Path(content_path, "templates/page.html"), }, }, "template_dir": content_path + "/templates", } with self.assertRaises(ConfigurationError): generate(site_config, output_dir) def test_should_not_accept_endpoints_with_invalid_characters(self): site_config = { "item_config": { "P1": { "endpoint": "/xyö", "template": "page.html", }, }, "template_dir": content_path + "/templates", } with self.assertRaises(ConfigurationError): generate(site_config, output_dir) def test_should_not_accept_endpoints_with_space(self): site_config = { "item_config": { "P1": { "endpoint": "/xy zz", "template": "page.html", }, }, "template_dir": content_path + "/templates", } with self.assertRaises(ConfigurationError): generate(site_config, output_dir)

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1

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null

0

4

43bc1a799a8508aff2a9dff6ef99dbc8ae4b1eeb

359

py

Python

tasks/EPAM/python_course/foundation-python/l2/m2-15.py

AleksNeStu/projects

1a4c68dfbdcb77228f0f3617e58fd18fcb1f5dbb

[ "Apache-2.0" ]

2

2022-01-19T18:01:35.000Z

2022-02-06T06:54:38.000Z

tasks/EPAM/python_course/foundation-python/l2/m2-15.py

AleksNeStu/projects

1a4c68dfbdcb77228f0f3617e58fd18fcb1f5dbb

[ "Apache-2.0" ]

null

tasks/EPAM/python_course/foundation-python/l2/m2-15.py

AleksNeStu/projects

1a4c68dfbdcb77228f0f3617e58fd18fcb1f5dbb

[ "Apache-2.0" ]

null

# is Evaluates to true if the variables on either side of the operator point to the same object and false otherwise. x is y, here is results in 1 if id(x) equals id(y). # is not Evaluates to false if the variables on either side of the operator point to the same object and true otherwise. x is not y, here is not results in 1 if id(x) is not equal to id(y).

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0

1

0

1

0

null

0

1

0

4

78f6064317aa3269772fc5e1c4d7da5880a767bc

304

py

Python

csp/__init__.py

JacobChen258/AI-Constraints-Satisfaction

9b01cfce447e40678eb2e426413b4e2e437257f0

[ "MIT" ]

null

csp/__init__.py

JacobChen258/AI-Constraints-Satisfaction

9b01cfce447e40678eb2e426413b4e2e437257f0

[ "MIT" ]

null

csp/__init__.py

JacobChen258/AI-Constraints-Satisfaction

9b01cfce447e40678eb2e426413b4e2e437257f0

[ "MIT" ]

null

from .csp import CSP from .tetris_csp import TetrisCSP from .csp_algorithms import CSPAlgorithms from .variable import Variable from .constraint import Constraint from .tetromino_puzzle_constraint import TetrominoPuzzleConstraint from .tetris_variable import TetrisVariable from .csp_util import CSPUtil

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null

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1

0

1

0

1

0

4

601a4669ee0b03503a3661d1800f5462f100acbf

188

py

Python

froide/upload/serializers.py

kratz00/froide

f31e6dbe7f6d565058bde36461a6fa2d09e0388e

[ "MIT" ]

null

froide/upload/serializers.py

kratz00/froide

f31e6dbe7f6d565058bde36461a6fa2d09e0388e

[ "MIT" ]

null

froide/upload/serializers.py

kratz00/froide

f31e6dbe7f6d565058bde36461a6fa2d09e0388e

[ "MIT" ]

null

from rest_framework import serializers from .models import Upload class UploadSerializer(serializers.ModelSerializer): class Meta: model = Upload fields = '__all__'

18.8

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188

6.947368

0.736842

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0.218085

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9

53

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0

1

0

false

0

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0

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0

null

0

1

0

null

0

1

0

1

0

4

603a3febf50bf50252584143949326911364fed7

514

py

Python

ezidapp/models/datacite_queue.py

HEG-INCIPIT/ARKetype

bd2ced291292fa6cc2f101f59a9614698dae5102

[ "MIT" ]

9

2020-02-26T00:45:09.000Z

2021-11-07T23:07:06.000Z

ezidapp/models/datacite_queue.py

HEG-INCIPIT/ARKetype

bd2ced291292fa6cc2f101f59a9614698dae5102

[ "MIT" ]

213

2020-04-07T21:36:17.000Z

2022-03-29T21:26:04.000Z

ezidapp/models/datacite_queue.py

HEG-INCIPIT/ARKetype

bd2ced291292fa6cc2f101f59a9614698dae5102

[ "MIT" ]

7

2020-04-07T20:04:51.000Z

2021-08-19T01:11:55.000Z

# ============================================================================= # # EZID :: ezidapp/models/datacite_queue.py # # Database model for the DataCite queue. # # Author: # Greg Janee <gjanee@ucop.edu> # # License: # Copyright (c) 2015, Regents of the University of California # http://creativecommons.org/licenses/BSD/ # # ----------------------------------------------------------------------------- import registration_queue class DataciteQueue(registration_queue.RegistrationQueue): pass

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true

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0

null

0

null

0

1

0

1

0

4

603e8d3db9a72dc84eeac73a6fd500ce2b340db7

274

py

Python

clientes/gateway.py

lauraziebarth/vendas-backend

026a86f1995af63f02738dd6166ebb3e145878a3

[ "MIT" ]

null

clientes/gateway.py

lauraziebarth/vendas-backend

026a86f1995af63f02738dd6166ebb3e145878a3

[ "MIT" ]

null

clientes/gateway.py

lauraziebarth/vendas-backend

026a86f1995af63f02738dd6166ebb3e145878a3

[ "MIT" ]

null

from clientes.models import Cliente def busca_um_cliente(cliente_id): return Cliente.objects.get(id=cliente_id) def busca_clientes_nao_excluidos(): return Cliente.objects.filter(excluido=False) def busca_todos_os_clientes(): return Cliente.objects.all()

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null

0

1

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1

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4

6076ab4045f42373d8afc9df3315a7e7b053b69b

188

py

Python

F_Machine_learning/2_Supervised-Learning/solutions/ex0_4b.py

oercompbiomed/CBM101

20010dcb99fbf218c4789eb5918dcff8ceb94898

[ "MIT" ]

7

2019-07-03T07:41:55.000Z

2022-02-06T20:25:37.000Z

Lab2-ML-tissue-classification/solutions/ex0_4b.py

computational-medicine/BMED360-2021

2c6052b9affedf1fee23c89d23941bf08eb2614c

[ "MIT" ]

9

2019-03-14T15:15:09.000Z

2019-08-01T14:18:21.000Z

Lab2-ML-tissue-classification/solutions/ex0_4b.py

computational-medicine/BMED360-2021

2c6052b9affedf1fee23c89d23941bf08eb2614c

[ "MIT" ]

11

2019-03-12T10:43:11.000Z

2021-10-05T12:15:00.000Z

def data_splitter(data, idxs): subsample = data[idxs] return subsample ## Note: matrices are indexed like mat[rows, cols]. If only one is provided, it is interpreted as mat[rows].

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1

0

null

0

1

0

1

0

4

609011e393f39f606ec754ea21754db7263e684c

45

py

Python

run_tests.py

acud/py-swarm

50f4b0eaa8266c721453fb44f4e2312aa5de15f0

[ "BSD-3-Clause" ]

1

2021-07-27T07:45:53.000Z

run_tests.py

acud/py-swarm

50f4b0eaa8266c721453fb44f4e2312aa5de15f0

[ "BSD-3-Clause" ]

null

run_tests.py

acud/py-swarm

50f4b0eaa8266c721453fb44f4e2312aa5de15f0

[ "BSD-3-Clause" ]

null

#!/bin/bash python -m unittest test/*.py -v

11.25

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1

0

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1

0

null

0

1

0

4

6097a63bab820b8fe6085ed3b1fc0834611b451b

169

py

Python

hamiltorch/__init__.py

kakodkar/hamiltorch

0ed85dacb28a77b27b9cb9c55ed178284ca7f195

[ "BSD-2-Clause" ]

237

2019-10-06T02:41:50.000Z

2022-03-25T19:55:56.000Z

hamiltorch/__init__.py

leoduan/hamiltorch

ac8feb278df2abd238a3d50604645a247c9610fd

[ "BSD-2-Clause" ]

15

2020-01-06T17:21:49.000Z

2022-03-10T07:35:02.000Z

hamiltorch/__init__.py

leoduan/hamiltorch

ac8feb278df2abd238a3d50604645a247c9610fd

[ "BSD-2-Clause" ]

47

2019-12-20T20:05:34.000Z

2022-01-04T15:48:44.000Z

__version__ = '0.4.0.dev1' from .samplers import sample, sample_model, predict_model, sample_split_model, Sampler, Integrator, Metric from .util import set_random_seed

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null

0

1

0

1

0

4

609e4f40804e523cf72716523a36f877e41b594a

26,153

py

Python

code/k.py

davidbradway/klassysouthkarolina

0b4ccee55543bc759c7684d6326b5d3768eeb4f4

[ "Apache-2.0" ]

2

2017-07-10T15:39:43.000Z

2018-12-16T20:04:47.000Z

code/k.py

davidbradway/klassysouthkarolina

0b4ccee55543bc759c7684d6326b5d3768eeb4f4

[ "Apache-2.0" ]

null

code/k.py

davidbradway/klassysouthkarolina

0b4ccee55543bc759c7684d6326b5d3768eeb4f4

[ "Apache-2.0" ]

null

kwords=['ka', 'kababish', 'kabaka', 'kabaragoya', 'kabard', 'kabardian', 'kabaya', 'kabbeljaws', 'kabel', 'kaberu', 'kabiet', 'kabirpanthi', 'kabistan', 'kabonga', 'kabuki', 'kabuli', 'kabyle', 'kachari', 'kachin', 'kachin', 'kadaga', 'kadarite', 'kadaya', 'kadayan', 'kaddish', 'kadein', 'kadikane', 'kadischi', 'kadmi', 'kados', 'kadu', 'kaempferol', 'kaf', 'kafa', 'kaferita', 'kaffir', 'kaffir', 'kaffiyeh', 'kaffraria', 'kaffrarian', 'kafir', 'kafir', 'kafiri', 'kafirin', 'kafiz', 'kafka', 'kafkaesque', 'kafta', 'kago', 'kagu', 'kaha', 'kahar', 'kahau', 'kahikatea', 'kahili', 'kahu', 'kahuna', 'kai', 'kaibab', 'kaibartha', 'kaid', 'kaik', 'kaikara', 'kaikawaka', 'kail', 'kailyard', 'kailyarder', 'kailyardism', 'kaimo', 'kainah', 'kainga', 'kainite', 'kainsi', 'kainyn', 'kairine', 'kairoline', 'kaiser', 'kaiserdom', 'kaiserism', 'kaisership', 'kaitaka', 'kaithi', 'kaiwhiria', 'kaiwi', 'kaj', 'kajar', 'kajawah', 'kajugaru', 'kaka', 'kakan', 'kakapo', 'kakar', 'kakarali', 'kakariki', 'kakatoe', 'kakatoidae', 'kakawahie', 'kaki', 'kakidrosis', 'kakistocracy', 'kakkak', 'kakke', 'kakortokite', 'kala', 'kaladana', 'kalamalo', 'kalamansanai', 'kalamian', 'kalanchoe', 'kalandariyah', 'kalang', 'kalapooian', 'kalashnikov', 'kalasie', 'kaldani', 'kale', 'kaleidophon', 'kaleidophone', 'kaleidoscope', 'kaleidoscopic', 'kaleidoscopical', 'kaleidoscopically', 'kalekah', 'kalema', 'kalendae', 'kalends', 'kalewife', 'kaleyard', 'kali', 'kalian', 'kaliana', 'kaliborite', 'kalidium', 'kaliform', 'kaligenous', 'kalinga', 'kalinite', 'kaliophilite', 'kalipaya', 'kalispel', 'kalium', 'kallah', 'kallege', 'kallilite', 'kallima', 'kallitype', 'kalmarian', 'kalmia', 'kalmuck', 'kalo', 'kalogeros', 'kalokagathia', 'kalon', 'kalong', 'kalpis', 'kalsomine', 'kalsominer', 'kalumpang', 'kalumpit', 'kalwar', 'kalymmaukion', 'kalymmocyte', 'kamachile', 'kamacite', 'kamahi', 'kamala', 'kamaloka', 'kamansi', 'kamao', 'kamares', 'kamarezite', 'kamarupa', 'kamarupic', 'kamas', 'kamasin', 'kamass', 'kamassi', 'kamba', 'kambal', 'kamboh', 'kamchadal', 'kamchatkan', 'kame', 'kameeldoorn', 'kameelthorn', 'kamel', 'kamelaukion', 'kamerad', 'kamias', 'kamichi', 'kamik', 'kamikaze', 'kamiya', 'kammalan', 'kammererite', 'kamperite', 'kampong', 'kamptomorph', 'kan', 'kana', 'kanae', 'kanagi', 'kanaka', 'kanap', 'kanara', 'kanarese', 'kanari', 'kanat', 'kanauji', 'kanawari', 'kanawha', 'kanchil', 'kande', 'kandelia', 'kandol', 'kaneh', 'kanephore', 'kanephoros', 'kaneshite', 'kanesian', 'kang', 'kanga', 'kangani', 'kangaroo', 'kangarooer', 'kangli', 'kanji', 'kankanai', 'kankie', 'kannume', 'kanoon', 'kanred', 'kans', 'kansa', 'kansan', 'kantele', 'kanteletar', 'kanten', 'kanthan', 'kantian', 'kantianism', 'kantism', 'kantist', 'kanuri', 'kanwar', 'kaoliang', 'kaolin', 'kaolinate', 'kaolinic', 'kaolinite', 'kaolinization', 'kaolinize', 'kapa', 'kapai', 'kapeika', 'kapok', 'kapp', 'kappa', 'kappe', 'kappland', 'kapur', 'kaput', 'karabagh', 'karagan', 'karaism', 'karaite', 'karaitism', 'karaka', 'karakatchan', 'karakul', 'karakul', 'karamojo', 'karamu', 'karaoke', 'karatas', 'karate', 'karaya', 'karaya', 'karbi', 'karch', 'kareao', 'kareeta', 'karel', 'karela', 'karelian', 'karen', 'karharbari', 'kari', 'karite', 'karl', 'karling', 'karluk', 'karma', 'karmathian', 'karmic', 'karmouth', 'karo', 'kaross', 'karou', 'karree', 'karri', 'karroo', 'karroo', 'karrusel', 'karsha', 'karshuni', 'karst', 'karst', 'karstenite', 'karstic', 'kartel', 'karthli', 'kartometer', 'kartos', 'kartvel', 'kartvelian', 'karwar', 'karwinskia', 'karyaster', 'karyenchyma', 'karyochrome', 'karyochylema', 'karyogamic', 'karyogamy', 'karyokinesis', 'karyokinetic', 'karyologic', 'karyological', 'karyologically', 'karyology', 'karyolymph', 'karyolysidae', 'karyolysis', 'karyolysus', 'karyolytic', 'karyomere', 'karyomerite', 'karyomicrosome', 'karyomitoic', 'karyomitome', 'karyomiton', 'karyomitosis', 'karyomitotic', 'karyon', 'karyoplasm', 'karyoplasma', 'karyoplasmatic', 'karyoplasmic', 'karyopyknosis', 'karyorrhexis', 'karyoschisis', 'karyosome', 'karyotin', 'karyotype', 'kasa', 'kasbah', 'kasbeke', 'kascamiol', 'kasha', 'kashan', 'kasher', 'kashga', 'kashi', 'kashima', 'kashmiri', 'kashmirian', 'kashoubish', 'kashruth', 'kashube', 'kashubian', 'kashyapa', 'kasida', 'kasikumuk', 'kaska', 'kaskaskia', 'kasm', 'kasolite', 'kassabah', 'kassak', 'kassite', 'kassu', 'kastura', 'kasubian', 'kat', 'katabanian', 'katabasis', 'katabatic', 'katabella', 'katabolic', 'katabolically', 'katabolism', 'katabolite', 'katabolize', 'katabothron', 'katachromasis', 'katacrotic', 'katacrotism', 'katagenesis', 'katagenetic', 'katakana', 'katakinesis', 'katakinetic', 'katakinetomer', 'katakinetomeric', 'katakiribori', 'katalase', 'katalysis', 'katalyst', 'katalytic', 'katalyze', 'katamorphism', 'kataphoresis', 'kataphoretic', 'kataphoric', 'kataphrenia', 'kataplasia', 'kataplectic', 'kataplexy', 'katar', 'katastate', 'katastatic', 'katathermometer', 'katatonia', 'katatonic', 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'keelblock', 'keelboat', 'keelboatman', 'keeled', 'keeler', 'keelfat', 'keelhale', 'keelhaul', 'keelie', 'keeling', 'keelivine', 'keelless', 'keelman', 'keelrake', 'keelson', 'keen', 'keena', 'keened', 'keener', 'keenly', 'keenness', 'keep', 'keepable', 'keeper', 'keeperess', 'keepering', 'keeperless', 'keepership', 'keeping', 'keepsake', 'keepsaky', 'keepworthy', 'keerogue', 'kees', 'keeshond', 'keest', 'keet', 'keeve', 'keewatin', 'kef', 'keffel', 'kefir', 'kefiric', 'kefti', 'keftian', 'keftiu', 'keg', 'kegler', 'kehaya', 'kehillah', 'kehoeite', 'keid', 'keilhauite', 'keita', 'keith', 'keitloa', 'kekchi', 'kekotene', 'kekuna', 'kelchin', 'keld', 'kele', 'kele', 'kelebe', 'kelectome', 'keleh', 'kelek', 'kelep', 'kelima', 'kelk', 'kell', 'kella', 'kellion', 'kellupweed', 'kelly', 'kelly', 'keloid', 'keloidal', 'kelp', 'kelper', 'kelpfish', 'kelpie', 'kelpware', 'kelpwort', 'kelpy', 'kelt', 'kelter', 'keltoi', 'kelty', 'kelvin', 'kelvin', 'kelyphite', 'kemal', 'kemalism', 'kemalist', 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'kristin', 'kristinaux', 'krisuvigite', 'kritarchy', 'krithia', 'kriton', 'kritrima', 'krobyloi', 'krobylos', 'krocket', 'krohnkite', 'krome', 'kromeski', 'kromogram', 'kromskop', 'krona', 'krone', 'kronen', 'kroner', 'kronion', 'kronor', 'kronur', 'kroo', 'kroon', 'krosa', 'krouchka', 'kroushka', 'kru', 'krugerism', 'krugerite', 'kruman', 'krummhorn', 'kryokonite', 'krypsis', 'kryptic', 'krypticism', 'kryptocyanine', 'kryptol', 'kryptomere', 'krypton', 'krzysztof', 'kshatriya', 'kshatriyahood', 'kua', 'kuan', 'kuan', 'kuar', 'kuba', 'kuba', 'kubachi', 'kubanka', 'kubba', 'kubera', 'kubuklion', 'kuchean', 'kuchen', 'kudize', 'kudos', 'kudrun', 'kudu', 'kudzu', 'kuehneola', 'kuei', 'kufic', 'kuge', 'kugel', 'kuhnia', 'kui', 'kuichua', 'kuki', 'kukoline', 'kukri', 'kuku', 'kukui', 'kukulcan', 'kukupa', 'kukuruku', 'kula', 'kulack', 'kulah', 'kulah', 'kulaite', 'kulak', 'kulakism', 'kulanapan', 'kulang', 'kuldip', 'kuli', 'kulimit', 'kulkarni', 'kullaite', 'kullani', 'kulm', 'kulmet', 'kulturkampf', 'kulturkreis', 'kuman', 'kumbi', 'kumhar', 'kumiss', 'kummel', 'kumni', 'kumquat', 'kumrah', 'kumyk', 'kunai', 'kunbi', 'kundry', 'kuneste', 'kung', 'kunk', 'kunkur', 'kunmiut', 'kunzite', 'kuomintang', 'kupfernickel', 'kupfferite', 'kuphar', 'kupper', 'kuranko', 'kurbash', 'kurchatovium', 'kurchicine', 'kurchine', 'kurd', 'kurdish', 'kurdistan', 'kurgan', 'kuri', 'kurilian', 'kurku', 'kurmburra', 'kurmi', 'kuroshio', 'kurrajong', 'kurt', 'kurtosis', 'kuruba', 'kurukh', 'kuruma', 'kurumaya', 'kurumba', 'kurung', 'kurus', 'kurvey', 'kurveyor', 'kusa', 'kusam', 'kusan', 'kusha', 'kushshu', 'kusimansel', 'kuskite', 'kuskos', 'kuskus', 'kuskwogmiut', 'kustenau', 'kusti', 'kusum', 'kusum', 'kutcha', 'kutchin', 'kutenai', 'kuttab', 'kuttar', 'kuttaur', 'kuvasz', 'kuvera', 'kvass', 'kvint', 'kvinter', 'kwakiutl', 'kwamme', 'kwan', 'kwannon', 'kwapa', 'kwarta', 'kwarterka', 'kwazoku', 'kyack', 'kyah', 'kyar', 'kyat', 'kyaung', 'kybele', 'kyklopes', 'kyklops', 'kyl', 'kyle', 'kyle', 'kylite', 'kylix', 'kylo', 'kymation', 'kymatology', 'kymbalon', 'kymogram', 'kymograph', 'kymographic', 'kynurenic', 'kynurine', 'kyphoscoliosis', 'kyphoscoliotic', 'kyphosidae', 'kyphosis', 'kyphotic', 'kyrie', 'kyrine', 'kyschtymite', 'kyte', 'kyu', 'kyung', 'kyurin', 'kyurinish'];

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60e6008a0ea47071e572e2098c826d44ff862e6c

171

py

Python

mod_kvm/forms.py

T1duS/ccextractor-web

73e704640d13c9b5d84ae2e8bc5bdcf352caaa75

[ "MIT" ]

19

2018-05-18T13:55:54.000Z

2019-10-26T10:08:45.000Z

mod_kvm/forms.py

T1duS/ccextractor-web

73e704640d13c9b5d84ae2e8bc5bdcf352caaa75

[ "MIT" ]

23

2018-06-04T07:10:15.000Z

2019-10-27T18:45:21.000Z

mod_kvm/forms.py

T1duS/ccextractor-web

73e704640d13c9b5d84ae2e8bc5bdcf352caaa75

[ "MIT" ]

21

2018-07-07T07:54:12.000Z

2020-11-24T14:35:27.000Z

""" ccextractor-web | forms.py Author : Saurabh Shrivastava Email : saurabh.shrivastava54+ccextractorweb[at]gmail.com Link : https://github.com/saurabhshri """

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60eab9810e9971ebc6e1640abf5bd26c0f7d8b50

54

py

Python

wflow-py/wflow/bmimodel.py

edwinkost/wflow

ee9291d4b556d7b61f7f13bcb972774be9a16cec

[ "MIT" ]

null

wflow-py/wflow/bmimodel.py

edwinkost/wflow

ee9291d4b556d7b61f7f13bcb972774be9a16cec

[ "MIT" ]

null

wflow-py/wflow/bmimodel.py

edwinkost/wflow

ee9291d4b556d7b61f7f13bcb972774be9a16cec

[ "MIT" ]

null

# Simple script to link to python wflow bmi models

10.8

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null

0

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4

60fc4076cf8a1b43ce9adc265eba2909a91456bf

33

py

Python

ox_herd/core/__init__.py

empower-capital/ox_herd

2aa77db945296c152dc8d420f42a6d6455d514fa

[ "BSD-2-Clause" ]

1

2021-11-28T20:35:31.000Z

ox_herd/core/__init__.py

empower-capital/ox_herd

2aa77db945296c152dc8d420f42a6d6455d514fa

[ "BSD-2-Clause" ]

5

2017-11-21T00:21:13.000Z

2021-06-30T19:47:54.000Z

ox_herd/core/__init__.py

empower-capital/ox_herd

2aa77db945296c152dc8d420f42a6d6455d514fa

[ "BSD-2-Clause" ]

4

2021-12-17T10:58:15.000Z

2021-12-23T14:38:40.000Z

"""Core modules for ox_herd. """

11

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1

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4

71cd1afdacfa6b319174fec97a3ff7763260d03b

141

py

Python

annotateapp/apps.py

joerodd/POnSS

8f0317100dcd3515f24e321747e9e266760b19f0

[ "MIT" ]

1

2020-09-02T00:57:02.000Z

annotateapp/apps.py

joerodd/POnSS

8f0317100dcd3515f24e321747e9e266760b19f0

[ "MIT" ]

null

annotateapp/apps.py

joerodd/POnSS

8f0317100dcd3515f24e321747e9e266760b19f0

[ "MIT" ]

1

2021-04-01T17:39:28.000Z

from __future__ import unicode_literals from django.apps import AppConfig class AnnotateApp2Config(AppConfig): name = 'annotate_app2'

17.625

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7

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4

e0973d9a0b11ebb30d69d34e6189a759ca72c96e

251

py

Python

hardware/opentrons_hardware/firmware_bindings/message.py

anuwrag/opentrons

28c8d76a19e367c6bd38f5290faaa32abf378715

[ "Apache-2.0" ]

235

2017-10-27T20:37:27.000Z

2022-03-30T14:09:49.000Z

hardware/opentrons_hardware/firmware_bindings/message.py

anuwrag/opentrons

28c8d76a19e367c6bd38f5290faaa32abf378715

[ "Apache-2.0" ]

8,425

2017-10-26T15:25:43.000Z

2022-03-31T23:54:26.000Z

hardware/opentrons_hardware/firmware_bindings/message.py

anuwrag/opentrons

28c8d76a19e367c6bd38f5290faaa32abf378715

[ "Apache-2.0" ]

130

2017-11-09T21:02:37.000Z

2022-03-15T18:01:24.000Z

"""Can message.""" from __future__ import annotations from dataclasses import dataclass from .arbitration_id import ArbitrationId @dataclass(frozen=True) class CanMessage: """A can message.""" arbitration_id: ArbitrationId data: bytes

17.928571

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13

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0

4

e0c26560e36d59fb0f99a2f1310d2d64b6afc1bb

102

py

Python

Codewars/8kyu/find-the-position/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

7

2017-09-20T16:40:39.000Z

2021-08-31T18:15:08.000Z

Codewars/8kyu/find-the-position/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

Codewars/8kyu/find-the-position/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

# Python - 3.6.0 position = lambda alphabet: f'Position of alphabet: {ord(alphabet) - ord("a") + 1}'

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4

e0c392565b189a84b1cb5ab883a9dc67a421970c

15,753

py

Python

tensorflow_quantum/core/ops/noise/noisy_sampled_expectation_op_test.py

quantummind/quantum

fd952d0362c5445eef0da4437fb3e5ebb16b7948

[ "Apache-2.0" ]

2

2021-09-24T09:41:47.000Z

2021-10-04T20:55:09.000Z

tensorflow_quantum/core/ops/noise/noisy_sampled_expectation_op_test.py

quantummind/quantum

fd952d0362c5445eef0da4437fb3e5ebb16b7948

[ "Apache-2.0" ]

1

2021-11-15T04:47:04.000Z

tensorflow_quantum/core/ops/noise/noisy_sampled_expectation_op_test.py

quantummind/quantum

fd952d0362c5445eef0da4437fb3e5ebb16b7948

[ "Apache-2.0" ]

1

2021-05-10T09:12:40.000Z

# Copyright 2020 The TensorFlow Quantum Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests that specifically target noisy expectation calculation.""" import numpy as np from absl.testing import parameterized import tensorflow as tf import cirq from tensorflow_quantum.core.ops import batch_util from tensorflow_quantum.core.ops.noise import noisy_sampled_expectation_op from tensorflow_quantum.python import util class NoisyExpectationCalculationTest(tf.test.TestCase, parameterized.TestCase): """Tests tfq.noise.expectation.""" def test_noisy_expectation_inputs(self): """Make sure noisy expectation op fails gracefully on bad inputs.""" n_qubits = 5 batch_size = 5 symbol_names = ['alpha'] qubits = cirq.GridQubit.rect(1, n_qubits) circuit_batch, resolver_batch = \ util.random_symbol_circuit_resolver_batch( qubits, symbol_names, batch_size, include_channels=True) symbol_values_array = np.array( [[resolver[symbol] for symbol in symbol_names] for resolver in resolver_batch]) pauli_sums = util.random_pauli_sums(qubits, 3, batch_size) num_samples = [[10]] * batch_size with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'programs must be rank 1'): # Circuit tensor has too many dimensions. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor([circuit_batch]), symbol_names, symbol_values_array, util.convert_to_tensor([[x] for x in pauli_sums]), num_samples) with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'symbol_names must be rank 1.'): # symbol_names tensor has too many dimensions. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), np.array([symbol_names]), symbol_values_array, util.convert_to_tensor([[x] for x in pauli_sums]), num_samples) with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'symbol_values must be rank 2.'): # symbol_values_array tensor has too many dimensions. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, np.array([symbol_values_array]), util.convert_to_tensor([[x] for x in pauli_sums]), num_samples) with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'symbol_values must be rank 2.'): # symbol_values_array tensor has too few dimensions. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, symbol_values_array[0], util.convert_to_tensor([[x] for x in pauli_sums]), num_samples) with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'pauli_sums must be rank 2.'): # pauli_sums tensor has too few dimensions. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, symbol_values_array, util.convert_to_tensor(list(pauli_sums)), num_samples) with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'pauli_sums must be rank 2.'): # pauli_sums tensor has too many dimensions. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, symbol_values_array, [util.convert_to_tensor([[x] for x in pauli_sums])], num_samples) with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'num_samples must be rank 2'): # num_samples tensor has the wrong shape. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, symbol_values_array, util.convert_to_tensor([[x] for x in pauli_sums]), [num_samples]) with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'num_samples must be rank 2'): # num_samples tensor has the wrong shape. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, symbol_values_array, util.convert_to_tensor([[x] for x in pauli_sums]), num_samples[0]) with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'Unparseable proto'): # circuit tensor has the right type but invalid values. noisy_sampled_expectation_op.sampled_expectation( ['junk'] * batch_size, symbol_names, symbol_values_array, util.convert_to_tensor([[x] for x in pauli_sums]), num_samples) with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'Could not find symbol in parameter map'): # symbol_names tensor has the right type but invalid values. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), ['junk'], symbol_values_array, util.convert_to_tensor([[x] for x in pauli_sums]), num_samples) with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'qubits not found in circuit'): # pauli_sums tensor has the right type but invalid values. new_qubits = [cirq.GridQubit(5, 5), cirq.GridQubit(9, 9)] new_pauli_sums = util.random_pauli_sums(new_qubits, 2, batch_size) noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, symbol_values_array, util.convert_to_tensor([[x] for x in new_pauli_sums]), num_samples) with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'Unparseable proto'): # pauli_sums tensor has the right type but invalid values 2. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, symbol_values_array, [['junk']] * batch_size, num_samples) with self.assertRaisesRegex(TypeError, 'Cannot convert'): # circuits tensor has the wrong type. noisy_sampled_expectation_op.sampled_expectation( [1.0] * batch_size, symbol_names, symbol_values_array, util.convert_to_tensor([[x] for x in pauli_sums]), num_samples) with self.assertRaisesRegex(TypeError, 'Cannot convert'): # symbol_names tensor has the wrong type. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), [0.1234], symbol_values_array, util.convert_to_tensor([[x] for x in pauli_sums]), num_samples) with self.assertRaisesRegex(tf.errors.UnimplementedError, ''): # symbol_values tensor has the wrong type. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, [['junk']] * batch_size, util.convert_to_tensor([[x] for x in pauli_sums]), num_samples) with self.assertRaisesRegex(TypeError, 'Cannot convert'): # pauli_sums tensor has the wrong type. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, symbol_values_array, [[1.0]] * batch_size, num_samples) with self.assertRaisesRegex(TypeError, 'missing'): # we are missing an argument. # pylint: disable=no-value-for-parameter noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, symbol_values_array, num_samples) # pylint: enable=no-value-for-parameter with self.assertRaisesRegex(TypeError, 'positional arguments'): # pylint: disable=too-many-function-args noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, symbol_values_array, util.convert_to_tensor([[x] for x in pauli_sums]), [], num_samples) # pylint: enable=too-many-function-args with self.assertRaisesRegex(tf.errors.InvalidArgumentError, expected_regex='do not match'): # wrong op size. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor([cirq.Circuit()]), symbol_names, symbol_values_array.astype(np.float64), util.convert_to_tensor([[x] for x in pauli_sums]), num_samples) with self.assertRaisesRegex(tf.errors.InvalidArgumentError, 'greater than 0'): # pylint: disable=too-many-function-args noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, symbol_values_array, util.convert_to_tensor([[x] for x in pauli_sums]), [[-1]] * batch_size) # pylint: enable=too-many-function-args with self.assertRaisesRegex(tf.errors.InvalidArgumentError, expected_regex='do not match'): # wrong symbol_values size. noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, symbol_values_array[:int(batch_size * 0.5)], util.convert_to_tensor([[x] for x in pauli_sums]), num_samples) @parameterized.parameters([ { 'n_qubits': 13, 'batch_size': 1, 'noisy': False }, # ComputeLarge. { 'n_qubits': 6, 'batch_size': 25, 'noisy': False }, # ComputeSmall. { 'n_qubits': 6, 'batch_size': 10, 'noisy': True }, # ComputeSmall. { 'n_qubits': 8, 'batch_size': 1, 'noisy': True } # ComputeLarge. ]) def test_simulate_consistency(self, batch_size, n_qubits, noisy): """Test consistency with batch_util.py simulation.""" symbol_names = ['alpha', 'beta'] qubits = cirq.GridQubit.rect(1, n_qubits) circuit_batch, resolver_batch = \ util.random_symbol_circuit_resolver_batch( qubits, symbol_names, batch_size, include_channels=noisy) symbol_values_array = np.array( [[resolver[symbol] for symbol in symbol_names] for resolver in resolver_batch]) pauli_sums1 = util.random_pauli_sums(qubits, 3, batch_size) pauli_sums2 = util.random_pauli_sums(qubits, 3, batch_size) batch_pauli_sums = [[x, y] for x, y in zip(pauli_sums1, pauli_sums2)] num_samples = [[10000] * 2] * batch_size op_exps = noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, symbol_values_array, util.convert_to_tensor(batch_pauli_sums), num_samples) cirq_exps = batch_util.batch_calculate_expectation( circuit_batch, resolver_batch, batch_pauli_sums, cirq.DensityMatrixSimulator() if noisy else cirq.Simulator()) tol = 0.5 self.assertAllClose(cirq_exps, op_exps, atol=tol, rtol=tol) @parameterized.parameters([{ 'channel': x } for x in util.get_supported_channels()]) def test_single_channel(self, channel): """Individually test adding just a single channel type to circuits.""" symbol_names = [] batch_size = 5 n_qubits = 6 qubits = cirq.GridQubit.rect(1, n_qubits) circuit_batch, resolver_batch = \ util.random_circuit_resolver_batch( qubits, batch_size, include_channels=False) for i in range(batch_size): circuit_batch[i] = circuit_batch[i] + channel.on_each(*qubits) symbol_values_array = np.array( [[resolver[symbol] for symbol in symbol_names] for resolver in resolver_batch]) pauli_sums1 = util.random_pauli_sums(qubits, 3, batch_size) pauli_sums2 = util.random_pauli_sums(qubits, 3, batch_size) batch_pauli_sums = [[x, y] for x, y in zip(pauli_sums1, pauli_sums2)] num_samples = [[20000] * 2] * batch_size op_exps = noisy_sampled_expectation_op.sampled_expectation( util.convert_to_tensor(circuit_batch), symbol_names, symbol_values_array, util.convert_to_tensor(batch_pauli_sums), num_samples) cirq_exps = batch_util.batch_calculate_expectation( circuit_batch, resolver_batch, batch_pauli_sums, cirq.DensityMatrixSimulator()) self.assertAllClose(cirq_exps, op_exps, atol=0.35, rtol=0.35) def test_correctness_empty(self): """Test the expectation for empty circuits.""" empty_circuit = util.convert_to_tensor([cirq.Circuit()]) empty_symbols = tf.convert_to_tensor([], dtype=tf.dtypes.string) empty_values = tf.convert_to_tensor([[]]) empty_paulis = tf.convert_to_tensor([[]], dtype=tf.dtypes.string) empty_n_samples = tf.convert_to_tensor([[]], dtype=tf.int32) out = noisy_sampled_expectation_op.sampled_expectation( empty_circuit, empty_symbols, empty_values, empty_paulis, empty_n_samples) expected = np.array([[]], dtype=np.complex64) self.assertAllClose(out, expected) def test_correctness_no_circuit(self): """Test the correctness with the empty tensor.""" empty_circuit = tf.raw_ops.Empty(shape=(0,), dtype=tf.string) empty_symbols = tf.raw_ops.Empty(shape=(0,), dtype=tf.string) empty_values = tf.raw_ops.Empty(shape=(0, 0), dtype=tf.float32) empty_paulis = tf.raw_ops.Empty(shape=(0, 0), dtype=tf.string) empty_n_samples = tf.raw_ops.Empty(shape=(0, 0), dtype=tf.int32) out = noisy_sampled_expectation_op.sampled_expectation( empty_circuit, empty_symbols, empty_values, empty_paulis, empty_n_samples) self.assertShapeEqual(np.zeros((0, 0)), out) if __name__ == "__main__": tf.test.main()

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1

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null

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4

e0f29fdf785e76fa0c82155c03ab6a73bc274610

7,530

py

Python

followers/tests/test_views.py

HanSaloZu/drf-blog-api

966776e59ed7699a9e94aeb85fdd6785c2532a3a

[ "MIT" ]

null

followers/tests/test_views.py

HanSaloZu/drf-blog-api

966776e59ed7699a9e94aeb85fdd6785c2532a3a

[ "MIT" ]

null

followers/tests/test_views.py

HanSaloZu/drf-blog-api

966776e59ed7699a9e94aeb85fdd6785c2532a3a

[ "MIT" ]

null

from urllib.parse import urlencode from django.urls import reverse from utils.tests import APIViewTestCase, ListAPIViewTestCase from ..services import follow, is_following class FollowersListAPIViewTestCase(ListAPIViewTestCase): def url(self, parameters={}): url = reverse("followers_list") if parameters: url += "?" + urlencode(parameters) return url def setUp(self): self.first_user = self.UserModel.objects.create_user( login="FirstUser", email="first@gmail.com", password="pass") auth_credentials = self.generate_jwt_auth_credentials(self.first_user) self.client.credentials(HTTP_AUTHORIZATION=auth_credentials) self.second_user = self.UserModel.objects.create_user( login="SecondUser", email="second@gmail.com", password="pass") self.third_user = self.UserModel.objects.create_user( login="ThirdUser", email="third@gmail.com", password="pass") follow(self.second_user, self.first_user) follow(self.third_user, self.first_user) def test_request_by_unauthenticated_client(self): self.client.credentials() response = self.client.get(self.url()) self.unauthorized_client_error_response_test(response) def test_followers_list(self): response = self.client.get(self.url()) self.check_common_details_of_list_view_response( response, total_items=2, page_size=2 ) def test_followers_list_with_q_parameter(self): response = self.client.get(self.url({"q": "SecondUser"})) self.check_common_details_of_list_view_response( response, total_items=1, page_size=1 ) self.assertEqual(response.data["items"] [0]["id"], self.second_user.id) class FollowingListAPIViewTestCase(ListAPIViewTestCase): url = reverse("following_list") def setUp(self): self.first_user = self.UserModel.objects.create_user( login="FirstUser", email="first@gmail.com", password="pass") auth_credentials = self.generate_jwt_auth_credentials(self.first_user) self.client.credentials(HTTP_AUTHORIZATION=auth_credentials) self.second_user = self.UserModel.objects.create_user( login="SecondUser", email="second@gmail.com", password="pass") follow(self.first_user, self.second_user) def test_request_by_unauthenticated_client(self): self.client.credentials() response = self.client.get(self.url) self.unauthorized_client_error_response_test(response) def test_following_list(self): response = self.client.get(self.url) self.check_common_details_of_list_view_response( response, total_items=1, page_size=1 ) self.assertEqual(response.data["items"] [0]["id"], self.second_user.id) class FollowingAPIViewTestCase(APIViewTestCase): def url(self, kwargs): return reverse("following", kwargs=kwargs) def setUp(self): self.first_user = self.UserModel.objects.create_user( login="FirstUser", email="first_user_@gmail.com", password="pass") auth_credentials = self.generate_jwt_auth_credentials(self.first_user) self.client.credentials(HTTP_AUTHORIZATION=auth_credentials) self.second_user = self.UserModel.objects.create_user( login="SecondUser", email="second_user_@gmail.com", password="pass") def test_request_by_unauthenticated_client(self): self.client.credentials() response = self.client.get(self.url({"login": self.second_user.login})) self.unauthorized_client_error_response_test(response) def test_follow(self): """ A valid follow request should return isFollowed: True """ response = self.client.put(self.url({"login": self.second_user.login})) self.assertEqual(response.status_code, self.http_status.HTTP_200_OK) self.assertIs(is_following(self.first_user, self.second_user), True) self.assertIs(response.data["isFollowed"], True) def test_self_follow(self): """ Self follow should return a 400 error """ response = self.client.put(self.url({"login": self.first_user.login})) self.client_error_response_test( response, messages=["You cannot follow yourself"] ) self.assertIs(is_following(self.first_user, self.first_user), False) def test_double_follow(self): """ Duplicate follow should return isFollowed: True """ follow(self.first_user, self.second_user) response = self.client.put(self.url({"login": self.second_user.login})) self.assertEqual(response.status_code, self.http_status.HTTP_200_OK) self.assertIs(is_following(self.first_user, self.second_user), True) self.assertIs(response.data["isFollowed"], True) def test_follow_with_invalid_login(self): """ A follow request with an invalid login should return a 404 error """ response = self.client.put(self.url({"login": "login"})) self.client_error_response_test( response, code="notFound", status=self.http_status.HTTP_404_NOT_FOUND, messages=["Invalid login, user is not found"] ) def test_unfollow(self): """ A valid unfollow request should return isFollowed: False """ follow(self.first_user, self.second_user) response = self.client.delete( self.url({"login": self.second_user.login})) self.assertEqual(response.status_code, self.http_status.HTTP_200_OK) self.assertIs(is_following(self.first_user, self.second_user), False) self.assertIs(response.data["isFollowed"], False) def test_unfollow_not_followed_user(self): """ Unfollowing from an unfollowed user should return a 404 error """ response = self.client.delete( self.url({"login": self.second_user.login})) self.client_error_response_test( response, code="notFound", status=self.http_status.HTTP_404_NOT_FOUND, messages=["You are not yet followed this user"] ) self.assertIs(is_following(self.first_user, self.second_user), False) def test_is_following(self): """ If the user is being followed, isFollowed should be True, otherwise False """ response = self.client.get(self.url({"login": self.second_user.login})) self.assertEqual(response.status_code, self.http_status.HTTP_200_OK) self.assertIs(response.data["isFollowed"], False) follow(self.first_user, self.second_user) response = self.client.get(self.url({"login": self.second_user.login})) self.assertEqual(response.status_code, self.http_status.HTTP_200_OK) self.assertIs(response.data["isFollowed"], True) def test_is_following_with_invalid_login(self): response = self.client.get(self.url({"login": "invalid"})) self.client_error_response_test( response, code="notFound", status=self.http_status.HTTP_404_NOT_FOUND, messages=["Invalid login, user is not found"] )

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null

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1

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null

0

4

e0f7e58b1ba501b8d6abb6593275dfb88668af75

7,682

py

Python

ht_web_service/apps/ht/migrations/0004_auto_20180330_1138.py

Kit-Angelov/ht-web-service

9eabc0696634c2e5eba2c9789cc32f548d84cccb

[ "MIT" ]

1

2018-11-09T07:31:41.000Z

ht_web_service/apps/ht/migrations/0004_auto_20180330_1138.py

Kit-Angelov/ht-web-service

9eabc0696634c2e5eba2c9789cc32f548d84cccb

[ "MIT" ]

5

2020-06-05T17:15:40.000Z

2021-09-07T23:39:19.000Z

ht_web_service/apps/ht/migrations/0004_auto_20180330_1138.py

Kit-Angelov/ht-web-service

9eabc0696634c2e5eba2c9789cc32f548d84cccb

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.11 on 2018-03-30 08:38 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('ht', '0003_auto_20180329_1022'), ] operations = [ migrations.AddField( model_name='feature', name='prov_doc_FDA', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Предоставление документов для подготовки распоряжения ФДА о предоставлении в аренду '), ), migrations.AlterField( model_name='feature', name='cadastral_num_formed', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Кадастровый номер образованного объекта недвижимости'), ), migrations.AlterField( model_name='feature', name='cadastral_num_origin_14', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Кадастровый номер исходного объекта недвижимости ПМТ 2014'), ), migrations.AlterField( model_name='feature', name='cadastral_num_origin_17', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Кадастровый номер исходного объкта недвижимости ПМТ 2017'), ), migrations.AlterField( model_name='feature', name='category_origin', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Категория исходного объекта недвижимости'), ), migrations.AlterField( model_name='feature', name='comments', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Комментарий'), ), migrations.AlterField( model_name='feature', name='contacts_holder', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Контакты правообладателя'), ), migrations.AlterField( model_name='feature', name='district', field=models.CharField(blank=True, max_length=56, null=True, verbose_name='Район'), ), migrations.AlterField( model_name='feature', name='event', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Мероприятие'), ), migrations.AlterField( model_name='feature', name='form_area', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Формирование земельных участков (кадастровый учет)'), ), migrations.AlterField( model_name='feature', name='obj_costat', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Стоимость объектов недвижимости'), ), migrations.AlterField( model_name='feature', name='obj_type_origin', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='ВРИ исходного объекта недвижимости'), ), migrations.AlterField( model_name='feature', name='offer_to_holdering', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Направление оферты правообладателю'), ), migrations.AlterField( model_name='feature', name='order', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='№ п/п'), ), migrations.AlterField( model_name='feature', name='origin_area_17', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Площадь исходного объекта недвижимости, кв.м. ПМТ 2017'), ), migrations.AlterField( model_name='feature', name='piquetu', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Пикет'), ), migrations.AlterField( model_name='feature', name='plot', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='№ зу ПМТ'), ), migrations.AlterField( model_name='feature', name='pre_doc_transfer_type', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Подготовка документов для перевода (отнесения) в категорию земель транспорта и/или изменение (установление) ВРИ'), ), migrations.AlterField( model_name='feature', name='pre_lang_plan', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Подготовка межевого плана и передача его на кадастровый учет'), ), migrations.AlterField( model_name='feature', name='provision_doc', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Предоставление документов для подготовки распоряжения ФДА об изъятии'), ), migrations.AlterField( model_name='feature', name='requisites_agree_vac', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Реквизиты соглашения об изъятии '), ), migrations.AlterField( model_name='feature', name='requisites_assess', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Реквизиты отчета об оценке'), ), migrations.AlterField( model_name='feature', name='requisites_dir_vac', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Реквизиты распоряжения об изъятии'), ), migrations.AlterField( model_name='feature', name='requisites_lease', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Реквизиты распоряжения о предоставлении в аренду'), ), migrations.AlterField( model_name='feature', name='requisites_lease_agree', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Реквизиты договора аренды'), ), migrations.AlterField( model_name='feature', name='rights_14', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Права ПМТ 2014'), ), migrations.AlterField( model_name='feature', name='rights_17', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Права ПМТ 2017'), ), migrations.AlterField( model_name='feature', name='rights_august_14', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Право после августа 2014'), ), migrations.AlterField( model_name='feature', name='status_area', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Статус участка'), ), migrations.AlterField( model_name='feature', name='vac_area_14', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Площадь подлежащая изъятию, кв.м. ПМТ 2014'), ), migrations.AlterField( model_name='feature', name='vac_area_17', field=models.CharField(blank=True, max_length=256, null=True, verbose_name='Площадь подлежащая изъятию, кв.м. ПМТ 2017'), ), ]

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0

4

46045a2f93ad27b884b5a038fb3cb447429aa6cd

92

py

Python

prometheus_flask/__init__.py

lishulong16/mokitou

96c5af6f86b2786ffda203f023b4a5f11015853f

[ "MIT" ]

1

2018-04-25T01:32:15.000Z

prometheus_flask/__init__.py

lishulong16/mokitou

96c5af6f86b2786ffda203f023b4a5f11015853f

[ "MIT" ]

null

prometheus_flask/__init__.py

lishulong16/mokitou

96c5af6f86b2786ffda203f023b4a5f11015853f

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ @contact: lishulong.never@gmail.com @time: 2018/5/8 上午10:17 """

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null

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4

46061ef6e20ced89d91a0e256bf1797c6a0bb0d4

109

py

Python

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/openedx/core/djangoapps/schedules/exceptions.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

3

2021-12-15T04:58:18.000Z

2022-02-06T12:15:37.000Z

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/openedx/core/djangoapps/schedules/exceptions.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

null

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/openedx/core/djangoapps/schedules/exceptions.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

1

2019-01-02T14:38:50.000Z

# lint-amnesty, pylint: disable=missing-module-docstring class CourseUpdateDoesNotExist(Exception): pass

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461a580983356f9d90d59948b5dc6cbbf3373922

113

py

Python

decloud/__init__.py

CNES/decloud

6b06ae98bfe68821b4ebd0e7ba06723809cb9b42

[ "Apache-2.0" ]

8

2022-02-25T13:15:07.000Z

2022-03-20T18:29:49.000Z

decloud/__init__.py

CNES/decloud

6b06ae98bfe68821b4ebd0e7ba06723809cb9b42

[ "Apache-2.0" ]

1

2022-02-25T13:21:33.000Z

decloud/__init__.py

CNES/decloud

6b06ae98bfe68821b4ebd0e7ba06723809cb9b42

[ "Apache-2.0" ]

1

2022-03-31T23:43:12.000Z

""" Decloud provides all the things to perform experiments with Sentinel-1/2, and targets image de-clouding. """

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null

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1ca0f9cc3279f0794201d0d2445a81f22a943387

62

py

Python

muttdown/__main__.py

guygma/muttdown

cd596cfdd976cd719cb16e247c574b2778050f6f

[ "ISC" ]

50

2015-07-02T17:55:53.000Z

2021-11-20T18:53:55.000Z

muttdown/__main__.py

guygma/muttdown

cd596cfdd976cd719cb16e247c574b2778050f6f

[ "ISC" ]

19

2015-06-03T17:08:04.000Z

2020-09-16T21:19:56.000Z

muttdown/__main__.py

guygma/muttdown

cd596cfdd976cd719cb16e247c574b2778050f6f

[ "ISC" ]

11

2015-06-01T15:27:58.000Z

2019-12-03T20:11:00.000Z

import sys from muttdown.main import main sys.exit(main())

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1ca3e17a607ab3b8327c02023ef54e15e792ed45

91

py

Python

Accessible_Campus-master/Geodjango/firstgis/apps.py

zzrose/Campus_Locator

9262968165c198c15cffd0b3165c97b26bdafed2

[ "Apache-2.0" ]

1

2019-02-25T23:17:29.000Z

Geodjango/firstgis/apps.py

Harrymissi/Accessible_Campus

e20c14a18809e86e90b4aff528d2966a5b36f416

[ "Apache-2.0" ]

null

Geodjango/firstgis/apps.py

Harrymissi/Accessible_Campus

e20c14a18809e86e90b4aff528d2966a5b36f416

[ "Apache-2.0" ]

null

from django.apps import AppConfig class FirstgisConfig(AppConfig): name = 'firstgis'

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1

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null

0

1

0

1

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4

1cba67972eae70c38a5f159726a88bc4b37b549e

458

py

Python

network/serializers.py

pawangeek/PollsChain

6059796c671d3250f2cd8bb36171bf54013d176e

[ "MIT" ]

null

network/serializers.py

pawangeek/PollsChain

6059796c671d3250f2cd8bb36171bf54013d176e

[ "MIT" ]

null

network/serializers.py

pawangeek/PollsChain

6059796c671d3250f2cd8bb36171bf54013d176e

[ "MIT" ]

null

from rest_framework import serializers from .models import Transaction, Block, Peer class TransactionSerializer(serializers.ModelSerializer): class Meta: model = Transaction fields='__all__' class BlockSerializer(serializers.ModelSerializer): class Meta: model=Block fields='__all__' class PeerSerializer(serializers.ModelSerializer): class Meta: model = Peer fields = ('name', 'address',)

19.913043

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458

7.380952

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0.3

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0

0.224891

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22

58

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null

1

0

null

0

1

0

4

1cf675b9c4f0372a456cb39d55b19295e1898797

276

py

Python

ShopperMiles/vendor/admin_export/urls.py

juansahe/shoppy

265e6e5d3cfc0bc05df3c793e9b4f5921ce78ae5

[ "MIT" ]

null

ShopperMiles/vendor/admin_export/urls.py

juansahe/shoppy

265e6e5d3cfc0bc05df3c793e9b4f5921ce78ae5

[ "MIT" ]

null

ShopperMiles/vendor/admin_export/urls.py

juansahe/shoppy

265e6e5d3cfc0bc05df3c793e9b4f5921ce78ae5

[ "MIT" ]

null

# from django.conf.urls import url, patterns # from django.contrib.admin.views.decorators import staff_member_required # from .views import AdminExport # view = staff_member_required(AdminExport.as_view()) # urlpatterns = [ # url(r'^export/$', view, name="export"), # ]

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276

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1

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1

0

null

0

1

0

4

e81fe97b06635861c4434a4d7f1849781b8f46a5

425

py

Python

flex/void.py

centergy/flex

4fc11d3ad48e4b5016f53256015e3eed2157daae

[ "MIT" ]

null

flex/void.py

centergy/flex

4fc11d3ad48e4b5016f53256015e3eed2157daae

[ "MIT" ]

null

flex/void.py

centergy/flex

4fc11d3ad48e4b5016f53256015e3eed2157daae

[ "MIT" ]

null

import builtins class VoidType(object): __slots__ = () def __new__(cls): if not hasattr(builtins, 'Void'): builtins.Void = super(VoidType, cls).__new__(cls) return builtins.Void def __len__(self): return 0 def __bool__(self): return False __nonzero__ = __bool__ # def __getnewargs__(self): # return () def __str__(self): return 'Void' def __repr__(self): return 'Void' __VOID = VoidType()

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425

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0

null

0

1

0

1

0

4

e820d6a0b3d0db228100e64bfb41667b2ef6fb5f

1,693

py

Python

tests/test_160-security_policies.py

britive/python-api

2daa7693f1d4adf03626abd78598e30f62b6e2e6

[ "MIT" ]

null

tests/test_160-security_policies.py

britive/python-api

2daa7693f1d4adf03626abd78598e30f62b6e2e6

[ "MIT" ]

null

tests/test_160-security_policies.py

britive/python-api

2daa7693f1d4adf03626abd78598e30f62b6e2e6

[ "MIT" ]

null

from .cache import * # will also import some globals like `britive` def test_create(cached_security_policy): assert isinstance(cached_security_policy, dict) def test_list(cached_security_policy): policies = britive.security_policies.list() assert isinstance(policies, list) assert cached_security_policy['id'] in [p['id'] for p in policies] def test_get(cached_security_policy): policy = britive.security_policies.get(security_policy_id=cached_security_policy['id']) assert isinstance(policy, dict) def test_disable(cached_security_policy): response = britive.security_policies.disable(security_policy_id=cached_security_policy['id']) assert response is None policy = britive.security_policies.get(security_policy_id=cached_security_policy['id']) assert policy['status'] == 'Inactive' def test_enable(cached_security_policy): response = britive.security_policies.enable(security_policy_id=cached_security_policy['id']) assert response is None policy = britive.security_policies.get(security_policy_id=cached_security_policy['id']) assert policy['status'] == 'Active' def test_update(cached_security_policy): response = britive.security_policies.update( security_policy_id=cached_security_policy['id'], ips=['2.2.2.2'] ) assert response is None policy = britive.security_policies.get(security_policy_id=cached_security_policy['id']) assert policy['conditions'][0]['values'] == ['2.2.2.2'] def test_delete(cached_security_policy): response = britive.security_policies.delete(security_policy_id=cached_security_policy['id']) assert response is None cleanup('security-policy')

35.270833

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null

1

0

null

0

1

0

4

1c1576961bfaf65770fd47940e1dc49e91254e5d

209

py

Python

pacote-download/atividades-python/fatorial.py

bigmaster112/matheus-python

865a5420c9d5f5f38f7bba67aea0841c23896cee

[ "MIT" ]

null

pacote-download/atividades-python/fatorial.py

bigmaster112/matheus-python

865a5420c9d5f5f38f7bba67aea0841c23896cee

[ "MIT" ]

null

pacote-download/atividades-python/fatorial.py

bigmaster112/matheus-python

865a5420c9d5f5f38f7bba67aea0841c23896cee

[ "MIT" ]

null

numero = int(input('Digite um numero')) fatorial = numero contador = 1 while (numero - contador) > 1: fatorial = fatorial * (numero-contador) contador +=1 print('O fatorial de ', numero,'é', fatorial)

26.125

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1

0

null

1

0

1

0

null

0

4

1c264ac43a5f389b625f961b872cdca85ac616a3

412

py

Python

pystreamable/exceptions.py

jernejovc/pystreamable

78e342dd502bedac6214781bf4f4f85f5a444471

[ "MIT" ]

9

2017-08-09T19:29:16.000Z

2021-11-29T02:50:24.000Z

pystreamable/exceptions.py

jernejovc/pystreamable

78e342dd502bedac6214781bf4f4f85f5a444471

[ "MIT" ]

3

2017-10-11T18:41:05.000Z

2018-10-22T09:04:12.000Z

pystreamable/exceptions.py

jernejovc/pystreamable

78e342dd502bedac6214781bf4f4f85f5a444471

[ "MIT" ]

3

2019-08-25T13:17:25.000Z

2021-01-22T18:11:10.000Z

from __future__ import print_function class StreamableApiException(Exception): """ Base class for all Streamable API wrapper exceptions. """ pass class StreamableApiServerException(StreamableApiException): """ Streamable API server exception. """ pass class StreamableApiClientException(StreamableApiException): """ Streamable API client exception. """ pass

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1

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1

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4

1c3a864ae8eabfe2c5b0f38f8dad8915a9ad63fe

918

py

Python

fbdplc/wires.py

Jmeyer1292/block_diagram_z3

b7180d2dedc33ccb86aa3c58c898dd7adb9653fe

[ "Apache-2.0" ]

4

2021-09-18T13:32:57.000Z

2022-03-15T22:13:56.000Z

fbdplc/wires.py

Jmeyer1292/block_diagram_z3

b7180d2dedc33ccb86aa3c58c898dd7adb9653fe

[ "Apache-2.0" ]

null

fbdplc/wires.py

Jmeyer1292/block_diagram_z3

b7180d2dedc33ccb86aa3c58c898dd7adb9653fe

[ "Apache-2.0" ]

2

2021-12-06T20:19:04.000Z

2022-03-15T22:13:58.000Z

''' Edges in a block diagram computational graph. The edges themselves don't have direction, but the ports that they attach to may. ''' class WireConnection: pass class NamedConnection(WireConnection): def __init__(self, target_uid: int, target_port: str): self.target_uid = target_uid self.target_port = target_port def __str__(self): return f'NamedConnection(id={self.target_uid}, port={self.target_port})' class IdentConnection(WireConnection): def __init__(self, target_uid: int): self.target_uid = target_uid def __str__(self): return f'IdentConnection(id={self.target_uid})' class Wire: ''' Wires in TIA's S7 XML format can have more than two terminals, but we always decompose them into a series of two terminal blocks. ''' def __init__(self, a: WireConnection, b: WireConnection): self.a = a self.b = b

24.810811

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1

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1

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1

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4

1c470f3b148dd13ad815f7979d810003cd90888e

1,031

py

Python

tests/datastructures/test_shuffle.py

TristenSeth/campy

9e726c342d682239e1c19e6f5645c0b2167d7fab

[ "MIT" ]

5

2018-12-03T19:18:50.000Z

2021-05-31T07:17:06.000Z

tests/datastructures/test_shuffle.py

TristenSeth/campy

9e726c342d682239e1c19e6f5645c0b2167d7fab

[ "MIT" ]

1

2017-06-07T04:33:46.000Z

tests/datastructures/test_shuffle.py

TristenSeth/campy

9e726c342d682239e1c19e6f5645c0b2167d7fab

[ "MIT" ]

1

2017-06-06T07:29:07.000Z

"""Tests for the :mod:`campy.datastructures.shuffle` module.""" # These reference shuffled values are being generated by Python running # 3.7.2 (default, Dec 27 2018, 07:35:06) \n[Clang 10.0.0 (clang-1000.11.45.5)] # on macOS 10.14.2 from campy.datastructures.shuffle import shuffle import random def test_shuffle_list(): random.seed(41) assert shuffle([3, 1, 4, 1, 5, 9]) == [5, 9, 3, 1, 4, 1] def test_shuffle_tuple(): random.seed(41) assert shuffle((3, 1, 4, 1, 5, 9)) == (5, 9, 3, 1, 4, 1) def test_shuffle_string(): random.seed(41) assert shuffle('abcdefg') == 'afgebcd' def test_shuffle_bytes(): random.seed(41) assert shuffle(b'abcdefg') == b'afgebcd' def test_shuffle_bytearray(): random.seed(41) assert shuffle(bytearray(b'abcdefg')) == bytearray(b'afgebcd') # def test_shuffle_dict(): # def test_shuffle_set(): # def test_shuffle_frozenset(): # Other types to test: namedtuple? defaultdict? counter? collections abc subclasses? # def test_shuffle_noniterable():

22.413043

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1c5c01689b84baac6ad976451aff4b8f71dcb9ea

191

py

Python

psi/templates/io/__init__.py

NCRAR/psiexperiment

c3f8580b2b155ce42ebb936019d862c4343b545c

[ "MIT" ]

2

2020-07-10T07:49:52.000Z

2020-11-15T13:20:52.000Z

psi/templates/io/__init__.py

NCRAR/psiexperiment

c3f8580b2b155ce42ebb936019d862c4343b545c

[ "MIT" ]

1

2020-04-20T20:37:48.000Z

psi/templates/io/__init__.py

NCRAR/psiexperiment

c3f8580b2b155ce42ebb936019d862c4343b545c

[ "MIT" ]

3

2020-04-17T15:03:36.000Z

2022-01-14T23:19:29.000Z

# Configurations in this directory prefixed with _ are meant to be copied using # `psi-config creaate-io` and modified. Configurations without the underscore # prefix can be loaded directly.

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0

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1

0

null

0

1

0

4

1c65c8302cb402e20550924e947dda41320f571a

559

py

Python

email_manager/admin.py

SACGF/variantgrid

515195e2f03a0da3a3e5f2919d8e0431babfd9c9

[ "RSA-MD" ]

5

2021-01-14T03:34:42.000Z

2022-03-07T15:34:18.000Z

email_manager/admin.py

SACGF/variantgrid

515195e2f03a0da3a3e5f2919d8e0431babfd9c9

[ "RSA-MD" ]

551

2020-10-19T00:02:38.000Z

2022-03-30T02:18:22.000Z

email_manager/admin.py

SACGF/variantgrid

515195e2f03a0da3a3e5f2919d8e0431babfd9c9

[ "RSA-MD" ]

null

from django.contrib import admin from email_manager.models import EmailLog @admin.register(EmailLog) class EmailLogAdmin(admin.ModelAdmin): list_per_page = 500 ordering = ('-created',) list_display = ('created', 'subject', 'recipient_list', 'from_email', 'probably_sent',) search_fields = ('recipient_list',) def has_add_permission(self, request, obj=None): return False def has_change_permission(self, request, obj=None): return False def has_delete_permission(self, request, obj=None): return False

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4

1c75d6064875274b31b97ad8cae1f13bfb2d5de5

2,379

py

Python

tardis/default_settings/__init__.py

keithschulze/mytardis

8ed3562574ce990d42bfe96133185a82c31c27d4

[ "Apache-2.0" ]

null

tardis/default_settings/__init__.py

keithschulze/mytardis

8ed3562574ce990d42bfe96133185a82c31c27d4

[ "Apache-2.0" ]

null

tardis/default_settings/__init__.py

keithschulze/mytardis

8ed3562574ce990d42bfe96133185a82c31c27d4

[ "Apache-2.0" ]

null

# pylint: disable=wildcard-import # first apps, so other files can add to INSTALLED_APPS from tardis.default_settings.apps import * from tardis.default_settings.admins import * from tardis.default_settings.analytics import * from tardis.default_settings.auth import * from tardis.default_settings.caches import * from tardis.default_settings.celery import * from tardis.default_settings.custom_views import * from tardis.default_settings.database import * from tardis.default_settings.debug import * from tardis.default_settings.downloads import * from tardis.default_settings.email import * from tardis.default_settings.filters import * from tardis.default_settings.frontend import * from tardis.default_settings.i18n import * from tardis.default_settings.localisation import * from tardis.default_settings.logging import * from tardis.default_settings.middlewares import * from tardis.default_settings.publication import * from tardis.default_settings.search import * from tardis.default_settings.sftp import * from tardis.default_settings.sharing import * from tardis.default_settings.site_customisations import * from tardis.default_settings.staging import * from tardis.default_settings.static_files import * from tardis.default_settings.storage import * from tardis.default_settings.templates import * from tardis.default_settings.uploads import * from tardis.default_settings.urls import * # Get version from git to be displayed on About page. def get_git_version(): repo_dir = path.dirname(path.dirname(path.abspath(__file__))) def run_git(args): import subprocess process = subprocess.Popen('git %s' % args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, cwd=repo_dir, universal_newlines=True) return process.communicate()[0] try: info = { 'commit_id': run_git("log -1 --format='%H'").strip(), 'date': run_git("log -1 --format='%cd' --date=rfc").strip(), 'branch': run_git("rev-parse --abbrev-ref HEAD").strip(), 'tag': run_git("describe --abbrev=0 --tags").strip(), } except Exception: return ["unavailable"] return info MYTARDIS_VERSION = get_git_version()

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1c778325f7fa92db35a01d5d91f9f2732c6f24e7

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Python

DailyProgrammer/DP20150410W.py

DayGitH/Python-Challenges

bc32f1332a92fcc2dfa6f5ea4d95f8a8d64c3edf

[ "MIT" ]

2

2020-12-23T18:59:22.000Z

2021-04-14T13:16:09.000Z

DailyProgrammer/DP20150410W.py

DayGitH/Python-Challenges

bc32f1332a92fcc2dfa6f5ea4d95f8a8d64c3edf

[ "MIT" ]

null

DailyProgrammer/DP20150410W.py

DayGitH/Python-Challenges

bc32f1332a92fcc2dfa6f5ea4d95f8a8d64c3edf

[ "MIT" ]

null

""" [Weekly #22] Machine Learning https://www.reddit.com/r/dailyprogrammer/comments/3206mk/weekly_22_machine_learning/ # [](#WeeklyIcon) Asimov would be proud! [Machine learning](http://en.wikipedia.org/wiki/Machine_learning) is a diverse field spanning from optimization and data classification, to computer vision and pattern recognition. Modern algorithms for detecting spam email use machine learning to react to developing types of spam and spot them quicker than people could! Techniques include evolutionary programming and genetic algorithms, and models such as [artificial neural networks](http://en.wikipedia.org/wiki/Artificial_neural_network). Do you work in any of these fields, or study them in academics? Do you know something about them that's interesting, or have any cool resources or videos to share? Show them to the world! Libraries like [OpenCV](http://en.wikipedia.org/wiki/OpenCV) (available [here](http://opencv.org/)) use machine learning to some extent, in order to adapt to new situations. The United Kingdom makes extensive use of [automatic number plate recognition](http://en.wikipedia.org/wiki/Police-enforced_ANPR_in_the_UK) on speed cameras, which is a subset of optical character recognition that needs to work in high speeds and poor visibility. Of course, there's also /r/MachineLearning if you want to check out even more. They have a [simple questions thread](http://www.reddit.com/r/MachineLearning/comments/2xopnm/mondays_simple_questions_thread_20150302/) if you want some reading material! *This post was inspired by [this challenge submission](http://www.reddit.com/r/dailyprogrammer_ideas/comments/31wpzp/intermediate_hello_world_genetic_or_evolutionary/). Check out /r/DailyProgrammer_Ideas to submit your own challenges to the subreddit!* ### IRC We have an [IRC channel on Freenode](http://www.reddit.com/r/dailyprogrammer/comments/2dtqr7/), at **#reddit-dailyprogrammer**. Join the channel and lurk with us! ### Previously... The previous weekly thread was [**Recap and Updates**](http://www.reddit.com/r/dailyprogrammer/comments/2sx7nn/). """ def main(): pass if __name__ == "__main__": main()

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py

Python

openpds/core/management/commands/flumoji_firebase.py

eschloss/FluFuture

385506f5d12201b3909d08e42fb1ebb5c0cb323f

[ "MIT" ]

null

openpds/core/management/commands/flumoji_firebase.py

eschloss/FluFuture

385506f5d12201b3909d08e42fb1ebb5c0cb323f

[ "MIT" ]

null

openpds/core/management/commands/flumoji_firebase.py

eschloss/FluFuture

385506f5d12201b3909d08e42fb1ebb5c0cb323f

[ "MIT" ]

null

from django.core.management.base import BaseCommand import logging from openpds.questions.tasks import howAreYouFeelingTodayAllUsers class Command(BaseCommand): def handle(self, *args, **kwargs): howAreYouFeelingTodayAllUsers.delay()

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98cd4bedbf051d709881b2f5aecbf5d8bd582be8

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py

Python

oops_fhir/r4/value_set/loinccodes.py

Mikuana/oops_fhir

77963315d123756b7d21ae881f433778096a1d25

[ "MIT" ]

null

oops_fhir/r4/value_set/loinccodes.py

Mikuana/oops_fhir

77963315d123756b7d21ae881f433778096a1d25

[ "MIT" ]

null

oops_fhir/r4/value_set/loinccodes.py

Mikuana/oops_fhir

77963315d123756b7d21ae881f433778096a1d25

[ "MIT" ]

null

from pathlib import Path from fhir.resources.valueset import ValueSet as _ValueSet from oops_fhir.utils import ValueSet __all__ = ["LOINCCodes"] _resource = _ValueSet.parse_file(Path(__file__).with_suffix(".json")) class LOINCCodes(ValueSet): """ LOINC Codes This value set includes all LOINC codes Status: draft - Version: 4.0.1 http://hl7.org/fhir/ValueSet/observation-codes """ # TODO: fix this template issue1 pass class Meta: resource = _resource

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98e783cd42569d2e132137c2f145ad0161ef41c3

201

py

Python

tests/conftest.py

araneto/foodelivery

9c8c587307286d9f0b79206bf8464d8fff9073fa

[ "MIT" ]

null

tests/conftest.py

araneto/foodelivery

9c8c587307286d9f0b79206bf8464d8fff9073fa

[ "MIT" ]

1

2020-09-14T22:09:03.000Z

tests/conftest.py

araneto/foodelivery

9c8c587307286d9f0b79206bf8464d8fff9073fa

[ "MIT" ]

null

""" Run make install before run tests """ import pytest from foodelivery.app import create_app @pytest.fixture(scope="module") def app(): """Instance of Main flask app""" return create_app()

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98f753d1c4e5758d90059e57e232a2987db844a3

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py

Python

lectures/code/dict_print.py

naskoch/python_course

84adfd3f8d48ca3ad5837f7acc59d2fa051e95d3

[ "MIT" ]

4

2015-08-10T17:46:55.000Z

2020-04-18T21:09:03.000Z

lectures/code/dict_print.py

naskoch/python_course

84adfd3f8d48ca3ad5837f7acc59d2fa051e95d3

[ "MIT" ]

null

lectures/code/dict_print.py

naskoch/python_course

84adfd3f8d48ca3ad5837f7acc59d2fa051e95d3

[ "MIT" ]

2

2019-04-24T03:31:02.000Z

2019-05-13T07:36:06.000Z

>>> d = {1: 'one', 2: 'two', 3: 'three'} >>> for key, value in d.items(): ... print key, value ... 1 one 2 two 3 three

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98fe2100e443a72cbbb6f3d74c7b430f232ca6d3

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py

Python

textToBraille/admin.py

JoelVG/text-to-braille

9e6f5c7337d2d402378cd96f9476eb39d7b82328

[ "MIT" ]

null

textToBraille/admin.py

JoelVG/text-to-braille

9e6f5c7337d2d402378cd96f9476eb39d7b82328

[ "MIT" ]

1

2022-02-06T21:12:48.000Z

textToBraille/admin.py

JoelVG/text-to-braille

9e6f5c7337d2d402378cd96f9476eb39d7b82328

[ "MIT" ]

null

from django.contrib import admin from .models import Translation # Register your models here. class TranslationAdmin(admin.ModelAdmin): list_display = ('text', 'braille_translation') admin.site.register(Translation, TranslationAdmin)

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c7086ad17884ddb1f62e33bef40213c6c10d2055

883

py

Python

baselines/EMNLP2019/uri_features.py

ParikhKadam/knowledge-net

abc4ed3ebb88bfde8c1f02709371324ae6347ba0

[ "MIT" ]

240

2019-09-13T21:33:24.000Z

2022-03-28T02:35:00.000Z

baselines/EMNLP2019/uri_features.py

ParikhKadam/knowledge-net

abc4ed3ebb88bfde8c1f02709371324ae6347ba0

[ "MIT" ]

8

2020-01-28T23:04:59.000Z

2021-05-21T16:01:28.000Z

baselines/EMNLP2019/uri_features.py

ParikhKadam/knowledge-net

abc4ed3ebb88bfde8c1f02709371324ae6347ba0

[ "MIT" ]

34

2019-09-21T00:19:37.000Z

2022-02-04T19:59:23.000Z

import itertools import numpy as np import networkx as nx import vocab def coref_score(instance, property_id): return [ instance.subject_entity["coref_score"], instance.object_entity["coref_score"] ] def el_score(instance, property_id): return [ instance.subject_entity["el_score"], instance.object_entity["el_score"] ] def _entity_linker_types_from_mention(entity): arr = np.zeros(len(vocab.types), np.float32) for i, t in enumerate(vocab.types): if t in entity["types"]: arr[i] = 1.0 return arr def entity_linker_types(instance, property_id): return np.concatenate([ _entity_linker_types_from_mention(instance.subject_entity), _entity_linker_types_from_mention(instance.object_entity) ]) def wikidata_predicates(instance, property_id): return None def text_score(instance, property_id): return [ instance.text_instance.scores[property_id] ]

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c71c65dfc54a49c0ce16a8b9dccc0de2db33751d

881

py

Python

python/dgl/_dataloading/__init__.py

ketyi/dgl

a1b859c29b63a673c148d13231a49504740e0e01

[ "Apache-2.0" ]

9,516

2018-12-08T22:11:31.000Z

2022-03-31T13:04:33.000Z

python/dgl/_dataloading/__init__.py

ketyi/dgl

a1b859c29b63a673c148d13231a49504740e0e01

[ "Apache-2.0" ]

2,494

2018-12-08T22:43:00.000Z

2022-03-31T21:16:27.000Z

python/dgl/_dataloading/__init__.py

ketyi/dgl

a1b859c29b63a673c148d13231a49504740e0e01

[ "Apache-2.0" ]

2,529

2018-12-08T22:56:14.000Z

2022-03-31T13:07:41.000Z

"""The ``dgl.dataloading`` package contains: * Data loader classes for iterating over a set of nodes or edges in a graph and generates computation dependency via neighborhood sampling methods. * Various sampler classes that perform neighborhood sampling for multi-layer GNNs. * Negative samplers for link prediction. For a holistic explanation on how different components work together. Read the user guide :ref:`guide-minibatch`. .. note:: This package is experimental and the interfaces may be subject to changes in future releases. It currently only has implementations in PyTorch. """ from .neighbor import * from .dataloader import * from .cluster_gcn import * from .shadow import * from . import negative_sampler from .async_transferer import AsyncTransferer from .. import backend as F if F.get_preferred_backend() == 'pytorch': from .pytorch import *

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c738c4999ed8cdf2e8966d52f4e0656d6ad275a7

866

py

Python

bat/report/report.py

XUANLANcognition/Bat

4ee2e81aa4e41b8a355701fa6d24a1e00115c3a4

[ "BSD-2-Clause" ]

null

bat/report/report.py

XUANLANcognition/Bat

4ee2e81aa4e41b8a355701fa6d24a1e00115c3a4

[ "BSD-2-Clause" ]

null

bat/report/report.py

XUANLANcognition/Bat

4ee2e81aa4e41b8a355701fa6d24a1e00115c3a4

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python # -*- config : utf-8 -*- 'help' class Report(object): """ You can wirte the report has created into a html file. """ def __init__(self, headtitle, content): self.headtitle = 'Bat''s' + headtitle self.content = content def replace(self): self.content = self.content.replace('\n', '<br>') self.content = self.content.replace('###', '') self.content = self.content.replace('[', '<h3>') self.content = self.content.replace(']', '</h3>') def create(self): self.replace() self.content = '<h1>' + self.headtitle + ' Report</h1>' + self.content with open('./' + self.headtitle + '.html', 'w') as f: f.write(self.content) class ScanReport(Report): """ For scan """ def __init__(self, headtitle, content): pass

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c75374fcb3ef0a237f3413fc6e0302c93e408590

19,165

py

Python

epg/epgcpmg.py

jtamir/mri-sim-py

f606b835412bfc6a84dc0a8124807ea0979f663c

[ "MIT" ]

5

2019-09-06T18:51:56.000Z

2020-05-26T10:17:29.000Z

epg/epgcpmg.py

jtamir/mri-sim-py

f606b835412bfc6a84dc0a8124807ea0979f663c

[ "MIT" ]

null

epg/epgcpmg.py

jtamir/mri-sim-py

f606b835412bfc6a84dc0a8124807ea0979f663c

[ "MIT" ]

5

2017-02-19T14:28:43.000Z

2020-06-10T07:42:54.000Z

#!/usr/bin/python # EPG CPMG simulation code, based off of Matlab scripts from Brian Hargreaves <bah@stanford.edu> # 2015 Jonathan Tamir <jtamir@eecs.berkeley.edu> import numpy as np from warnings import warn def rf(FpFmZ, alpha): "Same as rf2, but only returns FpFmZ""" return rf2(FpFmZ, alpha)[0] def rf2(FpFmZ, alpha): """ Propagate EPG states through an RF rotation of alpha (radians). Assumes CPMG condition, i.e. magnetization lies on the real x axis. INPUT: FpFmZ = 3xN vector of F+, F- and Z states. alpha = RF pulse flip angle in radians OUTPUT: FpFmZ = Updated FpFmZ state. RR = RF rotation matrix (3x3). """ # -- From Weigel at al, JMRI 41(2015)266-295, Eq. 21. if abs(alpha) > 2 * np.pi: warn('rf2: Flip angle should be in radians! alpha=%f' % alpha) cosa2 = np.cos(alpha/2.)**2 sina2 = np.sin(alpha/2.)**2 cosa = np.cos(alpha) sina = np.sin(alpha) RR = np.array([ [cosa2, sina2, sina], [sina2, cosa2, -sina], [-0.5 * sina, 0.5 * sina, cosa] ]) FpFmZ = np.dot(RR, FpFmZ) return FpFmZ, RR def rf_ex(FpFmZ, alpha): "Same as rf2_ex, but only returns FpFmZ""" return rf2_ex(FpFmZ, alpha)[0] def rf2_ex(FpFmZ, alpha): """ Propagate EPG states through an RF excitation of alpha (radians) along the y direction, i.e. phase of pi/2. INPUT: FpFmZ = 3xN vector of F+, F- and Z states. alpha = RF pulse flip angle in radians OUTPUT: FpFmZ = Updated FpFmZ state. RR = RF rotation matrix (3x3). """ try: alpha = alpha[0] except: pass if abs(alpha) > 2 * np.pi: warn('rf2_ex: Flip angle should be in radians! alpha=%f' % alpha) cosa2 = np.cos(alpha/2.)**2 sina2 = np.sin(alpha/2.)**2 cosa = np.cos(alpha) sina = np.sin(alpha) RR = np.array([ [cosa2, -sina2, sina], [-sina2, cosa2, sina], [-0.5 * sina, -0.5 * sina, cosa] ]) FpFmZ = np.dot(RR, FpFmZ) return FpFmZ, RR def rf_prime(FpFmZ, alpha): """Same as rf_prime2, but only returns FpFmZ""" return rf_prime2(FpFmZ, alpha)[0] def rf_prime2(FpFmZ, alpha): """ Compute the gradient of the RF rotation operator, where alpha (radians) is the RF rotation. Assumes CPMG condition, i.e. magnetization lies on the real x axis. INPUT: FpFmZ = 3xN vector of F+, F- and Z states. alpha = RF pulse flip angle in radians OUTPUT: FpFmZ = Derivative of FpFmZ state w.r.t. alpha RR = Derivative of RF rotation matrix (3x3) w.r.t. alpha """ if abs(alpha) > 2 * np.pi: warn('rf_prime2: Flip angle should be in radians! alpha=%f' % alpha) RR = np.array([ [-np.cos(alpha/2.) * np.sin(alpha/2.), np.cos(alpha/2.) * np.sin(alpha/2.), np.cos(alpha)], [np.cos(alpha/2.) * np.sin(alpha/2.), -np.cos(alpha/2.) * np.sin(alpha/2.), -np.cos(alpha)], [-0.5 * np.cos(alpha), 0.5 * np.cos(alpha), -np.sin(alpha)] ]) FpFmZ = np.dot(RR, FpFmZ) return FpFmZ, RR def rf_B1_prime(FpFmZ, alpha, B1): """Same as rf_B1_prime2, but only returns FpFmZ""" return rf_B1_prime2(FpFmZ, alpha, B1)[0] def rf_B1_prime2(FpFmZ, alpha, B1): """ Compute the gradient of B1 inhomogeneity w.r.t. RF refocusing operator, where alpha (radians) is the RF rotation and B1 is the B1 homogeneity (0, 2). Assumes CPMG condition, i.e. magnetization lies on the real x axis. INPUT: FpFmZ = 3xN vector of F+, F- and Z states. alpha = RF pulse flip angle in radians B1 = B1 Homogeneity, where 1. is homogeneous OUTPUT: FpFmZ = Derivative of FpFmZ state w.r.t. alpha RR = Derivative of RF rotation matrix (3x3) w.r.t. B1 """ if abs(alpha) > 2 * np.pi: warn('rf_B1_prime2: Flip angle should be in radians! alpha=%f' % alpha) if B1 < 0 or B1 > 2: warn('rf_B1_prime2: B1 Homogeneity should be a percentage between (0, 2)') RR = np.array([ [-alpha*np.cos(B1*alpha/2.) * np.sin(B1*alpha/2.), alpha*np.cos(B1*alpha/2.) * np.sin(B1*alpha/2.), alpha*np.cos(B1*alpha)], [alpha*np.cos(B1*alpha/2.) * np.sin(B1*alpha/2.), -alpha*np.cos(B1*alpha/2.) * np.sin(B1*alpha/2.), -alpha*np.cos(B1*alpha)], [-0.5*alpha*np.cos(B1*alpha), 0.5*alpha*np.cos(B1*alpha), -alpha*np.sin(B1*alpha)] ]) FpFmZ = np.dot(RR, FpFmZ) return FpFmZ, RR def rf_ex_B1_prime(FpFmZ, alpha, B1): """Gradient of B1 inhomogeneity w.r.t. RF excitation operator, where alpha (radians) is the RF rotation and B1 is the B1 honogeneity (0, 2). Assumes CPMG condition, i.e. RF excitation in the y direction. INPUT: FpFmZ = 3xN vector of F+, F- and Z states. alpha = RF pulse flip angle in radians B1 = B1 Homogeneity, where 1. is homogeneous OUTPUT: FpFmZ = Derivative of FpFmZ state w.r.t. alpha """ if abs(alpha) > 2 * np.pi: warn('rf_ex_B1_prime2: Flip angle should be in radians! alpha=%f' % alpha) if B1 < 0 or B1 > 2: warn('rf_ex_B1_prime: B1 Homogeneity should be a percentage between (0, 2)') RR = np.array([ [-alpha*np.cos(B1*alpha/2.) * np.sin(B1*alpha/2.), alpha*np.cos(B1*alpha/2.) * np.sin(B1*alpha/2.), alpha*np.cos(B1*alpha)], [alpha*np.cos(B1*alpha/2.) * np.sin(B1*alpha/2.), -alpha*np.cos(B1*alpha/2.) * np.sin(B1*alpha/2.), alpha*np.cos(B1*alpha)], [-0.5*alpha*np.cos(B1*alpha), -0.5*alpha*np.cos(B1*alpha), -alpha*np.sin(B1*alpha)] ]) FpFmZ = np.dot(RR, FpFmZ) return FpFmZ def relax_mat(T, T1, T2): E2 = np.exp(-T/T2) E1 = np.exp(-T/T1) EE = np.diag([E2, E2, E1]) # Decay of states due to relaxation alone. return EE def relax_mat_prime_T1(T, T1, T2): E1_prime_T1 = T * np.exp(-T/T1) / T1**2 return np.diag([0, 0, E1_prime_T1]) def relax_mat_prime_T2(T, T1, T2): E2_prime_T2 = T * np.exp(-T/T2) / T2**2 return np.diag([E2_prime_T2, E2_prime_T2, 0]) def relax_prime_T1(FpFmZ, T, T1, T2): """returns E'(T1) FpFmZ + E0'(T1)""" EE_prime_T1 = relax_mat_prime_T1(T, T1, T2) RR = -EE_prime_T1[2,2] FpFmZ = np.dot(EE_prime_T1, FpFmZ) FpFmZ[2,0] = FpFmZ[2,0] + RR return FpFmZ def relax_prime_T2(FpFmZ, T, T1, T2): """returns E'(T2) FpFmZ""" EE_prime_T2 = relax_mat_prime_T2(T, T1, T2) FpFmZ = np.dot(EE_prime_T2, FpFmZ) return FpFmZ def relax(FpFmZ, T, T1, T2): """Same as relax2, but only returns FpFmZ""" return relax2(FpFmZ, T, T1, T2)[0] def relax2(FpFmZ, T, T1, T2): """ Propagate EPG states through a period of relaxation over an interval T. INPUT: FpFmZ = 3xN vector of F+, F- and Z states. T1, T2 = Relaxation times (same as T) T = Time interval (same as T1,T2) OUTPUT: FpFmZ = updated F+, F- and Z states. EE = decay matrix, 3x3 = diag([E2 E2 E1]); """ E2 = np.exp(-T/T2) E1 = np.exp(-T/T1) EE = np.diag([E2, E2, E1]) # Decay of states due to relaxation alone. RR = 1 - E1 # Mz Recovery, affects only Z0 state, as # recovered magnetization is not dephased. FpFmZ = np.dot(EE, FpFmZ) # Apply Relaxation FpFmZ[2,0] = FpFmZ[2,0] + RR # Recovery return FpFmZ, EE def grad(FpFmZ, noadd=False): """Propagate EPG states through a "unit" gradient. Assumes CPMG condition, i.e. all states are real-valued. INPUT: FpFmZ = 3xN vector of F+, F- and Z states. noadd = True to NOT add any higher-order states - assume that they just go to zero. Be careful - this speeds up simulations, but may compromise accuracy! OUTPUT: Updated FpFmZ state. """ # Gradient does not affect the Z states. if noadd == False: FpFmZ = np.hstack((FpFmZ, [[0],[0],[0]])) # add higher dephased state FpFmZ[0,:] = np.roll(FpFmZ[0,:], 1) # shift Fp states FpFmZ[1,:] = np.roll(FpFmZ[1,:], -1) # shift Fm states FpFmZ[1,-1] = 0 # Zero highest Fm state FpFmZ[0,0] = FpFmZ[1,0] # Fill in lowest Fp state return FpFmZ def FSE_TE(FpFmZ, alpha, TE, T1, T2, noadd=False, recovery=True): """ Propagate EPG states through a full TE, i.e. relax -> grad -> rf -> grad -> relax. Assumes CPMG condition, i.e. all states are real-valued. INPUT: FpFmZ = 3xN vector of F+, F- and Z states. alpha = RF pulse flip angle in radians T1, T2 = Relaxation times (same as TE) TE = Echo Time interval (same as T1, T2) noadd = True to NOT add any higher-order states - assume that they just go to zero. Be careful - this speeds up simulations, but may compromise accuracy! OUTPUT: FpFmZ = updated F+, F- and Z states. """ EE = relax_mat(TE/2., T1, T2) if recovery: FpFmZ = relax(FpFmZ, TE/2., T1, T2) else: FpFmZ = np.dot(EE, FpFmZ) FpFmZ = grad(FpFmZ, noadd) FpFmZ = rf(FpFmZ, alpha) FpFmZ = grad(FpFmZ, noadd) if recovery: FpFmZ = relax(FpFmZ, TE/2., T1, T2) else: FpFmZ = np.dot(EE, FpFmZ) return FpFmZ def FSE_TE_prime_alpha(FpFmZ, alpha, TE, T1, T2, noadd=False, recovery=True): """ Gradient of EPG over a full TE, w.r.t. flip angle alpha, i.e. relax -> grad -> rf_prime -> grad -> relax_hat, where rf_prime is the derivative of the RF pulse matrix w.r.t. alpha, and relax_hat is the relaxation without longitudinal recovery Assumes CPMG condition, i.e. all states are real-valued. INPUT: FpFmZ = 3xN vector of F+, F- and Z states. alpha = RF pulse flip angle in radians T1, T2 = Relaxation times (same as TE) TE = Echo Time interval (same as T1, T2) noadd = True to NOT add any higher-order states - assume that they just go to zero. Be careful - this speeds up simulations, but may compromise accuracy! recovery = True to include T1 recovery in the Z0 state. OUTPUT: FpFmZ = updated F+, F- and Z states. """ FpFmZ, EE = relax2(FpFmZ, TE/2., T1, T2) FpFmZ = grad(FpFmZ, noadd) FpFmZ = rf_prime(FpFmZ, alpha) FpFmZ = grad(FpFmZ, noadd) FpFmZ = np.dot(EE, FpFmZ) return FpFmZ def FSE_TE_prime1_T2(FpFmZ, alpha, TE, T1, T2, noadd=False): """ Returns E(T2) G R G E'(T2) FpFmZ""" EE = relax_mat(TE/2., T1, T2) EE_prime = relax_mat_prime_T2(TE/2., T1, T2) FpFmZ = np.dot(EE_prime, FpFmZ) FpFmZ = grad(FpFmZ, noadd) FpFmZ = rf(FpFmZ, alpha) FpFmZ = grad(FpFmZ, noadd) FpFmZ = np.dot(EE, FpFmZ) return FpFmZ def FSE_TE_prime2_T2(FpFmZ, alpha, TE, T1, T2, noadd=False): """ Returns E'(T2) G R G (E(T2) FpFmZ + E0)""" EE_prime = relax_mat_prime_T2(TE/2., T1, T2) FpFmZ = relax(FpFmZ, TE/2., T1, T2) FpFmZ = grad(FpFmZ, noadd) FpFmZ = rf(FpFmZ, alpha) FpFmZ = grad(FpFmZ, noadd) FpFmZ = np.dot(EE_prime, FpFmZ) return FpFmZ def FSE_TE_prime1_T1(FpFmZ, alpha, TE, T1, T2, noadd=False): """ Returns E(T1) G R G (E'(T1) FpFmZ + E0'(T1))""" EE = relax_mat(TE/2., T1, T2) FpFmZ = relax_prime_T1(FpFmZ, TE/2., T1, T2) # E'(T1) FpFmZ + E0'(T1) FpFmZ = grad(FpFmZ, noadd) FpFmZ = rf(FpFmZ, alpha) FpFmZ = grad(FpFmZ, noadd) FpFmZ = np.dot(EE, FpFmZ) return FpFmZ def FSE_TE_prime2_T1(FpFmZ, alpha, TE, T1, T2, noadd=False): """ Returns E'(T1) G R G E(T1) FpFmZ + E0'(T1)""" EE = relax_mat(TE/2., T1, T2) FpFmZ = np.dot(EE, FpFmZ) FpFmZ = grad(FpFmZ, noadd) FpFmZ = rf(FpFmZ, alpha) FpFmZ = grad(FpFmZ, noadd) FpFmZ = relax_prime_T1(FpFmZ, TE/2., T1, T2) # E'(T1) FpFmZ + E0'(T1) return FpFmZ def FSE_TE_prime_B1(FpFmZ, alpha, TE, T1, T2, B1, noadd=False): """ Gradient of EPG over a full TE, w.r.t. B1 homogeneity fraciton B1, i.e. relax -> grad -> rf_B1_prime -> grad -> relax_hat, where rf_B1_prime is the derivative of the RF pulse matrix w.r.t. B1, and relax_hat is the relaxation without longitudinal recovery Assumes CPMG condition, i.e. all states are real-valued. INPUT: FpFmZ = 3xN vector of F+, F- and Z states. alpha = RF pulse flip angle in radians T1, T2 = Relaxation times (same as TE) TE = Echo Time interval (same as T1, T2) B1 = fraction of B1 homogeneity (1 is fully homogeneous) noadd = True to NOT add any higher-order states - assume that they just go to zero. Be careful - this speeds up simulations, but may compromise accuracy! recovery = True to include T1 recovery in the Z0 state. OUTPUT: FpFmZ = updated F+, F- and Z states. """ FpFmZ, EE = relax2(FpFmZ, TE/2., T1, T2) FpFmZ = grad(FpFmZ, noadd) FpFmZ = rf_B1_prime(FpFmZ, alpha, B1) FpFmZ = grad(FpFmZ, noadd) FpFmZ = np.dot(EE, FpFmZ) return FpFmZ ### Gradients of full FSE EPG function across T time points def FSE_signal_prime_alpha_idx(angles_rad, TE, T1, T2, idx): """Gradient of EPG function at each time point w.r.t. RF pulse alpha_i""" T = len(angles_rad) zi = np.hstack((np.array([[1],[1],[0]]), np.zeros((3, T)))) z_prime = np.zeros((T, 1)) for i in range(T): alpha = angles_rad[i] if i < idx: zi = FSE_TE(zi, alpha, TE, T1, T2, noadd=True) z_prime[i] = 0 elif i == idx: wi = FSE_TE_prime_alpha(zi, alpha, TE, T1, T2, noadd=True) z_prime[i] = wi[0,0] else: wi = FSE_TE(wi, alpha, TE, T1, T2, noadd=True, recovery=False) z_prime[i] = wi[0,0] return z_prime def FSE_signal_prime_T1(angles_rad, TE, T1, T2): return FSE_signal_ex_prime_T1(np.pi/2, angles_rad, TE, T1, T2) def FSE_signal_ex_prime_T1(angle_ex_rad, angles_rad, TE, T1, T2, B1=1.): """Gradient of EPG function at each time point w.r.t. T1""" T = len(angles_rad) try: B1 = B1[0] except: pass # since the grad doesn't depend on B1 inhomog, can just pre-scale flip angles angle_ex_rad = B1 * np.copy(angle_ex_rad) angles_rad = B1 * np.copy(angles_rad) zi = np.hstack((rf_ex(np.array([[0],[0],[1]]), angle_ex_rad), np.zeros((3, T)))) z_prime = np.zeros((T, 1)) for i in range(T): alpha = angles_rad[i] if i == 0: wi = np.zeros((3, T+1)) else: wi = FSE_TE(wi, alpha, TE, T1, T2, noadd=True, recovery=False) wi += FSE_TE_prime1_T1(zi, alpha, TE, T1, T2, noadd=True) wi += FSE_TE_prime2_T1(zi, alpha, TE, T1, T2, noadd=True) zi = FSE_TE(zi, alpha, TE, T1, T2, noadd=True) z_prime[i] = wi[0,0] return z_prime def FSE_signal_prime_T2(angles_rad, TE, T1, T2): return FSE_signal_ex_prime_T2(np.pi/2, angles_rad, TE, T1, T2) def FSE_signal_ex_prime_T2(angle_ex_rad, angles_rad, TE, T1, T2, B1=1.): """Gradient of EPG function at each time point w.r.t. T2""" T = len(angles_rad) try: B1 = B1[0] except: pass # since the grad doesn't depend on B1 inhomog, can just pre-scale flip angles angle_ex_rad = B1 * np.copy(angle_ex_rad) angles_rad = B1 * np.copy(angles_rad) zi = np.hstack((rf_ex(np.array([[0],[0],[1]]), angle_ex_rad), np.zeros((3, T)))) z_prime = np.zeros((T, 1)) for i in range(T): alpha = angles_rad[i] if i == 0: wi = np.zeros((3, T+1)) else: wi = FSE_TE(wi, alpha, TE, T1, T2, noadd=True, recovery=False) wi += FSE_TE_prime1_T2(zi, alpha, TE, T1, T2, noadd=True) wi += FSE_TE_prime2_T2(zi, alpha, TE, T1, T2, noadd=True) zi = FSE_TE(zi, alpha, TE, T1, T2, noadd=True) z_prime[i] = wi[0,0] return z_prime def FSE_signal_ex_prime_B1(angle_ex_rad, angles_rad, TE, T1, T2, B1): """Gradient of EPG function at each time point w.r.t. B1 Homogeneity. Includes the excitation flip angle""" T = len(angles_rad) zi = np.hstack((np.array([[0],[0],[1]]), np.zeros((3, T+1)))) z_prime = np.zeros((T, 1)) wi = rf_ex_B1_prime(zi, angle_ex_rad, B1) zi = rf_ex(zi, angle_ex_rad * B1) for i in range(T): alpha = angles_rad[i] if i == 0: xi = FSE_TE(wi, alpha * B1, TE, T1, T2, noadd=True, recovery=False) else: xi = FSE_TE(wi, alpha * B1, TE, T1, T2, noadd=True) wi = FSE_TE_prime_B1(zi, alpha, TE, T1, T2, B1, noadd=True) + xi zi = FSE_TE(zi, alpha * B1, TE, T1, T2, noadd=True) z_prime[i] = wi[0,0] return z_prime ### Full FSE EPG function across T time points def FSE_signal_ex(angle_ex_rad, angles_rad, TE, T1, T2, B1=1.): """Same as FSE_signal2_ex, but only returns Mxy""" return FSE_signal2_ex(angle_ex_rad, angles_rad, TE, T1, T2, B1)[0] def FSE_signal(angles_rad, TE, T1, T2): """Same as FSE_signal2, but only returns Mxy""" return FSE_signal2(angles_rad, TE, T1, T2)[0] def FSE_signal2(angles_rad, TE, T1, T2): """Same as FSE_signal2_ex, but assumes excitation pulse is 90 degrees""" return FSE_signal2_ex(np.pi/2., angles_rad, TE, T1, T2) def FSE_signal2_ex(angle_ex_rad, angles_rad, TE, T1, T2, B1=1.): """Simulate Fast Spin-Echo CPMG sequence with specific flip angle train. Prior to the flip angle train, an excitation pulse of angle_ex_rad degrees is applied in the Y direction. The flip angle train is then applied in the X direction. INPUT: angles_rad = array of flip angles in radians equal to echo train length TE = echo time/spacing T1 = T1 value in seconds T2 = T2 value in seconds OUTPUT: Mxy = Transverse magnetization at each echo time Mz = Longitudinal magnetization at each echo time """ T = len(angles_rad) Mxy = np.zeros((T,1)) Mz = np.zeros((T,1)) P = np.array([[0],[0],[1]]) # initially on Mz try: B1 = B1[0] except: pass # pre-scale by B1 homogeneity angle_ex_rad = B1 * np.copy(angle_ex_rad) angles_rad = B1 * np.copy(angles_rad) P = rf_ex(P, angle_ex_rad) # initial tip for i in range(T): alpha = angles_rad[i] P = FSE_TE(P, alpha, TE, T1, T2) Mxy[i] = P[0,0] Mz[i] = P[2,0] return Mxy, Mz if __name__ == "__main__": import matplotlib.pyplot as plt T1 = 1000e-3 T2 = 200e-3 TE = 5e-3 N = 100 angles = 120 * np.ones((N,)) angles_rad = angles * np.pi / 180. S = FSE_signal(angles_rad, TE, T1, T2) S2 = abs(S) plt.plot(TE*1000*np.arange(1, N+1), S2) plt.xlabel('time (ms)') plt.ylabel('signal') plt.title('T1 = %.2f ms, T2 = %.2f ms' % (T1 * 1000, T2 * 1000)) plt.show()

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website/migrations/0006_auto_20181006_2147.py

Lewes/ecssweb

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website/migrations/0006_auto_20181006_2147.py

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# Generated by Django 2.1.2 on 2018-10-06 20:47 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('website', '0005_committeerolemember_role_short_name'), ] operations = [ migrations.AlterModelOptions( name='committeerolemember', options={'verbose_name_plural': 'committee roles members'}, ), migrations.AlterModelOptions( name='society', options={'verbose_name_plural': 'societies'}, ), migrations.AlterModelOptions( name='societylink', options={'verbose_name_plural': 'societies links'}, ), migrations.AlterModelOptions( name='sponsorlink', options={'verbose_name_plural': 'sponsors links'}, ), ]

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plnx_demo_parameterize/plnx_sanity/conf.py

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plnx_demo_parameterize/plnx_sanity/conf.py

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plnx_demo_parameterize/plnx_sanity/conf.py

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myweb/pub_form/form_upload.py

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myweb/pub_form/form_upload.py

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myweb/pub_form/form_upload.py

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scripts/arcrest/agol/__init__.py

datastark/crime-analysis-toolbox

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scripts/arcrest/agol/__init__.py

datastark/crime-analysis-toolbox

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scripts/arcrest/agol/__init__.py

datastark/crime-analysis-toolbox

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from __future__ import absolute_import from .services import FeatureService, FeatureLayer, TableLayer, TiledService from . import helperservices from ._uploads import Uploads __version__ = "3.5.3"

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game/gameplay/level.py

chadfraser/CluGame

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game/gameplay/level.py

chadfraser/CluGame

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game/gameplay/level.py

chadfraser/CluGame

090f3749e102c5331136298356d543c8b4e8a9a5

[ "MIT" ]

null

import pygame import random from game.tools.asset_cache import getImage import game.tools.constants as c class Level: """Create a new level object. This class should not be called directly. Only call its subclasses. Attributes: rubberTilesHorizontal: A list of tuples indicating which columns and rows to place horizontal rubber traps. rubberTilesVertical: A list of tuples indicating which columns and rows to place vertical rubber traps. goldTilesHorizontal: A list of tuples indicating which columns and rows to place horizontal gold sprites. goldTilesVertical: A list of tuples indicating which columns and rows to place vertical gold sprites. """ def __init__(self, rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, goldTilesVertical): """Init Level using the lists of tuples rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, and goldTilesVertical. Instance variables: image: A None type object. Subclasses replace this with a Surface object of the image to be drawn for the current level. standardImage: A None type object. Subclasses replace this with a Surface object of the image to be seen in standard play of the current level. lightImage: A None type object. Subclasses replace this with a Surface object of a lighter variant of the image to be seen in standard play of the current level. Designed to be used when an ItemClock object is active, or to give the illusion of the level flashing. backgroundColor: A tuple indicating the color of the level's background. activeRubberTraps: An empty list. Subclasses replace this with a list of tuples indicating which columns and rows have horizontal rubber traps that begin the game in an active state. playerStartPositions: A list of four tuples indicating which columns and rows each player starts on. blackHolePositions: An empty list. Subclasses replace this with a list of tuples indicating which columns and start with a black hole sprite. itemTiles: An empty list. Subclasses replace this with a list of tuples indicating which columns and rows can have items spawned on them. levelBorderRects: An empty list. Subclasses replace this with a list of rect objects that form the boundaries of the level. isFlashing: A boolean indicating if the level should be in a flashing animation, switching between its standardImage and lightImage. frameCount: An integer that increases whenever the flashBoard method is called. """ self.image = self.standardImage = self.lightImage = None self.backgroundColor = c.BLACK self.rubberTilesHorizontal = rubberTilesHorizontal self.rubberTilesVertical = rubberTilesVertical self.goldTilesHorizontal = goldTilesHorizontal self.goldTilesVertical = goldTilesVertical self.activeRubberTraps = [] self.playerStartPosition = [(0, 0), (0, 0), (0, 0), (0, 0)] self.blackHolePositions = [] self.itemTiles = [] self.levelBorderRects = [] self.isFlashing = False self.frameCount = 0 def initialize(self): """Set the relevant variables of the level to their initial values.""" self.isFlashing = False self.image = self.standardImage self.frameCount = 0 def flashBoard(self): """Switch the level's image between standardImage and flashingImage every 6 frames.""" if self.isFlashing: self.frameCount += 1 if self.frameCount % 12 < 6: self.image = self.standardImage else: self.image = self.lightImage class BoardOneLevel(Level): """Create a new object of the first variant of levels. Attributes: rubberTilesHorizontal: A list of tuples indicating which columns and rows to place horizontal rubber traps. rubberTilesVertical: A list of tuples indicating which columns and rows to place vertical rubber traps. goldTilesHorizontal: A list of tuples indicating which columns and rows to place horizontal gold sprites. goldTilesVertical: A list of tuples indicating which columns and rows to place vertical gold sprites. """ def __init__(self, rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, goldTilesVertical): """Init BoardOneLevel using the lists of tuples rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, and goldTilesVertical. Instance variables: standardImage: The image to be drawn for the level during standard gameplay. lightImage: A lighter variant of standardImage, designed to be used when an ItemClock object is active, or to give the illusion of the level flashing. image: The current image to be drawn for the level. Defaults to the standardImage. backgroundColor: A tuple indicating the color of the level's background. playerStartPositions: A list of four tuples indicating which columns and rows each player starts on. blackHolePositions: A list of four tuples indicating which columns and rows each black hole sprite starts on. itemTiles: A list of tuples indicating which columns and rows can have items spawned on them. This should include every tile that a player can reach, except those tiles that the players start on. levelBorderRects: A list of rect objects that form the boundaries of the level. """ super().__init__(rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, goldTilesVertical) self.standardImage = getImage(c.BACKGROUND_FOLDER, "background_1A.png") self.lightImage = getImage(c.BACKGROUND_FOLDER, "background_1B.png") self.image = self.standardImage self.backgroundColor = c.DARK_RED self.playerStartPosition = [(1, 1), (9, 1), (2, 7), (8, 7)] self.blackHolePositions = [(5, 4)] self.itemTiles = [(x, y) for x in range(1, 10) for y in range(0, 8) if (x, y) not in self.playerStartPosition and (x, y) not in self.blackHolePositions and (x, y) not in [(1, 0), (9, 0), (1, 7), (9, 7)]] self.levelBorderRects = [pygame.Rect(0, 0, 80, 84), pygame.Rect(0, 0, 512, 36), pygame.Rect(0, 0, 39, 448), pygame.Rect(432, 0, 80, 84), pygame.Rect(477, 0, 39, 448), pygame.Rect(0, 380, 80, 84), pygame.Rect(432, 380, 80, 84), pygame.Rect(0, 426, 512, 36)] class BoardTwoLevel(Level): """Create a new object of the second variant of levels. Attributes: rubberTilesHorizontal: A list of tuples indicating which columns and rows to place horizontal rubber traps. rubberTilesVertical: A list of tuples indicating which columns and rows to place vertical rubber traps. goldTilesHorizontal: A list of tuples indicating which columns and rows to place horizontal gold sprites. goldTilesVertical: A list of tuples indicating which columns and rows to place vertical gold sprites. """ def __init__(self, rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, goldTilesVertical): """Init BoardTwoLevel using the lists of tuples rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, and goldTilesVertical. Instance variables: standardImage: The image to be drawn for the level during standard gameplay. lightImage: A lighter variant of standardImage, designed to be used when an ItemClock object is active, or to give the illusion of the level flashing. image: The current image to be drawn for the level. Defaults to the standardImage. backgroundColor: A tuple indicating the color of the level's background. playerStartPositions: A list of four tuples indicating which columns and rows each player starts on. blackHolePositions: A list of four tuples indicating which columns and rows each black hole sprite starts on. itemTiles: A list of tuples indicating which columns and rows can have items spawned on them. This should include every tile that a player can reach, except those tiles that the players start on. levelBorderRects: A list of rect objects that form the boundaries of the level. """ super().__init__(rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, goldTilesVertical) self.standardImage = getImage(c.BACKGROUND_FOLDER, "background_2A.png") self.lightImage = getImage(c.BACKGROUND_FOLDER, "background_2B.png") self.image = self.standardImage self.backgroundColor = c.DARK_GREEN self.playerStartPosition = [(4, 0), (6, 0), (1, 5), (9, 5)] self.blackHolePositions = [(2, 6), (8, 6)] self.itemTiles = [(x, y) for x in range(1, 10) for y in range(0, 8) if (x, y) not in self.playerStartPosition and (x, y) not in self.blackHolePositions and (x, y) not in [(1, 0), (9, 0), (1, 7), (9, 7)]] self.levelBorderRects = [pygame.Rect(0, 0, 80, 84), pygame.Rect(0, 0, 512, 36), pygame.Rect(0, 0, 39, 448), pygame.Rect(432, 0, 80, 84), pygame.Rect(477, 0, 39, 448), pygame.Rect(0, 380, 80, 84), pygame.Rect(432, 380, 80, 84), pygame.Rect(0, 426, 512, 36)] class BoardThreeLevel(Level): """Create a new object of the third variant of levels. Attributes: rubberTilesHorizontal: A list of tuples indicating which columns and rows to place horizontal rubber traps. rubberTilesVertical: A list of tuples indicating which columns and rows to place vertical rubber traps. goldTilesHorizontal: A list of tuples indicating which columns and rows to place horizontal gold sprites. goldTilesVertical: A list of tuples indicating which columns and rows to place vertical gold sprites. """ def __init__(self, rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, goldTilesVertical): """Init BoardThreeLevel using the lists of tuples rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, and goldTilesVertical. Instance variables: standardImage: The image to be drawn for the level during standard gameplay. lightImage: A lighter variant of standardImage, designed to be used when an ItemClock object is active, or to give the illusion of the level flashing. image: The current image to be drawn for the level. Defaults to the standardImage. backgroundColor: A tuple indicating the color of the level's background. playerStartPositions: A list of four tuples indicating which columns and rows each player starts on. blackHolePositions: A list of four tuples indicating which columns and rows each black hole sprite starts on. itemTiles: A list of tuples indicating which columns and rows can have items spawned on them. This should include every tile that a player can reach, except those tiles that the players start on. levelBorderRects: A list of rect objects that form the boundaries of the level. """ super().__init__(rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, goldTilesVertical) self.standardImage = getImage(c.BACKGROUND_FOLDER, "background_3A.png") self.lightImage = getImage(c.BACKGROUND_FOLDER, "background_3B.png") self.image = self.standardImage self.backgroundColor = c.DARK_BLUE self.playerStartPosition = [(5, 1), (5, 6), (1, 3), (9, 3)] self.blackHolePositions = [(4, 4), (6, 4)] self.itemTiles = [(x, y) for x in range(1, 10) for y in range(0, 8) if (x, y) not in self.playerStartPosition and (x, y) not in self.blackHolePositions and (x, y) not in [(4, 0), (5, 0), (6, 0), (4, 7), (5, 7), (6, 7)]] self.levelBorderRects = [pygame.Rect(0, 0, 512, 36), pygame.Rect(0, 0, 39, 448), pygame.Rect(477, 0, 39, 448), pygame.Rect(0, 426, 512, 36), pygame.Rect(190, 0, 134, 84), pygame.Rect(190, 380, 134, 84)] class BoardFourLevel(Level): """Create a new object of the fourth variant of levels. Attributes: rubberTilesHorizontal: A list of tuples indicating which columns and rows to place horizontal rubber traps. rubberTilesVertical: A list of tuples indicating which columns and rows to place vertical rubber traps. goldTilesHorizontal: A list of tuples indicating which columns and rows to place horizontal gold sprites. goldTilesVertical: A list of tuples indicating which columns and rows to place vertical gold sprites. """ def __init__(self, rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, goldTilesVertical): """Init BoardFourLevel using the lists of tuples rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, and goldTilesVertical. Instance variables: standardImage: The image to be drawn for the level during standard gameplay. lightImage: A lighter variant of standardImage, designed to be used when an ItemClock object is active, or to give the illusion of the level flashing. image: The current image to be drawn for the level. Defaults to the standardImage. backgroundColor: A tuple indicating the color of the level's background. playerStartPositions: A list of four tuples indicating which columns and rows each player starts on. blackHolePositions: A list of four tuples indicating which columns and rows each black hole sprite starts on. itemTiles: A list of tuples indicating which columns and rows can have items spawned on them. This should include every tile that a player can reach, except those tiles that the players start on. levelBorderRects: A list of rect objects that form the boundaries of the level. """ super().__init__(rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, goldTilesVertical) self.standardImage = getImage(c.BACKGROUND_FOLDER, "background_4A.png") self.lightImage = getImage(c.BACKGROUND_FOLDER, "background_4B.png") self.image = self.standardImage self.backgroundColor = c.PURPLE self.playerStartPosition = [(4, 0), (6, 0), (1, 7), (9, 7)] self.blackHolePositions = [(2, 2), (8, 2), (4, 6), (6, 6)] self.itemTiles = [(x, y) for x in range(0, 11) for y in range(0, 8) if (x, y) not in self.playerStartPosition and (x, y) not in self.blackHolePositions and (x, y) not in [(5, 0), (0, 1), (5, 1), (10, 1), (0, 2), (10, 2), (0, 3), (10, 3), (0, 4), (10, 4), (0, 5), (10, 5), (0, 6), (5, 6), (10, 6), (5, 7)]] self.levelBorderRects = [pygame.Rect(0, 0, 512, 36), pygame.Rect(238, 0, 36, 132), pygame.Rect(238, 346, 36, 132), pygame.Rect(0, 426, 512, 36), pygame.Rect(0, 92, 38, 280), pygame.Rect(476, 92, 38, 280)] class BoardFiveLevel(Level): """Create a new object of the fifth variant of levels. Attributes: rubberTilesHorizontal: A list of tuples indicating which columns and rows to place horizontal rubber traps. rubberTilesVertical: A list of tuples indicating which columns and rows to place vertical rubber traps. goldTilesHorizontal: A list of tuples indicating which columns and rows to place horizontal gold sprites. goldTilesVertical: A list of tuples indicating which columns and rows to place vertical gold sprites. """ def __init__(self, rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, goldTilesVertical): """Init BoardFiveLevel using the lists of tuples rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, and goldTilesVertical. Instance variables: standardImage: The image to be drawn for the level during standard gameplay. lightImage: A lighter variant of standardImage, designed to be used when an ItemClock object is active, or to give the illusion of the level flashing. image: The current image to be drawn for the level. Defaults to the standardImage. backgroundColor: A tuple indicating the color of the level's background. activeRubberTraps: A list of tuples indicating which columns and rows have horizontal rubber traps which begin the game in an active state. playerStartPositions: A list of four tuples indicating which columns and rows each player starts on. blackHolePositions: A list of four tuples indicating which columns and rows each black hole sprite starts on. itemTiles: A list of tuples indicating which columns and rows can have items spawned on them. This should include every tile that a player can reach, except those tiles that the players start on. levelBorderRects: A list of rect objects that form the boundaries of the level. """ super().__init__(rubberTilesHorizontal, rubberTilesVertical, goldTilesHorizontal, goldTilesVertical) self.standardImage = getImage(c.BACKGROUND_FOLDER, "background_5A.png") self.lightImage = getImage(c.BACKGROUND_FOLDER, "background_5B.png") self.image = self.standardImage self.backgroundColor = c.DARK_ORANGE self.activeRubberTraps = [(1, 4), (9, 4)] self.playerStartPosition = [(1, 0), (9, 0), (4, 7), (6, 7)] self.blackHolePositions = [(2, 4), (4, 4), (6, 4), (8, 4)] self.itemTiles = [(x, y) for x in range(0, 11) for y in range(0, 8) if (x, y) not in self.playerStartPosition and (x, y) not in self.blackHolePositions and (x, y) not in [(0, 0), (5, 0), (10, 0), (0, 7), (5, 7), (10, 7)]] self.levelBorderRects = [pygame.Rect(0, 0, 512, 36), pygame.Rect(238, 0, 40, 84), pygame.Rect(0, 426, 512, 36), pygame.Rect(238, 380, 40, 84), pygame.Rect(0, 0, 36, 84), pygame.Rect(478, 0, 36, 84), pygame.Rect(0, 380, 36, 84), pygame.Rect(478, 380, 36, 84)] class BonusLevel(Level): """Create a new object of the sixth, bonus variant of levels. Note that since the bonus level layout is unique, the arguments that are usually passed to the other level variants are instead created as constant instance variables in the bonus level __init__ method. """ def __init__(self): """Init BonusLevel. Instance variables: goldTilesHorizontal: A list of tuples indicating which columns and rows to place horizontal gold sprites. goldTilesVertical: A list of tuples indicating which columns and rows to place vertical gold sprites. standardImage: The image to be drawn for the level during standard gameplay. lightImage: A lighter variant of standardImage, designed to be used when an ItemClock object is active, or to give the illusion of the level flashing. image: The current image to be drawn for the level. Defaults to the standardImage. backgroundColor: A tuple indicating the color of the level's background. playerStartPositions: A list of four tuples indicating which columns and rows each player starts on. levelBorderRects: A list of rect objects that form the boundaries of the level. """ goldTilesHorizontal = [(2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (2, 2), (3, 2), (4, 2), (5, 2), (6, 2), (7, 2), (8, 2), (2, 3), (8, 3), (2, 4), (8, 4), (2, 5), (8, 5), (2, 6), (3, 6), (4, 6), (5, 6), (6, 6), (7, 6), (8, 6), (2, 7), (3, 7), (4, 7), (5, 7), (6, 7), (7, 7), (8, 7)] goldTilesVertical = [(2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (2, 2), (3, 2), (8, 2), (9, 2), (2, 3), (3, 3), (8, 3), (9, 3), (2, 4), (3, 4), (8, 4), (9, 4), (2, 5), (3, 5), (8, 5), (9, 5), (2, 6), (3, 6), (4, 6), (5, 6), (6, 6), (7, 6), (8, 6), (9, 6)] super().__init__([], [], goldTilesHorizontal, goldTilesVertical) self.standardImage = getImage(c.BACKGROUND_FOLDER, "background_6A.png") self.lightImage = getImage(c.BACKGROUND_FOLDER, "background_6B.png") self.image = self.standardImage self.backgroundColor = c.DARK_RED self.playerStartPosition = [(4, 1), (6, 1), (3, 6), (7, 6)] self.levelBorderRects = [pygame.Rect(0, 0, 512, 36), pygame.Rect(188, 186, 136, 94), pygame.Rect(0, 426, 512, 36), pygame.Rect(0, 0, 39, 448), pygame.Rect(477, 0, 39, 448)] # Create an instance of each of the 41 different level patterns. This ensures that there is exactly one copy of each # level pattern at all times, with the gold tiles and rubber trap tiles in the proper locations. HEART = BoardOneLevel([], [(4, 3), (7, 3)], [(3, 1), (4, 1), (6, 1), (7, 1), (2, 2), (5, 2), (8, 2), (2, 4), (8, 4), (3, 5), (7, 5), (4, 6), (6, 6), (5, 7)], [(3, 1), (5, 1), (6, 1), (8, 1), (2, 2), (9, 2), (2, 3), (9, 3), (3, 4), (8, 4), (4, 5), (7, 5), (5, 6), (6, 6)]) HOUSE = BoardOneLevel([], [(4, 5), (7, 5)], [(4, 1), (5, 1), (6, 1), (3, 2), (7, 2), (2, 3), (8, 3), (2, 4), (3, 4), (4, 4), (5, 4), (6, 4), (7, 4), (8, 4), (3, 7), (4, 7), (5, 7), (6, 7), (7, 7)], [(4, 1), (7, 1), (3, 2), (8, 2), (2, 3), (9, 3), (3, 4), (8, 4), (3, 5), (8, 5), (3, 6), (8, 6)]) FACE = BoardOneLevel([(2, 4), (8, 4)], [], [(4, 1), (6, 1), (4, 3), (6, 3), (2, 5), (8, 5), (2, 6), (3, 6), (4, 6), (5, 6), (6, 6), (7, 6), (8, 6), (3, 7), (4, 7), (5, 7), (6, 7), (7, 7)], [(4, 1), (5, 1), (6, 1), (7, 1), (4, 2), (5, 2), (6, 2), (7, 2), (2, 5), (3, 5), (8, 5), (9, 5), (3, 6), (8, 6)]) HUMAN = BoardOneLevel([(5, 3)], [], [(5, 1), (3, 2), (4, 2), (5, 2), (6, 2), (7, 2), (2, 3), (3, 3), (7, 3), (8, 3), (2, 4), (8, 4), (5, 6), (4, 7), (6, 7)], [(5, 1), (6, 1), (3, 2), (8, 2), (2, 3), (3, 3), (4, 3), (7, 3), (8, 3), (9, 3), (4, 4), (7, 4), (4, 5), (7, 5), (4, 6), (5, 6), (6, 6), (7, 6)]) BUBBLES = BoardOneLevel([], [], [(3, 1), (4, 1), (5, 1), (2, 2), (6, 2), (8, 3), (7, 4), (8, 4), (2, 5), (6, 5), (2, 6), (3, 6), (4, 6), (5, 6), (6, 6)], [(3, 1), (6, 1), (2, 2), (4, 2), (5, 2), (7, 2), (2, 3), (7, 3), (8, 3), (9, 3), (2, 4), (7, 4), (3, 5), (6, 5), (4, 6), (5, 6)]) LETTER_KE = BoardOneLevel([], [(5, 6)], [(3, 1), (7, 1), (6, 2), (8, 2), (6, 3), (8, 3), (6, 6), (3, 7), (6, 7), (7, 7)], [(3, 1), (4, 1), (7, 1), (8, 1), (3, 2), (4, 2), (6, 2), (9, 2), (3, 3), (4, 3), (7, 3), (8, 3), (3, 4), (4, 4), (7, 4), (8, 4), (3, 5), (4, 5), (7, 5), (8, 5), (3, 6), (4, 6), (6, 6), (8, 6)]) TELEVISION = BoardOneLevel([], [(2, 4), (9, 4)], [(4, 1), (6, 1), (4, 2), (5, 2), (6, 2), (3, 3), (4, 3), (5, 3), (6, 3), (7, 3), (4, 4), (5, 4), (6, 4), (4, 6), (5, 6), (6, 6), (3, 7), (4, 7), (5, 7), (6, 7), (7, 7)], [(5, 1), (6, 1), (4, 2), (7, 2), (3, 3), (8, 3), (3, 4), (4, 4), (7, 4), (8, 4), (3, 5), (4, 5), (7, 5), (8, 5), (3, 6), (8, 6)]) KOOPA = BoardOneLevel([], [(7, 4)], [(3, 1), (2, 2), (5, 2), (6, 2), (2, 3), (4, 3), (7, 3), (3, 5), (3, 6), (4, 6), (5, 6), (6, 6), (7, 6), (3, 7), (7, 7)], [(3, 1), (4, 1), (2, 2), (4, 2), (5, 2), (7, 2), (3, 3), (4, 3), (8, 3), (3, 4), (4, 4), (8, 4), (3, 5), (8, 5), (4, 6), (7, 6)]) CLOWN = BoardTwoLevel([(5, 2)], [(4, 6), (7, 6)], [(3, 2), (7, 2), (2, 3), (4, 3), (6, 3), (8, 3), (2, 4), (4, 4), (6, 4), (8, 4), (3, 5), (5, 5), (7, 5), (5, 7)], [(3, 2), (4, 2), (7, 2), (8, 2), (2, 3), (5, 3), (6, 3), (9, 3), (3, 4), (4, 4), (7, 4), (8, 4), (5, 5), (6, 5), (5, 6), (6, 6)]) SPADE = BoardTwoLevel([(5, 3)], [], [(5, 1), (4, 2), (6, 2), (3, 3), (7, 3), (5, 4), (3, 5), (4, 5), (5, 5), (6, 5), (7, 5), (4, 6), (6, 6), (4, 7), (5, 7), (6, 7)], [(5, 1), (6, 1), (4, 2), (7, 2), (3, 3), (8, 3), (3, 4), (5, 4), (6, 4), (8, 4), (5, 5), (6, 5), (4, 6), (7, 6)]) MOUSE = BoardTwoLevel([], [(5, 3), (6, 3)], [(3, 1), (7, 1), (3, 2), (4, 2), (5, 2), (6, 2), (7, 2), (3, 3), (7, 3), (3, 5), (5, 5), (7, 5), (4, 6), (5, 6), (6, 6), (5, 7)], [(3, 1), (4, 1), (7, 1), (8, 1), (4, 2), (7, 2), (3, 3), (8, 3), (3, 4), (8, 4), (4, 5), (5, 5), (6, 5), (7, 5), (5, 6), (6, 6)]) EAGLE = BoardTwoLevel([(4, 4), (6, 4)], [], [(5, 1), (6, 1), (6, 2), (2, 3), (3, 3), (4, 3), (6, 3), (7, 3), (8, 3), (2, 4), (8, 4), (3, 5), (4, 5), (6, 5), (7, 5), (4, 6), (5, 6), (6, 6), (4, 7), (6, 7)], [(5, 1), (7, 1), (5, 2), (6, 2), (2, 3), (9, 3), (3, 4), (8, 4), (5, 5), (6, 5), (4, 6), (5, 6), (6, 6), (7, 6)]) RAIN = BoardTwoLevel([(5, 2)], [], [(4, 1), (5, 1), (6, 1), (3, 2), (7, 2), (2, 3), (8, 3), (2, 4), (3, 4), (4, 4), (5, 4), (6, 4), (7, 4), (8, 4)], [(4, 1), (7, 1), (3, 2), (8, 2), (2, 3), (9, 3), (4, 4), (6, 4), (8, 4), (3, 5), (5, 5), (7, 5)]) CAR = BoardTwoLevel([(3, 5), (7, 5)], [], [(4, 2), (5, 2), (6, 2), (7, 2), (3, 3), (8, 3), (2, 4), (5, 4), (7, 4), (8, 4), (2, 6), (3, 6), (4, 6), (5, 6), (6, 6), (7, 6), (8, 6)], [(4, 2), (7, 2), (8, 2), (3, 3), (7, 3), (9, 3), (2, 4), (5, 4), (6, 4), (9, 4), (2, 5), (5, 5), (6, 5), (9, 5)]) MUSHROOM = BoardTwoLevel([(5, 4)], [], [(4, 1), (5, 1), (6, 1), (3, 2), (7, 2), (2, 3), (8, 3), (2, 5), (3, 5), (4, 5), (5, 5), (6, 5), (7, 5), (8, 5), (4, 7), (5, 7), (6, 7)], [(4, 1), (7, 1), (3, 2), (8, 2), (2, 3), (9, 3), (2, 4), (9, 4), (4, 5), (5, 5), (6, 5), (7, 5), (4, 6), (7, 6)]) SKULL = BoardTwoLevel([(5, 7)], [], [(3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (4, 2), (6, 2), (3, 4), (4, 4), (6, 4), (7, 4), (3, 5), (5, 5), (7, 5), (4, 6), (5, 6), (6, 6), (3, 7), (7, 7)], [(3, 1), (8, 1), (3, 2), (5, 2), (6, 2), (8, 2), (3, 3), (8, 3), (5, 4), (6, 4), (4, 5), (7, 5), (4, 6), (7, 6)]) SUBMARINE = BoardThreeLevel([], [(3, 1), (8, 1)], [(4, 3), (5, 3), (8, 3), (2, 4), (3, 4), (4, 4), (5, 4), (6, 4), (7, 4), (8, 4), (7, 5), (8, 5), (2, 6), (3, 6), (4, 6), (5, 6), (6, 6), (8, 6)], [(5, 2), (4, 3), (6, 3), (8, 3), (9, 3), (2, 4), (8, 4), (2, 5), (7, 5), (8, 5), (9, 5)]) GLASSES = BoardThreeLevel([(3, 2), (7, 2)], [], [(3, 3), (4, 3), (6, 3), (7, 3), (2, 4), (5, 4), (8, 4), (3, 6), (4, 6), (6, 6), (7, 6)], [(2, 1), (9, 1), (2, 2), (9, 2), (2, 3), (3, 3), (5, 3), (6, 3), (8, 3), (9, 3), (3, 4), (5, 4), (6, 4), (8, 4), (3, 5), (5, 5), (6, 5), (8, 5)]) KOALA = BoardThreeLevel([(4, 3), (6, 3), (2, 6), (8, 6)], [], [(2, 1), (8, 1), (3, 2), (4, 2), (5, 2), (6, 2), (7, 2), (2, 3), (8, 3), (3, 5), (5, 5), (7, 5), (4, 6), (5, 6), (6, 6)], [(2, 1), (3, 1), (8, 1), (9, 1), (2, 2), (9, 2), (3, 3), (8, 3), (3, 4), (8, 4), (4, 5), (5, 5), (6, 5), (7, 5)]) BUTTERFLY = BoardThreeLevel([], [(5, 2), (6, 2)], [(2, 2), (8, 2), (3, 3), (7, 3), (4, 4), (5, 4), (6, 4), (4, 5), (6, 5), (2, 6), (3, 6), (5, 6), (7, 6), (8, 6)], [(2, 2), (3, 2), (8, 2), (9, 2), (2, 3), (4, 3), (7, 3), (9, 3), (2, 4), (5, 4), (6, 4), (9, 4), (2, 5), (4, 5), (5, 5), (6, 5), (7, 5), (9, 5)]) FISH = BoardThreeLevel([(2, 1), (8, 1), (7, 5)], [], [(2, 2), (6, 2), (7, 2), (8, 2), (3, 3), (4, 4), (5, 4), (4, 5), (5, 5), (3, 6), (6, 6), (7, 6), (8, 6), (2, 7)], [(2, 2), (3, 2), (6, 2), (9, 2), (2, 3), (4, 3), (6, 3), (8, 3), (9, 3), (2, 4), (5, 4), (9, 4), (2, 5), (4, 5), (6, 5), (9, 5), (2, 6), (3, 6)]) CLU_CLU = BoardThreeLevel([], [(8, 3), (3, 4)], [(2, 1), (8, 1), (8, 2), (2, 3), (4, 3), (6, 3), (4, 4), (2, 6), (4, 6), (6, 6), (8, 6), (2, 7)], [(2, 1), (8, 1), (9, 1), (2, 2), (4, 2), (6, 2), (7, 2), (4, 4), (4, 5), (6, 5), (8, 5), (9, 5), (2, 6), (3, 6)]) CROWN = BoardThreeLevel([(2, 7), (8, 7)], [(4, 2), (7, 2)], [(2, 1), (8, 1), (2, 2), (5, 2), (8, 2), (5, 3), (3, 4), (4, 4), (6, 4), (7, 4), (2, 6), (3, 6), (4, 6), (5, 6), (6, 6), (7, 6), (8, 6)], [(2, 1), (3, 1), (8, 1), (9, 1), (2, 2), (3, 2), (5, 2), (6, 2), (8, 2), (9, 2), (2, 3), (3, 3), (5, 3), (6, 3), (8, 3), (9, 3), (2, 4), (9, 4), (2, 5), (9, 5)]) SWORD_SHIELD = BoardThreeLevel([(7, 4), (2, 7), (8, 7)], [(2, 1)], [(3, 2), (6, 3), (7, 3), (8, 3), (2, 5), (3, 5), (4, 5), (6, 5), (8, 5), (3, 6), (7, 6)], [(3, 2), (4, 2), (3, 3), (4, 3), (6, 3), (9, 3), (3, 4), (4, 4), (6, 4), (9, 4), (3, 5), (4, 5), (7, 5), (8, 5)]) HOLE = BoardFourLevel([(3, 3), (7, 3)], [(4, 4), (7, 4)], [(3, 1), (7, 1), (3, 2), (7, 2), (5, 3), (2, 4), (5, 4), (8, 4), (2, 5), (5, 5), (8, 5), (3, 6), (7, 6), (3, 7), (7, 7)], [(3, 1), (4, 1), (7, 1), (8, 1), (5, 3), (6, 3), (2, 4), (3, 4), (5, 4), (6, 4), (8, 4), (9, 4), (3, 6), (4, 6), (7, 6), (8, 6)]) KEY = BoardFourLevel([(2, 4), (4, 4), (6, 4), (8, 4)], [], [(2, 1), (8, 1), (3, 2), (7, 2), (2, 3), (3, 3), (7, 3), (8, 3), (2, 5), (3, 5), (7, 5), (8, 5), (2, 6), (8, 6), (3, 7), (7, 7)], [(2, 1), (3, 1), (8, 1), (9, 1), (2, 2), (4, 2), (7, 2), (9, 2), (2, 5), (4, 5), (7, 5), (9, 5), (3, 6), (4, 6), (7, 6), (8, 6)]) RIBBON = BoardFourLevel([(2, 4), (5, 4), (8, 4)], [], [(2, 2), (3, 2), (7, 2), (8, 2), (2, 3), (3, 3), (4, 3), (5, 3), (6, 3), (7, 3), (8, 3), (2, 5), (3, 5), (4, 5), (5, 5), (6, 5), (7, 5), (8, 5), (2, 6), (3, 6), (7, 6), (8, 6)], [(2, 2), (4, 2), (7, 2), (9, 2), (4, 3), (7, 3), (4, 4), (7, 4), (2, 5), (4, 5), (7, 5), (9, 5)]) LETTER_H = BoardFourLevel([(4, 4), (6, 4)], [(3, 3), (8, 3), (3, 5), (8, 5)], [(2, 1), (3, 1), (7, 1), (8, 1), (4, 3), (5, 3), (6, 3), (4, 5), (5, 5), (6, 5), (2, 7), (3, 7), (7, 7), (8, 7)], [(2, 1), (4, 1), (7, 1), (9, 1), (2, 2), (4, 2), (7, 2), (9, 2), (2, 3), (9, 3), (2, 4), (9, 4), (2, 5), (4, 5), (7, 5), (9, 5), (2, 6), (4, 6), (7, 6), (9, 6)]) PUNCTUATION = BoardFourLevel([], [(6, 3), (5, 4), (2, 6), (9, 6)], [(3, 1), (7, 1), (8, 1), (7, 2), (7, 3), (8, 4), (3, 5), (7, 5), (3, 6), (7, 6), (3, 7), (7, 7)], [(3, 1), (4, 1), (7, 1), (9, 1), (3, 2), (4, 2), (8, 2), (9, 2), (3, 3), (4, 3), (7, 3), (9, 3), (3, 4), (4, 4), (7, 4), (8, 4), (3, 6), (4, 6), (7, 6), (8, 6)]) FROWN = BoardFourLevel([], [(2, 3), (9, 3)], [(3, 1), (7, 1), (3, 3), (7, 3), (3, 4), (4, 4), (5, 4), (6, 4), (7, 4), (4, 5), (5, 5), (6, 5), (3, 7), (7, 7)], [(3, 1), (4, 1), (7, 1), (8, 1), (3, 2), (4, 2), (7, 2), (8, 2), (3, 4), (8, 4), (3, 5), (4, 5), (7, 5), (8, 5), (3, 6), (4, 6), (7, 6), (8, 6)]) PYTHON = BoardFourLevel([(2, 1), (8, 7)], [], [(8, 1), (7, 2), (3, 3), (4, 3), (5, 3), (6, 3), (2, 4), (4, 4), (5, 4), (6, 4), (8, 4), (4, 5), (5, 5), (6, 5), (7, 5), (3, 6), (2, 7)], [(8, 1), (9, 1), (7, 2), (8, 2), (9, 2), (3, 3), (7, 3), (9, 3), (2, 4), (4, 4), (8, 4), (2, 5), (3, 5), (4, 5), (2, 6), (3, 6)]) FLIP = BoardFourLevel([(7, 2), (5, 3), (3, 6)], [], [(2, 1), (2, 2), (3, 2), (7, 3), (5, 4), (3, 5), (5, 5), (7, 6), (8, 6), (8, 7)], [(2, 1), (3, 1), (9, 1), (3, 2), (4, 2), (9, 2), (3, 3), (4, 3), (7, 3), (8, 3), (3, 4), (4, 4), (5, 4), (6, 4), (7, 4), (8, 4), (2, 5), (7, 5), (8, 5), (2, 6), (8, 6), (9, 6)]) SPIDER = BoardFiveLevel([(3, 4), (7, 4)], [(1, 2), (4, 2), (7, 2), (4, 5), (7, 5), (10, 5)], [(2, 1), (8, 1), (5, 2), (2, 3), (8, 3), (2, 5), (8, 5), (5, 6), (2, 7), (8, 7)], [(3, 1), (8, 1), (2, 2), (9, 2), (5, 3), (6, 3), (5, 4), (6, 4), (2, 5), (9, 5), (3, 6), (8, 6)]) LETTER_X = BoardFiveLevel([(5, 3), (5, 5)], [(3, 1), (8, 1), (10, 2), (1, 5), (3, 6), (8, 6)], [(4, 2), (5, 2), (6, 2), (2, 3), (8, 3), (5, 4), (2, 5), (8, 5), (4, 6), (5, 6), (6, 6)], [(4, 1), (7, 1), (2, 2), (9, 2), (3, 3), (8, 3), (3, 4), (8, 4), (2, 5), (9, 5), (4, 6), (7, 6)]) BOX = BoardFiveLevel([(5, 3), (3, 4), (7, 4), (5, 5)], [(3, 2), (8, 2), (3, 5), (8, 5)], [(2, 1), (3, 1), (7, 1), (8, 1), (4, 2), (6, 2), (4, 6), (6, 6), (2, 7), (3, 7), (7, 7), (8, 7)], [(1, 2), (2, 2), (4, 2), (7, 2), (9, 2), (10, 2), (1, 5), (2, 5), (4, 5), (7, 5), (9, 5), (10, 5)]) DIAMOND = BoardFiveLevel([(3, 4), (5, 4), (7, 4)], [(4, 1), (7, 1), (4, 6), (7, 6)], [(2, 2), (5, 2), (8, 2), (1, 3), (3, 3), (5, 3), (7, 3), (9, 3), (1, 5), (3, 5), (5, 5), (7, 5), (9, 5), (2, 6), (5, 6), (8, 6)], [(3, 1), (8, 1), (2, 2), (9, 2), (2, 5), (9, 5), (3, 6), (8, 6)]) INVERTED_DIAMOND = BoardFiveLevel([(3, 6), (7, 6)], [(3, 2), (8, 2)], [(2, 1), (8, 1), (1, 2), (9, 2), (5, 3), (3, 5), (5, 5), (7, 5), (1, 6), (9, 6), (2, 7), (8, 7)], [(2, 1), (9, 1), (1, 2), (10, 2), (3, 4), (8, 4), (1, 5), (10, 5), (2, 6), (9, 6)]) BOX_PLUS = BoardFiveLevel([], [(2, 2), (9, 2), (4, 5), (7, 5)], [(2, 1), (8, 1), (5, 2), (5, 3), (5, 5), (5, 6), (3, 7), (7, 7)], [(1, 2), (3, 2), (5, 2), (6, 2), (8, 2), (10, 2), (5, 3), (6, 3), (5, 4), (6, 4), (1, 5), (2, 5), (3, 5), (5, 5), (6, 5), (8, 5), (9, 5), (10, 5)]) CRUSHER = BoardFiveLevel([(4, 2), (6, 6)], [], [(2, 1), (8, 1), (1, 3), (9, 3), (5, 4), (1, 5), (9, 5), (2, 7), (8, 7)], [(2, 1), (3, 1), (8, 1), (9, 1), (2, 2), (3, 2), (8, 2), (9, 2), (3, 3), (8, 3), (3, 4), (8, 4), (2, 5), (3, 5), (8, 5), (9, 5), (2, 6), (3, 6), (8, 6), (9, 6)]) KEY_PLUS = BoardFiveLevel([(3, 4), (5, 4), (7, 4)], [], [(1, 2), (5, 2), (9, 2), (1, 3), (2, 3), (4, 3), (6, 3), (8, 3), (9, 3), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5), (9, 5), (1, 6), (5, 6), (9, 6)], [(2, 1), (3, 1), (8, 1), (9, 1), (3, 2), (5, 2), (6, 2), (8, 2), (3, 5), (5, 5), (6, 5), (8, 5), (2, 6), (3, 6), (8, 6), (9, 6)]) BONUS_LEVEL = BonusLevel() boardOneLevels = [HEART, HOUSE, FACE, HUMAN, BUBBLES, LETTER_KE, TELEVISION, KOOPA] boardTwoLevels = [CLOWN, SPADE, MOUSE, EAGLE, RAIN, CAR, MUSHROOM, SKULL] boardThreeLevels = [SUBMARINE, GLASSES, KOALA, BUTTERFLY, FISH, CLU_CLU, CROWN, SWORD_SHIELD] boardFourLevels = [HOLE, KEY, RIBBON, LETTER_H, PUNCTUATION, FROWN, PYTHON, FLIP] boardFiveLevels = [SPIDER, LETTER_X, BOX, DIAMOND, INVERTED_DIAMOND, BOX_PLUS, CRUSHER, KEY_PLUS] listOfAllBoardsPastOne = [boardTwoLevels, boardThreeLevels, boardFourLevels, boardFiveLevels] def getLevelOrder(): """Get a random order of the 21 levels to be played, including one of the boardOneLevels, one of the bonus levels, and four of each other variant of levels. Note that though the order of when each specific level instance is played is randomized, the order will always follow the following pattern, repeated endlessly: boardOneLevel, boardTwoLevel, boardThreeLevel, boardFourLevel, boardFiveLevel, BonusLevel boardTwoLevel, boardThreeLevel, boardFourLevel, boardFiveLevel, BonusLevel boardTwoLevel, boardThreeLevel, boardFourLevel, boardFiveLevel, BonusLevel boardTwoLevel, boardThreeLevel, boardFourLevel, boardFiveLevel, BonusLevel Returns: newLevelOrder: A list of Level objects in the order to be played. """ random.shuffle(boardOneLevels) newLevelOrder = [boardOneLevels[0]] for boardList in listOfAllBoardsPastOne: random.shuffle(boardList) for num in range(4): for boardList in listOfAllBoardsPastOne: newLevelOrder.append(boardList[num]) newLevelOrder.append(BONUS_LEVEL) return newLevelOrder

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c7b3f98bfbcc3eb8db3d0f88535ff41566e41fcd

185

py

Python

budgetbuddy/stocks/apps.py

michaelqknguyen/Budget-Buddy

d1d25648d29f9b398b399e63b187b54daf3be521

[ "MIT" ]

null

budgetbuddy/stocks/apps.py

michaelqknguyen/Budget-Buddy

d1d25648d29f9b398b399e63b187b54daf3be521

[ "MIT" ]

null

budgetbuddy/stocks/apps.py

michaelqknguyen/Budget-Buddy

d1d25648d29f9b398b399e63b187b54daf3be521

[ "MIT" ]

null

from django.apps import AppConfig from django.utils.translation import gettext_lazy as _ class StocksConfig(AppConfig): name = 'budgetbuddy.stocks' verbose_name = _('Stocks')

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c7bbd120d8b2d28d027984c87205f445484d5dfd

759

py

Python

stage/factory/base.py

daveshed/linearstage

79d34d02482cd6c34102d07f29f6d2c7b7088c08

[ "MIT" ]

null

stage/factory/base.py

daveshed/linearstage

79d34d02482cd6c34102d07f29f6d2c7b7088c08

[ "MIT" ]

null

stage/factory/base.py

daveshed/linearstage

79d34d02482cd6c34102d07f29f6d2c7b7088c08

[ "MIT" ]

null

#pylint: disable=missing-docstring #pyling: enable=missing-docstring import abc class StageFactoryBase(abc.ABC): """ The factory base class that provides necessary objects required to instantiate a Stage object. """ @abc.abstractproperty def minimum_position(self): """The stage minimum position""" return @abc.abstractproperty def maximum_position(self): """The stage maximum position""" return @abc.abstractproperty def motor(self): """The motor to be used by the stage""" return @abc.abstractproperty def end_stop(self): """ End stop object that is triggered when the stage reaches the end of its travel """ return

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c7e9561ef48f0b334718cd86a746e2aeb7d60039

532

py

Python

wagtail_editor_extensions/utils/feature.py

mattdood/wagtail-editor-extensions

ecd60ffa6adaa77470f8b1081f4a6a2d994e7f7c

[ "MIT" ]

null

wagtail_editor_extensions/utils/feature.py

mattdood/wagtail-editor-extensions

ecd60ffa6adaa77470f8b1081f4a6a2d994e7f7c

[ "MIT" ]

null

wagtail_editor_extensions/utils/feature.py

mattdood/wagtail-editor-extensions

ecd60ffa6adaa77470f8b1081f4a6a2d994e7f7c

[ "MIT" ]

null

from wagtail_editor_extensions.conf import get_setting def get_feature_choices(feature_setting): return tuple(get_setting(feature_setting).items()) def get_feature_name(feature_name, name): feature = '%s_%s' % (feature_name, name) return feature def get_feature_name_upper(feature_name, name): return get_feature_name(feature_name, name).upper() def get_feature_name_list(feature_setting, feature_name): return [get_feature_name_upper(feature_name, name) for name in get_setting(feature_setting).keys()]

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0.130769

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0.118421

532

18

104

29.555556

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0.4

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0

0.1

0.3

0.9

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1

0

null

0

1

0

1

0

4

c7f524dc0b5952d8ed6eeb0a6f5f053697b986c6

3,507

py

Python

COLORFUL-Python/colorful-server/run_examples.py

FFFFOX/COLORFUL

3a6fab6184f94b0a3a5b3e56a649cca076ddfc91

[ "MIT" ]

77

2021-12-09T03:14:31.000Z

2022-03-06T06:06:38.000Z

COLORFUL-Python/colorful-server/run_examples.py

FFFFOX/COLORFUL

3a6fab6184f94b0a3a5b3e56a649cca076ddfc91

[ "MIT" ]

1

2021-12-09T03:22:23.000Z

COLORFUL-Python/colorful-server/run_examples.py

FFFFOX/COLORFUL

3a6fab6184f94b0a3a5b3e56a649cca076ddfc91

[ "MIT" ]

9

2021-12-09T07:02:41.000Z

2021-12-17T07:51:04.000Z

from recolor import Core def main(): # Simulating Protanopia with diagnosed degree of 0.9 and saving the image to file. Core.simulate(input_path='Examples_Check/ex_original.jpg', return_type='save', save_path='Examples_Check/ex_simulate_protanopia.png', simulate_type='protanopia', simulate_degree_primary=0.9) # Simulating deuteranopia with diagnosed degree of 0.9 and saving the image to file. Core.simulate(input_path='Examples_Check/ex_original.jpg', return_type='save', save_path='Examples_Check/ex_simulate_deuteranopia.png', simulate_type='deuteranopia', simulate_degree_primary=0.9) # Simulating Tritanopia with diagnosed degree of 0.9 and saving the image to file. Core.simulate(input_path='Examples_Check/ex_original.jpg', return_type='save', save_path='Examples_Check/ex_simulate_tritanopia.png', simulate_type='tritanopia', simulate_degree_primary=0.9) # Simulating Hybrid (Protanomaly + Deutranomaly) with diagnosed degree of 0.9 and 1.0 and saving the image to file. Core.simulate(input_path='Examples_Check/ex_original.jpg', return_type='save', save_path='Examples_Check/ex_simulate_hybrid.png', simulate_type='hybrid', simulate_degree_primary=0.5, simulate_degree_sec=0.5) # Correcting Image for Protanopia with diagnosed degree of 1.0 and saving the image to file. Core.correct(input_path='Examples_Check/ex_original.jpg', return_type='save', save_path='Examples_Check/ex_corrected_protanopia.png', protanopia_degree=0.9, deuteranopia_degree=0.0) # Also simulate the corrected image to see difference. Core.simulate(input_path='Examples_Check/ex_corrected_protanopia.png', return_type='save', save_path='Examples_Check/ex_simulate_corrected_protanopia.png', simulate_type='protanopia', simulate_degree_primary=0.9) # Correcting Image for deuteranopia with diagnosed degree of 1.0 and saving the image to file. Core.correct(input_path='Examples_Check/ex_original.jpg', return_type='save', save_path='Examples_Check/ex_corrected_deuteranopia.png', protanopia_degree=0.0, deuteranopia_degree=1.0) # Also simulate the corrected image to see difference. Core.simulate(input_path='Examples_Check/ex_corrected_deuteranopia.png', return_type='save', save_path='Examples_Check/ex_simulate_corrected_deuteranopia.png', simulate_type='deuteranopia', simulate_degree_primary=0.9) # Correcting Image for Hybrid with diagnosed degree of 1.0 for both protanopia and # deuteranopia and saving the image to file. Core.correct(input_path='Examples_Check/ex_original.jpg', return_type='save', save_path='Examples_Check/ex_corrected_hybrid.png', protanopia_degree=0.5, deuteranopia_degree=0.5) # You can also use different return types and get numpy array or PIL.Image for further processing. # See recolor.py return if __name__ == '__main__': main()

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py

Python

imf/tests/test_imf.py

segasai/imf

9a33b9e68b0af677dab86511e343d6099d9ea530

[ "MIT", "BSD-3-Clause" ]

null

imf/tests/test_imf.py

segasai/imf

9a33b9e68b0af677dab86511e343d6099d9ea530

[ "MIT", "BSD-3-Clause" ]

null

imf/tests/test_imf.py

segasai/imf

9a33b9e68b0af677dab86511e343d6099d9ea530

[ "MIT", "BSD-3-Clause" ]

null

import pytest import numpy as np import itertools from .. import imf from ..imf import kroupa, chabrier2005 @pytest.mark.parametrize(('inp', 'out', 'rtol', 'atol'), [(0.05, 5.6159, 1e-3, 1e-3), (1.5, 0.0359, 1e-4, 1e-4), (1.0, 0.0914, 1e-4, 1e-4), (3.0, 0.0073, 1e-4, 1e-4), (1, 0.0914, 1e-4, 1e-4), (3, 0.0073, 1e-4, 1e-4)]) def test_kroupa_val(inp, out, rtol, atol): kroupa = imf.Kroupa() np.testing.assert_allclose(kroupa(inp), out, rtol=rtol, atol=atol) np.testing.assert_allclose(imf.kroupa(inp), out, rtol=rtol, atol=atol) @pytest.mark.parametrize('massfunc', imf.massfunctions.keys()) def test_mmax(massfunc): """ Regression test for issue #4 """ if (not hasattr(imf.get_massfunc(massfunc), 'mmin')): pytest.skip("{0} doesn't have mmin defined".format(massfunc)) c = imf.make_cluster(10000, mmax=1, mmin=0.01, massfunc=massfunc) assert c.max() <= 1 @pytest.mark.parametrize(('mlow', 'mhigh'), itertools.product((0.01, 0.08, 0.1, 0.5, 1.0, 0.03), (0.02, 0.08, 0.4, 0.5, 1.0, 120))) def test_kroupa_integral(mlow, mhigh): if mlow >= mhigh: pytest.skip("mmin >= mmax") num = kroupa.integrate(mlow, mhigh, numerical=True)[0] anl = kroupa.integrate(mlow, mhigh, numerical=False)[0] np.testing.assert_almost_equal(num, anl) if num != 0: assert anl != 0 @pytest.mark.parametrize(('mlow', 'mhigh'), itertools.product((0.01, 0.08, 0.1, 0.5, 1.0, 0.03), (0.02, 0.08, 0.4, 0.5, 1.0, 120))) def test_kroupa_mintegral(mlow, mhigh): if mlow >= mhigh: pytest.skip("mmin >= mmax") num = kroupa.m_integrate(mlow, mhigh, numerical=True)[0] anl = kroupa.m_integrate(mlow, mhigh, numerical=False)[0] print("{0} {1} {2:0.3f} {3:0.3f}".format(mlow, mhigh, num, anl)) np.testing.assert_almost_equal(num, anl) if num != 0: assert anl != 0 @pytest.mark.parametrize(('mlow', 'mhigh'), itertools.product((0.033, 0.01, 0.08, 0.1, 0.5, 1.0, 0.03), (0.02, 0.05, 0.08, 0.4, 0.5, 1.0, 120))) def test_chabrier_integral(mlow, mhigh): if mlow >= mhigh: pytest.skip("mmin >= mmax") num = chabrier2005.integrate(mlow, mhigh, numerical=True)[0] anl = chabrier2005.integrate(mlow, mhigh, numerical=False)[0] print("{0} {1} {2:0.3f} {3:0.3f}".format(mlow, mhigh, num, anl)) np.testing.assert_almost_equal(num, anl) # for mlow in (0.01, 0.08, 0.1, 0.5, 1.0): # for mhigh in (0.02, 0.08, 0.4, 0.5, 1.0): # try: # num = chabrier2005.m_integrate(mlow, mhigh, numerical=True)[0] # anl = chabrier2005.m_integrate(mlow, mhigh, numerical=False)[0] # except ValueError: # continue # print("{0} {1} {2:0.3f} {3:0.3f}".format(mlow, mhigh, num, anl)) # np.testing.assert_almost_equal(num, anl) def test_make_cluster(): cluster = imf.make_cluster(1000) assert np.abs(sum(cluster) - 1000 < 100) def test_kroupa_inverses(): assert np.abs(imf.inverse_imf(0, massfunc=imf.Kroupa(), mmin=0.01) - 0.01) < 2e-3 assert np.abs(imf.inverse_imf(0, massfunc=imf.Kroupa(mmin=0.01)) - 0.01) < 2e-3 assert np.abs(imf.inverse_imf(1, massfunc=imf.Kroupa(), mmax=200) - 200) < 1 assert np.abs(imf.inverse_imf(1, massfunc=imf.Kroupa(mmax=200)) - 200) < 1 @pytest.mark.parametrize(('inp', 'out', 'rtol', 'atol'), [(0.05, 5.6159, 1e-3, 1e-3), (1.5, 0.0359, 1e-4, 1e-4), (1.0, 0.0914, 1e-4, 1e-4), (3.0, 0.0073, 1e-4, 1e-4), (1, 0.0914, 1e-4, 1e-4), (3, 0.0073, 1e-4, 1e-4)]) def test_kroupa_val_unchanged(inp, out, rtol, atol): # regression: make sure that imf.kroupa = imf.Kroupa kroupa = imf.Kroupa() np.testing.assert_allclose(kroupa(inp), out, rtol=rtol, atol=atol) np.testing.assert_allclose(imf.kroupa(inp), out, rtol=rtol, atol=atol) np.testing.assert_allclose(kroupa(inp), imf.kroupa(inp))

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Python

RawArchiver/TimedTriggers/TriggerManage.py

fake-name/ReadableWebProxy

ed5c7abe38706acc2684a1e6cd80242a03c5f010

[ "BSD-3-Clause" ]

193

2016-08-02T22:04:35.000Z

2022-03-09T20:45:41.000Z

RawArchiver/TimedTriggers/TriggerManage.py

fake-name/ReadableWebProxy

ed5c7abe38706acc2684a1e6cd80242a03c5f010

[ "BSD-3-Clause" ]

533

2016-08-23T20:48:23.000Z

2022-03-28T15:55:13.000Z

RawArchiver/TimedTriggers/TriggerManage.py

rrosajp/ReadableWebProxy

ed5c7abe38706acc2684a1e6cd80242a03c5f010

[ "BSD-3-Clause" ]

19

2015-08-13T18:01:08.000Z

2021-07-12T17:13:09.000Z

import logging import abc import datetime import traceback import urllib.parse import sqlalchemy.exc import common.database as db # import RawArchiver.TimedTriggers.RawRollingRewalkTrigger # def exposed_raw_rewalk_old(): # ''' # Trigger the rewalking system on the rawarchiver # ''' # run = RawArchiver.TimedTriggers.RawRollingRewalkTrigger.RollingRawRewalkTrigger() # run.go()

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400e8fd638e662ffd2e604a84ee3770ee327d2d0

142

py

Python

run.py

arryaaas/Met-Num

4975efdd98b7d69dd3684eb2eb6a138134b51ce3

[ "MIT" ]

null

run.py

arryaaas/Met-Num

4975efdd98b7d69dd3684eb2eb6a138134b51ce3

[ "MIT" ]

null

run.py

arryaaas/Met-Num

4975efdd98b7d69dd3684eb2eb6a138134b51ce3

[ "MIT" ]

null

from app import app import os if __name__ == "__main__": port = int(os.environ.get("PORT", 5000)) app.run(debug=True, port=port)

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4038b1b4ae008c4e4a97fb17709b6104f7b17487

204

py

Python

mellon/path.py

LaudateCorpus1/mellon

a7a9f6d8abf1dd03b63a94ddb4439c6cc6c2e272

[ "MIT" ]

5

2016-12-20T19:39:01.000Z

2021-01-08T16:19:17.000Z

mellon/path.py

CrowdStrike/mellon

7216f255d397a41b1c2777a1b02f1c085d07ddfe

[ "MIT" ]

1

2018-03-21T17:05:13.000Z

mellon/path.py

LaudateCorpus1/mellon

a7a9f6d8abf1dd03b63a94ddb4439c6cc6c2e272

[ "MIT" ]

2

2017-11-01T15:03:27.000Z

2018-11-13T03:04:44.000Z

from zope.component.factory import Factory from zope import interface from . import IPath @interface.implementer(IPath) class FilesystemPath(str): pass filesystemPathFactory = Factory(FilesystemPath)

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py

Python

overholt/users/__init__.py

prdonahue/overholt

e03209f7a059d165a9154355d090738af3159028

[ "MIT" ]

1,152

2015-01-04T16:30:17.000Z

2022-03-27T19:50:52.000Z

overholt/users/__init__.py

prdonahue/overholt

e03209f7a059d165a9154355d090738af3159028

[ "MIT" ]

25

2020-07-06T08:49:08.000Z

2021-07-27T06:15:43.000Z

overholt/users/__init__.py

prdonahue/overholt

e03209f7a059d165a9154355d090738af3159028

[ "MIT" ]

218

2015-01-06T20:41:52.000Z

2022-03-25T19:07:53.000Z

# -*- coding: utf-8 -*- """ overholt.users ~~~~~~~~~~~~~~ overholt users package """ from ..core import Service from .models import User class UsersService(Service): __model__ = User

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4087934eb49184e801ab0929fdb930bd0267d5b1

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py

Python

python/src/aoc/year2017/day1.py

ocirne/adventofcode

ea9b5f1b48a04284521e85c96b420ed54adf55f0

[ "Unlicense" ]

1

2021-02-16T21:30:04.000Z

python/src/aoc/year2017/day1.py

ocirne/adventofcode

ea9b5f1b48a04284521e85c96b420ed54adf55f0

[ "Unlicense" ]

null

python/src/aoc/year2017/day1.py

ocirne/adventofcode

ea9b5f1b48a04284521e85c96b420ed54adf55f0

[ "Unlicense" ]

null

from aoc.util import load_input def solve_captcha(line): """ >>> solve_captcha('1122') 3 >>> solve_captcha('1111') 4 >>> solve_captcha('1234') 0 >>> solve_captcha('91212129') 9 """ return sum(int(c) for i, c in enumerate(line) if line[i - 1] == c) def part1(lines): return solve_captcha(lines[0].strip()) def solve_new_captcha(line): """ >>> solve_new_captcha('1212') 6 >>> solve_new_captcha('1221') 0 >>> solve_new_captcha('123425') 4 >>> solve_new_captcha('123123') 12 >>> solve_new_captcha('12131415') 4 """ return sum(int(x) for x, y in zip(line, line[len(line) // 2 :] + line[: len(line) // 2]) if x == y) def part2(lines): return solve_new_captcha(lines[0].strip()) if __name__ == "__main__": data = load_input(__file__, 2017, "1") print(part1(data)) print(part2(data))

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470

py

Python

tests/__init__.py

pauliacomi/pyGAPS

c4d45b710e171c937471686437e382e05aec4ed5

[ "MIT" ]

35

2018-01-24T14:59:08.000Z

2022-03-10T02:47:58.000Z

tests/__init__.py

pauliacomi/pyGAPS

c4d45b710e171c937471686437e382e05aec4ed5

[ "MIT" ]

29

2018-01-06T12:08:08.000Z

2022-03-11T20:26:53.000Z

tests/__init__.py

pauliacomi/pyGAPS

c4d45b710e171c937471686437e382e05aec4ed5

[ "MIT" ]

20

2019-06-12T19:20:29.000Z

2022-03-02T09:57:02.000Z

# TODO saturation_pressure for each point # TODO isotherm excess / absolute # TODO implement AIF # TODO osmotic ensemble # TODO universal adsorption isotherm model UAIM # TODO double check all model solvers # TODO volume_liquid loading mode # TODO universal adsorption isotherm model (UAIM) # TODO deeper integration with NIST isodb # TODO code copy behaviour # TODO check adsorbate lists from VOC # TODO tplot psd # TODO General Adsorbent Library and Evaluation (GALE)

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409ae6195baf24502a85c3bcc544eefd1d198fcc

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py

Python

tests/myapp/urls.py

clover-es/django-mptt

6c78234659e10b15e9a102bc19f8bd26a6bf3b58

[ "MIT" ]

5

2018-09-08T18:31:49.000Z

2021-07-17T02:05:06.000Z

tests/myapp/urls.py

clover-es/django-mptt

6c78234659e10b15e9a102bc19f8bd26a6bf3b58

[ "MIT" ]

null

tests/myapp/urls.py

clover-es/django-mptt

6c78234659e10b15e9a102bc19f8bd26a6bf3b58

[ "MIT" ]

1

2019-06-25T17:13:02.000Z

import django from django.conf.urls import include, url from django.contrib import admin urlpatterns = [url(r'^admin/', admin.site.urls)]

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py

Python

tests/test_api.py

aidanmcdonagh22/shorty

2a7a79bc35941eda4a535f25807a56c3bcf587c1

[ "MIT" ]

null

tests/test_api.py

aidanmcdonagh22/shorty

2a7a79bc35941eda4a535f25807a56c3bcf587c1

[ "MIT" ]

null

tests/test_api.py

aidanmcdonagh22/shorty

2a7a79bc35941eda4a535f25807a56c3bcf587c1

[ "MIT" ]

null

from .utils import check_api_response from requests_mock import Mocker # Integration Testing class TestShortlinkView: def test_no_url(self, client): # perform client post to endpoint response = client.post('/shortlinks', json={ "provider": "bitly" }) # check response check_api_response(response, 400, { "error": "parameter url must be provided and as a string" }) def test_bad_provider(self, client): # perform client post to endpoint response = client.post('/shortlinks', json={ "url": "https://google.com", "provider": "somethingrandom" }) # check response check_api_response(response, 400, { "error": "provider must be 'bitly' or 'tinyurl'" }) def test_successful_response_bitly(self, client): url = "https://google.com" shortlink = "http://short.com" with Mocker() as m: # mock response m.post("https://api-ssl.bitly.com/v4/shorten", json={ "link": shortlink }) # perform client post to endpoint response = client.post('/shortlinks', json={ "url": url, "provider": "bitly" }) # check response check_api_response(response, 200, { "url": url, "link": shortlink }) def test_unsuccessful_response_bitly(self, client): url = "https://google.com" errorMsg = "Error: we could not provide you a link" with Mocker() as m: # mock response m.post("https://api-ssl.bitly.com/v4/shorten", json={ "message": errorMsg }, status_code=400) # perform client post to endpoint response = client.post('/shortlinks', json={ "url": url, "provider": "bitly" }) # check response check_api_response(response, 400, { "error": errorMsg }) def test_successful_response_tinyurl(self, client): url = "https://facebook.com" shortlink = "http://short.com" with Mocker() as m: # mock response m.get(f"http://tinyurl.com/api-create.php?url={url}", text=shortlink) # perform client post to endpoint response = client.post('/shortlinks', json={ "url": url, "provider": "tinyurl" }) # check response check_api_response(response, 200, { "url": url, "link": shortlink }) def test_unsuccessful_response_tinyurl(self, client): url = "https://facebook.com" error = "error creating tinyurl link" with Mocker() as m: # mock response m.get(f"http://tinyurl.com/api-create.php?url={url}", text=error, status_code=400) # perform client post to endpoint response = client.post('/shortlinks', json={ "url": url, "provider": "tinyurl" }) # check response check_api_response(response, 400, { "error": error })

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40bc4a36759cf1fb735daafeaf1a9c7fe1a66580

504

py

Python

db_context/Context.py

crippledfaith/shop

fb6a520170968e9f90d4d70c3f6a4e793b105e84

[ "Apache-2.0" ]

null

db_context/Context.py

crippledfaith/shop

fb6a520170968e9f90d4d70c3f6a4e793b105e84

[ "Apache-2.0" ]

null

db_context/Context.py

crippledfaith/shop

fb6a520170968e9f90d4d70c3f6a4e793b105e84

[ "Apache-2.0" ]

null

from pymongo import MongoClient class Context: def __init__(self): self.client = MongoClient(port=27017) self.db = self.client.shop def save(self, collection_name, obj): self.db[collection_name].insert_one(obj) def update(self, collection_name, obj): self.db[collection_name].find_one_and_update({"_id": obj["_id"]}, {"$set": obj}, upsert=True) def delete(self, collection_name, obj): self.db[collection_name].delete_one({"_id": obj["_id"]})

28

101

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504

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504

17

102

29.647059

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0.363636

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0

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null

0

1

0

1

0

4

40be4052d0b2e6de09ff0edfe724246d49c56e0c

97

py

Python

src/api/jp/kanjivg.py

Xifax/suzu-web

ebe6b87093f73bf8a100d7b78b1d4a83cf203315

[ "BSD-2-Clause" ]

null

src/api/jp/kanjivg.py

Xifax/suzu-web

ebe6b87093f73bf8a100d7b78b1d4a83cf203315

[ "BSD-2-Clause" ]

null

src/api/jp/kanjivg.py

Xifax/suzu-web

ebe6b87093f73bf8a100d7b78b1d4a83cf203315

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ KanjiVG stub """ class KanjiVG: pass

9.7

23

0.536082

12

97

4.333333

0.916667

0

0.013699

0.247423

97

9

24

10.777778

0.69863

0.56701

0

1

0

true

0.5

0

0.5

0

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0

null

0

1

0

null

0

1

0

4

40dc02ef4eafdaa22394a1b06dc6ab6f7ccb8ddb

175

py

Python

src/natasy/neural_network/layers/hiddenlayer.py

disooqi/DNN

f87a10afba0810778ab3669f30e20128779f9da0

[ "AFL-3.0" ]

3

2019-03-03T11:01:26.000Z

2022-02-01T15:53:47.000Z

src/natasy/neural_network/layers/hiddenlayer.py

disooqi/DNN

f87a10afba0810778ab3669f30e20128779f9da0

[ "AFL-3.0" ]

20

2018-10-31T16:54:21.000Z

2021-08-28T06:05:56.000Z

src/natasy/neural_network/layers/hiddenlayer.py

disooqi/Natasy

f87a10afba0810778ab3669f30e20128779f9da0

[ "AFL-3.0" ]

null

from . import NeuralNetworkLayer class HiddenLayer(NeuralNetworkLayer): def __init__(self, *args, **kwargs): super(HiddenLayer, self).__init__(*args, **kwargs)

21.875

58

0.714286

17

175

6.882353

0.647059

0.17094

0

0.16

175

7

59

25

0.795918

0

1

0.25

false

0

0.25

0

0.75

0

1

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null

0

1

0

null

0

1

0

1

0

4

40f8f66a4d55e39902ce1951d1b7a771e6affc06

42

py

Python

jupyterhub/etc/jupyterhub/jupyterhub_config.py

nevdullcode/vagrant-jupyterhub

41a361a64dd38c07abb863da0e0a1585acad1bff

[ "MIT" ]

1

2021-04-18T19:56:28.000Z

jupyterhub/etc/jupyterhub/jupyterhub_config.py

nevdullcode/vagrant-jupyterhub

41a361a64dd38c07abb863da0e0a1585acad1bff

[ "MIT" ]

null

jupyterhub/etc/jupyterhub/jupyterhub_config.py

nevdullcode/vagrant-jupyterhub

41a361a64dd38c07abb863da0e0a1585acad1bff

[ "MIT" ]

null

c.Authenticator.admin_users = {'vagrant'}

21

41

0.761905

5

42

6.2

1

0

0.071429

42

1

42

0.794872

0

0.166667

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

4

40fcc69cdab9b5733a6cbbf5c796e97d633fde88

2,187

py

Python

pychip8/operations/__init__.py

edwin-jones/pychip8

da8c850cdba90cbb6d1244de3b6118be8119c67d

[ "MIT" ]

null

pychip8/operations/__init__.py

edwin-jones/pychip8

da8c850cdba90cbb6d1244de3b6118be8119c67d

[ "MIT" ]

4

2018-09-02T20:37:47.000Z

2018-09-08T19:11:08.000Z

pychip8/operations/__init__.py

edwin-jones/pychip8

da8c850cdba90cbb6d1244de3b6118be8119c67d

[ "MIT" ]

null

from pychip8.operations.set_x_to_y import SetXToY from pychip8.operations.set_x import SetX from pychip8.operations.set_i import SetI from pychip8.operations.random import Random from pychip8.operations.save_registers_zero_to_x import SaveRegistersZeroToX from pychip8.operations.load_registers_zero_to_x import LoadRegistersZeroToX from pychip8.operations.save_x_as_bcd import SaveXAsBcd from pychip8.operations.load_character_address import LoadCharacterAddress from pychip8.operations.graphics.clear_display import ClearDisplay from pychip8.operations.graphics.draw_sprite import DrawSprite from pychip8.operations.jumps.goto import Goto from pychip8.operations.jumps.goto_plus import GotoPlus from pychip8.operations.jumps.skip_if_equal import SkipIfEqual from pychip8.operations.jumps.skip_if_not_equal import SkipIfNotEqual from pychip8.operations.jumps.skip_if_x_y_equal import SkipIfXyEqual from pychip8.operations.jumps.skip_if_x_y_not_equal import SkipIfXyNotEqual from pychip8.operations.jumps.return_from_function import ReturnFromFunction from pychip8.operations.jumps.call_function import CallFunction from pychip8.operations.arithmetic.add_to_x import AddToX from pychip8.operations.arithmetic.add_y_to_x import AddYToX from pychip8.operations.arithmetic.take_y_from_x import TakeYFromX from pychip8.operations.arithmetic.take_x_from_y import TakeXFromY from pychip8.operations.arithmetic.add_x_to_i import AddXToI from pychip8.operations.bitwise.shift_x_left import ShiftXLeft from pychip8.operations.bitwise.shift_x_right import ShiftXRight from pychip8.operations.bitwise.bitwise_and import BitwiseAnd from pychip8.operations.bitwise.bitwise_or import BitwiseOr from pychip8.operations.bitwise.bitwise_xor import BitwiseXor from pychip8.operations.timers.set_x_to_delay_timer import SetXToDelayTimer from pychip8.operations.timers.set_sound_timer import SetSoundTimer from pychip8.operations.timers.set_delay_timer import SetDelayTimer from pychip8.operations.input.skip_if_key_pressed import SkipIfKeyPressed from pychip8.operations.input.skip_if_key_not_pressed import SkipIfKeyNotPressed from pychip8.operations.input.wait_for_key_press import WaitForKeyPress

53.341463

80

0.89209

307

2,187

6.104235

0.276873

0.199573

0.381003

0.110993

0.422092

0.144077

0.073639

0.036286

0

0.016626

0.064929

2,187

40

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54.675

0.899756

0

1

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0

1

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null

0

1

0

null

0

1

0

1

0

1

0

4

dc0ece8ea8024b7639e8aef0ff9259fc64b7906d

210

py

Python

crypto/large case/secret.py

n0trix/SUSCTF2022_official_wp

51224930c59cd732b9d80cc63d95dd1a06ebc308

[ "MIT" ]

32

2022-03-01T06:57:42.000Z

2022-03-27T09:23:07.000Z

crypto/large case/secret.py

n0trix/SUSCTF2022_official_wp

51224930c59cd732b9d80cc63d95dd1a06ebc308

[ "MIT" ]

null

crypto/large case/secret.py

n0trix/SUSCTF2022_official_wp

51224930c59cd732b9d80cc63d95dd1a06ebc308

[ "MIT" ]

6

2022-03-01T06:49:09.000Z

2022-03-21T13:21:26.000Z

e=259776235785533 message=b'For RSA, the wrong key generation method can also reveal information. You recover my secret message, and here is the flag:SUSCTF{N0n_c0prime_RSA_c1pher_cAn_a1s0_recover_me33age!!!}'

105

191

0.833333

34

210

4.941176

0.852941

0

0.117021

0.104762

210

2

191

105

0.776596

0

0.5

0.857143

0.3

0

1

0

false

0

null

0

1

0

1

0

1

0

null

0

4

9088f7e6a1eb4276bc6e187d21405ff6eb5f15a2

3,735

py

Python

web/sufee/views.py

shalevy1/Flask-Sufee-Admin

3a663c2a49ef07772de646317eab350d795e0e5e

[ "MIT" ]

1

2020-07-13T03:55:56.000Z

web/sufee/views.py

shalevy1/Flask-Sufee-Admin

3a663c2a49ef07772de646317eab350d795e0e5e

[ "MIT" ]

null

web/sufee/views.py

shalevy1/Flask-Sufee-Admin

3a663c2a49ef07772de646317eab350d795e0e5e

[ "MIT" ]

1

2020-09-11T17:30:37.000Z

from flask import Flask from flask import render_template from flask import abort from sufee import app @app.route("/") @app.route("/home") def index(): template = 'index.html' return render_template(template) @app.route("/about") def aboutindex(): template = 'about.html' return render_template(template) @app.route("/page-login") def login(): template = "page-login.html" return render_template(template) @app.route("/page-register") def register(): template = "page-register.html" return render_template(template) @app.route("/pages-forget") def page_forget(): template = 'pages-forget.html' return render_template(template) @app.route("/ui-buttons") def ui_buttons(): template = 'ui-buttons.html' return render_template(template) @app.route("/ui-badges") def ui_badges(): template = 'ui-badges.html' return render_template(template) @app.route("/ui-tabs") def ui_tabs(): template = 'ui-tabs.html' return render_template(template) @app.route("/charts-chartjs") def charts_chartjs(): template = 'charts-chartjs.html' return render_template(template) @app.route("/charts-flot") def charts_flot(): template = 'charts-flot.html' return render_template(template) @app.route("/charts-peity") def charts_peity(): template = '/charts-peity.html' return render_template(template) @app.route("/font-fontawesome") def font_fontawesome(): template = 'font-fontawesome.html' return render_template(template) @app.route("/font-themify") def font_themify(): template = 'font-themify.html' return render_template(template) @app.route("/font-advanced") def font_advanced(): template = 'font-advanced.html' return render_template(template) @app.route("/forms-basic") def forms_basic(): template = 'forms-basic.html' return render_template(template) @app.route("/forms-advanced") def forms_advanced(): template = 'forms-advanced.html' return render_template(template) @app.route("/frame") def frame(): template = 'frame.html' return render_template(template) @app.route("/maps-gmap") def maps_gmap(): template = 'maps-gmap.html' return render_template(template) @app.route("/maps-vector") def maps_vector(): template = 'maps-vector.html' return render_template(template) @app.route("/tables-basic") def tables_basic(): template = 'tables-basic.html' return render_template(template) @app.route("/tables-data") def tables_data(): template = 'tables-data.html' return render_template(template) @app.route("/ui-alerts") def ui_alerts(): template = 'ui-alerts.html' return render_template(template) @app.route("/ui-cards") def ui_cards(): template = 'ui-cards.html' return render_template(template) @app.route("/ui-grids") def ui_grids(): template = 'ui-grids.html' return render_template(template) @app.route("/ui-modals") def ui_modals(): template = 'ui-modals.html' return render_template(template) @app.route("/ui-progressbar") def ui_progressbar(): template = 'ui-progressbar.html' return render_template(template) @app.route("/ui-social-buttons") def ui_social_buttons(): template = 'ui-social-buttons.html' return render_template(template) @app.route("/ui-switches") def ui_switches(): template = 'ui-switches.html' return render_template(template) # @app.route("/ui-tabs") # def ui_tabs(): # template = 'ui-tabs.html' # return render_template(template) @app.route("/ui-typography") def ui_typography(): template = '/ui-typography.html' return render_template(template) @app.route("/widgets") def widgets(): template = 'widgets.html' return render_template(template)

22.91411

39

0.701473

473

3,735

5.41649

0.105708

0.174863

0.193599

0.290398

0.553474

0.540984

0.494145

0.123341

0.08509

0

0.147256

3,735

162

40

23.055556

0.804396

0.027845

0

0.24

0

0.225655

0.011862

0

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0.24

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0.032

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0.512

0

null

0

1

0

null

0

1

0

1

0

4

9090469fa96e932346e141d16f47b247dde38a8c

230

py

Python

src/sage/version.py

haiyashah/sage

55a711e3d6251f2ff4f3bcccc4c6a8b7a2a8d1b2

[ "BSL-1.0" ]

null

src/sage/version.py

haiyashah/sage

55a711e3d6251f2ff4f3bcccc4c6a8b7a2a8d1b2

[ "BSL-1.0" ]

null

src/sage/version.py

haiyashah/sage

55a711e3d6251f2ff4f3bcccc4c6a8b7a2a8d1b2

[ "BSL-1.0" ]

null

# Sage version information for Python scripts # This file is auto-generated by the sage-update-version script, do not edit! version = '9.6.beta5' date = '2022-03-12' banner = 'SageMath version 9.6.beta5, Release Date: 2022-03-12'

38.333333

77

0.743478

39

230

4.384615

0.717949

0.093567

0.105263

0.163743

0

0.111675

0.143478

230

5

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46

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0

1

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0

1

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false

0

null

0

1

0

1

0

1

0

1

0

null

0

4

90b254aa4492a406e7bdbaae580cc675d6f89529

381

py

Python

apps/staff/templatetags/staff_template_filters.py

mrtaalebi/sitigo

cce8b4f5299b58d7365789ead416d4568b443743

[ "Apache-2.0" ]

null

apps/staff/templatetags/staff_template_filters.py

mrtaalebi/sitigo

cce8b4f5299b58d7365789ead416d4568b443743

[ "Apache-2.0" ]

8

2020-02-12T01:02:15.000Z

2022-03-11T23:53:39.000Z

apps/staff/templatetags/staff_template_filters.py

mrtaalebi/sitigo

cce8b4f5299b58d7365789ead416d4568b443743

[ "Apache-2.0" ]

null

from django import template register = template.Library() @register.filter def len(a): return a.__len__() @register.filter def modulo(a, b): return a % b @register.filter def name(staff, lang): if lang == "fa": return staff.persian_firstname + " " + staff.persian_lastname else: return staff.english_firstname + " " + staff.english_lastname

16.565217

69

0.669291

48

381

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0.479167

0.17004

0.206478

0

0.217848

381

22

70

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0.828859

0

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0

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false

0

0.071429

0.142857

0.571429

0

null

0

1

0

null

0

1

0

1

0

4

90d9b9a8576d5d62c0259b9fe249745ff6afe877

1,380

py

Python

benchmarks/cinderella.py

stanlysamuel/gensys

87e924235dcda592bd37e694d54ccc0b62e4a34a

[ "MIT" ]

null

benchmarks/cinderella.py

stanlysamuel/gensys

87e924235dcda592bd37e694d54ccc0b62e4a34a

[ "MIT" ]

null

benchmarks/cinderella.py

stanlysamuel/gensys

87e924235dcda592bd37e694d54ccc0b62e4a34a

[ "MIT" ]

null

from gensys.helper import * from gensys.fixpoints import * from z3 import * #Cinderella-Stepmother game of 5 buckets with bucket size of C. # 1. Define Environment moves def environment(b1, b2, b3, b4, b5, b1_, b2_, b3_, b4_, b5_): return And(b1_ + b2_ + b3_ + b4_ + b5_ == b1 + b2 + b3 + b4 + b5 + 1, b1_>=b1, b2_>=b2, b3_>=b3, b4_>=b4, b5_>=b5) #2. Define Controller moves def move1(b1, b2, b3, b4, b5, b1_, b2_, b3_, b4_, b5_): return And( b1_ == 0.0, b2_ == 0.0, b3_ == b3, b4_ == b4, b5_ == b5) def move2(b1, b2, b3, b4, b5, b1_, b2_, b3_, b4_, b5_): return And( b2_ == 0.0, b3_ == 0.0, b4_ == b4, b5_ == b5, b1_ == b1) def move3(b1, b2, b3, b4, b5, b1_, b2_, b3_, b4_, b5_): return And( b3_ == 0.0, b4_ == 0.0, b5_ == b5, b1_ == b1, b2_ == b2) def move4(b1, b2, b3, b4, b5, b1_, b2_, b3_, b4_, b5_): return And( b4_ == 0.0, b5_ == 0.0, b1_ == b1, b2_ == b2, b3_ == b3) def move5(b1, b2, b3, b4, b5, b1_, b2_, b3_, b4_, b5_): return And( b5_ == 0.0, b1_ == 0.0, b2_ == b2, b3_ == b3, b4_ == b4) controller_moves = [move1, move2, move3, move4, move5] C = sys.argv[1] mode = sys.argv[2] # 3. Define Guarantee def guarantee(b1, b2, b3, b4, b5): return And(b1 <= C , b2 <=C , b3 <=C , b4 <=C , b5 <=C , b1 >= 0.0 , b2 >= 0.0 , b3 >= 0.0 , b4 >= 0.0 , b5 >= 0.0) safety_fixedpoint(controller_moves, environment, guarantee, int(mode))

37.297297

119

0.574638

255

1,380

2.843137

0.172549

0.09931

0.124138

0.165517

0.451034

0.362759

0.29931

0.270345

0

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0.230435

1,380

37

120

37.297297

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0

1

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0.333333

0.809524

0

null

0

1

0

null

0

1

0

1

0

4

29563b18f2ee7efac2a242cf2a303898b0505e3f

24

py

Python

data/studio21_generated/introductory/3172/starter_code.py

vijaykumawat256/Prompt-Summarization

614f5911e2acd2933440d909de2b4f86653dc214

[ "Apache-2.0" ]

null

data/studio21_generated/introductory/3172/starter_code.py

vijaykumawat256/Prompt-Summarization

614f5911e2acd2933440d909de2b4f86653dc214

[ "Apache-2.0" ]

null

data/studio21_generated/introductory/3172/starter_code.py

vijaykumawat256/Prompt-Summarization

614f5911e2acd2933440d909de2b4f86653dc214

[ "Apache-2.0" ]

null

def parse_fen(string):

12

22

0.75

4

24

4.25

1

0

0.125

24

2

23

12

0.809524

0

null

0

null

0

1

0

null

0

1

0

null

0

1

0

4

296f5eabbb26a7428b7eb5ce12e1c70bb2c5c798

199

py

Python

python/simple_keyword_arguments.py

idrougge/ple

fcf645e23fec68fea93a068eb3bb78a88ca8af46

[ "MIT" ]

59

2016-10-27T03:33:17.000Z

2022-03-03T06:10:10.000Z

python/simple_keyword_arguments.py

idrougge/ple

fcf645e23fec68fea93a068eb3bb78a88ca8af46

[ "MIT" ]

6

2017-01-07T19:27:33.000Z

2019-11-10T21:29:59.000Z

python/simple_keyword_arguments.py

idrougge/ple

fcf645e23fec68fea93a068eb3bb78a88ca8af46

[ "MIT" ]

27

2016-09-20T16:22:03.000Z

2022-01-15T09:28:06.000Z

def set_fill_color(red, green, blue): pass def draw_rectangle(corner, other_corner): pass set_fill_color(red=161, green=219, blue=114) draw_rectangle(corner=(105,20), other_corner=(60,60))

22.111111

53

0.743719

33

199

4.242424

0.545455

0.1

0.171429

0.214286

0

0.102857

0.120603

199

8

54

24.875

0.697143

0

0.333333

0

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0.333333

false

0.333333

0

0.333333

0

null

0

1

0

1

0

null

0

1

0

1

0

4

29845881af93bb75270c761046387972658af4e3

518

py

Python

skift/__init__.py

dimidd/skift

48ce16ff07710cde956ff3a7071d83cbbd0f9977

[ "MIT" ]

244

2018-02-04T09:33:32.000Z

2022-03-06T05:26:36.000Z

skift/__init__.py

dimidd/skift

48ce16ff07710cde956ff3a7071d83cbbd0f9977

[ "MIT" ]

19

2018-02-16T03:23:45.000Z

2022-02-14T13:06:41.000Z

skift/__init__.py

dimidd/skift

48ce16ff07710cde956ff3a7071d83cbbd0f9977

[ "MIT" ]

28

2018-02-05T06:54:46.000Z

2022-02-03T14:47:14.000Z

"""Utilities for pandas.""" from .core import FirstColFtClassifier # noqa: F401 from .core import IdxBasedFtClassifier # noqa: F401 from .core import FirstObjFtClassifier # noqa: F401 from .core import ColLblBasedFtClassifier # noqa: F401 from .core import SeriesFtClassifier # noqa: F401 from ._version import get_versions __version__ = get_versions()['version'] del get_versions for name in ['get_versions', '_version', 'core', 'name']: try: globals().pop(name) except KeyError: pass

28.777778

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0

null

0

1

0

null

0

1

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4

461e11134327f36ac57873ad2fa3db8c7445fa13

218

py

Python

mentor_mentee/mentor_mentee/doctype/relation/relation.py

sehjal408/SK

9796fa2d7754a8583db246abdb09341824f254c2

[ "MIT" ]

null

mentor_mentee/mentor_mentee/doctype/relation/relation.py

sehjal408/SK

9796fa2d7754a8583db246abdb09341824f254c2

[ "MIT" ]

null

mentor_mentee/mentor_mentee/doctype/relation/relation.py

sehjal408/SK

9796fa2d7754a8583db246abdb09341824f254c2

[ "MIT" ]

null

# Copyright (c) 2022, SK and contributors # For license information, please see license.txt import frappe from frappe.website.website_generator import WebsiteGenerator class Relation(WebsiteGenerator): pass

15.571429

61

0.788991

26

218

6.576923

0.807692

0

0.021739

0.155963

218

13

62

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0.907609

0.399083

0

1

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0

1

0

null

0

null

0

1

0

4

46595df494e81791f7fca742cdfdd7448f09a9ff

831

py

Python

matchbook/tests/test_apiclient.py

jackrhunt13/matchbook

a12ac26e272ddc004f2590b4f4ad8f4715f1df66

[ "MIT" ]

11

2017-07-11T10:08:19.000Z

2021-01-22T17:08:44.000Z

matchbook/tests/test_apiclient.py

oddoneuk/matchbook

eb37817c4f6604097be406edf2df7f711586dcf6

[ "MIT" ]

10

2017-07-14T23:43:25.000Z

2021-08-19T17:21:10.000Z

matchbook/tests/test_apiclient.py

oddoneuk/matchbook

eb37817c4f6604097be406edf2df7f711586dcf6

[ "MIT" ]

9

2017-12-13T13:25:42.000Z

2021-07-16T18:24:23.000Z

import unittest from matchbook.apiclient import APIClient from matchbook.endpoints import Betting, Account, KeepAlive, Login, Logout, MarketData, ReferenceData, Reporting class APIClientTest(unittest.TestCase): def test_apiclient_init(self): client = APIClient('username', 'password') assert str(client) == 'APIClient' assert repr(client) == '<APIClient [username]>' assert isinstance(client.account, Account) assert isinstance(client.betting, Betting) assert isinstance(client.keep_alive, KeepAlive) assert isinstance(client.login, Login) assert isinstance(client.logout, Logout) assert isinstance(client.market_data, MarketData) assert isinstance(client.reference_data, ReferenceData) assert isinstance(client.reporting, Reporting)

37.772727

112

0.726835

84

831

7.130952

0.392857

0.213689

0.293823

0

0.186522

831

21

113

39.571429

0.886095

0

0.056627

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0.625

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0.0625

false

0.0625

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0

0.3125

0

null

1

0

null

0

1

0

1

0

4

465ea013fb24a2d6820687e62ebe6b5f80c99a82

342

py

Python

ooer/models/user.py

williammck/ooer

d6dc07627acfa41de824b86addf4eb70ecc8fc76

[ "MIT" ]

null

ooer/models/user.py

williammck/ooer

d6dc07627acfa41de824b86addf4eb70ecc8fc76

[ "MIT" ]

null

ooer/models/user.py

williammck/ooer

d6dc07627acfa41de824b86addf4eb70ecc8fc76

[ "MIT" ]

null

from mongoengine import * from flask_login import UserMixin class User(Document, UserMixin, object): username = StringField(required=True, unique=True) email = StringField() def get_id(self): return str(self.id) def __repr__(self): return self.username def __str__(self): return self.username

20.117647

54

0.681287

41

342

5.439024

0.560976

0.134529

0.125561

0.197309

0

0.233918

342

16

55

21.375

0.851145

0

0.181818

0

1

0.272727

false

0

0.181818

0.272727

1

0

null

0

1

0

null

0

1

0

1

0

4

467196265c0667547d23bb24cd17be53c71cb8f3

137

py

Python

lender_books/apps.py

joyliao07/django_lender

332bae3be31842fbf4e43443bd04a9467fce5a3d

[ "MIT" ]

1

2019-02-27T01:51:30.000Z

lender_books/apps.py

joyliao07/django_lender

332bae3be31842fbf4e43443bd04a9467fce5a3d

[ "MIT" ]

4

2019-01-08T00:56:19.000Z

2019-01-11T03:14:21.000Z

lender_books/apps.py

joyliao07/django_lender

332bae3be31842fbf4e43443bd04a9467fce5a3d

[ "MIT" ]

null

"""To configurate app lender_books.""" from django.apps import AppConfig class LenderBooksConfig(AppConfig): name = 'lender_books'

19.571429

38

0.759124

16

137

6.375

0.8125

0.215686

0

0.138686

137

6

39

22.833333

0.864407

0.233577

0

0.121212

0

1

0

false

0

0.333333

0

1

0

1

0

null

1

0

1

0

null

0

1

0

4

467f5b347cbaa534e381c0d1b0b13ac58ef48616

117

py

Python

gputools/denoise/__init__.py

gmazzamuto/gputools

73a4dee76a119f94d8163781a85b691fd080d506

[ "BSD-3-Clause" ]

89

2015-08-28T14:17:33.000Z

2022-01-20T16:19:34.000Z

gputools/denoise/__init__.py

gmazzamuto/gputools

73a4dee76a119f94d8163781a85b691fd080d506

[ "BSD-3-Clause" ]

24

2015-08-28T19:06:22.000Z

2022-02-21T21:10:13.000Z

gputools/denoise/__init__.py

gmazzamuto/gputools

73a4dee76a119f94d8163781a85b691fd080d506

[ "BSD-3-Clause" ]

17

2015-08-28T18:56:43.000Z

2021-09-15T23:15:36.000Z

from .nlm3 import nlm3 from .nlm2 import nlm2 from .bilateral2 import bilateral2 from .bilateral3 import bilateral3

19.5

34

0.820513

16

117

6

0.375

0

0.08

0.145299

117

5

35

23.4

0.88

0

1

0

true

0

1

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

4

4686aca7a7aa468b4230731e1687c9dfcfee1f59

58

py

Python

src/Basket.py

MattBlue92/progettoGiocattolo

8d4c7f924d9ebc3358e1d575b968e1c695e3d312

[ "MIT" ]

null

src/Basket.py

MattBlue92/progettoGiocattolo

8d4c7f924d9ebc3358e1d575b968e1c695e3d312

[ "MIT" ]

3

2020-05-12T09:21:20.000Z

2020-05-12T20:27:55.000Z

src/Basket.py

MattBlue92/progettoGiocattolo

8d4c7f924d9ebc3358e1d575b968e1c695e3d312

[ "MIT" ]

null

class Basket: def getPrice(self): return 100;

14.5

23

0.603448

7

58

5

1

0

0.075

0.310345

58

4

24

14.5

0.8

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4