vikp/starcoder_labeled · Datasets at Hugging Face

import os import pathlib import numpy as np import pandas as pd import torch from torch.utils.data import TensorDataset, DataLoader, BatchSampler from torch.utils.data import WeightedRandomSampler import scipy.sparse as ssp import sklearn.preprocessing as prep import sklearn.pipeline as ppln from sklearn.utils import class_weight import matplotlib.pyplot as plt from utils import to_sparse_tensor, bin_data, normalize, split_bucketed_data, \ summarize_feature_matrix def get_composite_transformer(n_quantiles): transformer = ppln.Pipeline([ ("quantile", prep.QuantileTransformer(output_distribution="normal", n_quantiles=n_quantiles)), ("normalize", prep.StandardScaler()) ]) return transformer class BinnedTransformer: def __init__(self, num_bins, create_transformer_f): self.num_bins = num_bins self.transformers = [create_transformer_f() for _ in range(num_bins)] def fit_transform(self, x_reg, x_class): transformed_x_reg = np.copy(x_reg) for bin_idx in range(self.num_bins): sample_idcs = x_class == bin_idx transformer = self.transformers[bin_idx] transformed_x_reg[sample_idcs] = transformer.fit_transform( transformed_x_reg[sample_idcs]) return transformed_x_reg def inverse_transform(self, x_reg, x_class): x_reg = x_reg.reshape(-1) transformed_x_reg = np.copy(x_reg) for bin_idx in range(self.num_bins): sample_idcs = x_class == bin_idx if np.sum(sample_idcs) == 0: continue # no sample of that class transformer = self.transformers[bin_idx] transformed_x_reg[sample_idcs] = transformer.inverse_transform( x_reg[sample_idcs].reshape(-1, 1)).reshape(-1) return transformed_x_reg class GraphTopologicalData: def __init__(self, adj_matrix=None, unweighted_adj_matrix=None, inc_matrix=None, inc_matrix_dense=None, edge_indices=None, edge_weights=None): self.adj_matrix = adj_matrix # NxN sparse matrix self.unweighted_adj_matrix = unweighted_adj_matrix # NxN sparse matrix self.inc_matrix = inc_matrix # NxE sparse matrix self.inc_matrix_dense = inc_matrix_dense # NxE dense matrix self.edge_indices = edge_indices # Ex2 dense matrix self.edge_weights = edge_weights # E dense vector class UrbanPlanningDataset: def __init__(self, data_base_path="Data/", num_bins=4, batch_size=32, n_quantiles=1000, resample=False, excluded_node_feature_columns=tuple(), excluded_edge_feature_columns=tuple(), use_binned_transformer=False, include_approx_flows=False, flow_adj_threshold=0, seed=7): """ Loads city data set. :param data_base_path: Location at which to find the node features, edge features, and the adjacency matrix. :param num_bins: Number of bins for dividing the data set labels. The bin index may be a classification target or for computing MAEs for each bin separately. :param batch_size: :param n_quantiles: Number of quantiles to use for the quantile transformer that preprocesses features and labels. :param excluded_node_feature_columns: Tuple of names of the columns to remove from the node feature data set. :param excluded_edge_feature_columns: Tuple of names of the columns to remove from the edge feature data set. :param resample: If True, we use a weighted random sampler to ensure that each epoch contains an equal number of samples from each bin. :param use_binned_transformer: If True, the edge labels are rescaled using an individual transformer for each bin. Inverting the transformation then requires both a regression and classification prediction. :param include_approx_flows: If True, the edge features include the approximate flows (normally used just for flow adjacency matrix). :param flow_adj_threshold: When constructing the unweighted flow adjacency matrix, only include edges with a flow greater or equal that threshold. :param seed: Random seed to always obtain the same split into training, validation, and test set. :return: Tuple consisting of - Node features of shape [N, K] - Sparse adjacency matrix of shape [N, N] - Loader for the training set of edges - Loader for the validation set of edges - Loader for the test set of edges - Number of node features - Number of edge features - Scaler used for edge labels """ print("Loading data") self.num_bins = num_bins self.batch_size = batch_size self.n_quantiles = n_quantiles self.use_binned_transformer = use_binned_transformer get_composite_transformer_f = lambda: get_composite_transformer( n_quantiles=n_quantiles) # Load node data (self.node_feats, self.num_nodes, self.num_node_feats, self.node_scaler) = self._load_node_data(data_base_path, get_composite_transformer_f, excluded_node_feature_columns) # Load edge data (flow_edge_indices, self.edge_feats, self.edge_labels, self.edge_labels_unscaled, self.label_scaler, self.edge_scaler, self.num_edges, self.num_edge_feats) = self._load_edge_data( data_base_path, get_composite_transformer_f, include_approx_flows, excluded_edge_feature_columns) self.max_label = np.max(self.edge_labels_unscaled) print(f"\tMax label {self.max_label}") (train_idcs, val_idcs, test_idcs) = self._load_dataset_split( data_base_path) # Load flow graph data (flow_adj_matrix, flow_inc_matrix, flow_adj_indices, unweighted_flow_adj_matrix, flow_adj_values) = self._load_flow_graph_data( data_base_path, self.num_nodes, self.num_edges, flow_adj_threshold) self.flow_topology = GraphTopologicalData( adj_matrix=flow_adj_matrix, edge_indices=flow_adj_indices, unweighted_adj_matrix=unweighted_flow_adj_matrix, inc_matrix=flow_inc_matrix, edge_weights=flow_adj_values ) # Load geographical graph data (geo_adj_matrix, geo_inc_matrix, geo_edge_indices, geo_adj_values) = self._load_geo_graph_data( data_base_path, self.num_nodes, self.num_edges, self.flow_topology) self.geo_topology = GraphTopologicalData( adj_matrix=geo_adj_matrix, inc_matrix=geo_inc_matrix, edge_indices=geo_edge_indices, edge_weights=geo_adj_values) # Load bin data self.bin_bounds = [10.0, 100.0, 1000.0, 10000.0] (self.edge_buckets, self.train_bin_weights, self.val_bin_weights, self.test_bin_weights) = self._load_bin_data(self.bin_bounds, self.edge_labels_unscaled, num_bins, train_idcs, val_idcs, test_idcs) print(f"\tBin counts: {np.array([np.sum(self.edge_buckets == i) for i in range(num_bins)])}") print(f"\tTraining bin weights: {self.train_bin_weights}") print(f"\tValidation bin weights: {self.val_bin_weights}") print(f"\tTest bin weights: {self.test_bin_weights}") # If specified, use the binned transformer to transform labels if use_binned_transformer: self.label_scaler = BinnedTransformer(self.num_bins, get_composite_transformer_f) self.edge_labels = self.label_scaler.fit_transform( self.edge_labels_unscaled.reshape(-1, 1), self.edge_buckets).reshape(-1) # plt.hist(self.edge_labels, bins=100) # plt.show() # Create edge feature matrix indices = flow_edge_indices.transpose(1, 0) values = self.edge_feats edge_feat_matrix = torch.sparse.FloatTensor(torch.from_numpy(indices), torch.from_numpy(values)) self.edge_feat_matrix = edge_feat_matrix.to_dense() # Convert numpy arrays to tensors self.node_feats = torch.from_numpy(self.node_feats) self.edge_feats = torch.from_numpy(self.edge_feats) flow_edge_indices = torch.from_numpy(flow_edge_indices) self.flow_topology.edge_indices = torch.from_numpy(self.flow_topology.edge_indices) self.flow_topology.edge_weights = torch.from_numpy(self.flow_topology.edge_weights) self.geo_topology.edge_indices = torch.from_numpy(self.geo_topology.edge_indices) self.geo_topology.edge_weights = torch.from_numpy(self.geo_topology.edge_weights) self.edge_labels = torch.from_numpy(self.edge_labels) self.edge_labels_unscaled = torch.from_numpy(self.edge_labels_unscaled) self.edge_buckets = torch.from_numpy(self.edge_buckets) self.train_bin_weights = torch.from_numpy(self.train_bin_weights) self.val_bin_weights = torch.from_numpy(self.val_bin_weights) self.test_bin_weights = torch.from_numpy(self.test_bin_weights) # Matrices self.geo_topology.adj_matrix = to_sparse_tensor(normalize(self.geo_topology.adj_matrix)) self.geo_topology.inc_matrix = to_sparse_tensor(self.geo_topology.inc_matrix) self.flow_topology.adj_matrix = to_sparse_tensor(self.flow_topology.adj_matrix) # Sparse tensor of shape [N, N] containing the flow values between nodes. self.flow_topology.unweighted_adj_matrix = to_sparse_tensor(self.flow_topology.unweighted_adj_matrix) self.flow_topology.inc_matrix = to_sparse_tensor(self.flow_topology.inc_matrix) self._check_data_consistency() # Create data loaders (self.train_loader, self.val_loader, self.test_loader) = self._create_data_loaders(train_idcs, val_idcs, test_idcs, self.train_bin_weights, flow_edge_indices, # different from flow_graph_topology.edge_indices because of additional 0-flows self.edge_feats, self.edge_labels, self.edge_buckets, batch_size, resample, seed) print("Finished loading data") def _check_data_consistency(self): tensors = [self.node_feats, self.edge_feats, self.flow_topology.edge_indices, self.geo_topology.edge_indices, self.edge_labels, self.edge_labels_unscaled, self.edge_buckets, self.train_bin_weights, self.val_bin_weights, self.test_bin_weights, self.geo_topology.adj_matrix, self.geo_topology.inc_matrix, self.flow_topology.adj_matrix, self.flow_topology.unweighted_adj_matrix, self.flow_topology.inc_matrix, self.edge_feat_matrix] print("Checking ", end="") for idx, tensor in enumerate(tensors): print(f"{idx}, ", end="") if (isinstance(tensor, torch.sparse.FloatTensor) or isinstance(tensor, torch.sparse.LongTensor)): assert not torch.isnan(tensor.coalesce().indices()).any() assert not torch.isnan(tensor.coalesce().values()).any() else: assert not torch.isnan(tensor).any() print("done") def to(self, device): """ Moves all tensors of the dataset that will not be iterated over in minibatch to the specified device. :param device: Device specifier. """ self.node_feats = self.node_feats.to(device=device) self.edge_feats = self.edge_feats.to(device=device) self.flow_topology.edge_indices = self.flow_topology.edge_indices.to(device=device) self.geo_topology.edge_indices = self.geo_topology.edge_indices.to(device=device) self.train_bin_weights = self.train_bin_weights.to(device=device) self.geo_topology.adj_matrix = self.geo_topology.adj_matrix.to(device=device) self.geo_topology.inc_matrix = self.geo_topology.inc_matrix.to(device=device) self.geo_topology.edge_weights = self.geo_topology.edge_weights.to(device=device) self.flow_topology.adj_matrix = self.flow_topology.adj_matrix.to(device=device) self.flow_topology.unweighted_adj_matrix = self.flow_topology.unweighted_adj_matrix.to( device=device) self.flow_topology.inc_matrix = self.flow_topology.inc_matrix.to(device=device) self.flow_topology.edge_weights = self.flow_topology.edge_weights.to(device=device) self.edge_feat_matrix = self.edge_feat_matrix.to(device=device) @staticmethod def _load_node_data(data_base_path, get_composite_transformer_f, excluded_columns): # Node features node_data = pd.read_pickle(os.path.join(data_base_path, "node_data.pk")) if len(excluded_columns) > 0: node_data.drop(list(excluded_columns), axis=1, inplace=True) node_feats = node_data.values # Rescale continuous features node_scaler = get_composite_transformer_f() cont_feature_idcs = UrbanPlanningDataset._get_continuous_feature_idcs(node_data) node_feats[:, cont_feature_idcs] = node_scaler.fit_transform(node_feats[:, cont_feature_idcs]) node_feats = node_feats.astype(np.float32) num_nodes = node_feats.shape[0] num_node_feats = node_feats.shape[1] return node_feats, num_nodes, num_node_feats, node_scaler @staticmethod def _load_edge_data(data_base_path, get_composite_transformer_f, include_approx_flows, excluded_columns): # Edge data edge_data = pd.read_pickle(os.path.join(data_base_path, "edge_data.pk")) if len(excluded_columns) > 0: edge_data.drop(list(excluded_columns), axis=1, inplace=True) edge_feats = edge_data.values edge_indices = edge_feats[:, :2].astype(np.int) edge_feats = edge_feats[:, 2:] # Load approximate flows and potentially concatenate to edge features # approx_flows = np.load(os.path.join(data_base_path, # "approx_flows.npy")) if include_approx_flows: raise NotImplementedError # edge_feats = np.concatenate((edge_feats, approx_flows.reshape(-1, 1)), # axis=-1) num_edges = edge_feats.shape[0] edge_labels = np.load(os.path.join(data_base_path, "flows.npy")) edge_labels_unscaled = np.copy(edge_labels).astype(np.float32) # Transform edge features edge_scaler = get_composite_transformer_f() cont_feature_idcs = UrbanPlanningDataset._get_continuous_feature_idcs(edge_data.iloc[:, 2:]) edge_feats[:, cont_feature_idcs] = edge_scaler.fit_transform(edge_feats)[:, cont_feature_idcs] edge_feats = edge_feats.astype(np.float32) # Transform edge labels edge_labels = edge_labels.astype(np.float32) label_scaler = get_composite_transformer_f() edge_labels = label_scaler.fit_transform( edge_labels.reshape(-1, 1)).reshape(-1) num_edge_feats = edge_feats.shape[1] return (edge_indices, edge_feats, edge_labels, edge_labels_unscaled, label_scaler, edge_scaler, num_edges, num_edge_feats) @staticmethod def _load_dataset_split(data_base_path): data_base_path = pathlib.Path(data_base_path) train_idcs = np.load(data_base_path / "train_edge_indices.npy") val_idcs = np.load(data_base_path / "val_edge_indices.npy") test_idcs = np.load(data_base_path / "test_edge_indices.npy") return train_idcs, val_idcs, test_idcs @staticmethod def _load_bin_data(bin_bounds, edge_labels_unscaled, num_bins, train_idcs, val_idcs, test_idcs): # Get edge buckets (assign each edge to a bucket based on magnitude of # flow) edge_buckets = bin_data(edge_labels_unscaled, num_bins, scale="custom", bin_bounds=bin_bounds) # Compute weights for each bucket to counterbalance the imbalanced # class/bin distribution train_bin_weights = class_weight.compute_class_weight('balanced', np.unique(edge_buckets), edge_buckets[train_idcs]) val_bin_weights = class_weight.compute_class_weight('balanced', np.unique(edge_buckets), edge_buckets[val_idcs]) test_bin_weights = class_weight.compute_class_weight('balanced', np.unique(edge_buckets), edge_buckets[test_idcs]) train_bin_weights = train_bin_weights.astype(np.float32) val_bin_weights = val_bin_weights.astype(np.float32) test_bin_weights = test_bin_weights.astype(np.float32) return edge_buckets, train_bin_weights, val_bin_weights, test_bin_weights @staticmethod def _load_flow_graph_data(data_base_path, num_nodes, num_edges, flow_adj_threshold): # Flow adjacency matrix flow_adj_indices = np.load(os.path.join(data_base_path, "flow_adj_indices.npy")).T flow_adj_values = np.load(os.path.join(data_base_path, "flow_adj_values.npy")) flow_adj_matrix = ssp.coo_matrix((flow_adj_values, (flow_adj_indices[0], flow_adj_indices[1])), shape=(num_nodes, num_nodes)) flow_adj_matrix = flow_adj_matrix.tocsr() unweighted_flow_adj_indices = flow_adj_indices[:, flow_adj_values >= flow_adj_threshold] flow_adj_values = flow_adj_values[flow_adj_values >= flow_adj_threshold] unweighted_flow_adj_matrix = ssp.coo_matrix( (flow_adj_values, (unweighted_flow_adj_indices[0], unweighted_flow_adj_indices[1])), shape=(num_nodes, num_nodes)) unweighted_flow_adj_matrix.setdiag(np.ones(num_nodes)) flow_adj_values = unweighted_flow_adj_matrix.tocoo().data flow_adj_indices = np.stack((unweighted_flow_adj_matrix.row, unweighted_flow_adj_matrix.col), axis=-1) flow_adj_indices = flow_adj_indices.astype(np.int64) flow_adj_values = flow_adj_values.astype(np.float32) unweighted_flow_adj_matrix = (unweighted_flow_adj_matrix > 0.0).astype(np.float) # Flow incidence matrix for all edges flow_inc_indices = np.load(os.path.join(data_base_path, "flow_inc_indices.npy")) flow_inc_matrix = ssp.coo_matrix( (np.ones(flow_inc_indices.shape[1]), (flow_inc_indices[0], flow_inc_indices[1])), shape=(num_nodes, num_edges)) flow_inc_matrix = flow_inc_matrix.tocsr() return (flow_adj_matrix, flow_inc_matrix, flow_adj_indices, unweighted_flow_adj_matrix, flow_adj_values) @staticmethod def _load_geo_graph_data(data_base_path, num_nodes, num_edges, flow_topology): # Geographical adjacency matrix geo_adj_indices = np.load(os.path.join(data_base_path, "geo_adj_indices.npy")) geo_adj_matrix = ssp.coo_matrix((np.ones(geo_adj_indices.shape[1]), (geo_adj_indices[0], geo_adj_indices[1])), shape=(num_nodes, num_nodes)) geo_adj_matrix = geo_adj_matrix.tocsr() # Geographical incidence matrix for all edges geo_inc_indices = np.load(os.path.join(data_base_path, "geo_inc_indices.npy")) geo_inc_matrix = ssp.coo_matrix( (np.ones(geo_inc_indices.shape[1]), (geo_inc_indices[0], geo_inc_indices[1])), shape=(num_nodes, num_edges)) geo_inc_matrix = geo_inc_matrix.tocsr() # Get flows for the geographical edges all_edges = np.array(flow_topology.adj_matrix.todense()).reshape(-1) # N^2 matrix geo_indices_of_edges = np.array(geo_adj_matrix.todense()).reshape(-1).nonzero() # N^2 matrix geo_flows = all_edges[geo_indices_of_edges] del all_edges all_edges = None del geo_indices_of_edges geo_indices_of_edges = None geo_flows = (geo_flows+1e-5).astype(np.float32) return geo_adj_matrix, geo_inc_matrix, geo_adj_indices.T, geo_flows @staticmethod def _create_data_loaders(train_idcs, val_idcs, test_idcs, train_bin_weights, edge_indices, edge_feats, edge_labels, edge_buckets, batch_size, resample, seed): """ :param train_idcs: :param val_idcs: :param test_idcs: :param train_bin_weights: :param edge_indices: :param edge_feats: :param edge_labels: :param edge_buckets: :param flow_node_edges_matrix: Transpose of the incidence matrix for incoming edges. Shape [E, N]. :param batch_size: :param resample: :param seed: :return: """ assert (len(edge_indices) == len(edge_feats) == len(edge_labels) == len(edge_buckets)) train_idcs = torch.from_numpy(train_idcs) val_idcs = torch.from_numpy(val_idcs) test_idcs = torch.from_numpy(test_idcs) # Sample weights train_sample_weights = train_bin_weights[edge_buckets[train_idcs]] # Compute split into training, validation, and test set np.random.seed(seed) if resample: train_sampler = BatchSampler( WeightedRandomSampler(train_sample_weights, train_idcs.shape[0]), batch_size=batch_size, drop_last=False) train_loader = DataLoader(TensorDataset(edge_indices[train_idcs], edge_feats[train_idcs], edge_labels[train_idcs], edge_buckets[train_idcs]), batch_sampler=train_sampler) else: train_loader = DataLoader(TensorDataset(edge_indices[train_idcs], edge_feats[train_idcs], edge_labels[train_idcs], edge_buckets[train_idcs]), batch_size=batch_size, shuffle=False) val_loader = DataLoader(TensorDataset(edge_indices[val_idcs], edge_feats[val_idcs], edge_labels[val_idcs], edge_buckets[val_idcs]), batch_size=batch_size, shuffle=False) test_loader = DataLoader(TensorDataset(edge_indices[test_idcs], edge_feats[test_idcs], edge_labels[test_idcs], edge_buckets[test_idcs]), batch_size=batch_size, shuffle=False) return train_loader, val_loader, test_loader @staticmethod def _get_continuous_feature_idcs(df): continuous_feature_idcs = [] for idx, col in enumerate(df.columns): if len(df[col].unique()) > 2: continuous_feature_idcs.append(idx) return continuous_feature_idcs if __name__ == '__main__': ds = UrbanPlanningDataset(data_base_path="Data/London_high/", use_binned_transformer=True, excluded_node_feature_columns=tuple()) print("\n\nNode features") summarize_feature_matrix(ds.node_feats.numpy()) print("\n\nEdge features") summarize_feature_matrix(ds.edge_feats.numpy())

dataset.py

import os import pathlib import numpy as np import pandas as pd import torch from torch.utils.data import TensorDataset, DataLoader, BatchSampler from torch.utils.data import WeightedRandomSampler import scipy.sparse as ssp import sklearn.preprocessing as prep import sklearn.pipeline as ppln from sklearn.utils import class_weight import matplotlib.pyplot as plt from utils import to_sparse_tensor, bin_data, normalize, split_bucketed_data, \ summarize_feature_matrix def get_composite_transformer(n_quantiles): transformer = ppln.Pipeline([ ("quantile", prep.QuantileTransformer(output_distribution="normal", n_quantiles=n_quantiles)), ("normalize", prep.StandardScaler()) ]) return transformer class BinnedTransformer: def __init__(self, num_bins, create_transformer_f): self.num_bins = num_bins self.transformers = [create_transformer_f() for _ in range(num_bins)] def fit_transform(self, x_reg, x_class): transformed_x_reg = np.copy(x_reg) for bin_idx in range(self.num_bins): sample_idcs = x_class == bin_idx transformer = self.transformers[bin_idx] transformed_x_reg[sample_idcs] = transformer.fit_transform( transformed_x_reg[sample_idcs]) return transformed_x_reg def inverse_transform(self, x_reg, x_class): x_reg = x_reg.reshape(-1) transformed_x_reg = np.copy(x_reg) for bin_idx in range(self.num_bins): sample_idcs = x_class == bin_idx if np.sum(sample_idcs) == 0: continue # no sample of that class transformer = self.transformers[bin_idx] transformed_x_reg[sample_idcs] = transformer.inverse_transform( x_reg[sample_idcs].reshape(-1, 1)).reshape(-1) return transformed_x_reg class GraphTopologicalData: def __init__(self, adj_matrix=None, unweighted_adj_matrix=None, inc_matrix=None, inc_matrix_dense=None, edge_indices=None, edge_weights=None): self.adj_matrix = adj_matrix # NxN sparse matrix self.unweighted_adj_matrix = unweighted_adj_matrix # NxN sparse matrix self.inc_matrix = inc_matrix # NxE sparse matrix self.inc_matrix_dense = inc_matrix_dense # NxE dense matrix self.edge_indices = edge_indices # Ex2 dense matrix self.edge_weights = edge_weights # E dense vector class UrbanPlanningDataset: def __init__(self, data_base_path="Data/", num_bins=4, batch_size=32, n_quantiles=1000, resample=False, excluded_node_feature_columns=tuple(), excluded_edge_feature_columns=tuple(), use_binned_transformer=False, include_approx_flows=False, flow_adj_threshold=0, seed=7): """ Loads city data set. :param data_base_path: Location at which to find the node features, edge features, and the adjacency matrix. :param num_bins: Number of bins for dividing the data set labels. The bin index may be a classification target or for computing MAEs for each bin separately. :param batch_size: :param n_quantiles: Number of quantiles to use for the quantile transformer that preprocesses features and labels. :param excluded_node_feature_columns: Tuple of names of the columns to remove from the node feature data set. :param excluded_edge_feature_columns: Tuple of names of the columns to remove from the edge feature data set. :param resample: If True, we use a weighted random sampler to ensure that each epoch contains an equal number of samples from each bin. :param use_binned_transformer: If True, the edge labels are rescaled using an individual transformer for each bin. Inverting the transformation then requires both a regression and classification prediction. :param include_approx_flows: If True, the edge features include the approximate flows (normally used just for flow adjacency matrix). :param flow_adj_threshold: When constructing the unweighted flow adjacency matrix, only include edges with a flow greater or equal that threshold. :param seed: Random seed to always obtain the same split into training, validation, and test set. :return: Tuple consisting of - Node features of shape [N, K] - Sparse adjacency matrix of shape [N, N] - Loader for the training set of edges - Loader for the validation set of edges - Loader for the test set of edges - Number of node features - Number of edge features - Scaler used for edge labels """ print("Loading data") self.num_bins = num_bins self.batch_size = batch_size self.n_quantiles = n_quantiles self.use_binned_transformer = use_binned_transformer get_composite_transformer_f = lambda: get_composite_transformer( n_quantiles=n_quantiles) # Load node data (self.node_feats, self.num_nodes, self.num_node_feats, self.node_scaler) = self._load_node_data(data_base_path, get_composite_transformer_f, excluded_node_feature_columns) # Load edge data (flow_edge_indices, self.edge_feats, self.edge_labels, self.edge_labels_unscaled, self.label_scaler, self.edge_scaler, self.num_edges, self.num_edge_feats) = self._load_edge_data( data_base_path, get_composite_transformer_f, include_approx_flows, excluded_edge_feature_columns) self.max_label = np.max(self.edge_labels_unscaled) print(f"\tMax label {self.max_label}") (train_idcs, val_idcs, test_idcs) = self._load_dataset_split( data_base_path) # Load flow graph data (flow_adj_matrix, flow_inc_matrix, flow_adj_indices, unweighted_flow_adj_matrix, flow_adj_values) = self._load_flow_graph_data( data_base_path, self.num_nodes, self.num_edges, flow_adj_threshold) self.flow_topology = GraphTopologicalData( adj_matrix=flow_adj_matrix, edge_indices=flow_adj_indices, unweighted_adj_matrix=unweighted_flow_adj_matrix, inc_matrix=flow_inc_matrix, edge_weights=flow_adj_values ) # Load geographical graph data (geo_adj_matrix, geo_inc_matrix, geo_edge_indices, geo_adj_values) = self._load_geo_graph_data( data_base_path, self.num_nodes, self.num_edges, self.flow_topology) self.geo_topology = GraphTopologicalData( adj_matrix=geo_adj_matrix, inc_matrix=geo_inc_matrix, edge_indices=geo_edge_indices, edge_weights=geo_adj_values) # Load bin data self.bin_bounds = [10.0, 100.0, 1000.0, 10000.0] (self.edge_buckets, self.train_bin_weights, self.val_bin_weights, self.test_bin_weights) = self._load_bin_data(self.bin_bounds, self.edge_labels_unscaled, num_bins, train_idcs, val_idcs, test_idcs) print(f"\tBin counts: {np.array([np.sum(self.edge_buckets == i) for i in range(num_bins)])}") print(f"\tTraining bin weights: {self.train_bin_weights}") print(f"\tValidation bin weights: {self.val_bin_weights}") print(f"\tTest bin weights: {self.test_bin_weights}") # If specified, use the binned transformer to transform labels if use_binned_transformer: self.label_scaler = BinnedTransformer(self.num_bins, get_composite_transformer_f) self.edge_labels = self.label_scaler.fit_transform( self.edge_labels_unscaled.reshape(-1, 1), self.edge_buckets).reshape(-1) # plt.hist(self.edge_labels, bins=100) # plt.show() # Create edge feature matrix indices = flow_edge_indices.transpose(1, 0) values = self.edge_feats edge_feat_matrix = torch.sparse.FloatTensor(torch.from_numpy(indices), torch.from_numpy(values)) self.edge_feat_matrix = edge_feat_matrix.to_dense() # Convert numpy arrays to tensors self.node_feats = torch.from_numpy(self.node_feats) self.edge_feats = torch.from_numpy(self.edge_feats) flow_edge_indices = torch.from_numpy(flow_edge_indices) self.flow_topology.edge_indices = torch.from_numpy(self.flow_topology.edge_indices) self.flow_topology.edge_weights = torch.from_numpy(self.flow_topology.edge_weights) self.geo_topology.edge_indices = torch.from_numpy(self.geo_topology.edge_indices) self.geo_topology.edge_weights = torch.from_numpy(self.geo_topology.edge_weights) self.edge_labels = torch.from_numpy(self.edge_labels) self.edge_labels_unscaled = torch.from_numpy(self.edge_labels_unscaled) self.edge_buckets = torch.from_numpy(self.edge_buckets) self.train_bin_weights = torch.from_numpy(self.train_bin_weights) self.val_bin_weights = torch.from_numpy(self.val_bin_weights) self.test_bin_weights = torch.from_numpy(self.test_bin_weights) # Matrices self.geo_topology.adj_matrix = to_sparse_tensor(normalize(self.geo_topology.adj_matrix)) self.geo_topology.inc_matrix = to_sparse_tensor(self.geo_topology.inc_matrix) self.flow_topology.adj_matrix = to_sparse_tensor(self.flow_topology.adj_matrix) # Sparse tensor of shape [N, N] containing the flow values between nodes. self.flow_topology.unweighted_adj_matrix = to_sparse_tensor(self.flow_topology.unweighted_adj_matrix) self.flow_topology.inc_matrix = to_sparse_tensor(self.flow_topology.inc_matrix) self._check_data_consistency() # Create data loaders (self.train_loader, self.val_loader, self.test_loader) = self._create_data_loaders(train_idcs, val_idcs, test_idcs, self.train_bin_weights, flow_edge_indices, # different from flow_graph_topology.edge_indices because of additional 0-flows self.edge_feats, self.edge_labels, self.edge_buckets, batch_size, resample, seed) print("Finished loading data") def _check_data_consistency(self): tensors = [self.node_feats, self.edge_feats, self.flow_topology.edge_indices, self.geo_topology.edge_indices, self.edge_labels, self.edge_labels_unscaled, self.edge_buckets, self.train_bin_weights, self.val_bin_weights, self.test_bin_weights, self.geo_topology.adj_matrix, self.geo_topology.inc_matrix, self.flow_topology.adj_matrix, self.flow_topology.unweighted_adj_matrix, self.flow_topology.inc_matrix, self.edge_feat_matrix] print("Checking ", end="") for idx, tensor in enumerate(tensors): print(f"{idx}, ", end="") if (isinstance(tensor, torch.sparse.FloatTensor) or isinstance(tensor, torch.sparse.LongTensor)): assert not torch.isnan(tensor.coalesce().indices()).any() assert not torch.isnan(tensor.coalesce().values()).any() else: assert not torch.isnan(tensor).any() print("done") def to(self, device): """ Moves all tensors of the dataset that will not be iterated over in minibatch to the specified device. :param device: Device specifier. """ self.node_feats = self.node_feats.to(device=device) self.edge_feats = self.edge_feats.to(device=device) self.flow_topology.edge_indices = self.flow_topology.edge_indices.to(device=device) self.geo_topology.edge_indices = self.geo_topology.edge_indices.to(device=device) self.train_bin_weights = self.train_bin_weights.to(device=device) self.geo_topology.adj_matrix = self.geo_topology.adj_matrix.to(device=device) self.geo_topology.inc_matrix = self.geo_topology.inc_matrix.to(device=device) self.geo_topology.edge_weights = self.geo_topology.edge_weights.to(device=device) self.flow_topology.adj_matrix = self.flow_topology.adj_matrix.to(device=device) self.flow_topology.unweighted_adj_matrix = self.flow_topology.unweighted_adj_matrix.to( device=device) self.flow_topology.inc_matrix = self.flow_topology.inc_matrix.to(device=device) self.flow_topology.edge_weights = self.flow_topology.edge_weights.to(device=device) self.edge_feat_matrix = self.edge_feat_matrix.to(device=device) @staticmethod def _load_node_data(data_base_path, get_composite_transformer_f, excluded_columns): # Node features node_data = pd.read_pickle(os.path.join(data_base_path, "node_data.pk")) if len(excluded_columns) > 0: node_data.drop(list(excluded_columns), axis=1, inplace=True) node_feats = node_data.values # Rescale continuous features node_scaler = get_composite_transformer_f() cont_feature_idcs = UrbanPlanningDataset._get_continuous_feature_idcs(node_data) node_feats[:, cont_feature_idcs] = node_scaler.fit_transform(node_feats[:, cont_feature_idcs]) node_feats = node_feats.astype(np.float32) num_nodes = node_feats.shape[0] num_node_feats = node_feats.shape[1] return node_feats, num_nodes, num_node_feats, node_scaler @staticmethod def _load_edge_data(data_base_path, get_composite_transformer_f, include_approx_flows, excluded_columns): # Edge data edge_data = pd.read_pickle(os.path.join(data_base_path, "edge_data.pk")) if len(excluded_columns) > 0: edge_data.drop(list(excluded_columns), axis=1, inplace=True) edge_feats = edge_data.values edge_indices = edge_feats[:, :2].astype(np.int) edge_feats = edge_feats[:, 2:] # Load approximate flows and potentially concatenate to edge features # approx_flows = np.load(os.path.join(data_base_path, # "approx_flows.npy")) if include_approx_flows: raise NotImplementedError # edge_feats = np.concatenate((edge_feats, approx_flows.reshape(-1, 1)), # axis=-1) num_edges = edge_feats.shape[0] edge_labels = np.load(os.path.join(data_base_path, "flows.npy")) edge_labels_unscaled = np.copy(edge_labels).astype(np.float32) # Transform edge features edge_scaler = get_composite_transformer_f() cont_feature_idcs = UrbanPlanningDataset._get_continuous_feature_idcs(edge_data.iloc[:, 2:]) edge_feats[:, cont_feature_idcs] = edge_scaler.fit_transform(edge_feats)[:, cont_feature_idcs] edge_feats = edge_feats.astype(np.float32) # Transform edge labels edge_labels = edge_labels.astype(np.float32) label_scaler = get_composite_transformer_f() edge_labels = label_scaler.fit_transform( edge_labels.reshape(-1, 1)).reshape(-1) num_edge_feats = edge_feats.shape[1] return (edge_indices, edge_feats, edge_labels, edge_labels_unscaled, label_scaler, edge_scaler, num_edges, num_edge_feats) @staticmethod def _load_dataset_split(data_base_path): data_base_path = pathlib.Path(data_base_path) train_idcs = np.load(data_base_path / "train_edge_indices.npy") val_idcs = np.load(data_base_path / "val_edge_indices.npy") test_idcs = np.load(data_base_path / "test_edge_indices.npy") return train_idcs, val_idcs, test_idcs @staticmethod def _load_bin_data(bin_bounds, edge_labels_unscaled, num_bins, train_idcs, val_idcs, test_idcs): # Get edge buckets (assign each edge to a bucket based on magnitude of # flow) edge_buckets = bin_data(edge_labels_unscaled, num_bins, scale="custom", bin_bounds=bin_bounds) # Compute weights for each bucket to counterbalance the imbalanced # class/bin distribution train_bin_weights = class_weight.compute_class_weight('balanced', np.unique(edge_buckets), edge_buckets[train_idcs]) val_bin_weights = class_weight.compute_class_weight('balanced', np.unique(edge_buckets), edge_buckets[val_idcs]) test_bin_weights = class_weight.compute_class_weight('balanced', np.unique(edge_buckets), edge_buckets[test_idcs]) train_bin_weights = train_bin_weights.astype(np.float32) val_bin_weights = val_bin_weights.astype(np.float32) test_bin_weights = test_bin_weights.astype(np.float32) return edge_buckets, train_bin_weights, val_bin_weights, test_bin_weights @staticmethod def _load_flow_graph_data(data_base_path, num_nodes, num_edges, flow_adj_threshold): # Flow adjacency matrix flow_adj_indices = np.load(os.path.join(data_base_path, "flow_adj_indices.npy")).T flow_adj_values = np.load(os.path.join(data_base_path, "flow_adj_values.npy")) flow_adj_matrix = ssp.coo_matrix((flow_adj_values, (flow_adj_indices[0], flow_adj_indices[1])), shape=(num_nodes, num_nodes)) flow_adj_matrix = flow_adj_matrix.tocsr() unweighted_flow_adj_indices = flow_adj_indices[:, flow_adj_values >= flow_adj_threshold] flow_adj_values = flow_adj_values[flow_adj_values >= flow_adj_threshold] unweighted_flow_adj_matrix = ssp.coo_matrix( (flow_adj_values, (unweighted_flow_adj_indices[0], unweighted_flow_adj_indices[1])), shape=(num_nodes, num_nodes)) unweighted_flow_adj_matrix.setdiag(np.ones(num_nodes)) flow_adj_values = unweighted_flow_adj_matrix.tocoo().data flow_adj_indices = np.stack((unweighted_flow_adj_matrix.row, unweighted_flow_adj_matrix.col), axis=-1) flow_adj_indices = flow_adj_indices.astype(np.int64) flow_adj_values = flow_adj_values.astype(np.float32) unweighted_flow_adj_matrix = (unweighted_flow_adj_matrix > 0.0).astype(np.float) # Flow incidence matrix for all edges flow_inc_indices = np.load(os.path.join(data_base_path, "flow_inc_indices.npy")) flow_inc_matrix = ssp.coo_matrix( (np.ones(flow_inc_indices.shape[1]), (flow_inc_indices[0], flow_inc_indices[1])), shape=(num_nodes, num_edges)) flow_inc_matrix = flow_inc_matrix.tocsr() return (flow_adj_matrix, flow_inc_matrix, flow_adj_indices, unweighted_flow_adj_matrix, flow_adj_values) @staticmethod def _load_geo_graph_data(data_base_path, num_nodes, num_edges, flow_topology): # Geographical adjacency matrix geo_adj_indices = np.load(os.path.join(data_base_path, "geo_adj_indices.npy")) geo_adj_matrix = ssp.coo_matrix((np.ones(geo_adj_indices.shape[1]), (geo_adj_indices[0], geo_adj_indices[1])), shape=(num_nodes, num_nodes)) geo_adj_matrix = geo_adj_matrix.tocsr() # Geographical incidence matrix for all edges geo_inc_indices = np.load(os.path.join(data_base_path, "geo_inc_indices.npy")) geo_inc_matrix = ssp.coo_matrix( (np.ones(geo_inc_indices.shape[1]), (geo_inc_indices[0], geo_inc_indices[1])), shape=(num_nodes, num_edges)) geo_inc_matrix = geo_inc_matrix.tocsr() # Get flows for the geographical edges all_edges = np.array(flow_topology.adj_matrix.todense()).reshape(-1) # N^2 matrix geo_indices_of_edges = np.array(geo_adj_matrix.todense()).reshape(-1).nonzero() # N^2 matrix geo_flows = all_edges[geo_indices_of_edges] del all_edges all_edges = None del geo_indices_of_edges geo_indices_of_edges = None geo_flows = (geo_flows+1e-5).astype(np.float32) return geo_adj_matrix, geo_inc_matrix, geo_adj_indices.T, geo_flows @staticmethod def _create_data_loaders(train_idcs, val_idcs, test_idcs, train_bin_weights, edge_indices, edge_feats, edge_labels, edge_buckets, batch_size, resample, seed): """ :param train_idcs: :param val_idcs: :param test_idcs: :param train_bin_weights: :param edge_indices: :param edge_feats: :param edge_labels: :param edge_buckets: :param flow_node_edges_matrix: Transpose of the incidence matrix for incoming edges. Shape [E, N]. :param batch_size: :param resample: :param seed: :return: """ assert (len(edge_indices) == len(edge_feats) == len(edge_labels) == len(edge_buckets)) train_idcs = torch.from_numpy(train_idcs) val_idcs = torch.from_numpy(val_idcs) test_idcs = torch.from_numpy(test_idcs) # Sample weights train_sample_weights = train_bin_weights[edge_buckets[train_idcs]] # Compute split into training, validation, and test set np.random.seed(seed) if resample: train_sampler = BatchSampler( WeightedRandomSampler(train_sample_weights, train_idcs.shape[0]), batch_size=batch_size, drop_last=False) train_loader = DataLoader(TensorDataset(edge_indices[train_idcs], edge_feats[train_idcs], edge_labels[train_idcs], edge_buckets[train_idcs]), batch_sampler=train_sampler) else: train_loader = DataLoader(TensorDataset(edge_indices[train_idcs], edge_feats[train_idcs], edge_labels[train_idcs], edge_buckets[train_idcs]), batch_size=batch_size, shuffle=False) val_loader = DataLoader(TensorDataset(edge_indices[val_idcs], edge_feats[val_idcs], edge_labels[val_idcs], edge_buckets[val_idcs]), batch_size=batch_size, shuffle=False) test_loader = DataLoader(TensorDataset(edge_indices[test_idcs], edge_feats[test_idcs], edge_labels[test_idcs], edge_buckets[test_idcs]), batch_size=batch_size, shuffle=False) return train_loader, val_loader, test_loader @staticmethod def _get_continuous_feature_idcs(df): continuous_feature_idcs = [] for idx, col in enumerate(df.columns): if len(df[col].unique()) > 2: continuous_feature_idcs.append(idx) return continuous_feature_idcs if __name__ == '__main__': ds = UrbanPlanningDataset(data_base_path="Data/London_high/", use_binned_transformer=True, excluded_node_feature_columns=tuple()) print("\n\nNode features") summarize_feature_matrix(ds.node_feats.numpy()) print("\n\nEdge features") summarize_feature_matrix(ds.edge_feats.numpy())

0.873336

0.560493

import numpy as np from gensim import corpora from keras.preprocessing import sequence from nltk.tokenize import TreebankWordTokenizer from sklearn.base import BaseEstimator, TransformerMixin from sklearn.utils.validation import check_is_fitted, column_or_1d __author__ = "<NAME>" __credits__ = "https://github.com/octo-technology/bdacore" __license__ = "Apache 2.0" class Text2Sequence(BaseEstimator, TransformerMixin): """ Class used to transform text into integers sequence. Parameters ---------- pad_string : string, optional (default='') string value used for padding sequence. seq_len : int, optional, (default=None) fixed sequence length used to build output sequence. If None, this length will be computed during transform operation, as the mean of all original sequences + 2 times standard deviation. tokenizer : object, optional (default=TreebankWordTokenizer()) object used to tokenize text. The class used must implements a tokenize method. stemmer : object, optional (default=None) object used to stem text. The class used must implements a stem method. Attributes ---------- dictionary_ : gensim.corpora.Dictionary maps all tokens to an unique id. dictionary_size_ : int size of the dictionary_ attribute. Examples -------- >>> import numpy as np >>> from nltk.stem.snowball import EnglishStemmer >>> from dsbox.ml.neural_networks.processing import Text2Sequence >>> X = np.array(['this is really awesome !', \ 'this is really crap !!']) >>> text2seq = Text2Sequence(stemmer=EnglishStemmer()) >>> text2seq.fit_transform(X) array([[0, 5, 3, 4, 2, 1], [5, 3, 4, 6, 1, 1]], dtype=int32) """ def __init__(self, pad_string='', tokenizer=TreebankWordTokenizer(), stemmer=None): self.pad_string = pad_string self.seq_len = None self.tokenizer = tokenizer self.stemmer = stemmer self.attr_to_check = ["dictionary_", "dictionary_size_", ] def tokenize(self, text_list): """ Takes a list of texts and return a list of tokens for each text (stemmed or not). Parameters ---------- text_list : array-like, iterable list of strings Returns ------- list of token lists """ all_tokens = [] for text in text_list: tokens = self.tokenizer.tokenize(text) if self.stemmer is not None: tokens = [self.stemmer.stem(word) for word in tokens] all_tokens.append(tokens) return all_tokens def fit(self, X, y=None): """ Fit the transformer by building the word corpus. Parameters ---------- X : array-like, iterable Collection of str or an iterable which yields str y : array-like, shape (n_samples,) No used, only here for compatibility reason Returns ------- self : object Returns an instance of self. """ x = column_or_1d(X) all_tokens = self.tokenize(x) # adding blank line for considering padding value self.dictionary_ = corpora.Dictionary([[self.pad_string]] + all_tokens) self.dictionary_size_ = len(self.dictionary_.keys()) return self def transform(self, X): """ Transform a list of texts into sequences of integers (ids), based on a known corpus. If a word is not found in the corpus built during fit operation, it will be replaced by the padding string value. Parameters ---------- X : array-like, iterable Collection of str or an iterable which yields str Returns ------- numpy array of ids sequences. """ check_is_fitted(self, self.attr_to_check) x = column_or_1d(X) new_tokens = self.tokenize(x) pad_value = self.dictionary_.token2id[self.pad_string] # transform all tokens into unique ids word_ids, word_ids_len = [], [] for doc in new_tokens: word_id = [] for word in doc: if word in self.dictionary_.token2id: word_id.append(self.dictionary_.token2id[word]) else: word_id.append(pad_value) word_ids.append(word_id) word_ids_len.append(len(word_id)) # compute the length sequence if self.seq_len is None: self.seq_len = np.round((np.mean(word_ids_len) + 2 * np.std(word_ids_len))).astype(int) return sequence.pad_sequences(np.array(word_ids), maxlen=self.seq_len, value=pad_value)

dsbox/ml/neural_networks/processing/text_classification.py

import numpy as np from gensim import corpora from keras.preprocessing import sequence from nltk.tokenize import TreebankWordTokenizer from sklearn.base import BaseEstimator, TransformerMixin from sklearn.utils.validation import check_is_fitted, column_or_1d __author__ = "<NAME>" __credits__ = "https://github.com/octo-technology/bdacore" __license__ = "Apache 2.0" class Text2Sequence(BaseEstimator, TransformerMixin): """ Class used to transform text into integers sequence. Parameters ---------- pad_string : string, optional (default='') string value used for padding sequence. seq_len : int, optional, (default=None) fixed sequence length used to build output sequence. If None, this length will be computed during transform operation, as the mean of all original sequences + 2 times standard deviation. tokenizer : object, optional (default=TreebankWordTokenizer()) object used to tokenize text. The class used must implements a tokenize method. stemmer : object, optional (default=None) object used to stem text. The class used must implements a stem method. Attributes ---------- dictionary_ : gensim.corpora.Dictionary maps all tokens to an unique id. dictionary_size_ : int size of the dictionary_ attribute. Examples -------- >>> import numpy as np >>> from nltk.stem.snowball import EnglishStemmer >>> from dsbox.ml.neural_networks.processing import Text2Sequence >>> X = np.array(['this is really awesome !', \ 'this is really crap !!']) >>> text2seq = Text2Sequence(stemmer=EnglishStemmer()) >>> text2seq.fit_transform(X) array([[0, 5, 3, 4, 2, 1], [5, 3, 4, 6, 1, 1]], dtype=int32) """ def __init__(self, pad_string='', tokenizer=TreebankWordTokenizer(), stemmer=None): self.pad_string = pad_string self.seq_len = None self.tokenizer = tokenizer self.stemmer = stemmer self.attr_to_check = ["dictionary_", "dictionary_size_", ] def tokenize(self, text_list): """ Takes a list of texts and return a list of tokens for each text (stemmed or not). Parameters ---------- text_list : array-like, iterable list of strings Returns ------- list of token lists """ all_tokens = [] for text in text_list: tokens = self.tokenizer.tokenize(text) if self.stemmer is not None: tokens = [self.stemmer.stem(word) for word in tokens] all_tokens.append(tokens) return all_tokens def fit(self, X, y=None): """ Fit the transformer by building the word corpus. Parameters ---------- X : array-like, iterable Collection of str or an iterable which yields str y : array-like, shape (n_samples,) No used, only here for compatibility reason Returns ------- self : object Returns an instance of self. """ x = column_or_1d(X) all_tokens = self.tokenize(x) # adding blank line for considering padding value self.dictionary_ = corpora.Dictionary([[self.pad_string]] + all_tokens) self.dictionary_size_ = len(self.dictionary_.keys()) return self def transform(self, X): """ Transform a list of texts into sequences of integers (ids), based on a known corpus. If a word is not found in the corpus built during fit operation, it will be replaced by the padding string value. Parameters ---------- X : array-like, iterable Collection of str or an iterable which yields str Returns ------- numpy array of ids sequences. """ check_is_fitted(self, self.attr_to_check) x = column_or_1d(X) new_tokens = self.tokenize(x) pad_value = self.dictionary_.token2id[self.pad_string] # transform all tokens into unique ids word_ids, word_ids_len = [], [] for doc in new_tokens: word_id = [] for word in doc: if word in self.dictionary_.token2id: word_id.append(self.dictionary_.token2id[word]) else: word_id.append(pad_value) word_ids.append(word_id) word_ids_len.append(len(word_id)) # compute the length sequence if self.seq_len is None: self.seq_len = np.round((np.mean(word_ids_len) + 2 * np.std(word_ids_len))).astype(int) return sequence.pad_sequences(np.array(word_ids), maxlen=self.seq_len, value=pad_value)

0.858303

0.404684

from .parser import TokenGroup, TokenElement, TokenAttribute, is_quote, is_bracket from .tokenizer import tokens from .stringify import stringify class ConvertState: __slots__ = ('inserted', 'text', 'repeat_guard', 'repeaters', 'variables', '_text_inserted') def __init__(self, text: str = None, variables={}, max_repeat=None): self.inserted = False self.text = text self.repeat_guard = max_repeat if max_repeat is not None else 1000000 self.variables = variables self.repeaters = [] self._text_inserted = False def get_text(self, pos: int): self._text_inserted = True if isinstance(self.text, list): value = self.text[pos] if pos is not None else '\n'.join(self.text) else: value = self.text or '' return value def get_variable(self, name: str): return self.variables.get(name) if self.variables else name class Abbreviation: __slots__ = ('type', 'children') def __init__(self): self.type = 'Abbreviation' self.children = [] class AbbreviationNode: __slots__ = ('type', 'name', 'value', 'repeat', 'attributes', 'children', 'self_closing') def __init__(self, node: TokenElement, state: ConvertState): self.type = 'AbbreviationNode' self.name = stringify_name(node.name, state) if node.name else None self.value = stringify_value(node.value, state) if node.value else None self.attributes = None self.children = [] self.repeat = clone_repeater(node.repeat) if node.repeat else None self.self_closing = node.self_close "Indicates current element is self-closing, e.g. should not contain closing pair" class AbbreviationAttribute: __slots__ = ('name', 'value', 'value_type', 'boolean', 'implied') def __init__(self, name: str, value: list, value_type: str, boolean=False, implied=False): self.name = name self.value = value self.value_type = value_type "Indicates type of value stored in `.value` property" self.boolean = boolean "Attribute is boolean (e.g.name equals value)" self.implied = implied "Attribute is implied (e.g.must be outputted only if contains non-null value)" def copy(self): return AbbreviationAttribute(self.name, self.value, self.value_type, self.boolean, self.implied) def convert(abbr: TokenGroup, options={}): "Converts given token-based abbreviation into simplified and unrolled node-based abbreviation" text = options.get('text') state = ConvertState(text, options.get('variables'), options.get('max_repeat')) result = Abbreviation() result.children = convert_group(abbr, state) if text is not None and not state._text_inserted: # Text given but no implicitly repeated elements: insert it into deepest child deepest = deepest_node(result.children[-1]) if deepest: tx = '\n'.join(text) if isinstance(text, list) else text or '' insert_text(deepest, tx) return result def convert_statement(node: TokenElement, state: ConvertState): result = [] if node.repeat: # Node is repeated: we should create copies of given node # and supply context token with actual repeater state original = node.repeat repeat = clone_repeater(node.repeat) if repeat.implicit and isinstance(state.text, list): repeat.count = len(state.text) else: repeat.count = repeat.count or 1 state.repeaters.append(repeat) i = 0 while i < repeat.count: repeat.value = i node.repeat = repeat items = convert_group(node, state) if is_group(node) else convert_element(node, state) if repeat.implicit and not state.inserted: # It’s an implicit repeater but no repeater placeholders found inside, # we should insert text into deepest node target = items[-1] deepest = deepest_node(target) if target else None if deepest: insert_text(deepest, state.get_text(repeat.value)) result += items # We should output at least one repeated item even if it’s reached # repeat limit state.repeat_guard -= 1 if state.repeat_guard <= 0: break i += 1 state.repeaters.pop() node.repeat = original if repeat.implicit: state.inserted = True else: result += convert_group(node, state) if is_group(node) else convert_element(node, state) return result def convert_element(node: TokenElement, state: ConvertState): elem = AbbreviationNode(node, state) result = [elem] for child in node.elements: elem.children += convert_statement(child, state) if node.attributes: elem.attributes = [convert_attribute(attr, state) for attr in node.attributes] # In case if current node is a text-only snippet without fields, we should # put all children as siblings if not elem.name and elem.attributes is None and elem.value and not some(elem.value, is_field): result += elem.children elem.children = [] return result def convert_group(node: TokenGroup, state: ConvertState): result = [] for child in node.elements: result += convert_statement(child, state) if node.repeat: result = attach_repeater(result, node.repeat) return result def convert_attribute(node: TokenAttribute, state: ConvertState): attr = create_attribute(node, state) if node.value: tokens = node.value[:] if is_quote(tokens[0]): # It’s a quoted value: remove quotes from output but mark attribute # value as quoted quote = tokens.pop(0) if len(tokens) and tokens[-1].type == quote.type: tokens.pop() attr.value_type = 'singleQuote' if quote.single else 'doubleQuote' elif is_bracket(tokens[0], 'expression', True): # Value is expression: remove brackets but mark value type attr.value_type = 'expression' tokens.pop(0) if tokens and is_bracket(tokens[-1], 'expression', False): tokens.pop() attr.value = stringify_value(tokens, state) return attr def create_attribute(node: TokenAttribute, state: ConvertState): name = stringify_name(node.name, state) if node.name else None value_type = 'expression' if node.expression else 'raw' boolean = False implied = False if name: if name[-1] == '.': boolean = True name = name[0:-1] if name[0] == '!': implied = True name = name[1:] return AbbreviationAttribute(name, None, value_type, boolean, implied) def stringify_name(tokens: list, state: ConvertState): "Converts given token list to string" return ''.join([stringify(token, state) for token in tokens]) def stringify_value(token_list: list, state: ConvertState): "Converts given token list to value list" result = [] accum = [] for token in token_list: if is_field(token): # We should keep original fields in output since some editors has their # own syntax for field or doesn’t support fields at all so we should # capture actual field location in output stream if accum: result.append(''.join(accum)) accum = [] result.append(token) else: accum.append(stringify(token, state)) if (accum): result.append(''.join(accum)) return result def is_group(node): return isinstance(node, TokenGroup) def is_field(token): return isinstance(token, tokens.Field) and token.index is not None def deepest_node(node: AbbreviationNode): return deepest_node(node.children[-1]) if node.children else node def insert_text(node: AbbreviationNode, text: str): if node.value: last_token = node.value[-1] if isinstance(last_token, str): node.value[-1] += text else: node.value.append(text) else: node.value = [text] def attach_repeater(items: list, repeater: tokens.Repeater): for item in items: if not item.repeat: item.repeat = clone_repeater(repeater) return items def clone_repeater(repeater: tokens.Repeater): return tokens.Repeater(repeater.count, repeater.value, repeater.implicit) def some(items: list, fn: callable): for item in items: if fn(item): return True return False

emmet/abbreviation/convert.py

from .parser import TokenGroup, TokenElement, TokenAttribute, is_quote, is_bracket from .tokenizer import tokens from .stringify import stringify class ConvertState: __slots__ = ('inserted', 'text', 'repeat_guard', 'repeaters', 'variables', '_text_inserted') def __init__(self, text: str = None, variables={}, max_repeat=None): self.inserted = False self.text = text self.repeat_guard = max_repeat if max_repeat is not None else 1000000 self.variables = variables self.repeaters = [] self._text_inserted = False def get_text(self, pos: int): self._text_inserted = True if isinstance(self.text, list): value = self.text[pos] if pos is not None else '\n'.join(self.text) else: value = self.text or '' return value def get_variable(self, name: str): return self.variables.get(name) if self.variables else name class Abbreviation: __slots__ = ('type', 'children') def __init__(self): self.type = 'Abbreviation' self.children = [] class AbbreviationNode: __slots__ = ('type', 'name', 'value', 'repeat', 'attributes', 'children', 'self_closing') def __init__(self, node: TokenElement, state: ConvertState): self.type = 'AbbreviationNode' self.name = stringify_name(node.name, state) if node.name else None self.value = stringify_value(node.value, state) if node.value else None self.attributes = None self.children = [] self.repeat = clone_repeater(node.repeat) if node.repeat else None self.self_closing = node.self_close "Indicates current element is self-closing, e.g. should not contain closing pair" class AbbreviationAttribute: __slots__ = ('name', 'value', 'value_type', 'boolean', 'implied') def __init__(self, name: str, value: list, value_type: str, boolean=False, implied=False): self.name = name self.value = value self.value_type = value_type "Indicates type of value stored in `.value` property" self.boolean = boolean "Attribute is boolean (e.g.name equals value)" self.implied = implied "Attribute is implied (e.g.must be outputted only if contains non-null value)" def copy(self): return AbbreviationAttribute(self.name, self.value, self.value_type, self.boolean, self.implied) def convert(abbr: TokenGroup, options={}): "Converts given token-based abbreviation into simplified and unrolled node-based abbreviation" text = options.get('text') state = ConvertState(text, options.get('variables'), options.get('max_repeat')) result = Abbreviation() result.children = convert_group(abbr, state) if text is not None and not state._text_inserted: # Text given but no implicitly repeated elements: insert it into deepest child deepest = deepest_node(result.children[-1]) if deepest: tx = '\n'.join(text) if isinstance(text, list) else text or '' insert_text(deepest, tx) return result def convert_statement(node: TokenElement, state: ConvertState): result = [] if node.repeat: # Node is repeated: we should create copies of given node # and supply context token with actual repeater state original = node.repeat repeat = clone_repeater(node.repeat) if repeat.implicit and isinstance(state.text, list): repeat.count = len(state.text) else: repeat.count = repeat.count or 1 state.repeaters.append(repeat) i = 0 while i < repeat.count: repeat.value = i node.repeat = repeat items = convert_group(node, state) if is_group(node) else convert_element(node, state) if repeat.implicit and not state.inserted: # It’s an implicit repeater but no repeater placeholders found inside, # we should insert text into deepest node target = items[-1] deepest = deepest_node(target) if target else None if deepest: insert_text(deepest, state.get_text(repeat.value)) result += items # We should output at least one repeated item even if it’s reached # repeat limit state.repeat_guard -= 1 if state.repeat_guard <= 0: break i += 1 state.repeaters.pop() node.repeat = original if repeat.implicit: state.inserted = True else: result += convert_group(node, state) if is_group(node) else convert_element(node, state) return result def convert_element(node: TokenElement, state: ConvertState): elem = AbbreviationNode(node, state) result = [elem] for child in node.elements: elem.children += convert_statement(child, state) if node.attributes: elem.attributes = [convert_attribute(attr, state) for attr in node.attributes] # In case if current node is a text-only snippet without fields, we should # put all children as siblings if not elem.name and elem.attributes is None and elem.value and not some(elem.value, is_field): result += elem.children elem.children = [] return result def convert_group(node: TokenGroup, state: ConvertState): result = [] for child in node.elements: result += convert_statement(child, state) if node.repeat: result = attach_repeater(result, node.repeat) return result def convert_attribute(node: TokenAttribute, state: ConvertState): attr = create_attribute(node, state) if node.value: tokens = node.value[:] if is_quote(tokens[0]): # It’s a quoted value: remove quotes from output but mark attribute # value as quoted quote = tokens.pop(0) if len(tokens) and tokens[-1].type == quote.type: tokens.pop() attr.value_type = 'singleQuote' if quote.single else 'doubleQuote' elif is_bracket(tokens[0], 'expression', True): # Value is expression: remove brackets but mark value type attr.value_type = 'expression' tokens.pop(0) if tokens and is_bracket(tokens[-1], 'expression', False): tokens.pop() attr.value = stringify_value(tokens, state) return attr def create_attribute(node: TokenAttribute, state: ConvertState): name = stringify_name(node.name, state) if node.name else None value_type = 'expression' if node.expression else 'raw' boolean = False implied = False if name: if name[-1] == '.': boolean = True name = name[0:-1] if name[0] == '!': implied = True name = name[1:] return AbbreviationAttribute(name, None, value_type, boolean, implied) def stringify_name(tokens: list, state: ConvertState): "Converts given token list to string" return ''.join([stringify(token, state) for token in tokens]) def stringify_value(token_list: list, state: ConvertState): "Converts given token list to value list" result = [] accum = [] for token in token_list: if is_field(token): # We should keep original fields in output since some editors has their # own syntax for field or doesn’t support fields at all so we should # capture actual field location in output stream if accum: result.append(''.join(accum)) accum = [] result.append(token) else: accum.append(stringify(token, state)) if (accum): result.append(''.join(accum)) return result def is_group(node): return isinstance(node, TokenGroup) def is_field(token): return isinstance(token, tokens.Field) and token.index is not None def deepest_node(node: AbbreviationNode): return deepest_node(node.children[-1]) if node.children else node def insert_text(node: AbbreviationNode, text: str): if node.value: last_token = node.value[-1] if isinstance(last_token, str): node.value[-1] += text else: node.value.append(text) else: node.value = [text] def attach_repeater(items: list, repeater: tokens.Repeater): for item in items: if not item.repeat: item.repeat = clone_repeater(repeater) return items def clone_repeater(repeater: tokens.Repeater): return tokens.Repeater(repeater.count, repeater.value, repeater.implicit) def some(items: list, fn: callable): for item in items: if fn(item): return True return False

0.674908

0.231386

from rdkit import Chem import numpy as np class SmilesVectorizer(object): """SMILES vectorizer and devectorizer, with support for SMILES enumeration (atom order randomization) as data augmentation :parameter charset: string containing the characters for the vectorization can also be generated via the .fit() method :parameter pad: Length of the vectorization :parameter leftpad: Add spaces to the left of the SMILES :parameter isomericSmiles: Generate SMILES containing information about stereogenic centers :parameter augment: Enumerate the SMILES during transform :parameter canonical: use canonical SMILES during transform (overrides enum) :parameter binary: Use RDKit binary strings instead of molecule objects """ def __init__(self, charset = '@C)(=cOn1S2/H[N]\\', pad=5, maxlength=120, leftpad=True, isomericSmiles=True, augment=True, canonical=False, startchar = '^', endchar = '$', unknownchar = '?', binary=False): #Special Characters self.startchar = startchar self.endchar = endchar self.unknownchar = unknownchar #Vectorization and SMILES options self.binary = binary self.leftpad = leftpad self.isomericSmiles = isomericSmiles self.augment = augment self.canonical = canonical self._pad = pad self._maxlength = maxlength #The characterset self._charset = None self.charset = charset #Calculate the dimensions self.setdims() @property def charset(self): return self._charset @charset.setter def charset(self, charset): #Ensure start and endchars are in the charset for char in [self.startchar, self.endchar, self.unknownchar]: if char not in charset: charset = charset + char #Set the hidden properties self._charset = charset self._charlen = len(charset) self._char_to_int = dict((c,i) for i,c in enumerate(charset)) self._int_to_char = dict((i,c) for i,c in enumerate(charset)) self.setdims() @property def maxlength(self): return self._maxlength @maxlength.setter def maxlength(self, maxlength): self._maxlength = maxlength self.setdims() @property def pad(self): return self._pad @pad.setter def pad(self, pad): self._pad = pad self.setdims() def setdims(self): """Calculates and sets the output dimensions of the vectorized molecules from the current settings""" self.dims = (self.maxlength + self.pad, self._charlen) def fit(self, mols, extra_chars=[]): """Performs extraction of the charset and length of a SMILES datasets and sets self.maxlength and self.charset :parameter smiles: Numpy array or Pandas series containing smiles as strings :parameter extra_chars: List of extra chars to add to the charset (e.g. "\\\\" when "/" is present) """ smiles = [Chem.MolToSmiles(mol) for mol in mols] charset = set("".join(list(smiles))) #Is there a smarter way when the list of SMILES is HUGE! self.charset = "".join(charset.union(set(extra_chars))) self.maxlength = max([len(smile) for smile in smiles]) def randomize_smiles(self, smiles): """Perform a randomization of a SMILES string must be RDKit sanitizable""" mol = Chem.MolFromSmiles(smiles) nmol = self.randomize_mol(mol) return Chem.MolToSmiles(nmol, canonical=self.canonical, isomericSmiles=self.isomericSmiles) def randomize_mol(self, mol): """Performs a randomization of the atom order of an RDKit molecule""" ans = list(range(mol.GetNumAtoms())) np.random.shuffle(ans) return Chem.RenumberAtoms(mol,ans) def transform(self, mols, augment=None, canonical=None): """Perform an enumeration (atom order randomization) and vectorization of a Numpy array of RDkit molecules :parameter mols: The RDKit molecules to transform in a list or array :parameter augment: Override the objects .augment setting :parameter canonical: Override the objects .canonical setting :output: Numpy array with the vectorized molecules with shape [batch, maxlength+pad, charset] """ #TODO make it possible to use both SMILES, RDKit mols and RDKit binary strings in input one_hot = np.zeros([len(mols)] + list(self.dims), dtype=np.int8) #Possibl override object settings if augment is None: augment = self.augment if canonical is None: canonical = self.canonical for i,mol in enumerate(mols): #Fast convert from RDKit binary if self.binary: mol = Chem.Mol(mol) if augment: mol = self.randomize_mol(mol) ss = Chem.MolToSmiles(mol, canonical=canonical, isomericSmiles=self.isomericSmiles) #TODO, Improvement make it robust to too long SMILES strings #TODO, Improvement make a "jitter", with random offset within the possible frame #TODO, Improvement make it report to many "?"'s l = len(ss) if self.leftpad: offset = self.dims[0]-l-1 else: offset = 1 for j,c in enumerate(ss): charidx = self._char_to_int.get(c, self._char_to_int[self.unknownchar]) if j+offset >= one_hot.shape[1]: print('I am here') one_hot[i,j+offset,charidx] = 1 #Pad the start one_hot[i,offset-1,self._char_to_int[self.startchar]] = 1 #Pad the end one_hot[i,offset+l:,self._char_to_int[self.endchar]] = 1 #Pad the space in front of start (Could this lead to funky effects during sampling?) #one_hot[i,:offset-1,self._char_to_int[self.endchar]] = 1 return one_hot def reverse_transform(self, vect, strip=True): """ Performs a conversion of a vectorized SMILES to a SMILES strings charset must be the same as used for vectorization. :parameter vect: Numpy array of vectorized SMILES. :parameter strip: Strip start and end tokens from the SMILES string """ #TODO make it possible to take a single vectorized molecule, not a list smiles = [] for v in vect: #mask v v=v[v.sum(axis=1)==1] #Find one hot encoded index with argmax, translate to char and join to string smile = "".join(self._int_to_char[i] for i in v.argmax(axis=1)) if strip: smile = smile.strip(self.startchar + self.endchar) smiles.append(smile) return np.array(smiles) from rdkit import DataStructs from rdkit.Chem import AllChem class HashedMorganVectorizer(object): def __init__(self, radius=2, bits=2048, augment=None): self.bits = bits self.radius = radius self.augment = augment #Not used self.dims = (bits,) self.keys = None def transform_mol(self, mol): """ transforms the molecule into a numpy bit array with the morgan bits :parameter mol: the RDKit molecule to be transformed """ fp = AllChem.GetMorganFingerprintAsBitVect(mol,self.radius,nBits=self.bits) arr = np.zeros((self.bits,)) DataStructs.ConvertToNumpyArray(fp, arr) return arr def transform(self, mols): """Transforms a list or array of RDKit molecules into an array with the Morgan bits :parameter mols: list or array of RDKit molecules """ arr = np.zeros((len(mols), self.bits)) for i, mol in enumerate(mols): arr[i,:] = self.transform_mol(mol) return arr class MorganDictVectorizer(object): def __init__(self, radius=2, augment=None): self.radius = radius self.augment = augment #Not used self.dims = None def fit(self, mols): """Analyses the molecules and creates the key index for the creation of the dense array""" keys=set() for mol in mols: fp = AllChem.GetMorganFingerprint(mol,self.radius) keys.update(fp.GetNonzeroElements().keys()) keys = list(keys) keys.sort() self.keys= np.array(keys) self.dims = len(self.keys) def transform_mol(self, mol, misses=False): """ transforms the mol into a dense array using the fitted keys as index :parameter mol: the RDKit molecule to be transformed :parameter misses: wheter to return the number of key misses for the molecule """ assert type(self.keys) is np.ndarray, "keys are not defined or is not an np.array, has the .fit(mols) function been used?" #Get fingerprint as a dictionary fp = AllChem.GetMorganFingerprint(mol,self.radius) fp_d = fp.GetNonzeroElements() #Prepare the array, and set the values #TODO is there a way to vectorize and speed up this? arr = np.zeros((self.dims,)) _misses = 0 for key, value in fp_d.items(): if key in self.keys: arr[self.keys == key] = value else: _misses = _misses + 1 if misses: return arr, _misses else: return arr def transform(self, mols, misses=False): """Transforms a list or array of RDKit molecules into a dense array using the key dictionary (see .fit()) :parameter mols: list or array of RDKit molecules :parameter misses: Wheter to return the number of key misses for each molecule """ arr = np.zeros((len(mols), self.dims)) if misses: _misses = np.zeros((len(mols),1)) for i, mol in enumerate(mols): arr[i,:], _misses[i] = self.transform_mol(mol, misses=misses) return arr, _misses else: for i, mol in enumerate(mols): arr[i,:] = self.transform_mol(mol, misses=False) return arr

ddc_pub/vectorizers.py

from rdkit import Chem import numpy as np class SmilesVectorizer(object): """SMILES vectorizer and devectorizer, with support for SMILES enumeration (atom order randomization) as data augmentation :parameter charset: string containing the characters for the vectorization can also be generated via the .fit() method :parameter pad: Length of the vectorization :parameter leftpad: Add spaces to the left of the SMILES :parameter isomericSmiles: Generate SMILES containing information about stereogenic centers :parameter augment: Enumerate the SMILES during transform :parameter canonical: use canonical SMILES during transform (overrides enum) :parameter binary: Use RDKit binary strings instead of molecule objects """ def __init__(self, charset = '@C)(=cOn1S2/H[N]\\', pad=5, maxlength=120, leftpad=True, isomericSmiles=True, augment=True, canonical=False, startchar = '^', endchar = '$', unknownchar = '?', binary=False): #Special Characters self.startchar = startchar self.endchar = endchar self.unknownchar = unknownchar #Vectorization and SMILES options self.binary = binary self.leftpad = leftpad self.isomericSmiles = isomericSmiles self.augment = augment self.canonical = canonical self._pad = pad self._maxlength = maxlength #The characterset self._charset = None self.charset = charset #Calculate the dimensions self.setdims() @property def charset(self): return self._charset @charset.setter def charset(self, charset): #Ensure start and endchars are in the charset for char in [self.startchar, self.endchar, self.unknownchar]: if char not in charset: charset = charset + char #Set the hidden properties self._charset = charset self._charlen = len(charset) self._char_to_int = dict((c,i) for i,c in enumerate(charset)) self._int_to_char = dict((i,c) for i,c in enumerate(charset)) self.setdims() @property def maxlength(self): return self._maxlength @maxlength.setter def maxlength(self, maxlength): self._maxlength = maxlength self.setdims() @property def pad(self): return self._pad @pad.setter def pad(self, pad): self._pad = pad self.setdims() def setdims(self): """Calculates and sets the output dimensions of the vectorized molecules from the current settings""" self.dims = (self.maxlength + self.pad, self._charlen) def fit(self, mols, extra_chars=[]): """Performs extraction of the charset and length of a SMILES datasets and sets self.maxlength and self.charset :parameter smiles: Numpy array or Pandas series containing smiles as strings :parameter extra_chars: List of extra chars to add to the charset (e.g. "\\\\" when "/" is present) """ smiles = [Chem.MolToSmiles(mol) for mol in mols] charset = set("".join(list(smiles))) #Is there a smarter way when the list of SMILES is HUGE! self.charset = "".join(charset.union(set(extra_chars))) self.maxlength = max([len(smile) for smile in smiles]) def randomize_smiles(self, smiles): """Perform a randomization of a SMILES string must be RDKit sanitizable""" mol = Chem.MolFromSmiles(smiles) nmol = self.randomize_mol(mol) return Chem.MolToSmiles(nmol, canonical=self.canonical, isomericSmiles=self.isomericSmiles) def randomize_mol(self, mol): """Performs a randomization of the atom order of an RDKit molecule""" ans = list(range(mol.GetNumAtoms())) np.random.shuffle(ans) return Chem.RenumberAtoms(mol,ans) def transform(self, mols, augment=None, canonical=None): """Perform an enumeration (atom order randomization) and vectorization of a Numpy array of RDkit molecules :parameter mols: The RDKit molecules to transform in a list or array :parameter augment: Override the objects .augment setting :parameter canonical: Override the objects .canonical setting :output: Numpy array with the vectorized molecules with shape [batch, maxlength+pad, charset] """ #TODO make it possible to use both SMILES, RDKit mols and RDKit binary strings in input one_hot = np.zeros([len(mols)] + list(self.dims), dtype=np.int8) #Possibl override object settings if augment is None: augment = self.augment if canonical is None: canonical = self.canonical for i,mol in enumerate(mols): #Fast convert from RDKit binary if self.binary: mol = Chem.Mol(mol) if augment: mol = self.randomize_mol(mol) ss = Chem.MolToSmiles(mol, canonical=canonical, isomericSmiles=self.isomericSmiles) #TODO, Improvement make it robust to too long SMILES strings #TODO, Improvement make a "jitter", with random offset within the possible frame #TODO, Improvement make it report to many "?"'s l = len(ss) if self.leftpad: offset = self.dims[0]-l-1 else: offset = 1 for j,c in enumerate(ss): charidx = self._char_to_int.get(c, self._char_to_int[self.unknownchar]) if j+offset >= one_hot.shape[1]: print('I am here') one_hot[i,j+offset,charidx] = 1 #Pad the start one_hot[i,offset-1,self._char_to_int[self.startchar]] = 1 #Pad the end one_hot[i,offset+l:,self._char_to_int[self.endchar]] = 1 #Pad the space in front of start (Could this lead to funky effects during sampling?) #one_hot[i,:offset-1,self._char_to_int[self.endchar]] = 1 return one_hot def reverse_transform(self, vect, strip=True): """ Performs a conversion of a vectorized SMILES to a SMILES strings charset must be the same as used for vectorization. :parameter vect: Numpy array of vectorized SMILES. :parameter strip: Strip start and end tokens from the SMILES string """ #TODO make it possible to take a single vectorized molecule, not a list smiles = [] for v in vect: #mask v v=v[v.sum(axis=1)==1] #Find one hot encoded index with argmax, translate to char and join to string smile = "".join(self._int_to_char[i] for i in v.argmax(axis=1)) if strip: smile = smile.strip(self.startchar + self.endchar) smiles.append(smile) return np.array(smiles) from rdkit import DataStructs from rdkit.Chem import AllChem class HashedMorganVectorizer(object): def __init__(self, radius=2, bits=2048, augment=None): self.bits = bits self.radius = radius self.augment = augment #Not used self.dims = (bits,) self.keys = None def transform_mol(self, mol): """ transforms the molecule into a numpy bit array with the morgan bits :parameter mol: the RDKit molecule to be transformed """ fp = AllChem.GetMorganFingerprintAsBitVect(mol,self.radius,nBits=self.bits) arr = np.zeros((self.bits,)) DataStructs.ConvertToNumpyArray(fp, arr) return arr def transform(self, mols): """Transforms a list or array of RDKit molecules into an array with the Morgan bits :parameter mols: list or array of RDKit molecules """ arr = np.zeros((len(mols), self.bits)) for i, mol in enumerate(mols): arr[i,:] = self.transform_mol(mol) return arr class MorganDictVectorizer(object): def __init__(self, radius=2, augment=None): self.radius = radius self.augment = augment #Not used self.dims = None def fit(self, mols): """Analyses the molecules and creates the key index for the creation of the dense array""" keys=set() for mol in mols: fp = AllChem.GetMorganFingerprint(mol,self.radius) keys.update(fp.GetNonzeroElements().keys()) keys = list(keys) keys.sort() self.keys= np.array(keys) self.dims = len(self.keys) def transform_mol(self, mol, misses=False): """ transforms the mol into a dense array using the fitted keys as index :parameter mol: the RDKit molecule to be transformed :parameter misses: wheter to return the number of key misses for the molecule """ assert type(self.keys) is np.ndarray, "keys are not defined or is not an np.array, has the .fit(mols) function been used?" #Get fingerprint as a dictionary fp = AllChem.GetMorganFingerprint(mol,self.radius) fp_d = fp.GetNonzeroElements() #Prepare the array, and set the values #TODO is there a way to vectorize and speed up this? arr = np.zeros((self.dims,)) _misses = 0 for key, value in fp_d.items(): if key in self.keys: arr[self.keys == key] = value else: _misses = _misses + 1 if misses: return arr, _misses else: return arr def transform(self, mols, misses=False): """Transforms a list or array of RDKit molecules into a dense array using the key dictionary (see .fit()) :parameter mols: list or array of RDKit molecules :parameter misses: Wheter to return the number of key misses for each molecule """ arr = np.zeros((len(mols), self.dims)) if misses: _misses = np.zeros((len(mols),1)) for i, mol in enumerate(mols): arr[i,:], _misses[i] = self.transform_mol(mol, misses=misses) return arr, _misses else: for i, mol in enumerate(mols): arr[i,:] = self.transform_mol(mol, misses=False) return arr

0.71602

0.571049

import re from os import path from setuptools import setup from codecs import open here = path.abspath(path.dirname(__file__)) # Get the long description from the README file with open(path.join(here, 'README.md'), encoding='utf-8') as f: long_description = f.read() def read(*parts): return open(path.join(here, *parts), 'r').read() def find_version(*file_paths): version_file = read(*file_paths) version_match = re.search(r"^version = ['\"]([^'\"]*)['\"]", version_file, re.M) if version_match: return version_match.group(1) raise RuntimeError("Unable to find version string.") setup( name='jira-tracker', version=find_version('jira_tracker','__init__.py'), description='Simple Jira story point tracking tool', long_description=long_description, long_description_content_type="text/markdown", # The project's main homepage. url='https://github.com/josephbmanley/jira-tracker', # Author details author='<NAME>', author_email='<EMAIL>', # Choose your license license='MIT', # See https://pypi.org/classifiers/ classifiers=[ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'Intended Audience :: Information Technology', 'Intended Audience :: System Administrators', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9' ], keywords='Jira', packages=['jira_tracker'], install_requires=['argparse','jira','pyyaml','wheel'], package_data={}, entry_points={ 'console_scripts' : [ 'jira-tracker=jira_tracker:main' ] } )

setup.py

import re from os import path from setuptools import setup from codecs import open here = path.abspath(path.dirname(__file__)) # Get the long description from the README file with open(path.join(here, 'README.md'), encoding='utf-8') as f: long_description = f.read() def read(*parts): return open(path.join(here, *parts), 'r').read() def find_version(*file_paths): version_file = read(*file_paths) version_match = re.search(r"^version = ['\"]([^'\"]*)['\"]", version_file, re.M) if version_match: return version_match.group(1) raise RuntimeError("Unable to find version string.") setup( name='jira-tracker', version=find_version('jira_tracker','__init__.py'), description='Simple Jira story point tracking tool', long_description=long_description, long_description_content_type="text/markdown", # The project's main homepage. url='https://github.com/josephbmanley/jira-tracker', # Author details author='<NAME>', author_email='<EMAIL>', # Choose your license license='MIT', # See https://pypi.org/classifiers/ classifiers=[ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'Intended Audience :: Information Technology', 'Intended Audience :: System Administrators', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9' ], keywords='Jira', packages=['jira_tracker'], install_requires=['argparse','jira','pyyaml','wheel'], package_data={}, entry_points={ 'console_scripts' : [ 'jira-tracker=jira_tracker:main' ] } )

0.47025

0.160562

from __future__ import unicode_literals from .imapclient_test import IMAPClientTest from .util import Mock class TestFolderStatus(IMAPClientTest): def test_basic(self): self.client._imap.status.return_value = ( "OK", [b"foo (MESSAGES 3 RECENT 0 UIDNEXT 4 UIDVALIDITY 1435636895 UNSEEN 0)"], ) out = self.client.folder_status("foo") self.client._imap.status.assert_called_once_with( b'"foo"', "(MESSAGES RECENT UIDNEXT UIDVALIDITY UNSEEN)" ) self.assertDictEqual( out, { b"MESSAGES": 3, b"RECENT": 0, b"UIDNEXT": 4, b"UIDVALIDITY": 1435636895, b"UNSEEN": 0, }, ) def test_literal(self): self.client._imap.status.return_value = ( "OK", [(b"{3}", b"foo"), b" (UIDNEXT 4)"], ) out = self.client.folder_status("foo", ["UIDNEXT"]) self.client._imap.status.assert_called_once_with(b'"foo"', "(UIDNEXT)") self.assertDictEqual(out, {b"UIDNEXT": 4}) def test_extra_response(self): # In production, we've seen folder names containing spaces come back # like this and be broken into two components in the tuple. server_response = [b"My files (UIDNEXT 24369)"] mock = Mock(return_value=server_response) self.client._command_and_check = mock resp = self.client.folder_status("My files", ["UIDNEXT"]) self.assertEqual(resp, {b"UIDNEXT": 24369}) # We've also seen the response contain mailboxes we didn't # ask for. In all known cases, the desired mailbox is last. server_response = [b"sent (UIDNEXT 123)\nINBOX (UIDNEXT 24369)"] mock = Mock(return_value=server_response) self.client._command_and_check = mock resp = self.client.folder_status("INBOX", ["UIDNEXT"]) self.assertEqual(resp, {b"UIDNEXT": 24369})

tests/test_folder_status.py

from __future__ import unicode_literals from .imapclient_test import IMAPClientTest from .util import Mock class TestFolderStatus(IMAPClientTest): def test_basic(self): self.client._imap.status.return_value = ( "OK", [b"foo (MESSAGES 3 RECENT 0 UIDNEXT 4 UIDVALIDITY 1435636895 UNSEEN 0)"], ) out = self.client.folder_status("foo") self.client._imap.status.assert_called_once_with( b'"foo"', "(MESSAGES RECENT UIDNEXT UIDVALIDITY UNSEEN)" ) self.assertDictEqual( out, { b"MESSAGES": 3, b"RECENT": 0, b"UIDNEXT": 4, b"UIDVALIDITY": 1435636895, b"UNSEEN": 0, }, ) def test_literal(self): self.client._imap.status.return_value = ( "OK", [(b"{3}", b"foo"), b" (UIDNEXT 4)"], ) out = self.client.folder_status("foo", ["UIDNEXT"]) self.client._imap.status.assert_called_once_with(b'"foo"', "(UIDNEXT)") self.assertDictEqual(out, {b"UIDNEXT": 4}) def test_extra_response(self): # In production, we've seen folder names containing spaces come back # like this and be broken into two components in the tuple. server_response = [b"My files (UIDNEXT 24369)"] mock = Mock(return_value=server_response) self.client._command_and_check = mock resp = self.client.folder_status("My files", ["UIDNEXT"]) self.assertEqual(resp, {b"UIDNEXT": 24369}) # We've also seen the response contain mailboxes we didn't # ask for. In all known cases, the desired mailbox is last. server_response = [b"sent (UIDNEXT 123)\nINBOX (UIDNEXT 24369)"] mock = Mock(return_value=server_response) self.client._command_and_check = mock resp = self.client.folder_status("INBOX", ["UIDNEXT"]) self.assertEqual(resp, {b"UIDNEXT": 24369})

0.670177

0.291428

from datetime import datetime from math import floor import requests from requests.exceptions import HTTPError import json import schedule # https://schedule.readthedocs.io/en/stable/ import time def stahniOdjezdy(limit=1000): # https://golemioapi.docs.apiary.io/#reference/public-transport/departure-boards/get-departure-board?console=1 # https://api.golemio.cz/api-keys/dashboard with open('APIkey.secret','r') as f: accessToken=f.read() headers = { 'Content-Type': 'application/json; charset=utf-8', 'x-access-token': accessToken } minutPred = 0 minutPo = 60*24*7 id1 = 'U861Z1P' # VosmikovychA id2 = 'U861Z2P' # VosmikovychB url1 = 'https://api.golemio.cz/v2/departureboards/' url1+= '?ids=' + id1 + '&limit=' + str(limit) url1 += '&minutesBefore='+str(minutPred)+'&minutesAfter='+str(minutPo) url2 = 'https://api.golemio.cz/v2/departureboards/' url2+= '?ids=' + id2 + '&limit='+str(limit) url2 += '&minutesBefore='+str(minutPred)+'&minutesAfter='+str(minutPo) try: # api gives max 100 entries, even tho stated limit is 1000 response1 = requests.get(url1, headers=headers) response1.raise_for_status() # access JSOn content response2 = requests.get(url2, headers=headers) response2.raise_for_status() vystup = response2.json()+response1.json() return(vystup) except HTTPError as http_err: print('HTTP error occurred:',http_err) except Exception as err: print('Other error occurred:',err) def ulozOdjezdy(): try: odjezdy = stahniOdjezdy() zapis = json.dumps(odjezdy) with open("odjezdyCache.txt",'w',encoding = 'utf-8') as f: f.write(zapis) except Exception as err: print('ulozOdjezd error occurred:', err) def vypisOdjezdy(): odjezdNejdrive = 0 # min pocetOdjezduMax = 20 dolu = [] nahoru = [] doluUq = [] nahoruUq = [] try: vystup = stahniOdjezdy(limit=pocetOdjezduMax*2) except: print("Error: stazeni odjezdu pro vypis") try: with open("odjezdyCache.txt",'r',encoding = 'utf-8') as fCt: vystup = json.load(fCt) except: print("Error: nacteni odjezdu ze souboru") pocetOdjezdu = 0 for polozka in vystup: # deleting ':' in timezone polozkaCas = datetime.strptime(polozka['departure_timestamp']['predicted'].replace("+01:00","+0100"),"%Y-%m-%dT%H:%M:%S.%f%z") aktualniCas = datetime.now(polozkaCas.tzinfo) casDoOdjezdu = polozkaCas-aktualniCas linka = polozka["route"]["short_name"] if len(linka) < 2: linka=" "+linka if abs(casDoOdjezdu.total_seconds())<60: doOdjezdu = str(floor(casDoOdjezdu.total_seconds()))+"s" doOdjezduVal = floor(casDoOdjezdu.total_seconds()) else: doOdjezdu = str(floor(casDoOdjezdu.total_seconds()/60))+"m" doOdjezduVal = floor(casDoOdjezdu.total_seconds()/60) if doOdjezduVal>0: doOdjezdu = " "+doOdjezdu if abs(doOdjezduVal)<10: doOdjezdu = " "+doOdjezdu odjezd = linka+" "+doOdjezdu+" |" if casDoOdjezdu.total_seconds()/60<odjezdNejdrive: continue if polozka['stop']['platform_code']=="A": dolu.append(odjezd) doluUq.append({linka:odjezd}) else: nahoru.append(odjezd) nahoruUq.append({linka:odjezd}) pocetOdjezdu=pocetOdjezdu+1 print("nahoru:",len(nahoru)) print("dolu:",len(dolu)) # print("\u25BC |",*dolu) # print("\u25B2 |",*nahoru) txt = "\u25BC |"+dolu[0]+dolu[1]+dolu[2]+"\n\u25BC |"+dolu[3]+dolu[4]+dolu[5] txt = txt+"\n\u25B2 |"+nahoru[0]+nahoru[1]+nahoru[2]+"\n\u25B2 |"+nahoru[3]+nahoru[4]+nahoru[5] print(txt) try: ulozOdjezdy() schedule.every(10).seconds.do(vypisOdjezdy) schedule.every(1).hours.do(ulozOdjezdy) while 1: schedule.run_pending() time.sleep(1) finally: schedule.clear()

PyPragueDepartures.py

from datetime import datetime from math import floor import requests from requests.exceptions import HTTPError import json import schedule # https://schedule.readthedocs.io/en/stable/ import time def stahniOdjezdy(limit=1000): # https://golemioapi.docs.apiary.io/#reference/public-transport/departure-boards/get-departure-board?console=1 # https://api.golemio.cz/api-keys/dashboard with open('APIkey.secret','r') as f: accessToken=f.read() headers = { 'Content-Type': 'application/json; charset=utf-8', 'x-access-token': accessToken } minutPred = 0 minutPo = 60*24*7 id1 = 'U861Z1P' # VosmikovychA id2 = 'U861Z2P' # VosmikovychB url1 = 'https://api.golemio.cz/v2/departureboards/' url1+= '?ids=' + id1 + '&limit=' + str(limit) url1 += '&minutesBefore='+str(minutPred)+'&minutesAfter='+str(minutPo) url2 = 'https://api.golemio.cz/v2/departureboards/' url2+= '?ids=' + id2 + '&limit='+str(limit) url2 += '&minutesBefore='+str(minutPred)+'&minutesAfter='+str(minutPo) try: # api gives max 100 entries, even tho stated limit is 1000 response1 = requests.get(url1, headers=headers) response1.raise_for_status() # access JSOn content response2 = requests.get(url2, headers=headers) response2.raise_for_status() vystup = response2.json()+response1.json() return(vystup) except HTTPError as http_err: print('HTTP error occurred:',http_err) except Exception as err: print('Other error occurred:',err) def ulozOdjezdy(): try: odjezdy = stahniOdjezdy() zapis = json.dumps(odjezdy) with open("odjezdyCache.txt",'w',encoding = 'utf-8') as f: f.write(zapis) except Exception as err: print('ulozOdjezd error occurred:', err) def vypisOdjezdy(): odjezdNejdrive = 0 # min pocetOdjezduMax = 20 dolu = [] nahoru = [] doluUq = [] nahoruUq = [] try: vystup = stahniOdjezdy(limit=pocetOdjezduMax*2) except: print("Error: stazeni odjezdu pro vypis") try: with open("odjezdyCache.txt",'r',encoding = 'utf-8') as fCt: vystup = json.load(fCt) except: print("Error: nacteni odjezdu ze souboru") pocetOdjezdu = 0 for polozka in vystup: # deleting ':' in timezone polozkaCas = datetime.strptime(polozka['departure_timestamp']['predicted'].replace("+01:00","+0100"),"%Y-%m-%dT%H:%M:%S.%f%z") aktualniCas = datetime.now(polozkaCas.tzinfo) casDoOdjezdu = polozkaCas-aktualniCas linka = polozka["route"]["short_name"] if len(linka) < 2: linka=" "+linka if abs(casDoOdjezdu.total_seconds())<60: doOdjezdu = str(floor(casDoOdjezdu.total_seconds()))+"s" doOdjezduVal = floor(casDoOdjezdu.total_seconds()) else: doOdjezdu = str(floor(casDoOdjezdu.total_seconds()/60))+"m" doOdjezduVal = floor(casDoOdjezdu.total_seconds()/60) if doOdjezduVal>0: doOdjezdu = " "+doOdjezdu if abs(doOdjezduVal)<10: doOdjezdu = " "+doOdjezdu odjezd = linka+" "+doOdjezdu+" |" if casDoOdjezdu.total_seconds()/60<odjezdNejdrive: continue if polozka['stop']['platform_code']=="A": dolu.append(odjezd) doluUq.append({linka:odjezd}) else: nahoru.append(odjezd) nahoruUq.append({linka:odjezd}) pocetOdjezdu=pocetOdjezdu+1 print("nahoru:",len(nahoru)) print("dolu:",len(dolu)) # print("\u25BC |",*dolu) # print("\u25B2 |",*nahoru) txt = "\u25BC |"+dolu[0]+dolu[1]+dolu[2]+"\n\u25BC |"+dolu[3]+dolu[4]+dolu[5] txt = txt+"\n\u25B2 |"+nahoru[0]+nahoru[1]+nahoru[2]+"\n\u25B2 |"+nahoru[3]+nahoru[4]+nahoru[5] print(txt) try: ulozOdjezdy() schedule.every(10).seconds.do(vypisOdjezdy) schedule.every(1).hours.do(ulozOdjezdy) while 1: schedule.run_pending() time.sleep(1) finally: schedule.clear()

0.200362

0.207938

from finviz.async_connector import Connector from lxml import html from lxml import etree import requests import urllib3 import os urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) TABLE = { 'Overview': '110', 'Valuation': '120', 'Ownership': '130', 'Performance': '140', 'Custom': '150', 'Financial': '160', 'Technical': '170' } def http_request(url, payload=None): if payload is None: payload = {} content = requests.get(url, params=payload, verify=False) content.raise_for_status() # Raise HTTPError for bad requests (4xx or 5xx) return content, content.url class Screener(object): def __init__(self, tickers=None, filters=None, rows=None, order='', signal='', table='Overview'): if tickers is None: self.tickers = [] else: self.tickers = tickers if filters is None: self.filters = [] else: self.filters = filters self.rows = rows self.order = order self.signal = signal self.table = table self.page_content = None self.url = None self.headers = None self.page_urls = None self.data = None self.__search_screener() def to_csv(self, directory=None): from save_data import export_to_csv if directory is None: directory = os.getcwd() export_to_csv(self.headers, self.data, directory) def __get_total_rows(self): total_element = self.page_content.cssselect('td[width="140"]') self.rows = int(etree.tostring(total_element[0]).decode("utf-8").split('</b>')[1].split(' ')[0]) def __get_page_urls(self): try: total_pages = int([i.text.split('/')[1] for i in self.page_content.cssselect('option[value="1"]')][0]) except IndexError: # No results found return None urls = [] for page_number in range(1, total_pages + 1): sequence = 1 + (page_number - 1) * 20 if sequence - 20 <= self.rows < sequence: break else: urls.append(self.url + '&r={}'.format(str(sequence))) self.page_urls = urls def __get_table_headers(self): first_row = self.page_content.cssselect('tr[valign="middle"]') headers = [] for table_content in first_row[0]: if table_content.text is None: sorted_text_list = etree.tostring(table_content.cssselect('img')[0]).decode("utf-8").split('/>') headers.append(sorted_text_list[1]) else: headers.append(table_content.text) self.headers = headers def __get_table_data(self, page=None): def parse_row(line): row_data = [] for tags in line: if tags.text is not None: row_data.append(tags.text) else: row_data.append([span.text for span in tags.cssselect('span')][0]) return row_data data_sets = [] page = html.fromstring(page) all_rows = [i.cssselect('a') for i in page.cssselect('tr[valign="top"]')[1:]] for row in all_rows: if int(row[0].text) is self.rows: values = dict(zip(self.headers, parse_row(row))) data_sets.append(values) break else: values = dict(zip(self.headers, parse_row(row))) data_sets.append(values) return data_sets def __search_screener(self): payload = { 'v': TABLE[self.table], 't': ','.join(self.tickers), 'f': ','.join(self.filters), 'o': self.order, 's': self.signal } self.page_content, self.url = http_request('https://finviz.com/screener.ashx', payload) self.page_content = html.fromstring(self.page_content.text) # Parses the page with the default lxml parser self.__get_table_headers() if self.rows is None: self.__get_total_rows() self.__get_page_urls() if self.page_urls is None: raise Exception("No results matching the criteria: {}" .format(self.url.split('?', 1)[1])) async_connector = Connector(self.__get_table_data, self.page_urls) self.data = async_connector.run_connector()

finviz/screener.py

from finviz.async_connector import Connector from lxml import html from lxml import etree import requests import urllib3 import os urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) TABLE = { 'Overview': '110', 'Valuation': '120', 'Ownership': '130', 'Performance': '140', 'Custom': '150', 'Financial': '160', 'Technical': '170' } def http_request(url, payload=None): if payload is None: payload = {} content = requests.get(url, params=payload, verify=False) content.raise_for_status() # Raise HTTPError for bad requests (4xx or 5xx) return content, content.url class Screener(object): def __init__(self, tickers=None, filters=None, rows=None, order='', signal='', table='Overview'): if tickers is None: self.tickers = [] else: self.tickers = tickers if filters is None: self.filters = [] else: self.filters = filters self.rows = rows self.order = order self.signal = signal self.table = table self.page_content = None self.url = None self.headers = None self.page_urls = None self.data = None self.__search_screener() def to_csv(self, directory=None): from save_data import export_to_csv if directory is None: directory = os.getcwd() export_to_csv(self.headers, self.data, directory) def __get_total_rows(self): total_element = self.page_content.cssselect('td[width="140"]') self.rows = int(etree.tostring(total_element[0]).decode("utf-8").split('</b>')[1].split(' ')[0]) def __get_page_urls(self): try: total_pages = int([i.text.split('/')[1] for i in self.page_content.cssselect('option[value="1"]')][0]) except IndexError: # No results found return None urls = [] for page_number in range(1, total_pages + 1): sequence = 1 + (page_number - 1) * 20 if sequence - 20 <= self.rows < sequence: break else: urls.append(self.url + '&r={}'.format(str(sequence))) self.page_urls = urls def __get_table_headers(self): first_row = self.page_content.cssselect('tr[valign="middle"]') headers = [] for table_content in first_row[0]: if table_content.text is None: sorted_text_list = etree.tostring(table_content.cssselect('img')[0]).decode("utf-8").split('/>') headers.append(sorted_text_list[1]) else: headers.append(table_content.text) self.headers = headers def __get_table_data(self, page=None): def parse_row(line): row_data = [] for tags in line: if tags.text is not None: row_data.append(tags.text) else: row_data.append([span.text for span in tags.cssselect('span')][0]) return row_data data_sets = [] page = html.fromstring(page) all_rows = [i.cssselect('a') for i in page.cssselect('tr[valign="top"]')[1:]] for row in all_rows: if int(row[0].text) is self.rows: values = dict(zip(self.headers, parse_row(row))) data_sets.append(values) break else: values = dict(zip(self.headers, parse_row(row))) data_sets.append(values) return data_sets def __search_screener(self): payload = { 'v': TABLE[self.table], 't': ','.join(self.tickers), 'f': ','.join(self.filters), 'o': self.order, 's': self.signal } self.page_content, self.url = http_request('https://finviz.com/screener.ashx', payload) self.page_content = html.fromstring(self.page_content.text) # Parses the page with the default lxml parser self.__get_table_headers() if self.rows is None: self.__get_total_rows() self.__get_page_urls() if self.page_urls is None: raise Exception("No results matching the criteria: {}" .format(self.url.split('?', 1)[1])) async_connector = Connector(self.__get_table_data, self.page_urls) self.data = async_connector.run_connector()

0.394784

0.089455

from django.utils.translation import ugettext_noop as _ from api import status from api.api_views import APIView from api.exceptions import ObjectNotFound, PreconditionRequired, ObjectAlreadyExists from api.task.response import SuccessTaskResponse from api.utils.db import get_object from api.dc.storage.serializers import DcNodeStorageSerializer, ExtendedDcNodeStorageSerializer from api.dc.messages import LOG_STORAGE_ATTACH, LOG_STORAGE_DETACH from vms.models import NodeStorage, DcNode class DcStorageView(APIView): serializer = DcNodeStorageSerializer order_by_default = ('node__hostname', 'zpool') order_by_field_map = {'hostname': 'node__hostname', 'zpool': 'zpool'} def __init__(self, request, name, data): super(DcStorageView, self).__init__(request) self.data = data self.name = name dc = request.dc if name: try: zpool, hostname = name.split('@') if not (zpool and hostname): raise ValueError except ValueError: raise ObjectNotFound(model=NodeStorage) attrs = {'node__hostname': hostname, 'zpool': zpool} if request.method != 'POST': attrs['dc'] = dc ns = get_object(request, NodeStorage, attrs, sr=('node', 'storage', 'storage__owner',), exists_ok=True, noexists_fail=True) ns.set_dc(dc) try: # Bug #chili-525 + checks if node is attached to Dc (must be!) ns.set_dc_node(DcNode.objects.get(node=ns.node, dc=dc)) except DcNode.DoesNotExist: raise PreconditionRequired(_('Compute node is not available')) else: # many ns = NodeStorage.objects.filter(dc=dc).order_by(*self.order_by) if self.full or self.extended: dc_nodes = {dn.node.hostname: dn for dn in DcNode.objects.select_related('node').filter(dc=request.dc)} ns = ns.select_related('node', 'storage', 'storage__owner') for i in ns: # Bug #chili-525 i.set_dc_node(dc_nodes.get(i.node.hostname, None)) i.set_dc(dc) self.ns = ns def get(self, many=False): if self.extended: serializer = ExtendedDcNodeStorageSerializer else: serializer = self.serializer if many or not self.name: if self.full or self.extended: if self.ns: # noinspection PyUnresolvedReferences res = serializer(self.request, self.ns, many=True).data else: res = [] else: res = ['@'.join(i) for i in self.ns.values_list('zpool', 'node__hostname')] else: # noinspection PyUnresolvedReferences res = serializer(self.request, self.ns).data return SuccessTaskResponse(self.request, res) def post(self): ns, dc = self.ns, self.request.dc if ns.dc.filter(id=dc.id).exists(): raise ObjectAlreadyExists(model=NodeStorage) ser = self.serializer(self.request, ns) ns.dc.add(dc) return SuccessTaskResponse(self.request, ser.data, obj=ns, status=status.HTTP_201_CREATED, detail_dict=ser.detail_dict(), msg=LOG_STORAGE_ATTACH) def delete(self): ns, dc = self.ns, self.request.dc for vm in dc.vm_set.filter(node=ns.node): if ns.zpool in vm.get_used_disk_pools(): # active + current raise PreconditionRequired(_('Storage is used by some VMs')) if dc.backup_set.filter(zpool=ns).exists(): raise PreconditionRequired(_('Storage is used by some VM backups')) ser = self.serializer(self.request, ns) ns.dc.remove(dc) return SuccessTaskResponse(self.request, None, obj=ns, detail_dict=ser.detail_dict(), msg=LOG_STORAGE_DETACH)

api/dc/storage/api_views.py

from django.utils.translation import ugettext_noop as _ from api import status from api.api_views import APIView from api.exceptions import ObjectNotFound, PreconditionRequired, ObjectAlreadyExists from api.task.response import SuccessTaskResponse from api.utils.db import get_object from api.dc.storage.serializers import DcNodeStorageSerializer, ExtendedDcNodeStorageSerializer from api.dc.messages import LOG_STORAGE_ATTACH, LOG_STORAGE_DETACH from vms.models import NodeStorage, DcNode class DcStorageView(APIView): serializer = DcNodeStorageSerializer order_by_default = ('node__hostname', 'zpool') order_by_field_map = {'hostname': 'node__hostname', 'zpool': 'zpool'} def __init__(self, request, name, data): super(DcStorageView, self).__init__(request) self.data = data self.name = name dc = request.dc if name: try: zpool, hostname = name.split('@') if not (zpool and hostname): raise ValueError except ValueError: raise ObjectNotFound(model=NodeStorage) attrs = {'node__hostname': hostname, 'zpool': zpool} if request.method != 'POST': attrs['dc'] = dc ns = get_object(request, NodeStorage, attrs, sr=('node', 'storage', 'storage__owner',), exists_ok=True, noexists_fail=True) ns.set_dc(dc) try: # Bug #chili-525 + checks if node is attached to Dc (must be!) ns.set_dc_node(DcNode.objects.get(node=ns.node, dc=dc)) except DcNode.DoesNotExist: raise PreconditionRequired(_('Compute node is not available')) else: # many ns = NodeStorage.objects.filter(dc=dc).order_by(*self.order_by) if self.full or self.extended: dc_nodes = {dn.node.hostname: dn for dn in DcNode.objects.select_related('node').filter(dc=request.dc)} ns = ns.select_related('node', 'storage', 'storage__owner') for i in ns: # Bug #chili-525 i.set_dc_node(dc_nodes.get(i.node.hostname, None)) i.set_dc(dc) self.ns = ns def get(self, many=False): if self.extended: serializer = ExtendedDcNodeStorageSerializer else: serializer = self.serializer if many or not self.name: if self.full or self.extended: if self.ns: # noinspection PyUnresolvedReferences res = serializer(self.request, self.ns, many=True).data else: res = [] else: res = ['@'.join(i) for i in self.ns.values_list('zpool', 'node__hostname')] else: # noinspection PyUnresolvedReferences res = serializer(self.request, self.ns).data return SuccessTaskResponse(self.request, res) def post(self): ns, dc = self.ns, self.request.dc if ns.dc.filter(id=dc.id).exists(): raise ObjectAlreadyExists(model=NodeStorage) ser = self.serializer(self.request, ns) ns.dc.add(dc) return SuccessTaskResponse(self.request, ser.data, obj=ns, status=status.HTTP_201_CREATED, detail_dict=ser.detail_dict(), msg=LOG_STORAGE_ATTACH) def delete(self): ns, dc = self.ns, self.request.dc for vm in dc.vm_set.filter(node=ns.node): if ns.zpool in vm.get_used_disk_pools(): # active + current raise PreconditionRequired(_('Storage is used by some VMs')) if dc.backup_set.filter(zpool=ns).exists(): raise PreconditionRequired(_('Storage is used by some VM backups')) ser = self.serializer(self.request, ns) ns.dc.remove(dc) return SuccessTaskResponse(self.request, None, obj=ns, detail_dict=ser.detail_dict(), msg=LOG_STORAGE_DETACH)

0.390243

0.096025

from astropy import units as u, constants as const import numpy as np from ..nuclear import nuclear_binding_energy, nuclear_reaction_energy, mass_energy from plasmapy.utils.pytest_helpers import run_test, run_test_equivalent_calls from plasmapy.atomic.exceptions import AtomicError, InvalidParticleError import pytest test_nuclear_table = [ [nuclear_binding_energy, 'p', {}, 0 * u.J], [nuclear_binding_energy, 'n', {}, 0 * u.J], [nuclear_binding_energy, 'p', {}, 0 * u.J], [nuclear_binding_energy, "H", {}, AtomicError], [nuclear_binding_energy, 'He-99', {}, InvalidParticleError], [nuclear_binding_energy, "He", {"mass_numb": 99}, InvalidParticleError], [nuclear_binding_energy, 3.1415926535j, {}, TypeError], [mass_energy, 'e-', {}, (const.m_e * const.c ** 2).to(u.J)], [mass_energy, 'p+', {}, (const.m_p * const.c ** 2).to(u.J)], [mass_energy, 'H-1', {}, (const.m_p * const.c ** 2).to(u.J)], [mass_energy, 'H-1 0+', {}, (const.m_p * const.c ** 2).to(u.J)], [mass_energy, 'n', {}, (const.m_n * const.c ** 2).to(u.J)], [nuclear_reaction_energy, (), {'reactants': ['n'], 'products': 3}, TypeError], [nuclear_reaction_energy, (), {'reactants': ['n'], 'products': ['He-4']}, AtomicError], [nuclear_reaction_energy, (), {'reactants': ['h'], 'products': ['H-1']}, AtomicError], [nuclear_reaction_energy, (), {'reactants': ['e-', 'n'], 'products': ['p+']}, AtomicError], [nuclear_reaction_energy, (), {'reactants': ['e+', 'n'], 'products': ['p-']}, AtomicError], [nuclear_reaction_energy, (), {'reactants': ['ksdf'], 'products': ['H-3']}, AtomicError], [nuclear_reaction_energy, (), {'reactants': ['H'], 'products': ['H-1']}, AtomicError], [nuclear_reaction_energy, (), {'reactants': ['p'], 'products': ['n', 'n', 'e-']}, AtomicError], [nuclear_reaction_energy, 'H + H --> H', {}, AtomicError], [nuclear_reaction_energy, 'H + H', {}, AtomicError], [nuclear_reaction_energy, 1, {}, TypeError], [nuclear_reaction_energy, 'H-1 + H-1 --> H-1', {}, AtomicError], [nuclear_reaction_energy, 'p --> n', {}, AtomicError], [nuclear_reaction_energy, 'p --> p', {'reactants': 'p', 'products': 'p'}, AtomicError], ] @pytest.mark.parametrize('test_inputs', test_nuclear_table) def test_nuclear(test_inputs): run_test(*test_inputs, rtol=1e-3) test_nuclear_equivalent_calls_table = [ [nuclear_binding_energy, ['He-4', {}], ['alpha', {}], ['He', {'mass_numb': 4}]], ] @pytest.mark.parametrize('test_inputs', test_nuclear_equivalent_calls_table) def test_nuclear_equivalent_calls(test_inputs): run_test_equivalent_calls(test_inputs) def test_nuclear_binding_energy_D_T(): before = nuclear_binding_energy("D") + nuclear_binding_energy("T") after = nuclear_binding_energy("alpha") E_in_MeV = (after - before).to(u.MeV).value # D + T --> alpha + n + E assert np.isclose(E_in_MeV, 17.58, rtol=0.01) def test_nuclear_reaction_energy(): reaction1 = 'D + T --> alpha + n' reaction2 = 'T + D -> n + alpha' released_energy1 = nuclear_reaction_energy(reaction1) released_energy2 = nuclear_reaction_energy(reaction2) assert np.isclose((released_energy1.to(u.MeV)).value, 17.58, rtol=0.01) assert released_energy1 == released_energy2 assert nuclear_reaction_energy('n + p+ --> n + p+ + p- + p+') == \ nuclear_reaction_energy('n + p+ --> n + 2*p+ + p-') nuclear_reaction_energy('neutron + antineutron --> neutron + antineutron') def test_nuclear_reaction_energy_triple_alpha(): triple_alpha1 = 'alpha + He-4 --> Be-8' triple_alpha2 = 'Be-8 + alpha --> carbon-12' energy_triplealpha1 = nuclear_reaction_energy(triple_alpha1) energy_triplealpha2 = nuclear_reaction_energy(triple_alpha2) assert np.isclose(energy_triplealpha1.to(u.keV).value, -91.8, atol=0.1) assert np.isclose(energy_triplealpha2.to(u.MeV).value, 7.367, atol=0.1) reactants = ['He-4', 'alpha'] products = ['Be-8'] energy = nuclear_reaction_energy(reactants=reactants, products=products) assert np.isclose(energy.to(u.keV).value, -91.8, atol=0.1) def test_nuclear_reaction_energy_alpha_decay(): alpha_decay_example = 'U-238 --> Th-234 + alpha' energy_alpha_decay = nuclear_reaction_energy(alpha_decay_example) assert np.isclose(energy_alpha_decay.to(u.MeV).value, 4.26975, atol=1e-5) def test_nuclear_reaction_energy_triple_alpha_r(): triple_alpha1_r = '4*He-4 --> 2*Be-8' energy_triplealpha1_r = nuclear_reaction_energy(triple_alpha1_r) assert np.isclose(energy_triplealpha1_r.to(u.keV).value, -91.8 * 2, atol=0.1) def test_nuclear_reaction_energy_beta(): energy1 = nuclear_reaction_energy(reactants=['n'], products=['p', 'e-']) assert np.isclose(energy1.to(u.MeV).value, 0.78, atol=0.01) energy2 = nuclear_reaction_energy( reactants=['Mg-23'], products=['Na-23', 'e+']) assert np.isclose(energy2.to(u.MeV).value, 3.034591, atol=1e-5) # (reactants, products, expectedMeV, tol) nuclear_reaction_energy_kwargs_table = [ ('H-1', 'p', 0.0, 0.0), (['B-10', 'n'], ['Li-7', 'He-4'], 2.8, 0.06), (['Li-6', 'D'], ['2*alpha'], 22.2, 0.06), (['C-12', 'p'], 'N-13', 1.95, 0.006), (['N-13'], ['C-13', 'e+'], 1.20, 0.006), (['C-13', 'hydrogen-1'], ['Nitrogen-14'], 7.54, 0.006), (['N-14', 'H-1'], ['O-15'], 7.35, 0.006), (['O-15'], ['N-15', 'e+'], 1.73, 0.006), (('N-15', 'H-1'), ('C-12', 'He-4'), 4.96, 0.006), ] @pytest.mark.parametrize( "reactants, products, expectedMeV, tol", nuclear_reaction_energy_kwargs_table) def test_nuclear_reaction_energy_kwargs(reactants, products, expectedMeV, tol): energy = nuclear_reaction_energy(reactants=reactants, products=products).si expected = (expectedMeV * u.MeV).si assert np.isclose(expected.value, energy.value, atol=tol)

plasmapy/atomic/tests/test_nuclear.py

from astropy import units as u, constants as const import numpy as np from ..nuclear import nuclear_binding_energy, nuclear_reaction_energy, mass_energy from plasmapy.utils.pytest_helpers import run_test, run_test_equivalent_calls from plasmapy.atomic.exceptions import AtomicError, InvalidParticleError import pytest test_nuclear_table = [ [nuclear_binding_energy, 'p', {}, 0 * u.J], [nuclear_binding_energy, 'n', {}, 0 * u.J], [nuclear_binding_energy, 'p', {}, 0 * u.J], [nuclear_binding_energy, "H", {}, AtomicError], [nuclear_binding_energy, 'He-99', {}, InvalidParticleError], [nuclear_binding_energy, "He", {"mass_numb": 99}, InvalidParticleError], [nuclear_binding_energy, 3.1415926535j, {}, TypeError], [mass_energy, 'e-', {}, (const.m_e * const.c ** 2).to(u.J)], [mass_energy, 'p+', {}, (const.m_p * const.c ** 2).to(u.J)], [mass_energy, 'H-1', {}, (const.m_p * const.c ** 2).to(u.J)], [mass_energy, 'H-1 0+', {}, (const.m_p * const.c ** 2).to(u.J)], [mass_energy, 'n', {}, (const.m_n * const.c ** 2).to(u.J)], [nuclear_reaction_energy, (), {'reactants': ['n'], 'products': 3}, TypeError], [nuclear_reaction_energy, (), {'reactants': ['n'], 'products': ['He-4']}, AtomicError], [nuclear_reaction_energy, (), {'reactants': ['h'], 'products': ['H-1']}, AtomicError], [nuclear_reaction_energy, (), {'reactants': ['e-', 'n'], 'products': ['p+']}, AtomicError], [nuclear_reaction_energy, (), {'reactants': ['e+', 'n'], 'products': ['p-']}, AtomicError], [nuclear_reaction_energy, (), {'reactants': ['ksdf'], 'products': ['H-3']}, AtomicError], [nuclear_reaction_energy, (), {'reactants': ['H'], 'products': ['H-1']}, AtomicError], [nuclear_reaction_energy, (), {'reactants': ['p'], 'products': ['n', 'n', 'e-']}, AtomicError], [nuclear_reaction_energy, 'H + H --> H', {}, AtomicError], [nuclear_reaction_energy, 'H + H', {}, AtomicError], [nuclear_reaction_energy, 1, {}, TypeError], [nuclear_reaction_energy, 'H-1 + H-1 --> H-1', {}, AtomicError], [nuclear_reaction_energy, 'p --> n', {}, AtomicError], [nuclear_reaction_energy, 'p --> p', {'reactants': 'p', 'products': 'p'}, AtomicError], ] @pytest.mark.parametrize('test_inputs', test_nuclear_table) def test_nuclear(test_inputs): run_test(*test_inputs, rtol=1e-3) test_nuclear_equivalent_calls_table = [ [nuclear_binding_energy, ['He-4', {}], ['alpha', {}], ['He', {'mass_numb': 4}]], ] @pytest.mark.parametrize('test_inputs', test_nuclear_equivalent_calls_table) def test_nuclear_equivalent_calls(test_inputs): run_test_equivalent_calls(test_inputs) def test_nuclear_binding_energy_D_T(): before = nuclear_binding_energy("D") + nuclear_binding_energy("T") after = nuclear_binding_energy("alpha") E_in_MeV = (after - before).to(u.MeV).value # D + T --> alpha + n + E assert np.isclose(E_in_MeV, 17.58, rtol=0.01) def test_nuclear_reaction_energy(): reaction1 = 'D + T --> alpha + n' reaction2 = 'T + D -> n + alpha' released_energy1 = nuclear_reaction_energy(reaction1) released_energy2 = nuclear_reaction_energy(reaction2) assert np.isclose((released_energy1.to(u.MeV)).value, 17.58, rtol=0.01) assert released_energy1 == released_energy2 assert nuclear_reaction_energy('n + p+ --> n + p+ + p- + p+') == \ nuclear_reaction_energy('n + p+ --> n + 2*p+ + p-') nuclear_reaction_energy('neutron + antineutron --> neutron + antineutron') def test_nuclear_reaction_energy_triple_alpha(): triple_alpha1 = 'alpha + He-4 --> Be-8' triple_alpha2 = 'Be-8 + alpha --> carbon-12' energy_triplealpha1 = nuclear_reaction_energy(triple_alpha1) energy_triplealpha2 = nuclear_reaction_energy(triple_alpha2) assert np.isclose(energy_triplealpha1.to(u.keV).value, -91.8, atol=0.1) assert np.isclose(energy_triplealpha2.to(u.MeV).value, 7.367, atol=0.1) reactants = ['He-4', 'alpha'] products = ['Be-8'] energy = nuclear_reaction_energy(reactants=reactants, products=products) assert np.isclose(energy.to(u.keV).value, -91.8, atol=0.1) def test_nuclear_reaction_energy_alpha_decay(): alpha_decay_example = 'U-238 --> Th-234 + alpha' energy_alpha_decay = nuclear_reaction_energy(alpha_decay_example) assert np.isclose(energy_alpha_decay.to(u.MeV).value, 4.26975, atol=1e-5) def test_nuclear_reaction_energy_triple_alpha_r(): triple_alpha1_r = '4*He-4 --> 2*Be-8' energy_triplealpha1_r = nuclear_reaction_energy(triple_alpha1_r) assert np.isclose(energy_triplealpha1_r.to(u.keV).value, -91.8 * 2, atol=0.1) def test_nuclear_reaction_energy_beta(): energy1 = nuclear_reaction_energy(reactants=['n'], products=['p', 'e-']) assert np.isclose(energy1.to(u.MeV).value, 0.78, atol=0.01) energy2 = nuclear_reaction_energy( reactants=['Mg-23'], products=['Na-23', 'e+']) assert np.isclose(energy2.to(u.MeV).value, 3.034591, atol=1e-5) # (reactants, products, expectedMeV, tol) nuclear_reaction_energy_kwargs_table = [ ('H-1', 'p', 0.0, 0.0), (['B-10', 'n'], ['Li-7', 'He-4'], 2.8, 0.06), (['Li-6', 'D'], ['2*alpha'], 22.2, 0.06), (['C-12', 'p'], 'N-13', 1.95, 0.006), (['N-13'], ['C-13', 'e+'], 1.20, 0.006), (['C-13', 'hydrogen-1'], ['Nitrogen-14'], 7.54, 0.006), (['N-14', 'H-1'], ['O-15'], 7.35, 0.006), (['O-15'], ['N-15', 'e+'], 1.73, 0.006), (('N-15', 'H-1'), ('C-12', 'He-4'), 4.96, 0.006), ] @pytest.mark.parametrize( "reactants, products, expectedMeV, tol", nuclear_reaction_energy_kwargs_table) def test_nuclear_reaction_energy_kwargs(reactants, products, expectedMeV, tol): energy = nuclear_reaction_energy(reactants=reactants, products=products).si expected = (expectedMeV * u.MeV).si assert np.isclose(expected.value, energy.value, atol=tol)

0.846483

0.585012

import os # Third party imports from fastapi import Depends, APIRouter, HTTPException, status from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm from dotenv import load_dotenv # Local imports from .auth_db import get_user from .encryption import encrypt_text, decrypt_token, encrypt_token from .classes import User load_dotenv() oauth = APIRouter() oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token") @oauth.post("/token") async def login(form_data: OAuth2PasswordRequestForm = Depends()): """ Token endpoint - Login here and get your token Username and Password are required and must be valid, if correct you will get youre beautiful token Returns: Dict[str, str] """ user = await get_user(form_data.username) if not user: raise HTTPException(status_code=400, detail="Incorrect username or password") hashed_password = await encrypt_text(form_data.password) if hashed_password != user.hashed_password: raise HTTPException(status_code=400, detail="Incorrect username or password") key = os.environ["KEY"] token = await encrypt_token(key, user.username) return {"access_token": token, "token_type": "bearer"} async def get_current_user(token: str = Depends(oauth2_scheme)): """ Decrypts a token to extract the user info from it, then it fetches the correct user from the db If user doesnt exist or invalid token it returns and raises an HTTPException :param token (str) : The users token Return: User : The user or it returns none and raises an exception """ key = os.environ["KEY"] decrypted_token = await decrypt_token(key, token) user = await get_user(decrypted_token) if not user: raise HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid authentication credentials", headers={"WWW-Authenticate": "Bearer"}, ) return user async def get_current_active_user(current_user: User = Depends(get_current_user)): """ Gets a user and checks if they are disabled Returns: if not disabled it returns the User else it returns and raises an HTTPException """ if current_user.disabled == 1: raise HTTPException(status_code=400, detail="Inactive user") return current_user

api/auth/authenticate.py

import os # Third party imports from fastapi import Depends, APIRouter, HTTPException, status from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm from dotenv import load_dotenv # Local imports from .auth_db import get_user from .encryption import encrypt_text, decrypt_token, encrypt_token from .classes import User load_dotenv() oauth = APIRouter() oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token") @oauth.post("/token") async def login(form_data: OAuth2PasswordRequestForm = Depends()): """ Token endpoint - Login here and get your token Username and Password are required and must be valid, if correct you will get youre beautiful token Returns: Dict[str, str] """ user = await get_user(form_data.username) if not user: raise HTTPException(status_code=400, detail="Incorrect username or password") hashed_password = await encrypt_text(form_data.password) if hashed_password != user.hashed_password: raise HTTPException(status_code=400, detail="Incorrect username or password") key = os.environ["KEY"] token = await encrypt_token(key, user.username) return {"access_token": token, "token_type": "bearer"} async def get_current_user(token: str = Depends(oauth2_scheme)): """ Decrypts a token to extract the user info from it, then it fetches the correct user from the db If user doesnt exist or invalid token it returns and raises an HTTPException :param token (str) : The users token Return: User : The user or it returns none and raises an exception """ key = os.environ["KEY"] decrypted_token = await decrypt_token(key, token) user = await get_user(decrypted_token) if not user: raise HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid authentication credentials", headers={"WWW-Authenticate": "Bearer"}, ) return user async def get_current_active_user(current_user: User = Depends(get_current_user)): """ Gets a user and checks if they are disabled Returns: if not disabled it returns the User else it returns and raises an HTTPException """ if current_user.disabled == 1: raise HTTPException(status_code=400, detail="Inactive user") return current_user

0.695441

0.096663

import math import matplotlib.pyplot as plt import numpy as np import gtsam import gtsam.utils.plot as gtsam_plot def report_on_progress(graph: gtsam.NonlinearFactorGraph, current_estimate: gtsam.Values, key: int): """Print and plot incremental progress of the robot for 2D Pose SLAM using iSAM2.""" # Print the current estimates computed using iSAM2. print("*"*50 + f"\nInference after State {key+1}:\n") print(current_estimate) # Compute the marginals for all states in the graph. marginals = gtsam.Marginals(graph, current_estimate) # Plot the newly updated iSAM2 inference. fig = plt.figure(0) axes = fig.gca() plt.cla() i = 1 while current_estimate.exists(i): gtsam_plot.plot_pose2(0, current_estimate.atPose2(i), 0.5, marginals.marginalCovariance(i)) i += 1 plt.axis('equal') axes.set_xlim(-1, 5) axes.set_ylim(-1, 3) plt.pause(1) def determine_loop_closure(odom: np.ndarray, current_estimate: gtsam.Values, key: int, xy_tol=0.6, theta_tol=17) -> int: """Simple brute force approach which iterates through previous states and checks for loop closure. Args: odom: Vector representing noisy odometry (x, y, theta) measurement in the body frame. current_estimate: The current estimates computed by iSAM2. key: Key corresponding to the current state estimate of the robot. xy_tol: Optional argument for the x-y measurement tolerance, in meters. theta_tol: Optional argument for the theta measurement tolerance, in degrees. Returns: k: The key of the state which is helping add the loop closure constraint. If loop closure is not found, then None is returned. """ if current_estimate: prev_est = current_estimate.atPose2(key+1) rotated_odom = prev_est.rotation().matrix() @ odom[:2] curr_xy = np.array([prev_est.x() + rotated_odom[0], prev_est.y() + rotated_odom[1]]) curr_theta = prev_est.theta() + odom[2] for k in range(1, key+1): pose_xy = np.array([current_estimate.atPose2(k).x(), current_estimate.atPose2(k).y()]) pose_theta = current_estimate.atPose2(k).theta() if (abs(pose_xy - curr_xy) <= xy_tol).all() and \ (abs(pose_theta - curr_theta) <= theta_tol*np.pi/180): return k def Pose2SLAM_ISAM2_example(): """Perform 2D SLAM given the ground truth changes in pose as well as simple loop closure detection.""" plt.ion() # Declare the 2D translational standard deviations of the prior factor's Gaussian model, in meters. prior_xy_sigma = 0.3 # Declare the 2D rotational standard deviation of the prior factor's Gaussian model, in degrees. prior_theta_sigma = 5 # Declare the 2D translational standard deviations of the odometry factor's Gaussian model, in meters. odometry_xy_sigma = 0.2 # Declare the 2D rotational standard deviation of the odometry factor's Gaussian model, in degrees. odometry_theta_sigma = 5 # Although this example only uses linear measurements and Gaussian noise models, it is important # to note that iSAM2 can be utilized to its full potential during nonlinear optimization. This example # simply showcases how iSAM2 may be applied to a Pose2 SLAM problem. PRIOR_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([prior_xy_sigma, prior_xy_sigma, prior_theta_sigma*np.pi/180])) ODOMETRY_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([odometry_xy_sigma, odometry_xy_sigma, odometry_theta_sigma*np.pi/180])) # Create a Nonlinear factor graph as well as the data structure to hold state estimates. graph = gtsam.NonlinearFactorGraph() initial_estimate = gtsam.Values() # Create iSAM2 parameters which can adjust the threshold necessary to force relinearization and how many # update calls are required to perform the relinearization. parameters = gtsam.ISAM2Params() parameters.setRelinearizeThreshold(0.1) parameters.setRelinearizeSkip(1) isam = gtsam.ISAM2(parameters) # Create the ground truth odometry measurements of the robot during the trajectory. true_odometry = [(2, 0, 0), (2, 0, math.pi/2), (2, 0, math.pi/2), (2, 0, math.pi/2), (2, 0, math.pi/2)] # Corrupt the odometry measurements with gaussian noise to create noisy odometry measurements. odometry_measurements = [np.random.multivariate_normal(true_odom, ODOMETRY_NOISE.covariance()) for true_odom in true_odometry] # Add the prior factor to the factor graph, and poorly initialize the prior pose to demonstrate # iSAM2 incremental optimization. graph.push_back(gtsam.PriorFactorPose2(1, gtsam.Pose2(0, 0, 0), PRIOR_NOISE)) initial_estimate.insert(1, gtsam.Pose2(0.5, 0.0, 0.2)) # Initialize the current estimate which is used during the incremental inference loop. current_estimate = initial_estimate for i in range(len(true_odometry)): # Obtain the noisy odometry that is received by the robot and corrupted by gaussian noise. noisy_odom_x, noisy_odom_y, noisy_odom_theta = odometry_measurements[i] # Determine if there is loop closure based on the odometry measurement and the previous estimate of the state. loop = determine_loop_closure(odometry_measurements[i], current_estimate, i, xy_tol=0.8, theta_tol=25) # Add a binary factor in between two existing states if loop closure is detected. # Otherwise, add a binary factor between a newly observed state and the previous state. if loop: graph.push_back(gtsam.BetweenFactorPose2(i + 1, loop, gtsam.Pose2(noisy_odom_x, noisy_odom_y, noisy_odom_theta), ODOMETRY_NOISE)) else: graph.push_back(gtsam.BetweenFactorPose2(i + 1, i + 2, gtsam.Pose2(noisy_odom_x, noisy_odom_y, noisy_odom_theta), ODOMETRY_NOISE)) # Compute and insert the initialization estimate for the current pose using the noisy odometry measurement. computed_estimate = current_estimate.atPose2(i + 1).compose(gtsam.Pose2(noisy_odom_x, noisy_odom_y, noisy_odom_theta)) initial_estimate.insert(i + 2, computed_estimate) # Perform incremental update to iSAM2's internal Bayes tree, optimizing only the affected variables. isam.update(graph, initial_estimate) current_estimate = isam.calculateEstimate() # Report all current state estimates from the iSAM2 optimzation. report_on_progress(graph, current_estimate, i) initial_estimate.clear() # Print the final covariance matrix for each pose after completing inference on the trajectory. marginals = gtsam.Marginals(graph, current_estimate) i = 1 for i in range(1, len(true_odometry)+1): print(f"X{i} covariance:\n{marginals.marginalCovariance(i)}\n") plt.ioff() plt.show() if __name__ == "__main__": Pose2SLAM_ISAM2_example()

python/gtsam/examples/Pose2ISAM2Example.py

import math import matplotlib.pyplot as plt import numpy as np import gtsam import gtsam.utils.plot as gtsam_plot def report_on_progress(graph: gtsam.NonlinearFactorGraph, current_estimate: gtsam.Values, key: int): """Print and plot incremental progress of the robot for 2D Pose SLAM using iSAM2.""" # Print the current estimates computed using iSAM2. print("*"*50 + f"\nInference after State {key+1}:\n") print(current_estimate) # Compute the marginals for all states in the graph. marginals = gtsam.Marginals(graph, current_estimate) # Plot the newly updated iSAM2 inference. fig = plt.figure(0) axes = fig.gca() plt.cla() i = 1 while current_estimate.exists(i): gtsam_plot.plot_pose2(0, current_estimate.atPose2(i), 0.5, marginals.marginalCovariance(i)) i += 1 plt.axis('equal') axes.set_xlim(-1, 5) axes.set_ylim(-1, 3) plt.pause(1) def determine_loop_closure(odom: np.ndarray, current_estimate: gtsam.Values, key: int, xy_tol=0.6, theta_tol=17) -> int: """Simple brute force approach which iterates through previous states and checks for loop closure. Args: odom: Vector representing noisy odometry (x, y, theta) measurement in the body frame. current_estimate: The current estimates computed by iSAM2. key: Key corresponding to the current state estimate of the robot. xy_tol: Optional argument for the x-y measurement tolerance, in meters. theta_tol: Optional argument for the theta measurement tolerance, in degrees. Returns: k: The key of the state which is helping add the loop closure constraint. If loop closure is not found, then None is returned. """ if current_estimate: prev_est = current_estimate.atPose2(key+1) rotated_odom = prev_est.rotation().matrix() @ odom[:2] curr_xy = np.array([prev_est.x() + rotated_odom[0], prev_est.y() + rotated_odom[1]]) curr_theta = prev_est.theta() + odom[2] for k in range(1, key+1): pose_xy = np.array([current_estimate.atPose2(k).x(), current_estimate.atPose2(k).y()]) pose_theta = current_estimate.atPose2(k).theta() if (abs(pose_xy - curr_xy) <= xy_tol).all() and \ (abs(pose_theta - curr_theta) <= theta_tol*np.pi/180): return k def Pose2SLAM_ISAM2_example(): """Perform 2D SLAM given the ground truth changes in pose as well as simple loop closure detection.""" plt.ion() # Declare the 2D translational standard deviations of the prior factor's Gaussian model, in meters. prior_xy_sigma = 0.3 # Declare the 2D rotational standard deviation of the prior factor's Gaussian model, in degrees. prior_theta_sigma = 5 # Declare the 2D translational standard deviations of the odometry factor's Gaussian model, in meters. odometry_xy_sigma = 0.2 # Declare the 2D rotational standard deviation of the odometry factor's Gaussian model, in degrees. odometry_theta_sigma = 5 # Although this example only uses linear measurements and Gaussian noise models, it is important # to note that iSAM2 can be utilized to its full potential during nonlinear optimization. This example # simply showcases how iSAM2 may be applied to a Pose2 SLAM problem. PRIOR_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([prior_xy_sigma, prior_xy_sigma, prior_theta_sigma*np.pi/180])) ODOMETRY_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([odometry_xy_sigma, odometry_xy_sigma, odometry_theta_sigma*np.pi/180])) # Create a Nonlinear factor graph as well as the data structure to hold state estimates. graph = gtsam.NonlinearFactorGraph() initial_estimate = gtsam.Values() # Create iSAM2 parameters which can adjust the threshold necessary to force relinearization and how many # update calls are required to perform the relinearization. parameters = gtsam.ISAM2Params() parameters.setRelinearizeThreshold(0.1) parameters.setRelinearizeSkip(1) isam = gtsam.ISAM2(parameters) # Create the ground truth odometry measurements of the robot during the trajectory. true_odometry = [(2, 0, 0), (2, 0, math.pi/2), (2, 0, math.pi/2), (2, 0, math.pi/2), (2, 0, math.pi/2)] # Corrupt the odometry measurements with gaussian noise to create noisy odometry measurements. odometry_measurements = [np.random.multivariate_normal(true_odom, ODOMETRY_NOISE.covariance()) for true_odom in true_odometry] # Add the prior factor to the factor graph, and poorly initialize the prior pose to demonstrate # iSAM2 incremental optimization. graph.push_back(gtsam.PriorFactorPose2(1, gtsam.Pose2(0, 0, 0), PRIOR_NOISE)) initial_estimate.insert(1, gtsam.Pose2(0.5, 0.0, 0.2)) # Initialize the current estimate which is used during the incremental inference loop. current_estimate = initial_estimate for i in range(len(true_odometry)): # Obtain the noisy odometry that is received by the robot and corrupted by gaussian noise. noisy_odom_x, noisy_odom_y, noisy_odom_theta = odometry_measurements[i] # Determine if there is loop closure based on the odometry measurement and the previous estimate of the state. loop = determine_loop_closure(odometry_measurements[i], current_estimate, i, xy_tol=0.8, theta_tol=25) # Add a binary factor in between two existing states if loop closure is detected. # Otherwise, add a binary factor between a newly observed state and the previous state. if loop: graph.push_back(gtsam.BetweenFactorPose2(i + 1, loop, gtsam.Pose2(noisy_odom_x, noisy_odom_y, noisy_odom_theta), ODOMETRY_NOISE)) else: graph.push_back(gtsam.BetweenFactorPose2(i + 1, i + 2, gtsam.Pose2(noisy_odom_x, noisy_odom_y, noisy_odom_theta), ODOMETRY_NOISE)) # Compute and insert the initialization estimate for the current pose using the noisy odometry measurement. computed_estimate = current_estimate.atPose2(i + 1).compose(gtsam.Pose2(noisy_odom_x, noisy_odom_y, noisy_odom_theta)) initial_estimate.insert(i + 2, computed_estimate) # Perform incremental update to iSAM2's internal Bayes tree, optimizing only the affected variables. isam.update(graph, initial_estimate) current_estimate = isam.calculateEstimate() # Report all current state estimates from the iSAM2 optimzation. report_on_progress(graph, current_estimate, i) initial_estimate.clear() # Print the final covariance matrix for each pose after completing inference on the trajectory. marginals = gtsam.Marginals(graph, current_estimate) i = 1 for i in range(1, len(true_odometry)+1): print(f"X{i} covariance:\n{marginals.marginalCovariance(i)}\n") plt.ioff() plt.show() if __name__ == "__main__": Pose2SLAM_ISAM2_example()

0.869977

0.750256

from modules.extractors.doc import DocExtractor from modules.metadata import Metadata import rtf2xml.ParseRtf class RtfExtractor(DocExtractor): """Class for handling RTF file data extraction. We will mostly be using the same logic as for MSDoc files, but we need another way to extract metadata, as wVSummary doesn't work on RTF """ NS_RTF = "http://rtf2xml.sourceforge.net/" def _namespace(self, element): import re m = re.match('\{.*\}', element.tag) return m.group(0) if m else '' def _tag_name(self, element): import re m = re.match('\{.*\}(.*)', element.tag) return m.group(1) if m else '' def get_metadata(self): """Returns a metadata.Metadata object See http://www.biblioscape.com/rtf15_spec.htm for RTF metadata specification """ import os temp_filename = os.path.join("temp", "tmp.rtf.xml") parse_obj = rtf2xml.ParseRtf.ParseRtf(in_file=self.path, out_file=temp_filename) parse_obj.parse_rtf() metadata = Metadata() import xml.etree.ElementTree as ET tree = ET.parse(temp_filename) root = tree.getroot() section = root.find(".//{%s}doc-information" % self.NS_RTF) if len(section) > 0: for tag in section.iterfind(".//*"): tag_name = self._tag_name(tag) if tag.text is not None: metadata.add({tag_name: tag.text}) elif tag.get("year") is not None and tag.get("year") != "0": date_parts = [] date_parts.append(tag.get("year")) date_parts.append(tag.get("month").zfill(2) or "01") date_parts.append(tag.get("day").zfill(2) or "01") date_str = "-".join(date_parts) metadata.add({tag_name: date_str}) os.unlink(temp_filename) return metadata if __name__ == "__main__": print "This module is only intended to be called from other scripts." import sys sys.exit()

modules/extractors/rtf.py

from modules.extractors.doc import DocExtractor from modules.metadata import Metadata import rtf2xml.ParseRtf class RtfExtractor(DocExtractor): """Class for handling RTF file data extraction. We will mostly be using the same logic as for MSDoc files, but we need another way to extract metadata, as wVSummary doesn't work on RTF """ NS_RTF = "http://rtf2xml.sourceforge.net/" def _namespace(self, element): import re m = re.match('\{.*\}', element.tag) return m.group(0) if m else '' def _tag_name(self, element): import re m = re.match('\{.*\}(.*)', element.tag) return m.group(1) if m else '' def get_metadata(self): """Returns a metadata.Metadata object See http://www.biblioscape.com/rtf15_spec.htm for RTF metadata specification """ import os temp_filename = os.path.join("temp", "tmp.rtf.xml") parse_obj = rtf2xml.ParseRtf.ParseRtf(in_file=self.path, out_file=temp_filename) parse_obj.parse_rtf() metadata = Metadata() import xml.etree.ElementTree as ET tree = ET.parse(temp_filename) root = tree.getroot() section = root.find(".//{%s}doc-information" % self.NS_RTF) if len(section) > 0: for tag in section.iterfind(".//*"): tag_name = self._tag_name(tag) if tag.text is not None: metadata.add({tag_name: tag.text}) elif tag.get("year") is not None and tag.get("year") != "0": date_parts = [] date_parts.append(tag.get("year")) date_parts.append(tag.get("month").zfill(2) or "01") date_parts.append(tag.get("day").zfill(2) or "01") date_str = "-".join(date_parts) metadata.add({tag_name: date_str}) os.unlink(temp_filename) return metadata if __name__ == "__main__": print "This module is only intended to be called from other scripts." import sys sys.exit()

0.555918

0.302404

__author__ = '<NAME>' import argparse from RouToolPa.Routines import MatplotlibRoutines parser = argparse.ArgumentParser() parser.add_argument("-i", "--input", action="store", dest="input", required=True, type=lambda s : s.split(","), help="Comma separated list of two input files with data") parser.add_argument("-d", "--index", action="store", dest="index", type=lambda s: map(int, s.split(",")), help="Zero based indexes of file columns to use. Default: all ") parser.add_argument("-o", "--output_prefix", action="store", dest="output_prefix", help="Prefix of output files") parser.add_argument("-s", "--separator", action="store", dest="separator", default=None, help="Separator between values in input file. Default - any space symbol") parser.add_argument("-l", "--xlabel", action="store", dest="xlabel", type=lambda s: s.split(","), help="Comma-separated list of X labels") parser.add_argument("-y", "--ylabel", action="store", dest="ylabel", type=lambda s: s.split(","), help="Comma-separated list of Y labels") parser.add_argument("-b", "--number_of_bins", action="store", dest="number_of_bins", type=lambda s: map(int, s.split(",")), help="Comma-separated list of bin numbers in histograms. " "Incompatible with -w/--width_of_bins option. Default - 30") parser.add_argument("-w", "--width_of_bins", action="store", dest="width_of_bins", type=lambda s: map(float, s.split(",")), help="Comma-separated list of bin widths in histograms. " "Incompatible with -b/--number_of_bins option. Not set by default") parser.add_argument("-n", "--min_value", action="store", dest="min_value", type=lambda s: map(float, s.split(",")), default=0, help="Comma-separated list of minimum value to show. Default - 1") parser.add_argument("-x", "--max_value", action="store", dest="max_value", type=lambda s: map(float, s.split(",")), help="Comma-separated list of maximum value to show. Default - length of longest sequence") parser.add_argument("-g", "--logbase", action="store", dest="logbase", type=int, default=10, help="Logbase to use for log-scaled histograms") parser.add_argument("-e", "--extensions", action="store", dest="extensions", type=lambda x: x.split(","), default=["png"], help="Comma-separated list of extensions for histogram files. Default: png only") parser.add_argument("-t", "--title_list", action="store", dest="title_list", type=lambda s: s.split(","), help="Comma-separated ist of two title for histograms") parser.add_argument("-v", "--share_x_axis", action="store_true", dest="share_x_axis", default=False, help="Share X axis in all histograms. Default: False") parser.add_argument("-z", "--share_y_axis", action="store_true", dest="share_y_axis", default=False, help="Share Y axis in all histograms. Default: False") args = parser.parse_args() if args.index is None: args.index = [None for i in range(0, len(args.input))] if args.max_value is None: args.max_value = [None for i in range(0, len(args.input))] MatplotlibRoutines.draw_tetra_histogram_with_two_logscaled_from_file(args.input, args.index, args.output_prefix, figsize=(10, 10), number_of_bins_list=args.number_of_bins, width_of_bins_list=args.width_of_bins, max_threshold_list=args.max_value, min_threshold_list=args.min_value, xlabel=args.xlabel, ylabel=args.ylabel, title_list=args.title_list, logbase=args.logbase, label_list=None, extensions=args.extensions, suptitle=None, separator=args.separator, share_y_axis=args.share_y_axis, share_x_axis=args.share_x_axis) """ Example: ~/Dropbox/MAVR/scripts/draw/draw_tetra_histogram_with_two_logscaled.py -i kirill.dn.ds.w.tab,solenodon.raw_alns.all.tab -d 3,3 -o dnds.ratio.log -l 'dN/dS' -y "Number of genes" -w 20 -n 0 -x 999 -t "11 species,4 species" """ """ if (args.number_of_bins is not None) and (args.width_of_bins is not None): raise AttributeError("Options -w/--width_of_bins and -b/--number_of_bins mustn't be set simultaneously") lengths = np.fromfile(args.input_file, sep=args.separator) max_len = max(lengths) if args.max_length is None: args.max_length = max_len if (args.max_length != max_len) and (args.min_length != 1): filtered = [] for entry in lengths: if args.min_length <= entry <= args.max_length: filtered.append(entry) else: filtered = lengths figure = plt.figure(1, figsize=(6, 6)) subplot = plt.subplot(1, 1, 1) if args.number_of_bins: bins = args.number_of_bins elif args.width_of_bins: bins = np.arange(args.min_length, args.max_length, args.width_of_bins) #print bins #bins[0] += 1 bins = np.append(bins, [args.max_length]) else: bins = 30 n, bins, patches = plt.hist(lengths, bins=bins) bin_centers = (bins + ((bins[1] - bins[0])/2))[:-1] #print bin_centers #print len(n) #print len(bin_centers) plt.xlim(xmin=args.min_length, xmax=args.max_length) if args.xlabel: plt.xlabel(args.xlabel) if args.ylabel: plt.ylabel(args.ylabel) if args.title: plt.title(args.title) for ext in args.extensions: plt.savefig("%s.%s" % (args.output_prefix, ext)) subplot.set_yscale('log', basey=args.logbase) #subplot.set_xscale('log', basex=args.logbase) for ext in args.extensions: plt.savefig("%s.logscale.%s" % (args.output_prefix, ext)) # save histo values np.savetxt("%s.histo" % args.output_prefix, zip(bin_centers, n), fmt="%i\t%i") """

scripts/draw/draw_tetra_histogram_with_two_logscaled.py

__author__ = '<NAME>' import argparse from RouToolPa.Routines import MatplotlibRoutines parser = argparse.ArgumentParser() parser.add_argument("-i", "--input", action="store", dest="input", required=True, type=lambda s : s.split(","), help="Comma separated list of two input files with data") parser.add_argument("-d", "--index", action="store", dest="index", type=lambda s: map(int, s.split(",")), help="Zero based indexes of file columns to use. Default: all ") parser.add_argument("-o", "--output_prefix", action="store", dest="output_prefix", help="Prefix of output files") parser.add_argument("-s", "--separator", action="store", dest="separator", default=None, help="Separator between values in input file. Default - any space symbol") parser.add_argument("-l", "--xlabel", action="store", dest="xlabel", type=lambda s: s.split(","), help="Comma-separated list of X labels") parser.add_argument("-y", "--ylabel", action="store", dest="ylabel", type=lambda s: s.split(","), help="Comma-separated list of Y labels") parser.add_argument("-b", "--number_of_bins", action="store", dest="number_of_bins", type=lambda s: map(int, s.split(",")), help="Comma-separated list of bin numbers in histograms. " "Incompatible with -w/--width_of_bins option. Default - 30") parser.add_argument("-w", "--width_of_bins", action="store", dest="width_of_bins", type=lambda s: map(float, s.split(",")), help="Comma-separated list of bin widths in histograms. " "Incompatible with -b/--number_of_bins option. Not set by default") parser.add_argument("-n", "--min_value", action="store", dest="min_value", type=lambda s: map(float, s.split(",")), default=0, help="Comma-separated list of minimum value to show. Default - 1") parser.add_argument("-x", "--max_value", action="store", dest="max_value", type=lambda s: map(float, s.split(",")), help="Comma-separated list of maximum value to show. Default - length of longest sequence") parser.add_argument("-g", "--logbase", action="store", dest="logbase", type=int, default=10, help="Logbase to use for log-scaled histograms") parser.add_argument("-e", "--extensions", action="store", dest="extensions", type=lambda x: x.split(","), default=["png"], help="Comma-separated list of extensions for histogram files. Default: png only") parser.add_argument("-t", "--title_list", action="store", dest="title_list", type=lambda s: s.split(","), help="Comma-separated ist of two title for histograms") parser.add_argument("-v", "--share_x_axis", action="store_true", dest="share_x_axis", default=False, help="Share X axis in all histograms. Default: False") parser.add_argument("-z", "--share_y_axis", action="store_true", dest="share_y_axis", default=False, help="Share Y axis in all histograms. Default: False") args = parser.parse_args() if args.index is None: args.index = [None for i in range(0, len(args.input))] if args.max_value is None: args.max_value = [None for i in range(0, len(args.input))] MatplotlibRoutines.draw_tetra_histogram_with_two_logscaled_from_file(args.input, args.index, args.output_prefix, figsize=(10, 10), number_of_bins_list=args.number_of_bins, width_of_bins_list=args.width_of_bins, max_threshold_list=args.max_value, min_threshold_list=args.min_value, xlabel=args.xlabel, ylabel=args.ylabel, title_list=args.title_list, logbase=args.logbase, label_list=None, extensions=args.extensions, suptitle=None, separator=args.separator, share_y_axis=args.share_y_axis, share_x_axis=args.share_x_axis) """ Example: ~/Dropbox/MAVR/scripts/draw/draw_tetra_histogram_with_two_logscaled.py -i kirill.dn.ds.w.tab,solenodon.raw_alns.all.tab -d 3,3 -o dnds.ratio.log -l 'dN/dS' -y "Number of genes" -w 20 -n 0 -x 999 -t "11 species,4 species" """ """ if (args.number_of_bins is not None) and (args.width_of_bins is not None): raise AttributeError("Options -w/--width_of_bins and -b/--number_of_bins mustn't be set simultaneously") lengths = np.fromfile(args.input_file, sep=args.separator) max_len = max(lengths) if args.max_length is None: args.max_length = max_len if (args.max_length != max_len) and (args.min_length != 1): filtered = [] for entry in lengths: if args.min_length <= entry <= args.max_length: filtered.append(entry) else: filtered = lengths figure = plt.figure(1, figsize=(6, 6)) subplot = plt.subplot(1, 1, 1) if args.number_of_bins: bins = args.number_of_bins elif args.width_of_bins: bins = np.arange(args.min_length, args.max_length, args.width_of_bins) #print bins #bins[0] += 1 bins = np.append(bins, [args.max_length]) else: bins = 30 n, bins, patches = plt.hist(lengths, bins=bins) bin_centers = (bins + ((bins[1] - bins[0])/2))[:-1] #print bin_centers #print len(n) #print len(bin_centers) plt.xlim(xmin=args.min_length, xmax=args.max_length) if args.xlabel: plt.xlabel(args.xlabel) if args.ylabel: plt.ylabel(args.ylabel) if args.title: plt.title(args.title) for ext in args.extensions: plt.savefig("%s.%s" % (args.output_prefix, ext)) subplot.set_yscale('log', basey=args.logbase) #subplot.set_xscale('log', basex=args.logbase) for ext in args.extensions: plt.savefig("%s.logscale.%s" % (args.output_prefix, ext)) # save histo values np.savetxt("%s.histo" % args.output_prefix, zip(bin_centers, n), fmt="%i\t%i") """

0.328206

0.171755

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ class reverse_metric(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-isis-operational - based on the path /isis-state/router-isis-config/reverse-metric. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: IS-IS system level reverse-metric configuration """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__reverse_metric_value','__rev_metric_whole_lan','__rev_metric_te_def_metric','__rev_metric_tlv_type',) _yang_name = 'reverse-metric' _rest_name = 'reverse-metric' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__rev_metric_whole_lan = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-whole-lan", rest_name="rev-metric-whole-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) self.__rev_metric_te_def_metric = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-te-def-metric", rest_name="rev-metric-te-def-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) self.__rev_metric_tlv_type = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="rev-metric-tlv-type", rest_name="rev-metric-tlv-type", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) self.__reverse_metric_value = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="reverse-metric-value", rest_name="reverse-metric-value", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'isis-state', u'router-isis-config', u'reverse-metric'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'isis-state', u'router-isis-config', u'reverse-metric'] def _get_reverse_metric_value(self): """ Getter method for reverse_metric_value, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/reverse_metric_value (uint32) YANG Description: IS-IS reverse metric value """ return self.__reverse_metric_value def _set_reverse_metric_value(self, v, load=False): """ Setter method for reverse_metric_value, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/reverse_metric_value (uint32) If this variable is read-only (config: false) in the source YANG file, then _set_reverse_metric_value is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_reverse_metric_value() directly. YANG Description: IS-IS reverse metric value """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="reverse-metric-value", rest_name="reverse-metric-value", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """reverse_metric_value must be of a type compatible with uint32""", 'defined-type': "uint32", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="reverse-metric-value", rest_name="reverse-metric-value", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False)""", }) self.__reverse_metric_value = t if hasattr(self, '_set'): self._set() def _unset_reverse_metric_value(self): self.__reverse_metric_value = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="reverse-metric-value", rest_name="reverse-metric-value", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) def _get_rev_metric_whole_lan(self): """ Getter method for rev_metric_whole_lan, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/rev_metric_whole_lan (isis-status) YANG Description: If IS-IS metric to be changed across whole LAN """ return self.__rev_metric_whole_lan def _set_rev_metric_whole_lan(self, v, load=False): """ Setter method for rev_metric_whole_lan, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/rev_metric_whole_lan (isis-status) If this variable is read-only (config: false) in the source YANG file, then _set_rev_metric_whole_lan is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_rev_metric_whole_lan() directly. YANG Description: If IS-IS metric to be changed across whole LAN """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-whole-lan", rest_name="rev-metric-whole-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """rev_metric_whole_lan must be of a type compatible with isis-status""", 'defined-type': "brocade-isis-operational:isis-status", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-whole-lan", rest_name="rev-metric-whole-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False)""", }) self.__rev_metric_whole_lan = t if hasattr(self, '_set'): self._set() def _unset_rev_metric_whole_lan(self): self.__rev_metric_whole_lan = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-whole-lan", rest_name="rev-metric-whole-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) def _get_rev_metric_te_def_metric(self): """ Getter method for rev_metric_te_def_metric, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/rev_metric_te_def_metric (isis-status) YANG Description: If TE default-metric subtlv has to be updated """ return self.__rev_metric_te_def_metric def _set_rev_metric_te_def_metric(self, v, load=False): """ Setter method for rev_metric_te_def_metric, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/rev_metric_te_def_metric (isis-status) If this variable is read-only (config: false) in the source YANG file, then _set_rev_metric_te_def_metric is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_rev_metric_te_def_metric() directly. YANG Description: If TE default-metric subtlv has to be updated """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-te-def-metric", rest_name="rev-metric-te-def-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """rev_metric_te_def_metric must be of a type compatible with isis-status""", 'defined-type': "brocade-isis-operational:isis-status", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-te-def-metric", rest_name="rev-metric-te-def-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False)""", }) self.__rev_metric_te_def_metric = t if hasattr(self, '_set'): self._set() def _unset_rev_metric_te_def_metric(self): self.__rev_metric_te_def_metric = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-te-def-metric", rest_name="rev-metric-te-def-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) def _get_rev_metric_tlv_type(self): """ Getter method for rev_metric_tlv_type, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/rev_metric_tlv_type (uint32) YANG Description: IS-IS reverse metric TLV type """ return self.__rev_metric_tlv_type def _set_rev_metric_tlv_type(self, v, load=False): """ Setter method for rev_metric_tlv_type, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/rev_metric_tlv_type (uint32) If this variable is read-only (config: false) in the source YANG file, then _set_rev_metric_tlv_type is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_rev_metric_tlv_type() directly. YANG Description: IS-IS reverse metric TLV type """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="rev-metric-tlv-type", rest_name="rev-metric-tlv-type", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """rev_metric_tlv_type must be of a type compatible with uint32""", 'defined-type': "uint32", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="rev-metric-tlv-type", rest_name="rev-metric-tlv-type", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False)""", }) self.__rev_metric_tlv_type = t if hasattr(self, '_set'): self._set() def _unset_rev_metric_tlv_type(self): self.__rev_metric_tlv_type = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="rev-metric-tlv-type", rest_name="rev-metric-tlv-type", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) reverse_metric_value = __builtin__.property(_get_reverse_metric_value) rev_metric_whole_lan = __builtin__.property(_get_rev_metric_whole_lan) rev_metric_te_def_metric = __builtin__.property(_get_rev_metric_te_def_metric) rev_metric_tlv_type = __builtin__.property(_get_rev_metric_tlv_type) _pyangbind_elements = {'reverse_metric_value': reverse_metric_value, 'rev_metric_whole_lan': rev_metric_whole_lan, 'rev_metric_te_def_metric': rev_metric_te_def_metric, 'rev_metric_tlv_type': rev_metric_tlv_type, }

pybind/slxos/v16r_1_00b/isis_state/router_isis_config/reverse_metric/__init__.py

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ class reverse_metric(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-isis-operational - based on the path /isis-state/router-isis-config/reverse-metric. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: IS-IS system level reverse-metric configuration """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__reverse_metric_value','__rev_metric_whole_lan','__rev_metric_te_def_metric','__rev_metric_tlv_type',) _yang_name = 'reverse-metric' _rest_name = 'reverse-metric' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__rev_metric_whole_lan = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-whole-lan", rest_name="rev-metric-whole-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) self.__rev_metric_te_def_metric = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-te-def-metric", rest_name="rev-metric-te-def-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) self.__rev_metric_tlv_type = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="rev-metric-tlv-type", rest_name="rev-metric-tlv-type", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) self.__reverse_metric_value = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="reverse-metric-value", rest_name="reverse-metric-value", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'isis-state', u'router-isis-config', u'reverse-metric'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'isis-state', u'router-isis-config', u'reverse-metric'] def _get_reverse_metric_value(self): """ Getter method for reverse_metric_value, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/reverse_metric_value (uint32) YANG Description: IS-IS reverse metric value """ return self.__reverse_metric_value def _set_reverse_metric_value(self, v, load=False): """ Setter method for reverse_metric_value, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/reverse_metric_value (uint32) If this variable is read-only (config: false) in the source YANG file, then _set_reverse_metric_value is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_reverse_metric_value() directly. YANG Description: IS-IS reverse metric value """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="reverse-metric-value", rest_name="reverse-metric-value", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """reverse_metric_value must be of a type compatible with uint32""", 'defined-type': "uint32", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="reverse-metric-value", rest_name="reverse-metric-value", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False)""", }) self.__reverse_metric_value = t if hasattr(self, '_set'): self._set() def _unset_reverse_metric_value(self): self.__reverse_metric_value = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="reverse-metric-value", rest_name="reverse-metric-value", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) def _get_rev_metric_whole_lan(self): """ Getter method for rev_metric_whole_lan, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/rev_metric_whole_lan (isis-status) YANG Description: If IS-IS metric to be changed across whole LAN """ return self.__rev_metric_whole_lan def _set_rev_metric_whole_lan(self, v, load=False): """ Setter method for rev_metric_whole_lan, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/rev_metric_whole_lan (isis-status) If this variable is read-only (config: false) in the source YANG file, then _set_rev_metric_whole_lan is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_rev_metric_whole_lan() directly. YANG Description: If IS-IS metric to be changed across whole LAN """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-whole-lan", rest_name="rev-metric-whole-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """rev_metric_whole_lan must be of a type compatible with isis-status""", 'defined-type': "brocade-isis-operational:isis-status", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-whole-lan", rest_name="rev-metric-whole-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False)""", }) self.__rev_metric_whole_lan = t if hasattr(self, '_set'): self._set() def _unset_rev_metric_whole_lan(self): self.__rev_metric_whole_lan = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-whole-lan", rest_name="rev-metric-whole-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) def _get_rev_metric_te_def_metric(self): """ Getter method for rev_metric_te_def_metric, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/rev_metric_te_def_metric (isis-status) YANG Description: If TE default-metric subtlv has to be updated """ return self.__rev_metric_te_def_metric def _set_rev_metric_te_def_metric(self, v, load=False): """ Setter method for rev_metric_te_def_metric, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/rev_metric_te_def_metric (isis-status) If this variable is read-only (config: false) in the source YANG file, then _set_rev_metric_te_def_metric is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_rev_metric_te_def_metric() directly. YANG Description: If TE default-metric subtlv has to be updated """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-te-def-metric", rest_name="rev-metric-te-def-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """rev_metric_te_def_metric must be of a type compatible with isis-status""", 'defined-type': "brocade-isis-operational:isis-status", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-te-def-metric", rest_name="rev-metric-te-def-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False)""", }) self.__rev_metric_te_def_metric = t if hasattr(self, '_set'): self._set() def _unset_rev_metric_te_def_metric(self): self.__rev_metric_te_def_metric = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'is-enabled': {'value': 1}, u'is-disabled': {'value': 0}},), is_leaf=True, yang_name="rev-metric-te-def-metric", rest_name="rev-metric-te-def-metric", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='isis-status', is_config=False) def _get_rev_metric_tlv_type(self): """ Getter method for rev_metric_tlv_type, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/rev_metric_tlv_type (uint32) YANG Description: IS-IS reverse metric TLV type """ return self.__rev_metric_tlv_type def _set_rev_metric_tlv_type(self, v, load=False): """ Setter method for rev_metric_tlv_type, mapped from YANG variable /isis_state/router_isis_config/reverse_metric/rev_metric_tlv_type (uint32) If this variable is read-only (config: false) in the source YANG file, then _set_rev_metric_tlv_type is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_rev_metric_tlv_type() directly. YANG Description: IS-IS reverse metric TLV type """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="rev-metric-tlv-type", rest_name="rev-metric-tlv-type", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """rev_metric_tlv_type must be of a type compatible with uint32""", 'defined-type': "uint32", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="rev-metric-tlv-type", rest_name="rev-metric-tlv-type", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False)""", }) self.__rev_metric_tlv_type = t if hasattr(self, '_set'): self._set() def _unset_rev_metric_tlv_type(self): self.__rev_metric_tlv_type = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="rev-metric-tlv-type", rest_name="rev-metric-tlv-type", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-isis-operational', defining_module='brocade-isis-operational', yang_type='uint32', is_config=False) reverse_metric_value = __builtin__.property(_get_reverse_metric_value) rev_metric_whole_lan = __builtin__.property(_get_rev_metric_whole_lan) rev_metric_te_def_metric = __builtin__.property(_get_rev_metric_te_def_metric) rev_metric_tlv_type = __builtin__.property(_get_rev_metric_tlv_type) _pyangbind_elements = {'reverse_metric_value': reverse_metric_value, 'rev_metric_whole_lan': rev_metric_whole_lan, 'rev_metric_te_def_metric': rev_metric_te_def_metric, 'rev_metric_tlv_type': rev_metric_tlv_type, }

0.680348

0.066448

from typing import Dict, List, Optional, Tuple, Union import re SYNTAX_ERROR = "SyntaxError" RUNTIME_ERROR = "RuntimeError" class Number: def __init__(self, value: int) -> None: self.value = value def run(self, variables: Dict[str, int]) -> int: return self.value class Operator: def __init__(self, left, right) -> None: self.left = left self.right = right def add_num(self, variables: Dict[str, int]) -> None: if self.left not in variables: variables[self.left] = 0 if self.right not in variables: variables[self.right] = 0 class And(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if int(variables[self.left]) != 0 and int(variables[self.right]) != 0 else 0 class Or(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if int(variables[self.left]) != 0 or int(variables[self.right]) != 0 else 0 class Nand(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if not (int(variables[self.left]) != 0 and int(variables[self.right]) != 0) else 0 class Add(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return int(variables[self.left]) + int(variables[self.right]) class Sub(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return int(variables[self.left]) - int(variables[self.right]) class Mul(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return int(variables[self.left]) * int(variables[self.right]) class Div(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return int(int(variables[self.left]) / int(variables[self.right])) class Lt(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if int(variables[self.left]) < int(variables[self.right]) else 0 class Gt(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if variables[self.left] > variables[self.right] else 0 class Eq(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if int(variables[self.left]) == int(variables[self.right]) else 0 class Leq(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if int(variables[self.left]) <= int(variables[self.right]) else 0 class Geq(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if int(variables[self.left]) >= int(variables[self.right]) else 0 class Call(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int], functions: List['Function']) -> Union[int, str]: func_to_run = [fun for fun in functions if fun.function_name == self.left] if len(func_to_run) == 0: return "NameError" if len(func_to_run[0].arguments_names) != len(self.right): return "TypeError" arguments = [] for var in self.right: if var not in variables: variables[var] = 0 arguments.append(variables[var]) rt_val = func_to_run[0].run(arguments, functions) return rt_val built_in_func = { "add": Add, "sub": Sub, "mul": Mul, "div": Div, "lt" : Lt, "gt" : Gt, "eq" : Eq, "leq": Leq, "geq": Geq, "and": And, "or" : Or, "nand": Nand, } class Expression: def __init__(self) -> None: self.variable: Optional[str] = None self.right: Optional[Operator] = None self.code_line_num: Optional[int] = None def check(self, functions: List['Function']) -> Optional[int]: if isinstance(self.right, Call): fun = [fun for fun in functions if fun.function_name == self.right.left and len(fun.arguments_names) == len(self.right.right)] if len(fun) != 1: return self.code_line_num return None def build(self, index: int, line: str, functions: List['Function']) -> Union[int, Tuple[int, str]]: self.code_line_num = index + 1 left, right = line.split("=") self.left = left.split()[0] if not check_variable(self.left): return (self.code_line_num, SYNTAX_ERROR) right_parts = right.split() if len(right_parts) == 0: return (self.code_line_num, SYNTAX_ERROR) elif len(right_parts) == 1 and right_parts[0].lstrip("-").isdigit(): self.right = Number(int(right_parts[0])) elif len(right_parts) == 3 and right_parts[0] in built_in_func: if not (check_variable(right_parts[1]) and check_variable(right_parts[2])): return (self.code_line_num, SYNTAX_ERROR) self.right = built_in_func[right_parts[0]](right_parts[1], right_parts[2]) else: if right_parts[0] in built_in_func: return (self.code_line_num, SYNTAX_ERROR) for var in right_parts[1:]: if not check_variable(var): return (self.code_line_num, SYNTAX_ERROR) self.right = Call(right_parts[0], right_parts[1:]) return index + 1 def run(self, variables: Dict[str, int], functions: List['Function']) -> Optional[Tuple[int, str]]: try: if isinstance(self.right, Call): rt_val = self.right.run(variables, functions) else: rt_val = self.right.run(variables) except: return (self.code_line_num, RUNTIME_ERROR) if not isinstance(rt_val, int): return (self.code_line_num, rt_val) variables[self.left] = rt_val class Scope: def __init__(self) -> None: self.objects_to_run: List[Union["Scope", "Operator", "Number"]] = [] def check_valid_line(self, line: str, pattern: str) -> bool: return re.search(pattern, line) is not None or line.isspace() or line == "" def check(self, functions: List['Function']) -> Optional[int]: for object_to_run in self.objects_to_run: rt_val = object_to_run.check(functions) if rt_val is not None: return rt_val return None def build_scope(self, index: int, code_lines: List[str], functions: List['Function'], indent: str) \ -> Union[int, Tuple[int, str]]: pattern = "^" + indent + "[a-zA-Z].*$" while index < len(code_lines) and self.check_valid_line(code_lines[index], pattern): line = code_lines[index] if line.isspace() or line == "": index += 1 continue line_parts = line.split() if len(line_parts) < 2: return (index + 1, SYNTAX_ERROR) elif len(line_parts) == 2: if line_parts[0] != "if" and line_parts[1] != "while": return (index + 1, SYNTAX_ERROR) new_object = PredicateScope() rt_val = new_object.build(index, code_lines, functions, indent + " ") if isinstance(rt_val, int): index = rt_val else: return rt_val self.objects_to_run.append(new_object) elif line_parts[1] == "=": new_object = Expression() rt_val = new_object.build(index, line, functions) if isinstance(rt_val, int): index = rt_val else: return rt_val self.objects_to_run.append(new_object) else: return (index + 1, SYNTAX_ERROR) return index class PredicateScope(Scope): def __init__(self) -> None: super().__init__() self.predicate_var: Optional[str] = None self.code_line_num: Optional[int] = None self.type_scp: Optional[str] = None def build(self, index: int, code_lines: List[str], functions: List['Function'], indent: str) -> Union[int, Tuple[int, str]]: self.code_line_num = index + 1 line = code_lines[index] line_parts = line.split() self.type_scp = "if" if line_parts[0] == "if" else "while" if not check_variable(line_parts[1]): return (index + 1, SYNTAX_ERROR) self.predicate_var = line_parts[1] return self.build_scope(index + 1, code_lines, functions, indent) def run(self, variables: Dict[str, int], functions: List['Function']) -> Optional[Tuple[int, str]]: if self.predicate_var not in variables: variables[self.predicate_var] = 0 return None if self.type_scp == "if" and variables[self.predicate_var] != 0: for line in self.objects_to_run: rt_val = line.run(variables, functions) if rt_val is not None: return rt_val while self.type_scp == "while" and variables[self.predicate_var] != 0: for line in self.objects_to_run: rt_val = line.run(variables, functions) if rt_val is not None: return rt_val class Function(Scope): def __init__(self) -> None: super().__init__() self.function_name: Optional[str] = None self.arguments_names: List[str] = [] self.line_num: Optional[int] = None def check_function_name(self, functions: List['Function']) -> bool: if re.search("^[a-zA-Z][a-zA-Z0-9_]*$", self.function_name) is None: return False if self.function_name in built_in_func: return False if any(map(lambda func: func.function_name == self.function_name, functions)): return False return True def build(self, index: int, code_lines: List[str], functions: List['Function']) -> Union[int, Tuple[int, str]]: self.line_num = index + 1 line = code_lines[index] header = line.split() if len(header) < 2: return (self.line_num, SYNTAX_ERROR) _, self.function_name, *arguments = header self.arguments_names = arguments for var in self.arguments_names: if not check_variable(var): return (self.line_num, SYNTAX_ERROR) if not self.check_function_name(functions): return (self.line_num, SYNTAX_ERROR) functions.append(self) return self.build_scope(index + 1, code_lines, functions, " ") def run(self, variables: List[str], functions: List['Function']) -> Union[int, Tuple[int, str]]: if len(self.arguments_names) != len(variables): return (self.line_num, RUNTIME_ERROR) variables_dict = {} variables_dict[self.function_name] = 0 for var, val in zip(self.arguments_names, variables): variables_dict[var] = val for line in self.objects_to_run: rt_val = line.run(variables_dict, functions) if rt_val is not None: return rt_val return variables_dict[self.function_name] class Interpreter: def __init__(self, code_str: str) -> None: self.code_lines: List[str] = code_str.split("\n") self.functions: List["Function"] = [] def func_check(self) -> Optional[int]: for fun in self.functions: rt_val = fun.check(self.functions) if rt_val is not None: return rt_val return None def build(self) -> Optional[Tuple[int, str]]: index = 0 while index < len(self.code_lines): line = self.code_lines[index] if line.isspace() or line == "": index += 1 elif line.startswith("def"): new_function = Function() rt_val = new_function.build(index, self.code_lines, self.functions) if isinstance(rt_val, int): index = rt_val else: return rt_val else: return (index + 1, SYNTAX_ERROR) rt_val = self.func_check() if rt_val is not None: return (rt_val, SYNTAX_ERROR) def run(self, func_name: str, variables: List[str]) -> Union[int, Tuple[int, str]]: function_to_run = [fun for fun in self.functions if fun.function_name == func_name] if len(function_to_run) != 1: return (0, RUNTIME_ERROR) return function_to_run[0].run(variables, self.functions) def check_variable(name: str) -> bool: return re.search("^[a-zA-Z][a-zA-Z0-9_]*$", name) is not None def do_rec(code: str, func_name: str, *args) -> Union[int, Tuple[int, str]]: interpreter = Interpreter(code) rt_val = interpreter.build() if rt_val is not None: return rt_val return interpreter.run(func_name, args)

rec.py

from typing import Dict, List, Optional, Tuple, Union import re SYNTAX_ERROR = "SyntaxError" RUNTIME_ERROR = "RuntimeError" class Number: def __init__(self, value: int) -> None: self.value = value def run(self, variables: Dict[str, int]) -> int: return self.value class Operator: def __init__(self, left, right) -> None: self.left = left self.right = right def add_num(self, variables: Dict[str, int]) -> None: if self.left not in variables: variables[self.left] = 0 if self.right not in variables: variables[self.right] = 0 class And(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if int(variables[self.left]) != 0 and int(variables[self.right]) != 0 else 0 class Or(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if int(variables[self.left]) != 0 or int(variables[self.right]) != 0 else 0 class Nand(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if not (int(variables[self.left]) != 0 and int(variables[self.right]) != 0) else 0 class Add(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return int(variables[self.left]) + int(variables[self.right]) class Sub(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return int(variables[self.left]) - int(variables[self.right]) class Mul(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return int(variables[self.left]) * int(variables[self.right]) class Div(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return int(int(variables[self.left]) / int(variables[self.right])) class Lt(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if int(variables[self.left]) < int(variables[self.right]) else 0 class Gt(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if variables[self.left] > variables[self.right] else 0 class Eq(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if int(variables[self.left]) == int(variables[self.right]) else 0 class Leq(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if int(variables[self.left]) <= int(variables[self.right]) else 0 class Geq(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int]) -> int: self.add_num(variables) return 1 if int(variables[self.left]) >= int(variables[self.right]) else 0 class Call(Operator): def __init__(self, left, right) -> None: super().__init__(left, right) def run(self, variables: Dict[str, int], functions: List['Function']) -> Union[int, str]: func_to_run = [fun for fun in functions if fun.function_name == self.left] if len(func_to_run) == 0: return "NameError" if len(func_to_run[0].arguments_names) != len(self.right): return "TypeError" arguments = [] for var in self.right: if var not in variables: variables[var] = 0 arguments.append(variables[var]) rt_val = func_to_run[0].run(arguments, functions) return rt_val built_in_func = { "add": Add, "sub": Sub, "mul": Mul, "div": Div, "lt" : Lt, "gt" : Gt, "eq" : Eq, "leq": Leq, "geq": Geq, "and": And, "or" : Or, "nand": Nand, } class Expression: def __init__(self) -> None: self.variable: Optional[str] = None self.right: Optional[Operator] = None self.code_line_num: Optional[int] = None def check(self, functions: List['Function']) -> Optional[int]: if isinstance(self.right, Call): fun = [fun for fun in functions if fun.function_name == self.right.left and len(fun.arguments_names) == len(self.right.right)] if len(fun) != 1: return self.code_line_num return None def build(self, index: int, line: str, functions: List['Function']) -> Union[int, Tuple[int, str]]: self.code_line_num = index + 1 left, right = line.split("=") self.left = left.split()[0] if not check_variable(self.left): return (self.code_line_num, SYNTAX_ERROR) right_parts = right.split() if len(right_parts) == 0: return (self.code_line_num, SYNTAX_ERROR) elif len(right_parts) == 1 and right_parts[0].lstrip("-").isdigit(): self.right = Number(int(right_parts[0])) elif len(right_parts) == 3 and right_parts[0] in built_in_func: if not (check_variable(right_parts[1]) and check_variable(right_parts[2])): return (self.code_line_num, SYNTAX_ERROR) self.right = built_in_func[right_parts[0]](right_parts[1], right_parts[2]) else: if right_parts[0] in built_in_func: return (self.code_line_num, SYNTAX_ERROR) for var in right_parts[1:]: if not check_variable(var): return (self.code_line_num, SYNTAX_ERROR) self.right = Call(right_parts[0], right_parts[1:]) return index + 1 def run(self, variables: Dict[str, int], functions: List['Function']) -> Optional[Tuple[int, str]]: try: if isinstance(self.right, Call): rt_val = self.right.run(variables, functions) else: rt_val = self.right.run(variables) except: return (self.code_line_num, RUNTIME_ERROR) if not isinstance(rt_val, int): return (self.code_line_num, rt_val) variables[self.left] = rt_val class Scope: def __init__(self) -> None: self.objects_to_run: List[Union["Scope", "Operator", "Number"]] = [] def check_valid_line(self, line: str, pattern: str) -> bool: return re.search(pattern, line) is not None or line.isspace() or line == "" def check(self, functions: List['Function']) -> Optional[int]: for object_to_run in self.objects_to_run: rt_val = object_to_run.check(functions) if rt_val is not None: return rt_val return None def build_scope(self, index: int, code_lines: List[str], functions: List['Function'], indent: str) \ -> Union[int, Tuple[int, str]]: pattern = "^" + indent + "[a-zA-Z].*$" while index < len(code_lines) and self.check_valid_line(code_lines[index], pattern): line = code_lines[index] if line.isspace() or line == "": index += 1 continue line_parts = line.split() if len(line_parts) < 2: return (index + 1, SYNTAX_ERROR) elif len(line_parts) == 2: if line_parts[0] != "if" and line_parts[1] != "while": return (index + 1, SYNTAX_ERROR) new_object = PredicateScope() rt_val = new_object.build(index, code_lines, functions, indent + " ") if isinstance(rt_val, int): index = rt_val else: return rt_val self.objects_to_run.append(new_object) elif line_parts[1] == "=": new_object = Expression() rt_val = new_object.build(index, line, functions) if isinstance(rt_val, int): index = rt_val else: return rt_val self.objects_to_run.append(new_object) else: return (index + 1, SYNTAX_ERROR) return index class PredicateScope(Scope): def __init__(self) -> None: super().__init__() self.predicate_var: Optional[str] = None self.code_line_num: Optional[int] = None self.type_scp: Optional[str] = None def build(self, index: int, code_lines: List[str], functions: List['Function'], indent: str) -> Union[int, Tuple[int, str]]: self.code_line_num = index + 1 line = code_lines[index] line_parts = line.split() self.type_scp = "if" if line_parts[0] == "if" else "while" if not check_variable(line_parts[1]): return (index + 1, SYNTAX_ERROR) self.predicate_var = line_parts[1] return self.build_scope(index + 1, code_lines, functions, indent) def run(self, variables: Dict[str, int], functions: List['Function']) -> Optional[Tuple[int, str]]: if self.predicate_var not in variables: variables[self.predicate_var] = 0 return None if self.type_scp == "if" and variables[self.predicate_var] != 0: for line in self.objects_to_run: rt_val = line.run(variables, functions) if rt_val is not None: return rt_val while self.type_scp == "while" and variables[self.predicate_var] != 0: for line in self.objects_to_run: rt_val = line.run(variables, functions) if rt_val is not None: return rt_val class Function(Scope): def __init__(self) -> None: super().__init__() self.function_name: Optional[str] = None self.arguments_names: List[str] = [] self.line_num: Optional[int] = None def check_function_name(self, functions: List['Function']) -> bool: if re.search("^[a-zA-Z][a-zA-Z0-9_]*$", self.function_name) is None: return False if self.function_name in built_in_func: return False if any(map(lambda func: func.function_name == self.function_name, functions)): return False return True def build(self, index: int, code_lines: List[str], functions: List['Function']) -> Union[int, Tuple[int, str]]: self.line_num = index + 1 line = code_lines[index] header = line.split() if len(header) < 2: return (self.line_num, SYNTAX_ERROR) _, self.function_name, *arguments = header self.arguments_names = arguments for var in self.arguments_names: if not check_variable(var): return (self.line_num, SYNTAX_ERROR) if not self.check_function_name(functions): return (self.line_num, SYNTAX_ERROR) functions.append(self) return self.build_scope(index + 1, code_lines, functions, " ") def run(self, variables: List[str], functions: List['Function']) -> Union[int, Tuple[int, str]]: if len(self.arguments_names) != len(variables): return (self.line_num, RUNTIME_ERROR) variables_dict = {} variables_dict[self.function_name] = 0 for var, val in zip(self.arguments_names, variables): variables_dict[var] = val for line in self.objects_to_run: rt_val = line.run(variables_dict, functions) if rt_val is not None: return rt_val return variables_dict[self.function_name] class Interpreter: def __init__(self, code_str: str) -> None: self.code_lines: List[str] = code_str.split("\n") self.functions: List["Function"] = [] def func_check(self) -> Optional[int]: for fun in self.functions: rt_val = fun.check(self.functions) if rt_val is not None: return rt_val return None def build(self) -> Optional[Tuple[int, str]]: index = 0 while index < len(self.code_lines): line = self.code_lines[index] if line.isspace() or line == "": index += 1 elif line.startswith("def"): new_function = Function() rt_val = new_function.build(index, self.code_lines, self.functions) if isinstance(rt_val, int): index = rt_val else: return rt_val else: return (index + 1, SYNTAX_ERROR) rt_val = self.func_check() if rt_val is not None: return (rt_val, SYNTAX_ERROR) def run(self, func_name: str, variables: List[str]) -> Union[int, Tuple[int, str]]: function_to_run = [fun for fun in self.functions if fun.function_name == func_name] if len(function_to_run) != 1: return (0, RUNTIME_ERROR) return function_to_run[0].run(variables, self.functions) def check_variable(name: str) -> bool: return re.search("^[a-zA-Z][a-zA-Z0-9_]*$", name) is not None def do_rec(code: str, func_name: str, *args) -> Union[int, Tuple[int, str]]: interpreter = Interpreter(code) rt_val = interpreter.build() if rt_val is not None: return rt_val return interpreter.run(func_name, args)

0.84916

0.419172

import numpy as np from atom.api import (Enum, Str, set_default) from exopy.tasks.api import SimpleTask, validators ARR_VAL = validators.Feval(types=np.ndarray) class ArrayExtremaTask(SimpleTask): """ Store the pair(s) of index/value for the extrema(s) of an array. Wait for any parallel operation before execution. """ #: Name of the target in the database. target_array = Str().tag(pref=True, feval=ARR_VAL) #: Name of the column into which the extrema should be looked for. column_name = Str().tag(pref=True) #: Flag indicating which extremum shiul be lookd for. mode = Enum('Max', 'Min', 'Max & min').tag(pref=True) database_entries = set_default({'max_ind': 0, 'max_value': 1.0}) wait = set_default({'activated': True}) # Wait on all pools by default. def perform(self): """ Find extrema of database array and store index/value pairs. """ array = self.format_and_eval_string(self.target_array) if self.column_name: array = array[self.column_name] if self.mode == 'Max' or self.mode == 'Max & min': ind = np.argmax(array) val = array[ind] self.write_in_database('max_ind', ind) self.write_in_database('max_value', val) if self.mode == 'Min' or self.mode == 'Max & min': ind = np.argmin(array) val = array[ind] self.write_in_database('min_ind', ind) self.write_in_database('min_value', val) def check(self, *args, **kwargs): """ Check the target array can be found and has the right column. """ test, traceback = super(ArrayExtremaTask, self).check(*args, **kwargs) if not test: return test, traceback array = self.format_and_eval_string(self.target_array) err_path = self.get_error_path() if self.column_name: if array.dtype.names: names = array.dtype.names if self.column_name not in names: msg = 'No column named {} in array. (column are : {})' traceback[err_path] = msg.format(self.column_name, names) return False, traceback else: traceback[err_path] = 'Array has no named columns' return False, traceback else: if array.dtype.names: msg = 'The target array has names columns : {}. Choose one' traceback[err_path] = msg.format(array.dtype.names) return False, traceback elif len(array.shape) > 1: msg = 'Must use 1d array when using non record arrays.' traceback[err_path] = msg return False, traceback return test, traceback def _post_setattr_mode(self, old, new): """ Update the database entries according to the mode. """ if new == 'Max': self.database_entries = {'max_ind': 0, 'max_value': 2.0} elif new == 'Min': self.database_entries = {'min_ind': 0, 'min_value': 1.0} else: self.database_entries = {'max_ind': 0, 'max_value': 2.0, 'min_ind': 0, 'min_value': 1.0} class ArrayFindValueTask(SimpleTask): """ Store the index of the first occurence of a value in an array. Wait for any parallel operation before execution. """ #: Name of the target in the database. target_array = Str().tag(pref=True, feval=ARR_VAL) #: Name of the column into which the extrema should be looked for. column_name = Str().tag(pref=True) #: Value which should be looked for in the array. value = Str().tag(pref=True, feval=validators.Feval()) database_entries = set_default({'index': 0}) wait = set_default({'activated': True}) # Wait on all pools by default. def perform(self): """ Find index of value array and store index in database. """ array = self.format_and_eval_string(self.target_array) if self.column_name: array = array[self.column_name] val = self.format_and_eval_string(self.value) try: ind = np.where(np.abs(array - val) < 1e-12)[0][0] except IndexError as e: msg = 'Could not find {} in array {} ({})' raise ValueError(msg.format(val, self.target_array, array)) from e self.write_in_database('index', ind) def check(self, *args, **kwargs): """ Check the target array can be found and has the right column. """ test, traceback = super(ArrayFindValueTask, self).check(*args, **kwargs) if not test: return test, traceback err_path = self.get_error_path() array = self.format_and_eval_string(self.target_array) if self.column_name: if array.dtype.names: names = array.dtype.names if self.column_name not in names: msg = 'No column named {} in array. (column are : {})' traceback[err_path] = msg.format(self.column_name, names) return False, traceback else: traceback[err_path] = 'Array has no named columns' return False, traceback else: if array.dtype.names: msg = 'The target array has names columns : {}. Choose one' traceback[err_path] = msg.format(array.dtype.names) return False, traceback elif len(array.shape) > 1: msg = 'Must use 1d array when using non record arrays.' traceback[err_path] = msg return False, traceback return test, traceback

exopy_hqc_legacy/tasks/tasks/util/array_tasks.py

import numpy as np from atom.api import (Enum, Str, set_default) from exopy.tasks.api import SimpleTask, validators ARR_VAL = validators.Feval(types=np.ndarray) class ArrayExtremaTask(SimpleTask): """ Store the pair(s) of index/value for the extrema(s) of an array. Wait for any parallel operation before execution. """ #: Name of the target in the database. target_array = Str().tag(pref=True, feval=ARR_VAL) #: Name of the column into which the extrema should be looked for. column_name = Str().tag(pref=True) #: Flag indicating which extremum shiul be lookd for. mode = Enum('Max', 'Min', 'Max & min').tag(pref=True) database_entries = set_default({'max_ind': 0, 'max_value': 1.0}) wait = set_default({'activated': True}) # Wait on all pools by default. def perform(self): """ Find extrema of database array and store index/value pairs. """ array = self.format_and_eval_string(self.target_array) if self.column_name: array = array[self.column_name] if self.mode == 'Max' or self.mode == 'Max & min': ind = np.argmax(array) val = array[ind] self.write_in_database('max_ind', ind) self.write_in_database('max_value', val) if self.mode == 'Min' or self.mode == 'Max & min': ind = np.argmin(array) val = array[ind] self.write_in_database('min_ind', ind) self.write_in_database('min_value', val) def check(self, *args, **kwargs): """ Check the target array can be found and has the right column. """ test, traceback = super(ArrayExtremaTask, self).check(*args, **kwargs) if not test: return test, traceback array = self.format_and_eval_string(self.target_array) err_path = self.get_error_path() if self.column_name: if array.dtype.names: names = array.dtype.names if self.column_name not in names: msg = 'No column named {} in array. (column are : {})' traceback[err_path] = msg.format(self.column_name, names) return False, traceback else: traceback[err_path] = 'Array has no named columns' return False, traceback else: if array.dtype.names: msg = 'The target array has names columns : {}. Choose one' traceback[err_path] = msg.format(array.dtype.names) return False, traceback elif len(array.shape) > 1: msg = 'Must use 1d array when using non record arrays.' traceback[err_path] = msg return False, traceback return test, traceback def _post_setattr_mode(self, old, new): """ Update the database entries according to the mode. """ if new == 'Max': self.database_entries = {'max_ind': 0, 'max_value': 2.0} elif new == 'Min': self.database_entries = {'min_ind': 0, 'min_value': 1.0} else: self.database_entries = {'max_ind': 0, 'max_value': 2.0, 'min_ind': 0, 'min_value': 1.0} class ArrayFindValueTask(SimpleTask): """ Store the index of the first occurence of a value in an array. Wait for any parallel operation before execution. """ #: Name of the target in the database. target_array = Str().tag(pref=True, feval=ARR_VAL) #: Name of the column into which the extrema should be looked for. column_name = Str().tag(pref=True) #: Value which should be looked for in the array. value = Str().tag(pref=True, feval=validators.Feval()) database_entries = set_default({'index': 0}) wait = set_default({'activated': True}) # Wait on all pools by default. def perform(self): """ Find index of value array and store index in database. """ array = self.format_and_eval_string(self.target_array) if self.column_name: array = array[self.column_name] val = self.format_and_eval_string(self.value) try: ind = np.where(np.abs(array - val) < 1e-12)[0][0] except IndexError as e: msg = 'Could not find {} in array {} ({})' raise ValueError(msg.format(val, self.target_array, array)) from e self.write_in_database('index', ind) def check(self, *args, **kwargs): """ Check the target array can be found and has the right column. """ test, traceback = super(ArrayFindValueTask, self).check(*args, **kwargs) if not test: return test, traceback err_path = self.get_error_path() array = self.format_and_eval_string(self.target_array) if self.column_name: if array.dtype.names: names = array.dtype.names if self.column_name not in names: msg = 'No column named {} in array. (column are : {})' traceback[err_path] = msg.format(self.column_name, names) return False, traceback else: traceback[err_path] = 'Array has no named columns' return False, traceback else: if array.dtype.names: msg = 'The target array has names columns : {}. Choose one' traceback[err_path] = msg.format(array.dtype.names) return False, traceback elif len(array.shape) > 1: msg = 'Must use 1d array when using non record arrays.' traceback[err_path] = msg return False, traceback return test, traceback

0.589598

0.442335

from dataclasses import dataclass, field from typing import List import pandas as pd import xlwings as xw import datetime class PyMoiReader: def read(self) -> pd.DataFrame: pass class CsvReader(PyMoiReader): def __init__(self, fullname, delimiter=',', quotechar='"'): self.fullname = fullname self.delimiter = delimiter self.quotechar = quotechar def read(self): df = pd.read_csv(self.fullname, delimiter=self.delimiter, quotechar=self.quotechar) return df class ExcelReader(PyMoiReader): def __init__( self, fullname, seek_start: str, names: list, unit_row: int = 1, sheetname: str = None, ): self.fullname = fullname self.seek_start = seek_start self.unit_row = unit_row self.sheetname = sheetname self.names = names self.parameters = [] self.count = 0 def read(self): xw.App(visible=False) self._wb = xw.Book( self.fullname, read_only=True, ignore_read_only_recommended=True ) # sheetnameが指定されていない場合は最初のシートを対象とする self._sht = self._wb.sheets[self.sheetname if self.sheetname else 0] # 読込み行数を取得 while self._sht.range(self.seek_start).offset(row_offset=self.count).value: self.count += self.unit_row buffer = [] for param in self.parameters: # fixed, cell if isinstance(param, StaticParameter): if isinstance(param, FixedParameter): ser = pd.Series([param.value] * self.count) elif isinstance(param, CellParameter): ser = pd.Series( [self._sht.range(param.cell).value] * self.count) buffer.append(ser) # direction, repeat elif isinstance(param, DynamicParameter): for j in range(param.number): # 始点セルと終点セルを取得 r1 = self._sht.range( param.column + str(self._sht.range(self.seek_start).row) ).offset(column_offset=j) r2 = r1.offset(row_offset=self.count - 1) ser = pd.Series(self._sht.range(r1, r2).value) # Repeatの場合はnaを直前の値で埋める if isinstance(param, RepeatParameter): ser.ffill(inplace=True) buffer.append(ser) self._wb.close() df = pd.DataFrame({k: v for k, v in zip(range(len(buffer)), buffer)}) df.columns = self.names return df @dataclass class Parameter: pass @dataclass class StaticParameter(Parameter): pass @dataclass class DynamicParameter(Parameter): pass @dataclass class FixedParameter(StaticParameter): value: str # TODO: システム日付と本日の違いは？ __reserved_params = { "#システム日時": datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "#システム日付": datetime.datetime.today().strftime('%Y-%m-%d'), "#本日": datetime.datetime.today().strftime('%Y-%m-%d'), } def __post_init__(self): self.value = self.__reserved_params.get(self.value, self.value) @dataclass class CellParameter(StaticParameter): cell: str @dataclass class DirectionParameter(DynamicParameter): line: int column: str number: int def __init__(self, line: int, column: str, number: int): if line < 1: raise ValueError(f"line must > 0 but {line}") if number < 1: raise ValueError(f'argument "number" must > 0 but {number}') self.line = line self.column = column self.number = number @dataclass class RepeatParameter(DynamicParameter): line: int column: str number: int def __init__(self, line: int, column: str, number: int): if line < 1: raise ValueError if number < 1: raise ValueError self.line = line self.column = column self.number = number

pymoi/reader.py

from dataclasses import dataclass, field from typing import List import pandas as pd import xlwings as xw import datetime class PyMoiReader: def read(self) -> pd.DataFrame: pass class CsvReader(PyMoiReader): def __init__(self, fullname, delimiter=',', quotechar='"'): self.fullname = fullname self.delimiter = delimiter self.quotechar = quotechar def read(self): df = pd.read_csv(self.fullname, delimiter=self.delimiter, quotechar=self.quotechar) return df class ExcelReader(PyMoiReader): def __init__( self, fullname, seek_start: str, names: list, unit_row: int = 1, sheetname: str = None, ): self.fullname = fullname self.seek_start = seek_start self.unit_row = unit_row self.sheetname = sheetname self.names = names self.parameters = [] self.count = 0 def read(self): xw.App(visible=False) self._wb = xw.Book( self.fullname, read_only=True, ignore_read_only_recommended=True ) # sheetnameが指定されていない場合は最初のシートを対象とする self._sht = self._wb.sheets[self.sheetname if self.sheetname else 0] # 読込み行数を取得 while self._sht.range(self.seek_start).offset(row_offset=self.count).value: self.count += self.unit_row buffer = [] for param in self.parameters: # fixed, cell if isinstance(param, StaticParameter): if isinstance(param, FixedParameter): ser = pd.Series([param.value] * self.count) elif isinstance(param, CellParameter): ser = pd.Series( [self._sht.range(param.cell).value] * self.count) buffer.append(ser) # direction, repeat elif isinstance(param, DynamicParameter): for j in range(param.number): # 始点セルと終点セルを取得 r1 = self._sht.range( param.column + str(self._sht.range(self.seek_start).row) ).offset(column_offset=j) r2 = r1.offset(row_offset=self.count - 1) ser = pd.Series(self._sht.range(r1, r2).value) # Repeatの場合はnaを直前の値で埋める if isinstance(param, RepeatParameter): ser.ffill(inplace=True) buffer.append(ser) self._wb.close() df = pd.DataFrame({k: v for k, v in zip(range(len(buffer)), buffer)}) df.columns = self.names return df @dataclass class Parameter: pass @dataclass class StaticParameter(Parameter): pass @dataclass class DynamicParameter(Parameter): pass @dataclass class FixedParameter(StaticParameter): value: str # TODO: システム日付と本日の違いは？ __reserved_params = { "#システム日時": datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "#システム日付": datetime.datetime.today().strftime('%Y-%m-%d'), "#本日": datetime.datetime.today().strftime('%Y-%m-%d'), } def __post_init__(self): self.value = self.__reserved_params.get(self.value, self.value) @dataclass class CellParameter(StaticParameter): cell: str @dataclass class DirectionParameter(DynamicParameter): line: int column: str number: int def __init__(self, line: int, column: str, number: int): if line < 1: raise ValueError(f"line must > 0 but {line}") if number < 1: raise ValueError(f'argument "number" must > 0 but {number}') self.line = line self.column = column self.number = number @dataclass class RepeatParameter(DynamicParameter): line: int column: str number: int def __init__(self, line: int, column: str, number: int): if line < 1: raise ValueError if number < 1: raise ValueError self.line = line self.column = column self.number = number

0.591841

0.226891

import datajoint as dj schema = dj.Schema() def activate(schema_name, create_schema=True, create_tables=True): """ activate(schema_name, create_schema=True, create_tables=True) :param schema_name: schema name on the database server to activate the `lab` element :param create_schema: when True (default), create schema in the database if it does not yet exist. :param create_tables: when True (default), create tables in the database if they do not yet exist. """ schema.activate(schema_name, create_schema=create_schema, create_tables=create_tables) @schema class Lab(dj.Lookup): definition = """ lab : varchar(24) # Abbreviated lab name --- lab_name : varchar(255) # full lab name institution : varchar(255) address : varchar(255) time_zone : varchar(64) """ @schema class Location(dj.Lookup): definition = """ # location of animal housing or experimental rigs -> Lab location : varchar(32) --- location_description='' : varchar(255) """ @schema class UserRole(dj.Lookup): definition = """ user_role : varchar(16) """ @schema class User(dj.Lookup): definition = """ user : varchar(32) --- user_email='' : varchar(128) user_cellphone='' : varchar(32) """ @schema class LabMembership(dj.Lookup): definition = """ -> Lab -> User --- -> [nullable] UserRole """ @schema class ProtocolType(dj.Lookup): definition = """ protocol_type : varchar(32) """ @schema class Protocol(dj.Lookup): definition = """ # protocol approved by some institutions like IACUC, IRB protocol : varchar(16) --- -> ProtocolType protocol_description='' : varchar(255) """ @schema class Project(dj.Lookup): definition = """ project : varchar(32) --- project_description='' : varchar(1024) """ @schema class ProjectUser(dj.Manual): definition = """ -> Project -> User """ @schema class Source(dj.Lookup): definition = """ # source or supplier of animals source : varchar(32) # abbreviated source name --- source_name : varchar(255) contact_details='' : varchar(255) source_description='' : varchar(255) """

element_lab/lab.py

import datajoint as dj schema = dj.Schema() def activate(schema_name, create_schema=True, create_tables=True): """ activate(schema_name, create_schema=True, create_tables=True) :param schema_name: schema name on the database server to activate the `lab` element :param create_schema: when True (default), create schema in the database if it does not yet exist. :param create_tables: when True (default), create tables in the database if they do not yet exist. """ schema.activate(schema_name, create_schema=create_schema, create_tables=create_tables) @schema class Lab(dj.Lookup): definition = """ lab : varchar(24) # Abbreviated lab name --- lab_name : varchar(255) # full lab name institution : varchar(255) address : varchar(255) time_zone : varchar(64) """ @schema class Location(dj.Lookup): definition = """ # location of animal housing or experimental rigs -> Lab location : varchar(32) --- location_description='' : varchar(255) """ @schema class UserRole(dj.Lookup): definition = """ user_role : varchar(16) """ @schema class User(dj.Lookup): definition = """ user : varchar(32) --- user_email='' : varchar(128) user_cellphone='' : varchar(32) """ @schema class LabMembership(dj.Lookup): definition = """ -> Lab -> User --- -> [nullable] UserRole """ @schema class ProtocolType(dj.Lookup): definition = """ protocol_type : varchar(32) """ @schema class Protocol(dj.Lookup): definition = """ # protocol approved by some institutions like IACUC, IRB protocol : varchar(16) --- -> ProtocolType protocol_description='' : varchar(255) """ @schema class Project(dj.Lookup): definition = """ project : varchar(32) --- project_description='' : varchar(1024) """ @schema class ProjectUser(dj.Manual): definition = """ -> Project -> User """ @schema class Source(dj.Lookup): definition = """ # source or supplier of animals source : varchar(32) # abbreviated source name --- source_name : varchar(255) contact_details='' : varchar(255) source_description='' : varchar(255) """

0.552057

0.365174

from flask import Flask, request, render_template import pandas as pd import pickle import numpy as np from sklearn.externals import joblib from sklearn.preprocessing import StandardScaler import re app = Flask(__name__, template_folder="templates") # Load the model model = joblib.load('./models/model.p') scaler = joblib.load('./models/scaler.p') @app.route('/', methods=['GET','POST']) def home(): if request.method == 'POST': principal_balance = request.form.get('principal_balance') principal_ptd = request.form.get('principal_ptd') down = request.form.get('down') NADA = request.form.get('NADA') finance_charge = request.form.get('finance_charge') term = request.form.get('term') seasoning = request.form.get('seasoning') DPD = request.form.get('DPD') text = request.form.get('text') data = [principal_balance,principal_ptd,down,NADA,finance_charge,term,seasoning,DPD,text] input = pd.DataFrame([data], columns=['principal_balance', 'principal_ptd', 'down', 'NADA', 'finance_charge','term','seasoning', 'DPD', 'content']) input['LTV'] = float(input.principal_balance)/float(input.NADA) input['WLTV'] = input.LTV*float(input.principal_balance) input['down_%'] = float(input.down)/(float(input.principal_balance)+float(input.principal_ptd)) print(input) df = input[['DPD', 'term', 'seasoning', 'finance_charge', 'principal_ptd', 'down_%','LTV', 'WLTV']] # df = pd.DataFrame(input.content.str.split().tolist(), columns=['DPD', 'term', 'seasoning', 'finance_charge', 'principal_ptd', 'down_%','LTV', 'WLTV']) # Make prediction print(df) rescaled_df = scaler.transform(df) pred = model.predict(rescaled_df) pred = np.round(pred, decimals=2) #print(preds) #pred = pred*100 print(pred) return render_template('index.html', value=pred[0]) return render_template('index.html', value='') if __name__ == '__main__': app.run(port=3000, debug=True)

Deploying your ML Model/WebProject1/website.py

from flask import Flask, request, render_template import pandas as pd import pickle import numpy as np from sklearn.externals import joblib from sklearn.preprocessing import StandardScaler import re app = Flask(__name__, template_folder="templates") # Load the model model = joblib.load('./models/model.p') scaler = joblib.load('./models/scaler.p') @app.route('/', methods=['GET','POST']) def home(): if request.method == 'POST': principal_balance = request.form.get('principal_balance') principal_ptd = request.form.get('principal_ptd') down = request.form.get('down') NADA = request.form.get('NADA') finance_charge = request.form.get('finance_charge') term = request.form.get('term') seasoning = request.form.get('seasoning') DPD = request.form.get('DPD') text = request.form.get('text') data = [principal_balance,principal_ptd,down,NADA,finance_charge,term,seasoning,DPD,text] input = pd.DataFrame([data], columns=['principal_balance', 'principal_ptd', 'down', 'NADA', 'finance_charge','term','seasoning', 'DPD', 'content']) input['LTV'] = float(input.principal_balance)/float(input.NADA) input['WLTV'] = input.LTV*float(input.principal_balance) input['down_%'] = float(input.down)/(float(input.principal_balance)+float(input.principal_ptd)) print(input) df = input[['DPD', 'term', 'seasoning', 'finance_charge', 'principal_ptd', 'down_%','LTV', 'WLTV']] # df = pd.DataFrame(input.content.str.split().tolist(), columns=['DPD', 'term', 'seasoning', 'finance_charge', 'principal_ptd', 'down_%','LTV', 'WLTV']) # Make prediction print(df) rescaled_df = scaler.transform(df) pred = model.predict(rescaled_df) pred = np.round(pred, decimals=2) #print(preds) #pred = pred*100 print(pred) return render_template('index.html', value=pred[0]) return render_template('index.html', value='') if __name__ == '__main__': app.run(port=3000, debug=True)

0.491212

0.075892

import pytz from custom_components.hass_opensprinkler import CONF_CONFIG, CONF_STATIONS, DOMAIN from datetime import datetime, timedelta from homeassistant.util import Throttle from homeassistant.helpers.entity import Entity SCAN_INTERVAL = timedelta(seconds=5) utc_tz = pytz.timezone('UTC') def setup_platform(hass, config, add_devices, discovery_info=None): opensprinkler = hass.data[DOMAIN][DOMAIN] opensprinklerConfig = hass.data[DOMAIN][CONF_CONFIG] stationIndexes = opensprinklerConfig[CONF_STATIONS] or [] sensors = [] for station in opensprinkler.stations(): if len(stationIndexes) == 0 or (station.index in stationIndexes): sensors.append(StationSensor(station)) sensors.append(WaterLevelSensor(opensprinkler)) sensors.append(LastRunSensor(opensprinkler)) sensors.append(RainDelayStopTimeSensor(opensprinkler)) add_devices(sensors, True) class StationSensor(Entity): def __init__(self, station): self._station = station self._state = None self._status = None self._p_status = None @property def name(self): """Return the name of the binary sensor.""" return self._station.name @property def unit_of_measurement(self): """Return the units of measurement.""" return None @property def state(self): """Return the state of the sensor.""" return self._state @Throttle(SCAN_INTERVAL) def update(self): """Fetch new state data for the sensor.""" self._status = self._station.status() self._p_status = self._station.p_status() if(self._status==1): if(self._p_status[0]==99): self._state = "Running manual" elif(self._p_status[0]==254): self._state = "Running once prog." elif(self._p_status[0]==0): self._state = "Idle" else: self._state = "Running schedule" else: if(self._p_status[0]>0): self._state = "Waiting for run" else: self._state = "Idle" class WaterLevelSensor(Entity): def __init__(self, opensprinkler): self._opensprinkler = opensprinkler self._state = None @property def name(self): """Return the name of the binary sensor.""" return "Water Level" @property def unit_of_measurement(self): """Return the units of measurement.""" return "%" @property def icon(self): """Return icon.""" return "mdi:water" @property def state(self): """Return the state of the sensor.""" return self._state @Throttle(SCAN_INTERVAL) def update(self): """Fetch new state data for the sensor.""" self._state = self._opensprinkler.water_level() class LastRunSensor(Entity): def __init__(self, opensprinkler): self._opensprinkler = opensprinkler self._state = None self._last_run = None @property def name(self): """Return the name of the binary sensor.""" return "Last Run" @property def unit_of_measurement(self): """Return the units of measurement.""" return None @property def icon(self): """Return icon.""" return "mdi:history" @property def state(self): """Return the state of the sensor.""" return self._state @Throttle(SCAN_INTERVAL) def update(self): """Fetch new state data for the sensor.""" self._last_run = self._opensprinkler.last_run() utcTime = datetime.fromtimestamp(self._last_run[3], utc_tz) self._state = utcTime.strftime("%d/%m %H:%M") class RainDelayStopTimeSensor(Entity): def __init__(self, opensprinkler): self._opensprinkler = opensprinkler self._state = None self._rain_delay_stop_time = None @property def name(self): """Return the name of the binary sensor.""" return "Rain Delay Stop Time" @property def unit_of_measurement(self): """Return the units of measurement.""" return None @property def icon(self): """Return icon.""" return "mdi:weather-rainy" @property def state(self): """Return the state of the sensor.""" return self._state @Throttle(SCAN_INTERVAL) def update(self): """Fetch new state data for the sensor.""" self._rain_delay_stop_time = self._opensprinkler.rain_delay_stop_time() if self._rain_delay_stop_time == 0: self._state = 'Not in effect' else: utcTime = datetime.fromtimestamp(self._rain_delay_stop_time, utc_tz) self._state = utcTime.strftime("%d/%m %H:%M")

hass_opensprinkler/sensor.py

import pytz from custom_components.hass_opensprinkler import CONF_CONFIG, CONF_STATIONS, DOMAIN from datetime import datetime, timedelta from homeassistant.util import Throttle from homeassistant.helpers.entity import Entity SCAN_INTERVAL = timedelta(seconds=5) utc_tz = pytz.timezone('UTC') def setup_platform(hass, config, add_devices, discovery_info=None): opensprinkler = hass.data[DOMAIN][DOMAIN] opensprinklerConfig = hass.data[DOMAIN][CONF_CONFIG] stationIndexes = opensprinklerConfig[CONF_STATIONS] or [] sensors = [] for station in opensprinkler.stations(): if len(stationIndexes) == 0 or (station.index in stationIndexes): sensors.append(StationSensor(station)) sensors.append(WaterLevelSensor(opensprinkler)) sensors.append(LastRunSensor(opensprinkler)) sensors.append(RainDelayStopTimeSensor(opensprinkler)) add_devices(sensors, True) class StationSensor(Entity): def __init__(self, station): self._station = station self._state = None self._status = None self._p_status = None @property def name(self): """Return the name of the binary sensor.""" return self._station.name @property def unit_of_measurement(self): """Return the units of measurement.""" return None @property def state(self): """Return the state of the sensor.""" return self._state @Throttle(SCAN_INTERVAL) def update(self): """Fetch new state data for the sensor.""" self._status = self._station.status() self._p_status = self._station.p_status() if(self._status==1): if(self._p_status[0]==99): self._state = "Running manual" elif(self._p_status[0]==254): self._state = "Running once prog." elif(self._p_status[0]==0): self._state = "Idle" else: self._state = "Running schedule" else: if(self._p_status[0]>0): self._state = "Waiting for run" else: self._state = "Idle" class WaterLevelSensor(Entity): def __init__(self, opensprinkler): self._opensprinkler = opensprinkler self._state = None @property def name(self): """Return the name of the binary sensor.""" return "Water Level" @property def unit_of_measurement(self): """Return the units of measurement.""" return "%" @property def icon(self): """Return icon.""" return "mdi:water" @property def state(self): """Return the state of the sensor.""" return self._state @Throttle(SCAN_INTERVAL) def update(self): """Fetch new state data for the sensor.""" self._state = self._opensprinkler.water_level() class LastRunSensor(Entity): def __init__(self, opensprinkler): self._opensprinkler = opensprinkler self._state = None self._last_run = None @property def name(self): """Return the name of the binary sensor.""" return "Last Run" @property def unit_of_measurement(self): """Return the units of measurement.""" return None @property def icon(self): """Return icon.""" return "mdi:history" @property def state(self): """Return the state of the sensor.""" return self._state @Throttle(SCAN_INTERVAL) def update(self): """Fetch new state data for the sensor.""" self._last_run = self._opensprinkler.last_run() utcTime = datetime.fromtimestamp(self._last_run[3], utc_tz) self._state = utcTime.strftime("%d/%m %H:%M") class RainDelayStopTimeSensor(Entity): def __init__(self, opensprinkler): self._opensprinkler = opensprinkler self._state = None self._rain_delay_stop_time = None @property def name(self): """Return the name of the binary sensor.""" return "Rain Delay Stop Time" @property def unit_of_measurement(self): """Return the units of measurement.""" return None @property def icon(self): """Return icon.""" return "mdi:weather-rainy" @property def state(self): """Return the state of the sensor.""" return self._state @Throttle(SCAN_INTERVAL) def update(self): """Fetch new state data for the sensor.""" self._rain_delay_stop_time = self._opensprinkler.rain_delay_stop_time() if self._rain_delay_stop_time == 0: self._state = 'Not in effect' else: utcTime = datetime.fromtimestamp(self._rain_delay_stop_time, utc_tz) self._state = utcTime.strftime("%d/%m %H:%M")

0.722723

0.144149

import dao.PersonDAO as PersonDAO import manager.PermissionManager as PermissionManager def get_all(): get_first_name_last_name(None, None) def get_first_name(first_name): get_first_name_last_name(first_name, None) def get_last_name(last_name): get_first_name_last_name(None, last_name) def get_first_name_last_name(first_name, last_name): results = PersonDAO.get_all(first_name, last_name) print(format_person_header()) for person in results: print(format_person(person)) def get_id_username(person_id): result = PersonDAO.get_id_or_username(person_id) if result: print(format_person_header()) print(format_person(result)) else: print("User with '" + person_id + "' as ID or username not found!") def login(username, password): user = PersonDAO.get_username_password(username, password) if user: print("Welcome " + user['first_name'] + " " + user['last_name']) return user else: print("Invalid username or password") return None def add_person(person): if PermissionManager.has_permission_add_user(person): print('''You will be guided to enter the new user's data, if you want to cancel type /cancel''') first_name = input("First name >> ") if first_name != '/cancel': last_name = input("Last name >> ") if last_name != '/cancel': username = input("Username used to login >> ") if username != '/cancel': password = input("Password used to login >> ") if password != '/cancel': print("User data" "\n========================" "\nFirst name: " + first_name, "\nLast name: " + last_name, "\nUsername: " + username, "\nPassword: " + password) answer = input("Do you want to add user with data above (yes/no) >> ") while answer != "yes" and answer != "no": answer = input("Unrecognized '" + answer + "'\nDo you want to add user with data above (yes/no) >> ") if answer == "yes": return PersonDAO.add_person(first_name, last_name, username, password) else: print("Forbidden. You are missing necessary permissions.") return False def format_person_header(): return \ " ID| First name | Last name | Username \n" \ "-----+---------------------+--------------------------+----------------" def format_person(person): return u"{0:5}| {1:20}| {2:25}| {3:15}".format( person['id'], person['first_name'], person['last_name'], person['username'])

manager/PersonManager.py

import dao.PersonDAO as PersonDAO import manager.PermissionManager as PermissionManager def get_all(): get_first_name_last_name(None, None) def get_first_name(first_name): get_first_name_last_name(first_name, None) def get_last_name(last_name): get_first_name_last_name(None, last_name) def get_first_name_last_name(first_name, last_name): results = PersonDAO.get_all(first_name, last_name) print(format_person_header()) for person in results: print(format_person(person)) def get_id_username(person_id): result = PersonDAO.get_id_or_username(person_id) if result: print(format_person_header()) print(format_person(result)) else: print("User with '" + person_id + "' as ID or username not found!") def login(username, password): user = PersonDAO.get_username_password(username, password) if user: print("Welcome " + user['first_name'] + " " + user['last_name']) return user else: print("Invalid username or password") return None def add_person(person): if PermissionManager.has_permission_add_user(person): print('''You will be guided to enter the new user's data, if you want to cancel type /cancel''') first_name = input("First name >> ") if first_name != '/cancel': last_name = input("Last name >> ") if last_name != '/cancel': username = input("Username used to login >> ") if username != '/cancel': password = input("Password used to login >> ") if password != '/cancel': print("User data" "\n========================" "\nFirst name: " + first_name, "\nLast name: " + last_name, "\nUsername: " + username, "\nPassword: " + password) answer = input("Do you want to add user with data above (yes/no) >> ") while answer != "yes" and answer != "no": answer = input("Unrecognized '" + answer + "'\nDo you want to add user with data above (yes/no) >> ") if answer == "yes": return PersonDAO.add_person(first_name, last_name, username, password) else: print("Forbidden. You are missing necessary permissions.") return False def format_person_header(): return \ " ID| First name | Last name | Username \n" \ "-----+---------------------+--------------------------+----------------" def format_person(person): return u"{0:5}| {1:20}| {2:25}| {3:15}".format( person['id'], person['first_name'], person['last_name'], person['username'])

0.265024

0.105487

from abc import abstractmethod from typing import ( List, Union, Dict, Tuple ) from mockintosh.constants import PYBARS, JINJA from mockintosh.performance import PerformanceProfile from mockintosh.exceptions import ( CommaInTagIsForbidden ) from mockintosh.templating import TemplateRenderer class ConfigService: services = [] def __init__( self, _type: str, name: Union[str, None], internal_service_id: Union[int, None] ): self.type = _type self.name = name self.external_file_paths = [] self._impl = None if internal_service_id is None: self.internal_service_id = len(ConfigService.services) ConfigService.services.append(self) else: self.internal_service_id = internal_service_id ConfigService.services[internal_service_id] = self def get_name(self) -> str: return self.name if self.name is not None else '' @abstractmethod def get_hint(self): raise NotImplementedError def add_external_file_path(self, external_file_path) -> None: self.external_file_paths.append(external_file_path) def destroy(self) -> None: for external_file_path in self.external_file_paths: external_file_path.destroy() class ConfigContainsTag: def forbid_comma_in_tag(self, data: list): for row in data: if isinstance(row, (str, ConfigExternalFilePath)): return elif isinstance(row, dict): for key, value in row.items(): if key != 'tag': continue if ',' in value: # pragma: no cover raise CommaInTagIsForbidden(value) else: if row.tag is not None and ',' in row.tag: raise CommaInTagIsForbidden(row.tag) class ConfigExternalFilePath: files = [] def __init__(self, path: str, service: ConfigService = None): self.path = path self._index = len(ConfigExternalFilePath.files) ConfigExternalFilePath.files.append(self) if service is not None: service.add_external_file_path(self) def destroy(self) -> None: ConfigExternalFilePath.files.pop(self._index) for i, external_file_path in enumerate(ConfigExternalFilePath.files): external_file_path._index = i class ConfigDataset(ConfigContainsTag): def __init__(self, payload: Union[List[dict], str, ConfigExternalFilePath]): self.payload = payload if isinstance(self.payload, list): self.forbid_comma_in_tag(self.payload) class ConfigSchema: def __init__(self, payload: Union[dict, ConfigExternalFilePath]): self.payload = payload class ConfigHeaders: def __init__(self, payload: Dict[str, Union[str, List[str], ConfigExternalFilePath]]): self.payload = payload class ConfigAmqpProperties: def __init__( self, content_type=None, content_encoding=None, delivery_mode=None, priority=None, correlation_id=None, reply_to=None, expiration=None, message_id=None, timestamp=None, _type=None, user_id=None, app_id=None, cluster_id=None ): self.content_type = content_type self.content_encoding = content_encoding self.delivery_mode = delivery_mode self.priority = priority self.correlation_id = correlation_id self.reply_to = reply_to self.expiration = expiration self.message_id = message_id self.timestamp = timestamp self.type = _type self.user_id = user_id self.app_id = app_id self.cluster_id = cluster_id class ConfigConsume: def __init__( self, queue: str, group: Union[str, None] = None, key: Union[str, None] = None, schema: Union[ConfigSchema, None] = None, value: Union[str, None] = None, headers: Union[ConfigHeaders, None] = None, amqp_properties: Union[ConfigAmqpProperties, None] = None, capture: int = 1 ): self.queue = queue self.group = group self.key = key self.schema = schema self.value = value self.headers = headers self.amqp_properties = amqp_properties self.capture = capture class ConfigProduce: def __init__( self, queue: str, value: Union[str, ConfigExternalFilePath], create: bool = False, tag: Union[str, None] = None, key: Union[str, None] = None, headers: Union[ConfigHeaders, None] = None, amqp_properties: Union[ConfigAmqpProperties, None] = None ): self.queue = queue self.value = value self.create = create self.tag = tag self.key = key self.headers = headers self.amqp_properties = amqp_properties class ConfigMultiProduce: def __init__(self, produce_list: List[ConfigProduce]): self.produce_list = produce_list class ConfigActor: def __init__( self, name: Union[str, None] = None, dataset: Union[ConfigDataset, None] = None, produce: Union[ConfigMultiProduce, ConfigProduce, None] = None, consume: Union[ConfigConsume, None] = None, delay: Union[int, float, None] = None, limit: Union[int, None] = None, multi_payloads_looped: bool = True, dataset_looped: bool = True, ): self.name = name self.dataset = dataset self.produce = produce self.consume = consume self.delay = delay self.limit = limit self.multi_payloads_looped = multi_payloads_looped self.dataset_looped = dataset_looped class ConfigAsyncService(ConfigService): services = [] def __init__( self, _type: str, address: Union[str, None] = None, actors: List[ConfigActor] = [], name: Union[str, None] = None, ssl: bool = False, internal_service_id: Union[int, None] = None ): super().__init__(_type, name, internal_service_id) ConfigAsyncService.services.append(self) self.type = _type self.address = address self.actors = actors self.ssl = ssl def get_hint(self): return '%s://%s' % (self.type, self.address) if self.name is None else self.name def address_template_renderer( self, template_engine: str, rendering_queue, ) -> Tuple[str, dict]: if template_engine == PYBARS: from mockintosh.hbs.methods import env elif template_engine == JINJA: from mockintosh.j2.methods import env renderer = TemplateRenderer() self.address, _ = renderer.render( template_engine, self.address, rendering_queue, inject_methods=[ env ] ) class ConfigResponse: def __init__( self, headers: Union[ConfigHeaders, None] = None, status: Union[str, int] = 200, body: Union[str, ConfigExternalFilePath, None] = None, use_templating: bool = True, templating_engine: str = PYBARS, tag: Union[str, None] = None, trigger_async_producer: Union[str, int, None] = None ): self.headers = headers self.status = status self.body = body self.use_templating = use_templating self.templating_engine = templating_engine self.tag = tag self.trigger_async_producer = trigger_async_producer def oas(self, status_data: dict): new_headers = {k.title(): v for k, v in self.headers.payload.items()} if 'Content-Type' in new_headers: if new_headers['Content-Type'].startswith('application/json'): status_data = { 'content': { 'application/json': { 'schema': {} } } } status_data['headers'] = {} for key in new_headers.keys(): status_data['headers'][key] = { 'schema': { 'type': 'string' } } class ConfigMultiResponse(ConfigContainsTag): def __init__(self, payload: List[Union[ConfigResponse, ConfigExternalFilePath, str]]): self.payload = payload self.forbid_comma_in_tag(self.payload) class ConfigBody: def __init__( self, schema: ConfigSchema = None, text: Union[str, None] = None, graphql_query: Union[str, ConfigExternalFilePath, None] = None, graphql_variables: Dict[str, str] = None, urlencoded: Dict[str, str] = None, multipart: Dict[str, str] = None ): self.schema = schema self.text = text self.urlencoded = urlencoded self.multipart = multipart self.graphql_query = graphql_query self.graphql_variables = graphql_variables class ConfigEndpoint: def __init__( self, path: str, _id: Union[str, None] = None, comment: Union[str, None] = None, method: str = 'GET', query_string: Dict[str, str] = {}, headers: Dict[str, str] = {}, body: Union[ConfigBody, None] = None, dataset: Union[ConfigDataset, None] = None, response: Union[ConfigResponse, ConfigExternalFilePath, str, ConfigMultiResponse, None] = None, multi_responses_looped: bool = True, dataset_looped: bool = True, performance_profile: Union[str, None] = None ): self.path = path self.id = _id self.comment = comment self.method = method.upper() self.query_string = query_string self.headers = headers self.body = body self.dataset = dataset self.response = response self.multi_responses_looped = multi_responses_looped self.dataset_looped = dataset_looped self.performance_profile = performance_profile class ConfigHttpService(ConfigService): def __init__( self, port: int, name: Union[str, None] = None, hostname: Union[str, None] = None, ssl: bool = False, ssl_cert_file: Union[str, None] = None, ssl_key_file: Union[str, None] = None, management_root: Union[str, None] = None, oas: Union[str, ConfigExternalFilePath, None] = None, endpoints: List[ConfigEndpoint] = [], performance_profile: Union[str, None] = None, fallback_to: Union[str, None] = None, internal_service_id: Union[int, None] = None ): super().__init__('http', name, internal_service_id) self.port = port self.hostname = hostname self.ssl = ssl self.ssl_cert_file = ssl_cert_file self.ssl_key_file = ssl_key_file self.management_root = management_root self.oas = oas self.endpoints = endpoints self.performance_profile = performance_profile self.fallback_to = fallback_to def get_hint(self): return '%s://%s:%s%s' % ( 'https' if self.ssl else 'http', self.hostname if self.hostname is not None else ( 'localhost' ), self.port, ' - %s' % self.name if self.name is not None else '' ) class ConfigGlobals: def __init__( self, headers: Union[ConfigHeaders, None], performance_profile: Union[str, None] = None ): self.headers = headers self.performance_profile = performance_profile class ConfigManagement: def __init__( self, port: str, ssl: bool = False, ssl_cert_file: Union[str, None] = None, ssl_key_file: Union[str, None] = None ): self.port = port self.ssl = ssl self.ssl_cert_file = ssl_cert_file self.ssl_key_file = ssl_key_file class ConfigPerformanceProfile: def __init__( self, ratio: Union[int, float], delay: Union[int, float] = 0.0, faults: Union[dict, None] = None ): self.ratio = ratio self.delay = delay self.faults = {} if faults is None else faults self.actuator = PerformanceProfile( self.ratio, delay=self.delay, faults=self.faults ) class ConfigRoot: def __init__( self, services: List[Union[ConfigHttpService, ConfigAsyncService]], management: Union[ConfigManagement, None] = None, templating_engine: str = PYBARS, _globals: Union[ConfigGlobals, None] = None, performance_profiles: Dict[str, ConfigPerformanceProfile] = {} ): self.services = services self.management = management self.templating_engine = templating_engine self.globals = _globals self.performance_profiles = performance_profiles

mockintosh/config.py

from abc import abstractmethod from typing import ( List, Union, Dict, Tuple ) from mockintosh.constants import PYBARS, JINJA from mockintosh.performance import PerformanceProfile from mockintosh.exceptions import ( CommaInTagIsForbidden ) from mockintosh.templating import TemplateRenderer class ConfigService: services = [] def __init__( self, _type: str, name: Union[str, None], internal_service_id: Union[int, None] ): self.type = _type self.name = name self.external_file_paths = [] self._impl = None if internal_service_id is None: self.internal_service_id = len(ConfigService.services) ConfigService.services.append(self) else: self.internal_service_id = internal_service_id ConfigService.services[internal_service_id] = self def get_name(self) -> str: return self.name if self.name is not None else '' @abstractmethod def get_hint(self): raise NotImplementedError def add_external_file_path(self, external_file_path) -> None: self.external_file_paths.append(external_file_path) def destroy(self) -> None: for external_file_path in self.external_file_paths: external_file_path.destroy() class ConfigContainsTag: def forbid_comma_in_tag(self, data: list): for row in data: if isinstance(row, (str, ConfigExternalFilePath)): return elif isinstance(row, dict): for key, value in row.items(): if key != 'tag': continue if ',' in value: # pragma: no cover raise CommaInTagIsForbidden(value) else: if row.tag is not None and ',' in row.tag: raise CommaInTagIsForbidden(row.tag) class ConfigExternalFilePath: files = [] def __init__(self, path: str, service: ConfigService = None): self.path = path self._index = len(ConfigExternalFilePath.files) ConfigExternalFilePath.files.append(self) if service is not None: service.add_external_file_path(self) def destroy(self) -> None: ConfigExternalFilePath.files.pop(self._index) for i, external_file_path in enumerate(ConfigExternalFilePath.files): external_file_path._index = i class ConfigDataset(ConfigContainsTag): def __init__(self, payload: Union[List[dict], str, ConfigExternalFilePath]): self.payload = payload if isinstance(self.payload, list): self.forbid_comma_in_tag(self.payload) class ConfigSchema: def __init__(self, payload: Union[dict, ConfigExternalFilePath]): self.payload = payload class ConfigHeaders: def __init__(self, payload: Dict[str, Union[str, List[str], ConfigExternalFilePath]]): self.payload = payload class ConfigAmqpProperties: def __init__( self, content_type=None, content_encoding=None, delivery_mode=None, priority=None, correlation_id=None, reply_to=None, expiration=None, message_id=None, timestamp=None, _type=None, user_id=None, app_id=None, cluster_id=None ): self.content_type = content_type self.content_encoding = content_encoding self.delivery_mode = delivery_mode self.priority = priority self.correlation_id = correlation_id self.reply_to = reply_to self.expiration = expiration self.message_id = message_id self.timestamp = timestamp self.type = _type self.user_id = user_id self.app_id = app_id self.cluster_id = cluster_id class ConfigConsume: def __init__( self, queue: str, group: Union[str, None] = None, key: Union[str, None] = None, schema: Union[ConfigSchema, None] = None, value: Union[str, None] = None, headers: Union[ConfigHeaders, None] = None, amqp_properties: Union[ConfigAmqpProperties, None] = None, capture: int = 1 ): self.queue = queue self.group = group self.key = key self.schema = schema self.value = value self.headers = headers self.amqp_properties = amqp_properties self.capture = capture class ConfigProduce: def __init__( self, queue: str, value: Union[str, ConfigExternalFilePath], create: bool = False, tag: Union[str, None] = None, key: Union[str, None] = None, headers: Union[ConfigHeaders, None] = None, amqp_properties: Union[ConfigAmqpProperties, None] = None ): self.queue = queue self.value = value self.create = create self.tag = tag self.key = key self.headers = headers self.amqp_properties = amqp_properties class ConfigMultiProduce: def __init__(self, produce_list: List[ConfigProduce]): self.produce_list = produce_list class ConfigActor: def __init__( self, name: Union[str, None] = None, dataset: Union[ConfigDataset, None] = None, produce: Union[ConfigMultiProduce, ConfigProduce, None] = None, consume: Union[ConfigConsume, None] = None, delay: Union[int, float, None] = None, limit: Union[int, None] = None, multi_payloads_looped: bool = True, dataset_looped: bool = True, ): self.name = name self.dataset = dataset self.produce = produce self.consume = consume self.delay = delay self.limit = limit self.multi_payloads_looped = multi_payloads_looped self.dataset_looped = dataset_looped class ConfigAsyncService(ConfigService): services = [] def __init__( self, _type: str, address: Union[str, None] = None, actors: List[ConfigActor] = [], name: Union[str, None] = None, ssl: bool = False, internal_service_id: Union[int, None] = None ): super().__init__(_type, name, internal_service_id) ConfigAsyncService.services.append(self) self.type = _type self.address = address self.actors = actors self.ssl = ssl def get_hint(self): return '%s://%s' % (self.type, self.address) if self.name is None else self.name def address_template_renderer( self, template_engine: str, rendering_queue, ) -> Tuple[str, dict]: if template_engine == PYBARS: from mockintosh.hbs.methods import env elif template_engine == JINJA: from mockintosh.j2.methods import env renderer = TemplateRenderer() self.address, _ = renderer.render( template_engine, self.address, rendering_queue, inject_methods=[ env ] ) class ConfigResponse: def __init__( self, headers: Union[ConfigHeaders, None] = None, status: Union[str, int] = 200, body: Union[str, ConfigExternalFilePath, None] = None, use_templating: bool = True, templating_engine: str = PYBARS, tag: Union[str, None] = None, trigger_async_producer: Union[str, int, None] = None ): self.headers = headers self.status = status self.body = body self.use_templating = use_templating self.templating_engine = templating_engine self.tag = tag self.trigger_async_producer = trigger_async_producer def oas(self, status_data: dict): new_headers = {k.title(): v for k, v in self.headers.payload.items()} if 'Content-Type' in new_headers: if new_headers['Content-Type'].startswith('application/json'): status_data = { 'content': { 'application/json': { 'schema': {} } } } status_data['headers'] = {} for key in new_headers.keys(): status_data['headers'][key] = { 'schema': { 'type': 'string' } } class ConfigMultiResponse(ConfigContainsTag): def __init__(self, payload: List[Union[ConfigResponse, ConfigExternalFilePath, str]]): self.payload = payload self.forbid_comma_in_tag(self.payload) class ConfigBody: def __init__( self, schema: ConfigSchema = None, text: Union[str, None] = None, graphql_query: Union[str, ConfigExternalFilePath, None] = None, graphql_variables: Dict[str, str] = None, urlencoded: Dict[str, str] = None, multipart: Dict[str, str] = None ): self.schema = schema self.text = text self.urlencoded = urlencoded self.multipart = multipart self.graphql_query = graphql_query self.graphql_variables = graphql_variables class ConfigEndpoint: def __init__( self, path: str, _id: Union[str, None] = None, comment: Union[str, None] = None, method: str = 'GET', query_string: Dict[str, str] = {}, headers: Dict[str, str] = {}, body: Union[ConfigBody, None] = None, dataset: Union[ConfigDataset, None] = None, response: Union[ConfigResponse, ConfigExternalFilePath, str, ConfigMultiResponse, None] = None, multi_responses_looped: bool = True, dataset_looped: bool = True, performance_profile: Union[str, None] = None ): self.path = path self.id = _id self.comment = comment self.method = method.upper() self.query_string = query_string self.headers = headers self.body = body self.dataset = dataset self.response = response self.multi_responses_looped = multi_responses_looped self.dataset_looped = dataset_looped self.performance_profile = performance_profile class ConfigHttpService(ConfigService): def __init__( self, port: int, name: Union[str, None] = None, hostname: Union[str, None] = None, ssl: bool = False, ssl_cert_file: Union[str, None] = None, ssl_key_file: Union[str, None] = None, management_root: Union[str, None] = None, oas: Union[str, ConfigExternalFilePath, None] = None, endpoints: List[ConfigEndpoint] = [], performance_profile: Union[str, None] = None, fallback_to: Union[str, None] = None, internal_service_id: Union[int, None] = None ): super().__init__('http', name, internal_service_id) self.port = port self.hostname = hostname self.ssl = ssl self.ssl_cert_file = ssl_cert_file self.ssl_key_file = ssl_key_file self.management_root = management_root self.oas = oas self.endpoints = endpoints self.performance_profile = performance_profile self.fallback_to = fallback_to def get_hint(self): return '%s://%s:%s%s' % ( 'https' if self.ssl else 'http', self.hostname if self.hostname is not None else ( 'localhost' ), self.port, ' - %s' % self.name if self.name is not None else '' ) class ConfigGlobals: def __init__( self, headers: Union[ConfigHeaders, None], performance_profile: Union[str, None] = None ): self.headers = headers self.performance_profile = performance_profile class ConfigManagement: def __init__( self, port: str, ssl: bool = False, ssl_cert_file: Union[str, None] = None, ssl_key_file: Union[str, None] = None ): self.port = port self.ssl = ssl self.ssl_cert_file = ssl_cert_file self.ssl_key_file = ssl_key_file class ConfigPerformanceProfile: def __init__( self, ratio: Union[int, float], delay: Union[int, float] = 0.0, faults: Union[dict, None] = None ): self.ratio = ratio self.delay = delay self.faults = {} if faults is None else faults self.actuator = PerformanceProfile( self.ratio, delay=self.delay, faults=self.faults ) class ConfigRoot: def __init__( self, services: List[Union[ConfigHttpService, ConfigAsyncService]], management: Union[ConfigManagement, None] = None, templating_engine: str = PYBARS, _globals: Union[ConfigGlobals, None] = None, performance_profiles: Dict[str, ConfigPerformanceProfile] = {} ): self.services = services self.management = management self.templating_engine = templating_engine self.globals = _globals self.performance_profiles = performance_profiles

0.7773

0.134236

from apitools.base.protorpclite import messages as _messages from apitools.base.py import encoding from apitools.base.py import extra_types package = 'dataproc' class AcceleratorConfig(_messages.Message): r"""Specifies the type and number of accelerator cards attached to the instances of an instance group (see GPUs on Compute Engine). Fields: acceleratorCount: The number of the accelerator cards of this type exposed to this instance. acceleratorTypeUri: Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes( /compute/docs/reference/beta/acceleratorTypes)Examples * https://www.googleapis.com/compute/beta/projects/[project_id]/zones /us-east1-a/acceleratorTypes/nvidia-tesla-k80 * projects/[project_id]/zones/us-east1-a/acceleratorTypes/nvidia-tesla-k80 * nvidia-tesla-k80Auto Zone Exception: If you are using the Cloud Dataproc Auto Zone Placement feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-k80. """ acceleratorCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) acceleratorTypeUri = _messages.StringField(2) class AllocationAffinity(_messages.Message): r"""Allocation Affinity for consuming Zonal allocation. Enums: ConsumeAllocationTypeValueValuesEnum: Fields: consumeAllocationType: A ConsumeAllocationTypeValueValuesEnum attribute. key: Corresponds to the label key of Allocation resource. values: Corresponds to the label values of allocation resource. """ class ConsumeAllocationTypeValueValuesEnum(_messages.Enum): r"""ConsumeAllocationTypeValueValuesEnum enum type. Values: TYPE_UNSPECIFIED: <no description> NO_ALLOCATION: Do not consume from any allocated capacity. ANY_ALLOCATION: Consume any allocation available. SPECIFIC_ALLOCATION: Must consume from a specific allocation. Must specify key value fields for specifying the allocations. """ TYPE_UNSPECIFIED = 0 NO_ALLOCATION = 1 ANY_ALLOCATION = 2 SPECIFIC_ALLOCATION = 3 consumeAllocationType = _messages.EnumField('ConsumeAllocationTypeValueValuesEnum', 1) key = _messages.StringField(2) values = _messages.StringField(3, repeated=True) class AutoscalingConfig(_messages.Message): r"""Autoscaling Policy config associated with the cluster. Fields: policyUri: Optional. The autoscaling policy used by the cluster.Only resource names including projectid and location (region) are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/lo cations/[dataproc_region]/autoscalingPolicies/[policy_id] projects/[proj ect_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id]Note that the policy must be in the same project and Cloud Dataproc region. """ policyUri = _messages.StringField(1) class AutoscalingPolicy(_messages.Message): r"""Describes an autoscaling policy for Dataproc cluster autoscaler. Fields: basicAlgorithm: A BasicAutoscalingAlgorithm attribute. id: Required. The policy id.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters. name: Output only. The "resource name" of the policy, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id}. secondaryWorkerConfig: Required. Describes how the autoscaler will operate for secondary workers. workerConfig: Required. Describes how the autoscaler will operate for primary workers. """ basicAlgorithm = _messages.MessageField('BasicAutoscalingAlgorithm', 1) id = _messages.StringField(2) name = _messages.StringField(3) secondaryWorkerConfig = _messages.MessageField('InstanceGroupAutoscalingPolicyConfig', 4) workerConfig = _messages.MessageField('InstanceGroupAutoscalingPolicyConfig', 5) class BasicAutoscalingAlgorithm(_messages.Message): r"""Basic algorithm for autoscaling. Fields: cooldownPeriod: Required. Cooldown time in between scaling. yarnConfig: Required. YARN autoscaling configuration. """ cooldownPeriod = _messages.StringField(1) yarnConfig = _messages.MessageField('BasicYarnAutoscalingConfig', 2) class BasicYarnAutoscalingConfig(_messages.Message): r"""Basic autoscaling configurations for YARN. Fields: gracefulDecommissionTimeout: Optional. Timeout used during an autoscaling event (cluster update) between 0 seconds (no graceful decommission) and 1 day.Default: 0s. scaleDownFactor: Optional. Fraction of suggested decrease in workers to scale down by between 0 and 1. Suggested decrease when scaling down is determined by the amount of average available memory since the last cooldown period.Default: 1.0. scaleDownMinWorkerFraction: Optional. Minimum workers as a fraction of the current cluster size to to scale down by between 0 and 1.Default: 0.0. scaleUpFactor: Required. Fraction of suggested increase in workers to scale up by between 0 and 1. Suggested increase when scaling up is determined by the amount of average pending memory since the last cooldown period. scaleUpMinWorkerFraction: Optional. Minimum workers as a fraction of the current cluster size to to scale up by between 0 and 1.Default: 0.0. """ gracefulDecommissionTimeout = _messages.StringField(1) scaleDownFactor = _messages.FloatField(2) scaleDownMinWorkerFraction = _messages.FloatField(3) scaleUpFactor = _messages.FloatField(4) scaleUpMinWorkerFraction = _messages.FloatField(5) class Binding(_messages.Message): r"""Associates members with a role. Fields: condition: Unimplemented. The condition that is associated with this binding. NOTE: an unsatisfied condition will not allow user access via current binding. Different bindings, including their conditions, are examined independently. members: Specifies the identities requesting access for a Cloud Platform resource. members can have the following values: allUsers: A special identifier that represents anyone who is on the internet; with or without a Google account. allAuthenticatedUsers: A special identifier that represents anyone who is authenticated with a Google account or a service account. user:{emailid}: An email address that represents a specific Google account. For example, <EMAIL> . serviceAccount:{emailid}: An email address that represents a service account. For example, <EMAIL>. group:{emailid}: An email address that represents a Google group. For example, <EMAIL>. domain:{domain}: A Google Apps domain name that represents all the users of that domain. For example, google.com or example.com. role: Role that is assigned to members. For example, roles/viewer, roles/editor, or roles/owner. """ condition = _messages.MessageField('Expr', 1) members = _messages.StringField(2, repeated=True) role = _messages.StringField(3) class CancelJobRequest(_messages.Message): r"""A request to cancel a job.""" class Cluster(_messages.Message): r"""Describes the identifying information, config, and status of a cluster of Compute Engine instances. Messages: LabelsValue: Optional. The labels to associate with this cluster. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a cluster. Fields: clusterName: Required. The cluster name. Cluster names within a project must be unique. Names of deleted clusters can be reused. clusterUuid: Output only. A cluster UUID (Unique Universal Identifier). Cloud Dataproc generates this value when it creates the cluster. config: Required. The cluster config. Note that Cloud Dataproc may set default values, and values may change when clusters are updated. labels: Optional. The labels to associate with this cluster. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a cluster. metrics: Output only. Contains cluster daemon metrics such as HDFS and YARN stats.Beta Feature: This report is available for testing purposes only. It may be changed before final release. projectId: Required. The Google Cloud Platform project ID that the cluster belongs to. status: Output only. Cluster status. statusHistory: Output only. The previous cluster status. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels to associate with this cluster. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a cluster. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) clusterName = _messages.StringField(1) clusterUuid = _messages.StringField(2) config = _messages.MessageField('ClusterConfig', 3) labels = _messages.MessageField('LabelsValue', 4) metrics = _messages.MessageField('ClusterMetrics', 5) projectId = _messages.StringField(6) status = _messages.MessageField('ClusterStatus', 7) statusHistory = _messages.MessageField('ClusterStatus', 8, repeated=True) class ClusterConfig(_messages.Message): r"""The cluster config. Fields: autoscalingConfig: Optional. Autoscaling config for the policy associated with the cluster. Cluster does not autoscale if this field is unset. configBucket: Optional. A Cloud Storage staging bucket used for sharing generated SSH keys and config. If you do not specify a staging bucket, Cloud Dataproc will determine an appropriate Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Google Compute Engine zone where your cluster is deployed, and then it will create and manage this project-level, per-location bucket for you. encryptionConfig: Optional. Encryption settings for the cluster. endpointConfig: Optional. Port/endpoint configuration for this cluster gceClusterConfig: Required. The shared Compute Engine config settings for all instances in a cluster. initializationActions: Optional. Commands to execute on each node after config is completed. By default, executables are run on master and all worker nodes. You can test a node's <code>role</code> metadata to run an executable on a master or worker node, as shown below using curl (you can also use wget): ROLE=$(curl -H Metadata-Flavor:Google http://metadata/computeMetadata/v1beta2/instance/attributes/dataproc- role) if [[ "${ROLE}" == 'Master' ]]; then ... master specific actions ... else ... worker specific actions ... fi lifecycleConfig: Optional. The config setting for auto delete cluster schedule. masterConfig: Optional. The Compute Engine config settings for the master instance in a cluster. secondaryWorkerConfig: Optional. The Compute Engine config settings for additional worker instances in a cluster. securityConfig: Optional. Security related configuration. softwareConfig: Optional. The config settings for software inside the cluster. workerConfig: Optional. The Compute Engine config settings for worker instances in a cluster. """ autoscalingConfig = _messages.MessageField('AutoscalingConfig', 1) configBucket = _messages.StringField(2) encryptionConfig = _messages.MessageField('EncryptionConfig', 3) endpointConfig = _messages.MessageField('EndpointConfig', 4) gceClusterConfig = _messages.MessageField('GceClusterConfig', 5) initializationActions = _messages.MessageField('NodeInitializationAction', 6, repeated=True) lifecycleConfig = _messages.MessageField('LifecycleConfig', 7) masterConfig = _messages.MessageField('InstanceGroupConfig', 8) secondaryWorkerConfig = _messages.MessageField('InstanceGroupConfig', 9) securityConfig = _messages.MessageField('SecurityConfig', 10) softwareConfig = _messages.MessageField('SoftwareConfig', 11) workerConfig = _messages.MessageField('InstanceGroupConfig', 12) class ClusterMetrics(_messages.Message): r"""Contains cluster daemon metrics, such as HDFS and YARN stats.Beta Feature: This report is available for testing purposes only. It may be changed before final release. Messages: HdfsMetricsValue: The HDFS metrics. YarnMetricsValue: The YARN metrics. Fields: hdfsMetrics: The HDFS metrics. yarnMetrics: The YARN metrics. """ @encoding.MapUnrecognizedFields('additionalProperties') class HdfsMetricsValue(_messages.Message): r"""The HDFS metrics. Messages: AdditionalProperty: An additional property for a HdfsMetricsValue object. Fields: additionalProperties: Additional properties of type HdfsMetricsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a HdfsMetricsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.IntegerField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class YarnMetricsValue(_messages.Message): r"""The YARN metrics. Messages: AdditionalProperty: An additional property for a YarnMetricsValue object. Fields: additionalProperties: Additional properties of type YarnMetricsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a YarnMetricsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.IntegerField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) hdfsMetrics = _messages.MessageField('HdfsMetricsValue', 1) yarnMetrics = _messages.MessageField('YarnMetricsValue', 2) class ClusterOperation(_messages.Message): r"""The cluster operation triggered by a workflow. Fields: done: Output only. Indicates the operation is done. error: Output only. Error, if operation failed. operationId: Output only. The id of the cluster operation. """ done = _messages.BooleanField(1) error = _messages.StringField(2) operationId = _messages.StringField(3) class ClusterOperationMetadata(_messages.Message): r"""Metadata describing the operation. Messages: LabelsValue: Output only. Labels associated with the operation Fields: clusterName: Output only. Name of the cluster for the operation. clusterUuid: Output only. Cluster UUID for the operation. description: Output only. Short description of operation. labels: Output only. Labels associated with the operation operationType: Output only. The operation type. status: Output only. Current operation status. statusHistory: Output only. The previous operation status. warnings: Output only. Errors encountered during operation execution. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Output only. Labels associated with the operation Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) clusterName = _messages.StringField(1) clusterUuid = _messages.StringField(2) description = _messages.StringField(3) labels = _messages.MessageField('LabelsValue', 4) operationType = _messages.StringField(5) status = _messages.MessageField('ClusterOperationStatus', 6) statusHistory = _messages.MessageField('ClusterOperationStatus', 7, repeated=True) warnings = _messages.StringField(8, repeated=True) class ClusterOperationStatus(_messages.Message): r"""The status of the operation. Enums: StateValueValuesEnum: Output only. A message containing the operation state. Fields: details: Output only. A message containing any operation metadata details. innerState: Output only. A message containing the detailed operation state. state: Output only. A message containing the operation state. stateStartTime: Output only. The time this state was entered. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. A message containing the operation state. Values: UNKNOWN: Unused. PENDING: The operation has been created. RUNNING: The operation is running. DONE: The operation is done; either cancelled or completed. """ UNKNOWN = 0 PENDING = 1 RUNNING = 2 DONE = 3 details = _messages.StringField(1) innerState = _messages.StringField(2) state = _messages.EnumField('StateValueValuesEnum', 3) stateStartTime = _messages.StringField(4) class ClusterSelector(_messages.Message): r"""A selector that chooses target cluster for jobs based on metadata. Messages: ClusterLabelsValue: Required. The cluster labels. Cluster must have all labels to match. Fields: clusterLabels: Required. The cluster labels. Cluster must have all labels to match. zone: Optional. The zone where workflow process executes. This parameter does not affect the selection of the cluster.If unspecified, the zone of the first cluster matching the selector is used. """ @encoding.MapUnrecognizedFields('additionalProperties') class ClusterLabelsValue(_messages.Message): r"""Required. The cluster labels. Cluster must have all labels to match. Messages: AdditionalProperty: An additional property for a ClusterLabelsValue object. Fields: additionalProperties: Additional properties of type ClusterLabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ClusterLabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) clusterLabels = _messages.MessageField('ClusterLabelsValue', 1) zone = _messages.StringField(2) class ClusterStatus(_messages.Message): r"""The status of a cluster and its instances. Enums: StateValueValuesEnum: Output only. The cluster's state. SubstateValueValuesEnum: Output only. Additional state information that includes status reported by the agent. Fields: detail: Output only. Optional details of cluster's state. state: Output only. The cluster's state. stateStartTime: Output only. Time when this state was entered. substate: Output only. Additional state information that includes status reported by the agent. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The cluster's state. Values: UNKNOWN: The cluster state is unknown. CREATING: The cluster is being created and set up. It is not ready for use. RUNNING: The cluster is currently running and healthy. It is ready for use. ERROR: The cluster encountered an error. It is not ready for use. DELETING: The cluster is being deleted. It cannot be used. UPDATING: The cluster is being updated. It continues to accept and process jobs. """ UNKNOWN = 0 CREATING = 1 RUNNING = 2 ERROR = 3 DELETING = 4 UPDATING = 5 class SubstateValueValuesEnum(_messages.Enum): r"""Output only. Additional state information that includes status reported by the agent. Values: UNSPECIFIED: The cluster substate is unknown. UNHEALTHY: The cluster is known to be in an unhealthy state (for example, critical daemons are not running or HDFS capacity is exhausted).Applies to RUNNING state. STALE_STATUS: The agent-reported status is out of date (may occur if Cloud Dataproc loses communication with Agent).Applies to RUNNING state. """ UNSPECIFIED = 0 UNHEALTHY = 1 STALE_STATUS = 2 detail = _messages.StringField(1) state = _messages.EnumField('StateValueValuesEnum', 2) stateStartTime = _messages.StringField(3) substate = _messages.EnumField('SubstateValueValuesEnum', 4) class DataprocProjectsLocationsAutoscalingPoliciesCreateRequest(_messages.Message): r"""A DataprocProjectsLocationsAutoscalingPoliciesCreateRequest object. Fields: autoscalingPolicy: A AutoscalingPolicy resource to be passed as the request body. parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}. """ autoscalingPolicy = _messages.MessageField('AutoscalingPolicy', 1) parent = _messages.StringField(2, required=True) class DataprocProjectsLocationsAutoscalingPoliciesDeleteRequest(_messages.Message): r"""A DataprocProjectsLocationsAutoscalingPoliciesDeleteRequest object. Fields: name: Required. The "resource name" of the autoscaling policy, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id}. """ name = _messages.StringField(1, required=True) class DataprocProjectsLocationsAutoscalingPoliciesGetRequest(_messages.Message): r"""A DataprocProjectsLocationsAutoscalingPoliciesGetRequest object. Fields: name: Required. The "resource name" of the autoscaling policy, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id}. """ name = _messages.StringField(1, required=True) class DataprocProjectsLocationsAutoscalingPoliciesListRequest(_messages.Message): r"""A DataprocProjectsLocationsAutoscalingPoliciesListRequest object. Fields: pageSize: Optional. The maximum number of results to return in each response. pageToken: Optional. The page token, returned by a previous call, to request the next page of results. parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class DataprocProjectsLocationsWorkflowTemplatesCreateRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesCreateRequest object. Fields: parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} workflowTemplate: A WorkflowTemplate resource to be passed as the request body. """ parent = _messages.StringField(1, required=True) workflowTemplate = _messages.MessageField('WorkflowTemplate', 2) class DataprocProjectsLocationsWorkflowTemplatesDeleteRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesDeleteRequest object. Fields: name: Required. The "resource name" of the workflow template, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/workflowTemplates/{template_id} version: Optional. The version of workflow template to delete. If specified, will only delete the template if the current server version matches specified version. """ name = _messages.StringField(1, required=True) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class DataprocProjectsLocationsWorkflowTemplatesGetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesGetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being requested. See the operation documentation for the appropriate value for this field. """ resource = _messages.StringField(1, required=True) class DataprocProjectsLocationsWorkflowTemplatesGetRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesGetRequest object. Fields: name: Required. The "resource name" of the workflow template, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/workflowTemplates/{template_id} version: Optional. The version of workflow template to retrieve. Only previously instatiated versions can be retrieved.If unspecified, retrieves the current version. """ name = _messages.StringField(1, required=True) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class DataprocProjectsLocationsWorkflowTemplatesInstantiateInlineRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesInstantiateInlineRequest object. Fields: instanceId: Deprecated. Please use request_id field instead. parent: Required. The "resource name" of the workflow template region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} requestId: Optional. A tag that prevents multiple concurrent workflow instances with the same tag from running. This mitigates risk of concurrent instances started due to retries.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The tag must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters. workflowTemplate: A WorkflowTemplate resource to be passed as the request body. """ instanceId = _messages.StringField(1) parent = _messages.StringField(2, required=True) requestId = _messages.StringField(3) workflowTemplate = _messages.MessageField('WorkflowTemplate', 4) class DataprocProjectsLocationsWorkflowTemplatesInstantiateRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesInstantiateRequest object. Fields: instantiateWorkflowTemplateRequest: A InstantiateWorkflowTemplateRequest resource to be passed as the request body. name: Required. The "resource name" of the workflow template, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/workflowTemplates/{template_id} """ instantiateWorkflowTemplateRequest = _messages.MessageField('InstantiateWorkflowTemplateRequest', 1) name = _messages.StringField(2, required=True) class DataprocProjectsLocationsWorkflowTemplatesListRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesListRequest object. Fields: pageSize: Optional. The maximum number of results to return in each response. pageToken: Optional. The page token, returned by a previous call, to request the next page of results. parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class DataprocProjectsLocationsWorkflowTemplatesSetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesSetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being specified. See the operation documentation for the appropriate value for this field. setIamPolicyRequest: A SetIamPolicyRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) setIamPolicyRequest = _messages.MessageField('SetIamPolicyRequest', 2) class DataprocProjectsLocationsWorkflowTemplatesTestIamPermissionsRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesTestIamPermissionsRequest object. Fields: resource: REQUIRED: The resource for which the policy detail is being requested. See the operation documentation for the appropriate value for this field. testIamPermissionsRequest: A TestIamPermissionsRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) testIamPermissionsRequest = _messages.MessageField('TestIamPermissionsRequest', 2) class DataprocProjectsRegionsAutoscalingPoliciesCreateRequest(_messages.Message): r"""A DataprocProjectsRegionsAutoscalingPoliciesCreateRequest object. Fields: autoscalingPolicy: A AutoscalingPolicy resource to be passed as the request body. parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}. """ autoscalingPolicy = _messages.MessageField('AutoscalingPolicy', 1) parent = _messages.StringField(2, required=True) class DataprocProjectsRegionsAutoscalingPoliciesDeleteRequest(_messages.Message): r"""A DataprocProjectsRegionsAutoscalingPoliciesDeleteRequest object. Fields: name: Required. The "resource name" of the autoscaling policy, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id}. """ name = _messages.StringField(1, required=True) class DataprocProjectsRegionsAutoscalingPoliciesGetRequest(_messages.Message): r"""A DataprocProjectsRegionsAutoscalingPoliciesGetRequest object. Fields: name: Required. The "resource name" of the autoscaling policy, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id}. """ name = _messages.StringField(1, required=True) class DataprocProjectsRegionsAutoscalingPoliciesListRequest(_messages.Message): r"""A DataprocProjectsRegionsAutoscalingPoliciesListRequest object. Fields: pageSize: Optional. The maximum number of results to return in each response. pageToken: Optional. The page token, returned by a previous call, to request the next page of results. parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class DataprocProjectsRegionsClustersCreateRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersCreateRequest object. Fields: cluster: A Cluster resource to be passed as the request body. projectId: Required. The ID of the Google Cloud Platform project that the cluster belongs to. region: Required. The Cloud Dataproc region in which to handle the request. requestId: Optional. A unique id used to identify the request. If the server receives two CreateClusterRequest requests with the same id, then the second request will be ignored and the first google.longrunning.Operation created and stored in the backend is returned.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters. """ cluster = _messages.MessageField('Cluster', 1) projectId = _messages.StringField(2, required=True) region = _messages.StringField(3, required=True) requestId = _messages.StringField(4) class DataprocProjectsRegionsClustersDeleteRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersDeleteRequest object. Fields: clusterName: Required. The cluster name. clusterUuid: Optional. Specifying the cluster_uuid means the RPC should fail (with error NOT_FOUND) if cluster with specified UUID does not exist. projectId: Required. The ID of the Google Cloud Platform project that the cluster belongs to. region: Required. The Cloud Dataproc region in which to handle the request. requestId: Optional. A unique id used to identify the request. If the server receives two DeleteClusterRequest requests with the same id, then the second request will be ignored and the first google.longrunning.Operation created and stored in the backend is returned.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters. """ clusterName = _messages.StringField(1, required=True) clusterUuid = _messages.StringField(2) projectId = _messages.StringField(3, required=True) region = _messages.StringField(4, required=True) requestId = _messages.StringField(5) class DataprocProjectsRegionsClustersDiagnoseRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersDiagnoseRequest object. Fields: clusterName: Required. The cluster name. diagnoseClusterRequest: A DiagnoseClusterRequest resource to be passed as the request body. projectId: Required. The ID of the Google Cloud Platform project that the cluster belongs to. region: Required. The Cloud Dataproc region in which to handle the request. """ clusterName = _messages.StringField(1, required=True) diagnoseClusterRequest = _messages.MessageField('DiagnoseClusterRequest', 2) projectId = _messages.StringField(3, required=True) region = _messages.StringField(4, required=True) class DataprocProjectsRegionsClustersGetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersGetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being requested. See the operation documentation for the appropriate value for this field. """ resource = _messages.StringField(1, required=True) class DataprocProjectsRegionsClustersGetRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersGetRequest object. Fields: clusterName: Required. The cluster name. projectId: Required. The ID of the Google Cloud Platform project that the cluster belongs to. region: Required. The Cloud Dataproc region in which to handle the request. """ clusterName = _messages.StringField(1, required=True) projectId = _messages.StringField(2, required=True) region = _messages.StringField(3, required=True) class DataprocProjectsRegionsClustersListRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersListRequest object. Fields: filter: Optional. A filter constraining the clusters to list. Filters are case-sensitive and have the following syntax:field = value AND field = value ...where field is one of status.state, clusterName, or labels.[KEY], and [KEY] is a label key. value can be * to match all values. status.state can be one of the following: ACTIVE, INACTIVE, CREATING, RUNNING, ERROR, DELETING, or UPDATING. ACTIVE contains the CREATING, UPDATING, and RUNNING states. INACTIVE contains the DELETING and ERROR states. clusterName is the name of the cluster provided at creation time. Only the logical AND operator is supported; space- separated items are treated as having an implicit AND operator.Example filter:status.state = ACTIVE AND clusterName = mycluster AND labels.env = staging AND labels.starred = * pageSize: Optional. The standard List page size. pageToken: Optional. The standard List page token. projectId: Required. The ID of the Google Cloud Platform project that the cluster belongs to. region: Required. The Cloud Dataproc region in which to handle the request. """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) projectId = _messages.StringField(4, required=True) region = _messages.StringField(5, required=True) class DataprocProjectsRegionsClustersPatchRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersPatchRequest object. Fields: cluster: A Cluster resource to be passed as the request body. clusterName: Required. The cluster name. gracefulDecommissionTimeout: Optional. Timeout for graceful YARN decomissioning. Graceful decommissioning allows removing nodes from the cluster without interrupting jobs in progress. Timeout specifies how long to wait for jobs in progress to finish before forcefully removing nodes (and potentially interrupting jobs). Default timeout is 0 (for forceful decommission), and the maximum allowed timeout is 1 day.Only supported on Dataproc image versions 1.2 and higher. projectId: Required. The ID of the Google Cloud Platform project the cluster belongs to. region: Required. The Cloud Dataproc region in which to handle the request. requestId: Optional. A unique id used to identify the request. If the server receives two UpdateClusterRequest requests with the same id, then the second request will be ignored and the first google.longrunning.Operation created and stored in the backend is returned.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters. updateMask: Required. Specifies the path, relative to Cluster, of the field to update. For example, to change the number of workers in a cluster to 5, the update_mask parameter would be specified as config.worker_config.num_instances, and the PATCH request body would specify the new value, as follows: { "config":{ "workerConfig":{ "numInstances":"5" } } } Similarly, to change the number of preemptible workers in a cluster to 5, the update_mask parameter would be config.secondary_worker_config.num_instances, and the PATCH request body would be set as follows: { "config":{ "secondaryWorkerConfig":{ "numInstances":"5" } } } <strong>Note:</strong> currently only the following fields can be updated: <table> <tr> <td><strong>Mask</strong></td><td><strong>Purpose</strong></td> </tr> <tr> <td>labels</td><td>Updates labels</td> </tr> <tr> <td>config.worker_config.num_instances</td><td>Resize primary worker group</td> </tr> <tr> <td>config.secondary_worker_config.num_instances</td><td>Resize secondary worker group</td> </tr> <tr> <td>config.lifecycle_config.auto_delete_ttl</td><td>Reset MAX TTL duration</td> </tr> <tr> <td>config.lifecycle_config.auto_delete_time</td><td>Update MAX TTL deletion timestamp</td> </tr> <tr> <td>config.lifecycle_config.idle_delete_ttl</td><td>Update Idle TTL duration</td> </tr> </table> """ cluster = _messages.MessageField('Cluster', 1) clusterName = _messages.StringField(2, required=True) gracefulDecommissionTimeout = _messages.StringField(3) projectId = _messages.StringField(4, required=True) region = _messages.StringField(5, required=True) requestId = _messages.StringField(6) updateMask = _messages.StringField(7) class DataprocProjectsRegionsClustersSetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersSetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being specified. See the operation documentation for the appropriate value for this field. setIamPolicyRequest: A SetIamPolicyRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) setIamPolicyRequest = _messages.MessageField('SetIamPolicyRequest', 2) class DataprocProjectsRegionsClustersTestIamPermissionsRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersTestIamPermissionsRequest object. Fields: resource: REQUIRED: The resource for which the policy detail is being requested. See the operation documentation for the appropriate value for this field. testIamPermissionsRequest: A TestIamPermissionsRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) testIamPermissionsRequest = _messages.MessageField('TestIamPermissionsRequest', 2) class DataprocProjectsRegionsJobsCancelRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsCancelRequest object. Fields: cancelJobRequest: A CancelJobRequest resource to be passed as the request body. jobId: Required. The job ID. projectId: Required. The ID of the Google Cloud Platform project that the job belongs to. region: Required. The Cloud Dataproc region in which to handle the request. """ cancelJobRequest = _messages.MessageField('CancelJobRequest', 1) jobId = _messages.StringField(2, required=True) projectId = _messages.StringField(3, required=True) region = _messages.StringField(4, required=True) class DataprocProjectsRegionsJobsDeleteRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsDeleteRequest object. Fields: jobId: Required. The job ID. projectId: Required. The ID of the Google Cloud Platform project that the job belongs to. region: Required. The Cloud Dataproc region in which to handle the request. """ jobId = _messages.StringField(1, required=True) projectId = _messages.StringField(2, required=True) region = _messages.StringField(3, required=True) class DataprocProjectsRegionsJobsGetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsGetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being requested. See the operation documentation for the appropriate value for this field. """ resource = _messages.StringField(1, required=True) class DataprocProjectsRegionsJobsGetRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsGetRequest object. Fields: jobId: Required. The job ID. projectId: Required. The ID of the Google Cloud Platform project that the job belongs to. region: Required. The Cloud Dataproc region in which to handle the request. """ jobId = _messages.StringField(1, required=True) projectId = _messages.StringField(2, required=True) region = _messages.StringField(3, required=True) class DataprocProjectsRegionsJobsListRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsListRequest object. Enums: JobStateMatcherValueValuesEnum: Optional. Specifies enumerated categories of jobs to list. (default = match ALL jobs).If filter is provided, jobStateMatcher will be ignored. Fields: clusterName: Optional. If set, the returned jobs list includes only jobs that were submitted to the named cluster. filter: Optional. A filter constraining the jobs to list. Filters are case-sensitive and have the following syntax:field = value AND field = value ...where field is status.state or labels.[KEY], and [KEY] is a label key. value can be * to match all values. status.state can be either ACTIVE or NON_ACTIVE. Only the logical AND operator is supported; space-separated items are treated as having an implicit AND operator.Example filter:status.state = ACTIVE AND labels.env = staging AND labels.starred = * jobStateMatcher: Optional. Specifies enumerated categories of jobs to list. (default = match ALL jobs).If filter is provided, jobStateMatcher will be ignored. pageSize: Optional. The number of results to return in each response. pageToken: Optional. The page token, returned by a previous call, to request the next page of results. projectId: Required. The ID of the Google Cloud Platform project that the job belongs to. region: Required. The Cloud Dataproc region in which to handle the request. """ class JobStateMatcherValueValuesEnum(_messages.Enum): r"""Optional. Specifies enumerated categories of jobs to list. (default = match ALL jobs).If filter is provided, jobStateMatcher will be ignored. Values: ALL: <no description> ACTIVE: <no description> NON_ACTIVE: <no description> """ ALL = 0 ACTIVE = 1 NON_ACTIVE = 2 clusterName = _messages.StringField(1) filter = _messages.StringField(2) jobStateMatcher = _messages.EnumField('JobStateMatcherValueValuesEnum', 3) pageSize = _messages.IntegerField(4, variant=_messages.Variant.INT32) pageToken = _messages.StringField(5) projectId = _messages.StringField(6, required=True) region = _messages.StringField(7, required=True) class DataprocProjectsRegionsJobsPatchRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsPatchRequest object. Fields: job: A Job resource to be passed as the request body. jobId: Required. The job ID. projectId: Required. The ID of the Google Cloud Platform project that the job belongs to. region: Required. The Cloud Dataproc region in which to handle the request. updateMask: Required. Specifies the path, relative to <code>Job</code>, of the field to update. For example, to update the labels of a Job the <code>update_mask</code> parameter would be specified as <code>labels</code>, and the PATCH request body would specify the new value. <strong>Note:</strong> Currently, <code>labels</code> is the only field that can be updated. """ job = _messages.MessageField('Job', 1) jobId = _messages.StringField(2, required=True) projectId = _messages.StringField(3, required=True) region = _messages.StringField(4, required=True) updateMask = _messages.StringField(5) class DataprocProjectsRegionsJobsSetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsSetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being specified. See the operation documentation for the appropriate value for this field. setIamPolicyRequest: A SetIamPolicyRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) setIamPolicyRequest = _messages.MessageField('SetIamPolicyRequest', 2) class DataprocProjectsRegionsJobsSubmitRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsSubmitRequest object. Fields: projectId: Required. The ID of the Google Cloud Platform project that the job belongs to. region: Required. The Cloud Dataproc region in which to handle the request. submitJobRequest: A SubmitJobRequest resource to be passed as the request body. """ projectId = _messages.StringField(1, required=True) region = _messages.StringField(2, required=True) submitJobRequest = _messages.MessageField('SubmitJobRequest', 3) class DataprocProjectsRegionsJobsTestIamPermissionsRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsTestIamPermissionsRequest object. Fields: resource: REQUIRED: The resource for which the policy detail is being requested. See the operation documentation for the appropriate value for this field. testIamPermissionsRequest: A TestIamPermissionsRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) testIamPermissionsRequest = _messages.MessageField('TestIamPermissionsRequest', 2) class DataprocProjectsRegionsOperationsCancelRequest(_messages.Message): r"""A DataprocProjectsRegionsOperationsCancelRequest object. Fields: name: The name of the operation resource to be cancelled. """ name = _messages.StringField(1, required=True) class DataprocProjectsRegionsOperationsDeleteRequest(_messages.Message): r"""A DataprocProjectsRegionsOperationsDeleteRequest object. Fields: name: The name of the operation resource to be deleted. """ name = _messages.StringField(1, required=True) class DataprocProjectsRegionsOperationsGetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsOperationsGetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being requested. See the operation documentation for the appropriate value for this field. """ resource = _messages.StringField(1, required=True) class DataprocProjectsRegionsOperationsGetRequest(_messages.Message): r"""A DataprocProjectsRegionsOperationsGetRequest object. Fields: name: The name of the operation resource. """ name = _messages.StringField(1, required=True) class DataprocProjectsRegionsOperationsListRequest(_messages.Message): r"""A DataprocProjectsRegionsOperationsListRequest object. Fields: filter: The standard list filter. name: The name of the operation's parent resource. pageSize: The standard list page size. pageToken: The standard list page token. """ filter = _messages.StringField(1) name = _messages.StringField(2, required=True) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) class DataprocProjectsRegionsOperationsSetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsOperationsSetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being specified. See the operation documentation for the appropriate value for this field. setIamPolicyRequest: A SetIamPolicyRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) setIamPolicyRequest = _messages.MessageField('SetIamPolicyRequest', 2) class DataprocProjectsRegionsOperationsTestIamPermissionsRequest(_messages.Message): r"""A DataprocProjectsRegionsOperationsTestIamPermissionsRequest object. Fields: resource: REQUIRED: The resource for which the policy detail is being requested. See the operation documentation for the appropriate value for this field. testIamPermissionsRequest: A TestIamPermissionsRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) testIamPermissionsRequest = _messages.MessageField('TestIamPermissionsRequest', 2) class DataprocProjectsRegionsWorkflowTemplatesCreateRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesCreateRequest object. Fields: parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} workflowTemplate: A WorkflowTemplate resource to be passed as the request body. """ parent = _messages.StringField(1, required=True) workflowTemplate = _messages.MessageField('WorkflowTemplate', 2) class DataprocProjectsRegionsWorkflowTemplatesDeleteRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesDeleteRequest object. Fields: name: Required. The "resource name" of the workflow template, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/workflowTemplates/{template_id} version: Optional. The version of workflow template to delete. If specified, will only delete the template if the current server version matches specified version. """ name = _messages.StringField(1, required=True) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class DataprocProjectsRegionsWorkflowTemplatesGetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesGetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being requested. See the operation documentation for the appropriate value for this field. """ resource = _messages.StringField(1, required=True) class DataprocProjectsRegionsWorkflowTemplatesGetRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesGetRequest object. Fields: name: Required. The "resource name" of the workflow template, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/workflowTemplates/{template_id} version: Optional. The version of workflow template to retrieve. Only previously instatiated versions can be retrieved.If unspecified, retrieves the current version. """ name = _messages.StringField(1, required=True) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class DataprocProjectsRegionsWorkflowTemplatesInstantiateInlineRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesInstantiateInlineRequest object. Fields: instanceId: Deprecated. Please use request_id field instead. parent: Required. The "resource name" of the workflow template region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} requestId: Optional. A tag that prevents multiple concurrent workflow instances with the same tag from running. This mitigates risk of concurrent instances started due to retries.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The tag must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters. workflowTemplate: A WorkflowTemplate resource to be passed as the request body. """ instanceId = _messages.StringField(1) parent = _messages.StringField(2, required=True) requestId = _messages.StringField(3) workflowTemplate = _messages.MessageField('WorkflowTemplate', 4) class DataprocProjectsRegionsWorkflowTemplatesInstantiateRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesInstantiateRequest object. Fields: instantiateWorkflowTemplateRequest: A InstantiateWorkflowTemplateRequest resource to be passed as the request body. name: Required. The "resource name" of the workflow template, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/workflowTemplates/{template_id} """ instantiateWorkflowTemplateRequest = _messages.MessageField('InstantiateWorkflowTemplateRequest', 1) name = _messages.StringField(2, required=True) class DataprocProjectsRegionsWorkflowTemplatesListRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesListRequest object. Fields: pageSize: Optional. The maximum number of results to return in each response. pageToken: Optional. The page token, returned by a previous call, to request the next page of results. parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class DataprocProjectsRegionsWorkflowTemplatesSetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesSetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being specified. See the operation documentation for the appropriate value for this field. setIamPolicyRequest: A SetIamPolicyRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) setIamPolicyRequest = _messages.MessageField('SetIamPolicyRequest', 2) class DataprocProjectsRegionsWorkflowTemplatesTestIamPermissionsRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesTestIamPermissionsRequest object. Fields: resource: REQUIRED: The resource for which the policy detail is being requested. See the operation documentation for the appropriate value for this field. testIamPermissionsRequest: A TestIamPermissionsRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) testIamPermissionsRequest = _messages.MessageField('TestIamPermissionsRequest', 2) class DiagnoseClusterRequest(_messages.Message): r"""A request to collect cluster diagnostic information.""" class DiagnoseClusterResults(_messages.Message): r"""The location of diagnostic output. Fields: outputUri: Output only. The Cloud Storage URI of the diagnostic output. The output report is a plain text file with a summary of collected diagnostics. """ outputUri = _messages.StringField(1) class DiskConfig(_messages.Message): r"""Specifies the config of disk options for a group of VM instances. Fields: bootDiskSizeGb: Optional. Size in GB of the boot disk (default is 500GB). bootDiskType: Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd- standard" (Persistent Disk Hard Disk Drive). numLocalSsds: Optional. Number of attached SSDs, from 0 to 4 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries. """ bootDiskSizeGb = _messages.IntegerField(1, variant=_messages.Variant.INT32) bootDiskType = _messages.StringField(2) numLocalSsds = _messages.IntegerField(3, variant=_messages.Variant.INT32) class Empty(_messages.Message): r"""A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}. """ class EncryptionConfig(_messages.Message): r"""Encryption settings for the cluster. Fields: gcePdKmsKeyName: Optional. The Cloud KMS key name to use for PD disk encryption for all instances in the cluster. """ gcePdKmsKeyName = _messages.StringField(1) class EndpointConfig(_messages.Message): r"""Endpoint config for this cluster Messages: HttpPortsValue: Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true. Fields: enableHttpPortAccess: Optional. If true, enable http access to specific ports on the cluster from external sources. Defaults to false. httpPorts: Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true. """ @encoding.MapUnrecognizedFields('additionalProperties') class HttpPortsValue(_messages.Message): r"""Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true. Messages: AdditionalProperty: An additional property for a HttpPortsValue object. Fields: additionalProperties: Additional properties of type HttpPortsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a HttpPortsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) enableHttpPortAccess = _messages.BooleanField(1) httpPorts = _messages.MessageField('HttpPortsValue', 2) class Expr(_messages.Message): r"""Represents an expression text. Example: title: "User account presence" description: "Determines whether the request has a user account" expression: "size(request.user) > 0" Fields: description: An optional description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI. expression: Textual representation of an expression in Common Expression Language syntax.The application context of the containing message determines which well-known feature set of CEL is supported. location: An optional string indicating the location of the expression for error reporting, e.g. a file name and a position in the file. title: An optional title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression. """ description = _messages.StringField(1) expression = _messages.StringField(2) location = _messages.StringField(3) title = _messages.StringField(4) class GceClusterConfig(_messages.Message): r"""Common config settings for resources of Compute Engine cluster instances, applicable to all instances in the cluster. Messages: MetadataValue: The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs/storing-retrieving- metadata#project_and_instance_metadata)). Fields: allocationAffinity: Allocation Affinity for consuming Zonal allocation. internalIpOnly: Optional. If true, all instances in the cluster will only have internal IP addresses. By default, clusters are not restricted to internal IP addresses, and will have ephemeral external IP addresses assigned to each instance. This internal_ip_only restriction can only be enabled for subnetwork enabled networks, and all off-cluster dependencies must be configured to be accessible without external IP addresses. metadata: The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs /storing-retrieving-metadata#project_and_instance_metadata)). networkUri: Optional. The Compute Engine network to be used for machine communications. Cannot be specified with subnetwork_uri. If neither network_uri nor subnetwork_uri is specified, the "default" network of the project is used, if it exists. Cannot be a "Custom Subnet Network" (see Using Subnetworks for more information).A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/pr ojects/[project_id]/regions/global/default projects/[project_id]/regions/global/default default serviceAccount: Optional. The service account of the instances. Defaults to the default Compute Engine service account. Custom service accounts need permissions equivalent to the following IAM roles: roles/logging.logWriter roles/storage.objectAdmin(see https://cloud.google.com/compute/docs/access/service- accounts#custom_service_accounts for more information). Example: [account_id]@[project_id].iam.gserviceaccount.com serviceAccountScopes: Optional. The URIs of service account scopes to be included in Compute Engine instances. The following base set of scopes is always included: https://www.googleapis.com/auth/cloud.useraccounts.readonly https://www.googleapis.com/auth/devstorage.read_write https://www.googleapis.com/auth/logging.writeIf no scopes are specified, the following defaults are also provided: https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigtable.admin.table https://www.googleapis.com/auth/bigtable.data https://www.googleapis.com/auth/devstorage.full_control subnetworkUri: Optional. The Compute Engine subnetwork to be used for machine communications. Cannot be specified with network_uri.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/regions/us- east1/sub0 projects/[project_id]/regions/us-east1/sub0 sub0 tags: The Compute Engine tags to add to all instances (see Tagging instances). zoneUri: Optional. The zone where the Compute Engine cluster will be located. On a create request, it is required in the "global" region. If omitted in a non-global Cloud Dataproc region, the service will pick a zone in the corresponding Compute Engine region. On a get request, zone will always be present.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone] projects/[project_id]/zones/[zone] us-central1-f """ @encoding.MapUnrecognizedFields('additionalProperties') class MetadataValue(_messages.Message): r"""The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs /storing-retrieving-metadata#project_and_instance_metadata)). Messages: AdditionalProperty: An additional property for a MetadataValue object. Fields: additionalProperties: Additional properties of type MetadataValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a MetadataValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) allocationAffinity = _messages.MessageField('AllocationAffinity', 1) internalIpOnly = _messages.BooleanField(2) metadata = _messages.MessageField('MetadataValue', 3) networkUri = _messages.StringField(4) serviceAccount = _messages.StringField(5) serviceAccountScopes = _messages.StringField(6, repeated=True) subnetworkUri = _messages.StringField(7) tags = _messages.StringField(8, repeated=True) zoneUri = _messages.StringField(9) class GetIamPolicyRequest(_messages.Message): r"""Request message for GetIamPolicy method.""" class HadoopJob(_messages.Message): r"""A Cloud Dataproc job for running Apache Hadoop MapReduce (https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop- mapreduce-client-core/MapReduceTutorial.html) jobs on Apache Hadoop YARN (https://hadoop.apache.org/docs/r2.7.1/hadoop-yarn/hadoop-yarn- site/YARN.html). Messages: PropertiesValue: Optional. A mapping of property names to values, used to configure Hadoop. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site and classes in user code. Fields: archiveUris: Optional. HCFS URIs of archives to be extracted in the working directory of Hadoop drivers and tasks. Supported file types: .jar, .tar, .tar.gz, .tgz, or .zip. args: Optional. The arguments to pass to the driver. Do not include arguments, such as -libjars or -Dfoo=bar, that can be set as job properties, since a collision may occur that causes an incorrect job submission. fileUris: Optional. HCFS (Hadoop Compatible Filesystem) URIs of files to be copied to the working directory of Hadoop drivers and distributed tasks. Useful for naively parallel tasks. jarFileUris: Optional. Jar file URIs to add to the CLASSPATHs of the Hadoop driver and tasks. loggingConfig: Optional. The runtime log config for job execution. mainClass: The name of the driver's main class. The jar file containing the class must be in the default CLASSPATH or specified in jar_file_uris. mainJarFileUri: The HCFS URI of the jar file containing the main class. Examples: 'gs://foo-bucket/analytics-binaries/extract-useful-metrics- mr.jar' 'hdfs:/tmp/test-samples/custom-wordcount.jar' 'file:///home/usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar' properties: Optional. A mapping of property names to values, used to configure Hadoop. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site and classes in user code. """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names to values, used to configure Hadoop. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site and classes in user code. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) archiveUris = _messages.StringField(1, repeated=True) args = _messages.StringField(2, repeated=True) fileUris = _messages.StringField(3, repeated=True) jarFileUris = _messages.StringField(4, repeated=True) loggingConfig = _messages.MessageField('LoggingConfig', 5) mainClass = _messages.StringField(6) mainJarFileUri = _messages.StringField(7) properties = _messages.MessageField('PropertiesValue', 8) class HiveJob(_messages.Message): r"""A Cloud Dataproc job for running Apache Hive (https://hive.apache.org/) queries on YARN. Messages: PropertiesValue: Optional. A mapping of property names and values, used to configure Hive. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/hive/conf/hive-site.xml, and classes in user code. ScriptVariablesValue: Optional. Mapping of query variable names to values (equivalent to the Hive command: SET name="value";). Fields: continueOnFailure: Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries. jarFileUris: Optional. HCFS URIs of jar files to add to the CLASSPATH of the Hive server and Hadoop MapReduce (MR) tasks. Can contain Hive SerDes and UDFs. properties: Optional. A mapping of property names and values, used to configure Hive. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/hive/conf/hive-site.xml, and classes in user code. queryFileUri: The HCFS URI of the script that contains Hive queries. queryList: A list of queries. scriptVariables: Optional. Mapping of query variable names to values (equivalent to the Hive command: SET name="value";). """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names and values, used to configure Hive. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/hive/conf/hive-site.xml, and classes in user code. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class ScriptVariablesValue(_messages.Message): r"""Optional. Mapping of query variable names to values (equivalent to the Hive command: SET name="value";). Messages: AdditionalProperty: An additional property for a ScriptVariablesValue object. Fields: additionalProperties: Additional properties of type ScriptVariablesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ScriptVariablesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) continueOnFailure = _messages.BooleanField(1) jarFileUris = _messages.StringField(2, repeated=True) properties = _messages.MessageField('PropertiesValue', 3) queryFileUri = _messages.StringField(4) queryList = _messages.MessageField('QueryList', 5) scriptVariables = _messages.MessageField('ScriptVariablesValue', 6) class InstanceGroupAutoscalingPolicyConfig(_messages.Message): r"""Configuration for the size bounds of an instance group, including its proportional size to other groups. Fields: maxInstances: Required. Maximum number of instances for this group. Must be >= min_instances. minInstances: Optional. Minimum number of instances for this group.Default for primary workers is 2, default for secondary workers is 0. weight: Optional. Weight for instance group. Determines fraction of total workers in cluster that will be composed of instances from this instance group (e.g. if primary workers have weight 2 and secondary workers have weight 1, then the cluster should have approximately 2 primary workers to each secondary worker. Cluster may not reach these exact weights if constrained by min/max bounds or other autoscaling configurations.Default 1. Note that all groups have equal an equal weight by default, so the cluster will attempt to maintain an equal number of workers in each group within configured size bounds per group. """ maxInstances = _messages.IntegerField(1, variant=_messages.Variant.INT32) minInstances = _messages.IntegerField(2, variant=_messages.Variant.INT32) weight = _messages.IntegerField(3, variant=_messages.Variant.INT32) class InstanceGroupConfig(_messages.Message): r"""Optional. The config settings for Compute Engine resources in an instance group, such as a master or worker group. Fields: accelerators: Optional. The Compute Engine accelerator configuration for these instances.Beta Feature: This feature is still under development. It may be changed before final release. diskConfig: Optional. Disk option config settings. imageUri: Optional. The Compute Engine image resource used for cluster instances. It can be specified or may be inferred from SoftwareConfig.image_version. instanceNames: Output only. The list of instance names. Cloud Dataproc derives the names from cluster_name, num_instances, and the instance group. isPreemptible: Optional. Specifies that this instance group contains preemptible instances. machineTypeUri: Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/us- east1-a/machineTypes/n1-standard-2 projects/[project_id]/zones/us- east1-a/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Cloud Dataproc Auto Zone Placement feature, you must use the short name of the machine type resource, for example, n1-standard-2. managedGroupConfig: Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups. minCpuPlatform: Optional. Specifies the minimum cpu platform for the Instance Group. See Cloud Dataproc→Minimum CPU Platform. numInstances: Optional. The number of VM instances in the instance group. For master instance groups, must be set to 1. """ accelerators = _messages.MessageField('AcceleratorConfig', 1, repeated=True) diskConfig = _messages.MessageField('DiskConfig', 2) imageUri = _messages.StringField(3) instanceNames = _messages.StringField(4, repeated=True) isPreemptible = _messages.BooleanField(5) machineTypeUri = _messages.StringField(6) managedGroupConfig = _messages.MessageField('ManagedGroupConfig', 7) minCpuPlatform = _messages.StringField(8) numInstances = _messages.IntegerField(9, variant=_messages.Variant.INT32) class InstantiateWorkflowTemplateRequest(_messages.Message): r"""A request to instantiate a workflow template. Messages: ParametersValue: Optional. Map from parameter names to values that should be used for those parameters. Values may not exceed 100 characters. Fields: instanceId: Deprecated. Please use request_id field instead. parameters: Optional. Map from parameter names to values that should be used for those parameters. Values may not exceed 100 characters. requestId: Optional. A tag that prevents multiple concurrent workflow instances with the same tag from running. This mitigates risk of concurrent instances started due to retries.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The tag must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters. version: Optional. The version of workflow template to instantiate. If specified, the workflow will be instantiated only if the current version of the workflow template has the supplied version.This option cannot be used to instantiate a previous version of workflow template. """ @encoding.MapUnrecognizedFields('additionalProperties') class ParametersValue(_messages.Message): r"""Optional. Map from parameter names to values that should be used for those parameters. Values may not exceed 100 characters. Messages: AdditionalProperty: An additional property for a ParametersValue object. Fields: additionalProperties: Additional properties of type ParametersValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ParametersValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) instanceId = _messages.StringField(1) parameters = _messages.MessageField('ParametersValue', 2) requestId = _messages.StringField(3) version = _messages.IntegerField(4, variant=_messages.Variant.INT32) class Job(_messages.Message): r"""A Cloud Dataproc job resource. Messages: LabelsValue: Optional. The labels to associate with this job. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a job. Fields: driverControlFilesUri: Output only. If present, the location of miscellaneous control files which may be used as part of job setup and handling. If not present, control files may be placed in the same location as driver_output_uri. driverOutputResourceUri: Output only. A URI pointing to the location of the stdout of the job's driver program. hadoopJob: Job is a Hadoop job. hiveJob: Job is a Hive job. jobUuid: Output only. A UUID that uniquely identifies a job within the project over time. This is in contrast to a user-settable reference.job_id that may be reused over time. labels: Optional. The labels to associate with this job. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a job. pigJob: Job is a Pig job. placement: Required. Job information, including how, when, and where to run the job. prestoJob: Job is a Presto job pysparkJob: Job is a Pyspark job. reference: Optional. The fully qualified reference to the job, which can be used to obtain the equivalent REST path of the job resource. If this property is not specified when a job is created, the server generates a <code>job_id</code>. scheduling: Optional. Job scheduling configuration. sparkJob: Job is a Spark job. sparkRJob: Job is a SparkR job. sparkSqlJob: Job is a SparkSql job. status: Output only. The job status. Additional application-specific status information may be contained in the <code>type_job</code> and <code>yarn_applications</code> fields. statusHistory: Output only. The previous job status. submittedBy: Output only. The email address of the user submitting the job. For jobs submitted on the cluster, the address is <code>username@hostname</code>. yarnApplications: Output only. The collection of YARN applications spun up by this job.Beta Feature: This report is available for testing purposes only. It may be changed before final release. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels to associate with this job. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a job. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) driverControlFilesUri = _messages.StringField(1) driverOutputResourceUri = _messages.StringField(2) hadoopJob = _messages.MessageField('HadoopJob', 3) hiveJob = _messages.MessageField('HiveJob', 4) jobUuid = _messages.StringField(5) labels = _messages.MessageField('LabelsValue', 6) pigJob = _messages.MessageField('PigJob', 7) placement = _messages.MessageField('JobPlacement', 8) prestoJob = _messages.MessageField('PrestoJob', 9) pysparkJob = _messages.MessageField('PySparkJob', 10) reference = _messages.MessageField('JobReference', 11) scheduling = _messages.MessageField('JobScheduling', 12) sparkJob = _messages.MessageField('SparkJob', 13) sparkRJob = _messages.MessageField('SparkRJob', 14) sparkSqlJob = _messages.MessageField('SparkSqlJob', 15) status = _messages.MessageField('JobStatus', 16) statusHistory = _messages.MessageField('JobStatus', 17, repeated=True) submittedBy = _messages.StringField(18) yarnApplications = _messages.MessageField('YarnApplication', 19, repeated=True) class JobPlacement(_messages.Message): r"""Cloud Dataproc job config. Fields: clusterName: Required. The name of the cluster where the job will be submitted. clusterUuid: Output only. A cluster UUID generated by the Cloud Dataproc service when the job is submitted. """ clusterName = _messages.StringField(1) clusterUuid = _messages.StringField(2) class JobReference(_messages.Message): r"""Encapsulates the full scoping used to reference a job. Fields: jobId: Optional. The job ID, which must be unique within the project. The job ID is generated by the server upon job submission or provided by the user as a means to perform retries without creating duplicate jobs. The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), or hyphens (-). The maximum length is 100 characters. projectId: Required. The ID of the Google Cloud Platform project that the job belongs to. """ jobId = _messages.StringField(1) projectId = _messages.StringField(2) class JobScheduling(_messages.Message): r"""Job scheduling options. Fields: maxFailuresPerHour: Optional. Maximum number of times per hour a driver may be restarted as a result of driver terminating with non-zero code before job is reported failed.A job may be reported as thrashing if driver exits with non-zero code 4 times within 10 minute window.Maximum value is 10. """ maxFailuresPerHour = _messages.IntegerField(1, variant=_messages.Variant.INT32) class JobStatus(_messages.Message): r"""Cloud Dataproc job status. Enums: StateValueValuesEnum: Output only. A state message specifying the overall job state. SubstateValueValuesEnum: Output only. Additional state information, which includes status reported by the agent. Fields: details: Output only. Optional job state details, such as an error description if the state is <code>ERROR</code>. state: Output only. A state message specifying the overall job state. stateStartTime: Output only. The time when this state was entered. substate: Output only. Additional state information, which includes status reported by the agent. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. A state message specifying the overall job state. Values: STATE_UNSPECIFIED: The job state is unknown. PENDING: The job is pending; it has been submitted, but is not yet running. SETUP_DONE: Job has been received by the service and completed initial setup; it will soon be submitted to the cluster. RUNNING: The job is running on the cluster. CANCEL_PENDING: A CancelJob request has been received, but is pending. CANCEL_STARTED: Transient in-flight resources have been canceled, and the request to cancel the running job has been issued to the cluster. CANCELLED: The job cancellation was successful. DONE: The job has completed successfully. ERROR: The job has completed, but encountered an error. ATTEMPT_FAILURE: Job attempt has failed. The detail field contains failure details for this attempt.Applies to restartable jobs only. """ STATE_UNSPECIFIED = 0 PENDING = 1 SETUP_DONE = 2 RUNNING = 3 CANCEL_PENDING = 4 CANCEL_STARTED = 5 CANCELLED = 6 DONE = 7 ERROR = 8 ATTEMPT_FAILURE = 9 class SubstateValueValuesEnum(_messages.Enum): r"""Output only. Additional state information, which includes status reported by the agent. Values: UNSPECIFIED: The job substate is unknown. SUBMITTED: The Job is submitted to the agent.Applies to RUNNING state. QUEUED: The Job has been received and is awaiting execution (it may be waiting for a condition to be met). See the "details" field for the reason for the delay.Applies to RUNNING state. STALE_STATUS: The agent-reported status is out of date, which may be caused by a loss of communication between the agent and Cloud Dataproc. If the agent does not send a timely update, the job will fail.Applies to RUNNING state. """ UNSPECIFIED = 0 SUBMITTED = 1 QUEUED = 2 STALE_STATUS = 3 details = _messages.StringField(1) state = _messages.EnumField('StateValueValuesEnum', 2) stateStartTime = _messages.StringField(3) substate = _messages.EnumField('SubstateValueValuesEnum', 4) class KerberosConfig(_messages.Message): r"""Specifies Kerberos related configuration. Fields: crossRealmTrustAdminServer: Optional. The admin server (IP or hostname) for the remote trusted realm in a cross realm trust relationship. crossRealmTrustKdc: Optional. The KDC (IP or hostname) for the remote trusted realm in a cross realm trust relationship. crossRealmTrustRealm: Optional. The remote realm the Dataproc on-cluster KDC will trust, should the user enable cross realm trust. crossRealmTrustSharedPasswordUri: Optional. The GCS uri of a KMS encrypted file containing the shared password between the on-cluster Kerberos realm and the remote trusted realm, in a cross realm trust relationship. enableKerberos: Optional. Flag to indicate whether to Kerberize the cluster. kdcDbKeyUri: Optional. The GCS uri of a KMS encrypted file containing the master key of the KDC database. keyPasswordUri: Optional. The GCS uri of a KMS encrypted file containing the password to the user provided key. For the self-signed certificate, this password is generated by Dataproc. keystorePasswordUri: Optional. The GCS uri of a KMS encrypted file containing the password to the user provided keystore. For the self- signed certificate, this password is generated by Dataproc. keystoreUri: Optional. The GCS uri of the keystore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate. kmsKeyUri: Required. The uri of the KMS key used to encrypt various sensitive files. rootPrincipalPasswordUri: Required. The GCS uri of a KMS encrypted file containing the root principal password. tgtLifetimeHours: Optional. The lifetime of the ticket granting ticket, in hours. If not specified, or user specifies 0, then default value 10 will be used. truststorePasswordUri: Optional. The GCS uri of a KMS encrypted file containing the password to the user provided truststore. For the self- signed certificate, this password is generated by Dataproc. truststoreUri: Optional. The GCS uri of the truststore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate. """ crossRealmTrustAdminServer = _messages.StringField(1) crossRealmTrustKdc = _messages.StringField(2) crossRealmTrustRealm = _messages.StringField(3) crossRealmTrustSharedPasswordUri = _messages.StringField(4) enableKerberos = _messages.BooleanField(5) kdcDbKeyUri = _messages.StringField(6) keyPasswordUri = _messages.StringField(7) keystorePasswordUri = _messages.StringField(8) keystoreUri = _messages.StringField(9) kmsKeyUri = _messages.StringField(10) rootPrincipalPasswordUri = _messages.StringField(11) tgtLifetimeHours = _messages.IntegerField(12, variant=_messages.Variant.INT32) truststorePasswordUri = _messages.StringField(13) truststoreUri = _messages.StringField(14) class LifecycleConfig(_messages.Message): r"""Specifies the cluster auto-delete schedule configuration. Fields: autoDeleteTime: Optional. The time when cluster will be auto-deleted. autoDeleteTtl: Optional. The lifetime duration of cluster. The cluster will be auto-deleted at the end of this period. Valid range: 10m, 14d.Example: "1d", to delete the cluster 1 day after its creation.. idleDeleteTtl: Optional. The duration to keep the cluster alive while idling. Passing this threshold will cause the cluster to be deleted. Valid range: 10m, 14d.Example: "10m", the minimum value, to delete the cluster when it has had no jobs running for 10 minutes. idleStartTime: Output only. The time when cluster became idle (most recent job finished) and became eligible for deletion due to idleness. """ autoDeleteTime = _messages.StringField(1) autoDeleteTtl = _messages.StringField(2) idleDeleteTtl = _messages.StringField(3) idleStartTime = _messages.StringField(4) class ListAutoscalingPoliciesResponse(_messages.Message): r"""A response to a request to list autoscaling policies in a project. Fields: nextPageToken: Output only. This token is included in the response if there are more results to fetch. policies: Output only. Autoscaling policies list. """ nextPageToken = _messages.StringField(1) policies = _messages.MessageField('AutoscalingPolicy', 2, repeated=True) class ListClustersResponse(_messages.Message): r"""The list of all clusters in a project. Fields: clusters: Output only. The clusters in the project. nextPageToken: Output only. This token is included in the response if there are more results to fetch. To fetch additional results, provide this value as the page_token in a subsequent <code>ListClustersRequest</code>. """ clusters = _messages.MessageField('Cluster', 1, repeated=True) nextPageToken = _messages.StringField(2) class ListJobsResponse(_messages.Message): r"""A list of jobs in a project. Fields: jobs: Output only. Jobs list. nextPageToken: Optional. This token is included in the response if there are more results to fetch. To fetch additional results, provide this value as the page_token in a subsequent <code>ListJobsRequest</code>. """ jobs = _messages.MessageField('Job', 1, repeated=True) nextPageToken = _messages.StringField(2) class ListOperationsResponse(_messages.Message): r"""The response message for Operations.ListOperations. Fields: nextPageToken: The standard List next-page token. operations: A list of operations that matches the specified filter in the request. """ nextPageToken = _messages.StringField(1) operations = _messages.MessageField('Operation', 2, repeated=True) class ListWorkflowTemplatesResponse(_messages.Message): r"""A response to a request to list workflow templates in a project. Fields: nextPageToken: Output only. This token is included in the response if there are more results to fetch. To fetch additional results, provide this value as the page_token in a subsequent <code>ListWorkflowTemplatesRequest</code>. templates: Output only. WorkflowTemplates list. """ nextPageToken = _messages.StringField(1) templates = _messages.MessageField('WorkflowTemplate', 2, repeated=True) class LoggingConfig(_messages.Message): r"""The runtime logging config of the job. Messages: DriverLogLevelsValue: The per-package log levels for the driver. This may include "root" package name to configure rootLogger. Examples: 'com.google = FATAL', 'root = INFO', 'org.apache = DEBUG' Fields: driverLogLevels: The per-package log levels for the driver. This may include "root" package name to configure rootLogger. Examples: 'com.google = FATAL', 'root = INFO', 'org.apache = DEBUG' """ @encoding.MapUnrecognizedFields('additionalProperties') class DriverLogLevelsValue(_messages.Message): r"""The per-package log levels for the driver. This may include "root" package name to configure rootLogger. Examples: 'com.google = FATAL', 'root = INFO', 'org.apache = DEBUG' Messages: AdditionalProperty: An additional property for a DriverLogLevelsValue object. Fields: additionalProperties: Additional properties of type DriverLogLevelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a DriverLogLevelsValue object. Enums: ValueValueValuesEnum: Fields: key: Name of the additional property. value: A ValueValueValuesEnum attribute. """ class ValueValueValuesEnum(_messages.Enum): r"""ValueValueValuesEnum enum type. Values: LEVEL_UNSPECIFIED: <no description> ALL: <no description> TRACE: <no description> DEBUG: <no description> INFO: <no description> WARN: <no description> ERROR: <no description> FATAL: <no description> OFF: <no description> """ LEVEL_UNSPECIFIED = 0 ALL = 1 TRACE = 2 DEBUG = 3 INFO = 4 WARN = 5 ERROR = 6 FATAL = 7 OFF = 8 key = _messages.StringField(1) value = _messages.EnumField('ValueValueValuesEnum', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) driverLogLevels = _messages.MessageField('DriverLogLevelsValue', 1) class ManagedCluster(_messages.Message): r"""Cluster that is managed by the workflow. Messages: LabelsValue: Optional. The labels to associate with this cluster.Label keys must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given cluster. Fields: clusterName: Required. The cluster name prefix. A unique cluster name will be formed by appending a random suffix.The name must contain only lower- case letters (a-z), numbers (0-9), and hyphens (-). Must begin with a letter. Cannot begin or end with hyphen. Must consist of between 2 and 35 characters. config: Required. The cluster configuration. labels: Optional. The labels to associate with this cluster.Label keys must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given cluster. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels to associate with this cluster.Label keys must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given cluster. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) clusterName = _messages.StringField(1) config = _messages.MessageField('ClusterConfig', 2) labels = _messages.MessageField('LabelsValue', 3) class ManagedGroupConfig(_messages.Message): r"""Specifies the resources used to actively manage an instance group. Fields: instanceGroupManagerName: Output only. The name of the Instance Group Manager for this group. instanceTemplateName: Output only. The name of the Instance Template used for the Managed Instance Group. """ instanceGroupManagerName = _messages.StringField(1) instanceTemplateName = _messages.StringField(2) class NodeInitializationAction(_messages.Message): r"""Specifies an executable to run on a fully configured node and a timeout period for executable completion. Fields: executableFile: Required. Cloud Storage URI of executable file. executionTimeout: Optional. Amount of time executable has to complete. Default is 10 minutes. Cluster creation fails with an explanatory error message (the name of the executable that caused the error and the exceeded timeout period) if the executable is not completed at end of the timeout period. """ executableFile = _messages.StringField(1) executionTimeout = _messages.StringField(2) class Operation(_messages.Message): r"""This resource represents a long-running operation that is the result of a network API call. Messages: MetadataValue: Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. ResponseValue: The normal response of the operation in case of success. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse. Fields: done: If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available. error: The error result of the operation in case of failure or cancellation. metadata: Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. name: The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should have the format of operations/some/unique/name. response: The normal response of the operation in case of success. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse. """ @encoding.MapUnrecognizedFields('additionalProperties') class MetadataValue(_messages.Message): r"""Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. Messages: AdditionalProperty: An additional property for a MetadataValue object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. """ class AdditionalProperty(_messages.Message): r"""An additional property for a MetadataValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class ResponseValue(_messages.Message): r"""The normal response of the operation in case of success. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse. Messages: AdditionalProperty: An additional property for a ResponseValue object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. """ class AdditionalProperty(_messages.Message): r"""An additional property for a ResponseValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) done = _messages.BooleanField(1) error = _messages.MessageField('Status', 2) metadata = _messages.MessageField('MetadataValue', 3) name = _messages.StringField(4) response = _messages.MessageField('ResponseValue', 5) class OrderedJob(_messages.Message): r"""A job executed by the workflow. Messages: LabelsValue: Optional. The labels to associate with this job.Label keys must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given job. Fields: hadoopJob: Job is a Hadoop job. hiveJob: Job is a Hive job. labels: Optional. The labels to associate with this job.Label keys must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given job. pigJob: Job is a Pig job. prerequisiteStepIds: Optional. The optional list of prerequisite job step_ids. If not specified, the job will start at the beginning of workflow. prestoJob: Job is a Presto job. pysparkJob: Job is a Pyspark job. scheduling: Optional. Job scheduling configuration. sparkJob: Job is a Spark job. sparkRJob: Job is a SparkR job. sparkSqlJob: Job is a SparkSql job. stepId: Required. The step id. The id must be unique among all jobs within the template.The step id is used as prefix for job id, as job goog- dataproc-workflow-step-id label, and in prerequisiteStepIds field from other steps.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels to associate with this job.Label keys must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given job. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) hadoopJob = _messages.MessageField('HadoopJob', 1) hiveJob = _messages.MessageField('HiveJob', 2) labels = _messages.MessageField('LabelsValue', 3) pigJob = _messages.MessageField('PigJob', 4) prerequisiteStepIds = _messages.StringField(5, repeated=True) prestoJob = _messages.MessageField('PrestoJob', 6) pysparkJob = _messages.MessageField('PySparkJob', 7) scheduling = _messages.MessageField('JobScheduling', 8) sparkJob = _messages.MessageField('SparkJob', 9) sparkRJob = _messages.MessageField('SparkRJob', 10) sparkSqlJob = _messages.MessageField('SparkSqlJob', 11) stepId = _messages.StringField(12) class ParameterValidation(_messages.Message): r"""Configuration for parameter validation. Fields: regex: Validation based on regular expressions. values: Validation based on a list of allowed values. """ regex = _messages.MessageField('RegexValidation', 1) values = _messages.MessageField('ValueValidation', 2) class PigJob(_messages.Message): r"""A Cloud Dataproc job for running Apache Pig (https://pig.apache.org/) queries on YARN. Messages: PropertiesValue: Optional. A mapping of property names to values, used to configure Pig. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/pig/conf/pig.properties, and classes in user code. ScriptVariablesValue: Optional. Mapping of query variable names to values (equivalent to the Pig command: name=[value]). Fields: continueOnFailure: Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries. jarFileUris: Optional. HCFS URIs of jar files to add to the CLASSPATH of the Pig Client and Hadoop MapReduce (MR) tasks. Can contain Pig UDFs. loggingConfig: Optional. The runtime log config for job execution. properties: Optional. A mapping of property names to values, used to configure Pig. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/pig/conf/pig.properties, and classes in user code. queryFileUri: The HCFS URI of the script that contains the Pig queries. queryList: A list of queries. scriptVariables: Optional. Mapping of query variable names to values (equivalent to the Pig command: name=[value]). """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names to values, used to configure Pig. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/pig/conf/pig.properties, and classes in user code. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class ScriptVariablesValue(_messages.Message): r"""Optional. Mapping of query variable names to values (equivalent to the Pig command: name=[value]). Messages: AdditionalProperty: An additional property for a ScriptVariablesValue object. Fields: additionalProperties: Additional properties of type ScriptVariablesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ScriptVariablesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) continueOnFailure = _messages.BooleanField(1) jarFileUris = _messages.StringField(2, repeated=True) loggingConfig = _messages.MessageField('LoggingConfig', 3) properties = _messages.MessageField('PropertiesValue', 4) queryFileUri = _messages.StringField(5) queryList = _messages.MessageField('QueryList', 6) scriptVariables = _messages.MessageField('ScriptVariablesValue', 7) class Policy(_messages.Message): r"""Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources.A Policy consists of a list of bindings. A binding binds a list of members to a role, where the members can be user accounts, Google groups, Google domains, and service accounts. A role is a named list of permissions defined by IAM.JSON Example { "bindings": [ { "role": "roles/owner", "members": [ "user:<EMAIL>", "group:<EMAIL>", "domain:google.com", "serviceAccount:<EMAIL>" ] }, { "role": "roles/viewer", "members": ["user:<EMAIL>"] } ] } YAML Example bindings: - members: - user:<EMAIL> - group:<EMAIL> - domain:google.com - serviceAccount:my-<EMAIL>- <EMAIL> role: roles/owner - members: - user:<EMAIL> role: roles/viewer For a description of IAM and its features, see the IAM developer's guide (https://cloud.google.com/iam/docs). Fields: bindings: Associates a list of members to a role. bindings with no members will result in an error. etag: etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the etag in the read- modify-write cycle to perform policy updates in order to avoid race conditions: An etag is returned in the response to getIamPolicy, and systems are expected to put that etag in the request to setIamPolicy to ensure that their change will be applied to the same version of the policy.If no etag is provided in the call to setIamPolicy, then the existing policy is overwritten blindly. version: Deprecated. """ bindings = _messages.MessageField('Binding', 1, repeated=True) etag = _messages.BytesField(2) version = _messages.IntegerField(3, variant=_messages.Variant.INT32) class PrestoJob(_messages.Message): r"""A Cloud Dataproc job for running Presto (https://prestosql.io/) queries Messages: PropertiesValue: Optional. A mapping of property names to values. Used to set Presto session properties (https://prestodb.io/docs/current/sql/set- session.html) Equivalent to using the --session flag in the Presto CLI Fields: clientTags: Optional. Presto client tags to attach to this query continueOnFailure: Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries. loggingConfig: Optional. The runtime log config for job execution. outputFormat: Optional. The format in which query output will be displayed. See the Presto documentation for supported output formats properties: Optional. A mapping of property names to values. Used to set Presto session properties (https://prestodb.io/docs/current/sql/set- session.html) Equivalent to using the --session flag in the Presto CLI queryFileUri: The HCFS URI of the script that contains SQL queries. queryList: A list of queries. """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names to values. Used to set Presto session properties (https://prestodb.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Presto CLI Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) clientTags = _messages.StringField(1, repeated=True) continueOnFailure = _messages.BooleanField(2) loggingConfig = _messages.MessageField('LoggingConfig', 3) outputFormat = _messages.StringField(4) properties = _messages.MessageField('PropertiesValue', 5) queryFileUri = _messages.StringField(6) queryList = _messages.MessageField('QueryList', 7) class PySparkJob(_messages.Message): r"""A Cloud Dataproc job for running Apache PySpark (https://spark.apache.org/docs/0.9.0/python-programming-guide.html) applications on YARN. Messages: PropertiesValue: Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code. Fields: archiveUris: Optional. HCFS URIs of archives to be extracted in the working directory of .jar, .tar, .tar.gz, .tgz, and .zip. args: Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission. fileUris: Optional. HCFS URIs of files to be copied to the working directory of Python drivers and distributed tasks. Useful for naively parallel tasks. jarFileUris: Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Python driver and tasks. loggingConfig: Optional. The runtime log config for job execution. mainPythonFileUri: Required. The HCFS URI of the main Python file to use as the driver. Must be a .py file. properties: Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code. pythonFileUris: Optional. HCFS file URIs of Python files to pass to the PySpark framework. Supported file types: .py, .egg, and .zip. """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf /spark-defaults.conf and classes in user code. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) archiveUris = _messages.StringField(1, repeated=True) args = _messages.StringField(2, repeated=True) fileUris = _messages.StringField(3, repeated=True) jarFileUris = _messages.StringField(4, repeated=True) loggingConfig = _messages.MessageField('LoggingConfig', 5) mainPythonFileUri = _messages.StringField(6) properties = _messages.MessageField('PropertiesValue', 7) pythonFileUris = _messages.StringField(8, repeated=True) class QueryList(_messages.Message): r"""A list of queries to run on a cluster. Fields: queries: Required. The queries to execute. You do not need to terminate a query with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of an Cloud Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } } """ queries = _messages.StringField(1, repeated=True) class RegexValidation(_messages.Message): r"""Validation based on regular expressions. Fields: regexes: Required. RE2 regular expressions used to validate the parameter's value. The value must match the regex in its entirety (substring matches are not sufficient). """ regexes = _messages.StringField(1, repeated=True) class SecurityConfig(_messages.Message): r"""Security related configuration, including encryption, Kerberos, etc. Fields: kerberosConfig: Kerberos related configuration. """ kerberosConfig = _messages.MessageField('KerberosConfig', 1) class SetIamPolicyRequest(_messages.Message): r"""Request message for SetIamPolicy method. Fields: policy: REQUIRED: The complete policy to be applied to the resource. The size of the policy is limited to a few 10s of KB. An empty policy is a valid policy but certain Cloud Platform services (such as Projects) might reject them. """ policy = _messages.MessageField('Policy', 1) class SoftwareConfig(_messages.Message): r"""Specifies the selection and config of software inside the cluster. Enums: OptionalComponentsValueListEntryValuesEnum: Messages: PropertiesValue: Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, such as core:fs.defaultFS. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties. Fields: imageVersion: Optional. The version of software inside the cluster. It must be one of the supported Cloud Dataproc Versions, such as "1.2" (including a subminor version, such as "1.2.29"), or the "preview" version. If unspecified, it defaults to the latest version. optionalComponents: The set of optional components to activate on the cluster. properties: Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, such as core:fs.defaultFS. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties. """ class OptionalComponentsValueListEntryValuesEnum(_messages.Enum): r"""OptionalComponentsValueListEntryValuesEnum enum type. Values: COMPONENT_UNSPECIFIED: <no description> JUPYTER: <no description> HIVE_WEBHCAT: <no description> ZEPPELIN: <no description> ANACONDA: <no description> PRESTO: <no description> KERBEROS: <no description> """ COMPONENT_UNSPECIFIED = 0 JUPYTER = 1 HIVE_WEBHCAT = 2 ZEPPELIN = 3 ANACONDA = 4 PRESTO = 5 KERBEROS = 6 @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, such as core:fs.defaultFS. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) imageVersion = _messages.StringField(1) optionalComponents = _messages.EnumField('OptionalComponentsValueListEntryValuesEnum', 2, repeated=True) properties = _messages.MessageField('PropertiesValue', 3) class SparkJob(_messages.Message): r"""A Cloud Dataproc job for running Apache Spark (http://spark.apache.org/) applications on YARN. Messages: PropertiesValue: Optional. A mapping of property names to values, used to configure Spark. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code. Fields: archiveUris: Optional. HCFS URIs of archives to be extracted in the working directory of Spark drivers and tasks. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip. args: Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission. fileUris: Optional. HCFS URIs of files to be copied to the working directory of Spark drivers and distributed tasks. Useful for naively parallel tasks. jarFileUris: Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Spark driver and tasks. loggingConfig: Optional. The runtime log config for job execution. mainClass: The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in jar_file_uris. mainJarFileUri: The HCFS URI of the jar file that contains the main class. properties: Optional. A mapping of property names to values, used to configure Spark. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code. """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names to values, used to configure Spark. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark- defaults.conf and classes in user code. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) archiveUris = _messages.StringField(1, repeated=True) args = _messages.StringField(2, repeated=True) fileUris = _messages.StringField(3, repeated=True) jarFileUris = _messages.StringField(4, repeated=True) loggingConfig = _messages.MessageField('LoggingConfig', 5) mainClass = _messages.StringField(6) mainJarFileUri = _messages.StringField(7) properties = _messages.MessageField('PropertiesValue', 8) class SparkRJob(_messages.Message): r"""A Cloud Dataproc job for running Apache SparkR (https://spark.apache.org/docs/latest/sparkr.html) applications on YARN. Messages: PropertiesValue: Optional. A mapping of property names to values, used to configure SparkR. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code. Fields: archiveUris: Optional. HCFS URIs of archives to be extracted in the working directory of Spark drivers and tasks. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip. args: Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission. fileUris: Optional. HCFS URIs of files to be copied to the working directory of R drivers and distributed tasks. Useful for naively parallel tasks. loggingConfig: Optional. The runtime log config for job execution. mainRFileUri: Required. The HCFS URI of the main R file to use as the driver. Must be a .R file. properties: Optional. A mapping of property names to values, used to configure SparkR. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code. """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names to values, used to configure SparkR. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark- defaults.conf and classes in user code. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) archiveUris = _messages.StringField(1, repeated=True) args = _messages.StringField(2, repeated=True) fileUris = _messages.StringField(3, repeated=True) loggingConfig = _messages.MessageField('LoggingConfig', 4) mainRFileUri = _messages.StringField(5) properties = _messages.MessageField('PropertiesValue', 6) class SparkSqlJob(_messages.Message): r"""A Cloud Dataproc job for running Apache Spark SQL (http://spark.apache.org/sql/) queries. Messages: PropertiesValue: Optional. A mapping of property names to values, used to configure Spark SQL's SparkConf. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. ScriptVariablesValue: Optional. Mapping of query variable names to values (equivalent to the Spark SQL command: SET name="value";). Fields: jarFileUris: Optional. HCFS URIs of jar files to be added to the Spark CLASSPATH. loggingConfig: Optional. The runtime log config for job execution. properties: Optional. A mapping of property names to values, used to configure Spark SQL's SparkConf. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. queryFileUri: The HCFS URI of the script that contains SQL queries. queryList: A list of queries. scriptVariables: Optional. Mapping of query variable names to values (equivalent to the Spark SQL command: SET name="value";). """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names to values, used to configure Spark SQL's SparkConf. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class ScriptVariablesValue(_messages.Message): r"""Optional. Mapping of query variable names to values (equivalent to the Spark SQL command: SET name="value";). Messages: AdditionalProperty: An additional property for a ScriptVariablesValue object. Fields: additionalProperties: Additional properties of type ScriptVariablesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ScriptVariablesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) jarFileUris = _messages.StringField(1, repeated=True) loggingConfig = _messages.MessageField('LoggingConfig', 2) properties = _messages.MessageField('PropertiesValue', 3) queryFileUri = _messages.StringField(4) queryList = _messages.MessageField('QueryList', 5) scriptVariables = _messages.MessageField('ScriptVariablesValue', 6) class StandardQueryParameters(_messages.Message): r"""Query parameters accepted by all methods. Enums: FXgafvValueValuesEnum: V1 error format. AltValueValuesEnum: Data format for response. Fields: f__xgafv: V1 error format. access_token: OAuth access token. alt: Data format for response. callback: JSONP fields: Selector specifying which fields to include in a partial response. key: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token. oauth_token: OAuth 2.0 token for the current user. prettyPrint: Returns response with indentations and line breaks. quotaUser: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters. trace: A tracing token of the form "token:<tokenid>" to include in api requests. uploadType: Legacy upload protocol for media (e.g. "media", "multipart"). upload_protocol: Upload protocol for media (e.g. "raw", "multipart"). """ class AltValueValuesEnum(_messages.Enum): r"""Data format for response. Values: json: Responses with Content-Type of application/json media: Media download with context-dependent Content-Type proto: Responses with Content-Type of application/x-protobuf """ json = 0 media = 1 proto = 2 class FXgafvValueValuesEnum(_messages.Enum): r"""V1 error format. Values: _1: v1 error format _2: v2 error format """ _1 = 0 _2 = 1 f__xgafv = _messages.EnumField('FXgafvValueValuesEnum', 1) access_token = _messages.StringField(2) alt = _messages.EnumField('AltValueValuesEnum', 3, default=u'json') callback = _messages.StringField(4) fields = _messages.StringField(5) key = _messages.StringField(6) oauth_token = _messages.StringField(7) prettyPrint = _messages.BooleanField(8, default=True) quotaUser = _messages.StringField(9) trace = _messages.StringField(10) uploadType = _messages.StringField(11) upload_protocol = _messages.StringField(12) class Status(_messages.Message): r"""The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). The error model is designed to be: Simple to use and understand for most users Flexible enough to meet unexpected needsOverviewThe Status message contains three pieces of data: error code, error message, and error details. The error code should be an enum value of google.rpc.Code, but it may accept additional error codes if needed. The error message should be a developer-facing English message that helps developers understand and resolve the error. If a localized user- facing error message is needed, put the localized message in the error details or localize it in the client. The optional error details may contain arbitrary information about the error. There is a predefined set of error detail types in the package google.rpc that can be used for common error conditions.Language mappingThe Status message is the logical representation of the error model, but it is not necessarily the actual wire format. When the Status message is exposed in different client libraries and different wire protocols, it can be mapped differently. For example, it will likely be mapped to some exceptions in Java, but more likely mapped to some error codes in C.Other usesThe error model and the Status message can be used in a variety of environments, either with or without APIs, to provide a consistent developer experience across different environments.Example uses of this error model include: Partial errors. If a service needs to return partial errors to the client, it may embed the Status in the normal response to indicate the partial errors. Workflow errors. A typical workflow has multiple steps. Each step may have a Status message for error reporting. Batch operations. If a client uses batch request and batch response, the Status message should be used directly inside batch response, one for each error sub-response. Asynchronous operations. If an API call embeds asynchronous operation results in its response, the status of those operations should be represented directly using the Status message. Logging. If some API errors are stored in logs, the message Status could be used directly after any stripping needed for security/privacy reasons. Messages: DetailsValueListEntry: A DetailsValueListEntry object. Fields: code: The status code, which should be an enum value of google.rpc.Code. details: A list of messages that carry the error details. There is a common set of message types for APIs to use. message: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client. """ @encoding.MapUnrecognizedFields('additionalProperties') class DetailsValueListEntry(_messages.Message): r"""A DetailsValueListEntry object. Messages: AdditionalProperty: An additional property for a DetailsValueListEntry object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. """ class AdditionalProperty(_messages.Message): r"""An additional property for a DetailsValueListEntry object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) code = _messages.IntegerField(1, variant=_messages.Variant.INT32) details = _messages.MessageField('DetailsValueListEntry', 2, repeated=True) message = _messages.StringField(3) class SubmitJobRequest(_messages.Message): r"""A request to submit a job. Fields: job: Required. The job resource. requestId: Optional. A unique id used to identify the request. If the server receives two SubmitJobRequest requests with the same id, then the second request will be ignored and the first Job created and stored in the backend is returned.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters. """ job = _messages.MessageField('Job', 1) requestId = _messages.StringField(2) class TemplateParameter(_messages.Message): r"""A configurable parameter that replaces one or more fields in the template. Parameterizable fields: - Labels - File uris - Job properties - Job arguments - Script variables - Main class (in HadoopJob and SparkJob) - Zone (in ClusterSelector) Fields: description: Optional. Brief description of the parameter. Must not exceed 1024 characters. fields: Required. Paths to all fields that the parameter replaces. A field is allowed to appear in at most one parameter's list of field paths.A field path is similar in syntax to a google.protobuf.FieldMask. For example, a field path that references the zone field of a workflow template's cluster selector would be specified as placement.clusterSelector.zone.Also, field paths can reference fields using the following syntax: Values in maps can be referenced by key: labels'key' placement.clusterSelector.clusterLabels'key' placement.managedCluster.labels'key' placement.clusterSelector.clusterLabels'key' jobs'step-id'.labels'key' Jobs in the jobs list can be referenced by step-id: jobs'step- id'.hadoopJob.mainJarFileUri jobs'step-id'.hiveJob.queryFileUri jobs 'step-id'.pySparkJob.mainPythonFileUri jobs'step- id'.hadoopJob.jarFileUris0 jobs'step-id'.hadoopJob.archiveUris0 jobs 'step-id'.hadoopJob.fileUris0 jobs'step-id'.pySparkJob.pythonFileUris0 Items in repeated fields can be referenced by a zero-based index: jobs 'step-id'.sparkJob.args0 Other examples: jobs'step- id'.hadoopJob.properties'key' jobs'step-id'.hadoopJob.args0 jobs'step- id'.hiveJob.scriptVariables'key' jobs'step-id'.hadoopJob.mainJarFileUri placement.clusterSelector.zoneIt may not be possible to parameterize maps and repeated fields in their entirety since only individual map values and individual items in repeated fields can be referenced. For example, the following field paths are invalid: placement.clusterSelector.clusterLabels jobs'step-id'.sparkJob.args name: Required. Parameter name. The parameter name is used as the key, and paired with the parameter value, which are passed to the template when the template is instantiated. The name must contain only capital letters (A-Z), numbers (0-9), and underscores (_), and must not start with a number. The maximum length is 40 characters. validation: Optional. Validation rules to be applied to this parameter's value. """ description = _messages.StringField(1) fields = _messages.StringField(2, repeated=True) name = _messages.StringField(3) validation = _messages.MessageField('ParameterValidation', 4) class TestIamPermissionsRequest(_messages.Message): r"""Request message for TestIamPermissions method. Fields: permissions: The set of permissions to check for the resource. Permissions with wildcards (such as '*' or 'storage.*') are not allowed. For more information see IAM Overview (https://cloud.google.com/iam/docs/overview#permissions). """ permissions = _messages.StringField(1, repeated=True) class TestIamPermissionsResponse(_messages.Message): r"""Response message for TestIamPermissions method. Fields: permissions: A subset of TestPermissionsRequest.permissions that the caller is allowed. """ permissions = _messages.StringField(1, repeated=True) class ValueValidation(_messages.Message): r"""Validation based on a list of allowed values. Fields: values: Required. List of allowed values for the parameter. """ values = _messages.StringField(1, repeated=True) class WorkflowGraph(_messages.Message): r"""The workflow graph. Fields: nodes: Output only. The workflow nodes. """ nodes = _messages.MessageField('WorkflowNode', 1, repeated=True) class WorkflowMetadata(_messages.Message): r"""A Cloud Dataproc workflow template resource. Enums: StateValueValuesEnum: Output only. The workflow state. Messages: ParametersValue: Map from parameter names to values that were used for those parameters. Fields: clusterName: Output only. The name of the target cluster. clusterUuid: Output only. The UUID of target cluster. createCluster: Output only. The create cluster operation metadata. deleteCluster: Output only. The delete cluster operation metadata. endTime: Output only. Workflow end time. graph: Output only. The workflow graph. parameters: Map from parameter names to values that were used for those parameters. startTime: Output only. Workflow start time. state: Output only. The workflow state. template: Output only. The "resource name" of the template. version: Output only. The version of template at the time of workflow instantiation. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The workflow state. Values: UNKNOWN: Unused. PENDING: The operation has been created. RUNNING: The operation is running. DONE: The operation is done; either cancelled or completed. """ UNKNOWN = 0 PENDING = 1 RUNNING = 2 DONE = 3 @encoding.MapUnrecognizedFields('additionalProperties') class ParametersValue(_messages.Message): r"""Map from parameter names to values that were used for those parameters. Messages: AdditionalProperty: An additional property for a ParametersValue object. Fields: additionalProperties: Additional properties of type ParametersValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ParametersValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) clusterName = _messages.StringField(1) clusterUuid = _messages.StringField(2) createCluster = _messages.MessageField('ClusterOperation', 3) deleteCluster = _messages.MessageField('ClusterOperation', 4) endTime = _messages.StringField(5) graph = _messages.MessageField('WorkflowGraph', 6) parameters = _messages.MessageField('ParametersValue', 7) startTime = _messages.StringField(8) state = _messages.EnumField('StateValueValuesEnum', 9) template = _messages.StringField(10) version = _messages.IntegerField(11, variant=_messages.Variant.INT32) class WorkflowNode(_messages.Message): r"""The workflow node. Enums: StateValueValuesEnum: Output only. The node state. Fields: error: Output only. The error detail. jobId: Output only. The job id; populated after the node enters RUNNING state. prerequisiteStepIds: Output only. Node's prerequisite nodes. state: Output only. The node state. stepId: Output only. The name of the node. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The node state. Values: NODE_STATUS_UNSPECIFIED: State is unspecified. BLOCKED: The node is awaiting prerequisite node to finish. RUNNABLE: The node is runnable but not running. RUNNING: The node is running. COMPLETED: The node completed successfully. FAILED: The node failed. A node can be marked FAILED because its ancestor or peer failed. """ NODE_STATUS_UNSPECIFIED = 0 BLOCKED = 1 RUNNABLE = 2 RUNNING = 3 COMPLETED = 4 FAILED = 5 error = _messages.StringField(1) jobId = _messages.StringField(2) prerequisiteStepIds = _messages.StringField(3, repeated=True) state = _messages.EnumField('StateValueValuesEnum', 4) stepId = _messages.StringField(5) class WorkflowTemplate(_messages.Message): r"""A Cloud Dataproc workflow template resource. Messages: LabelsValue: Optional. The labels to associate with this template. These labels will be propagated to all jobs and clusters created by the workflow instance.Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).No more than 32 labels can be associated with a template. Fields: createTime: Output only. The time template was created. id: Required. The template id.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.. jobs: Required. The Directed Acyclic Graph of Jobs to submit. labels: Optional. The labels to associate with this template. These labels will be propagated to all jobs and clusters created by the workflow instance.Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).No more than 32 labels can be associated with a template. name: Output only. The "resource name" of the template, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/workflowTemplates/{template_id} parameters: Optional. Template parameters whose values are substituted into the template. Values for parameters must be provided when the template is instantiated. placement: Required. WorkflowTemplate scheduling information. updateTime: Output only. The time template was last updated. version: Optional. Used to perform a consistent read-modify-write.This field should be left blank for a CreateWorkflowTemplate request. It is required for an UpdateWorkflowTemplate request, and must match the current server version. A typical update template flow would fetch the current template with a GetWorkflowTemplate request, which will return the current template with the version field filled in with the current server version. The user updates other fields in the template, then returns it as part of the UpdateWorkflowTemplate request. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels to associate with this template. These labels will be propagated to all jobs and clusters created by the workflow instance.Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).No more than 32 labels can be associated with a template. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) id = _messages.StringField(2) jobs = _messages.MessageField('OrderedJob', 3, repeated=True) labels = _messages.MessageField('LabelsValue', 4) name = _messages.StringField(5) parameters = _messages.MessageField('TemplateParameter', 6, repeated=True) placement = _messages.MessageField('WorkflowTemplatePlacement', 7) updateTime = _messages.StringField(8) version = _messages.IntegerField(9, variant=_messages.Variant.INT32) class WorkflowTemplatePlacement(_messages.Message): r"""Specifies workflow execution target.Either managed_cluster or cluster_selector is required. Fields: clusterSelector: Optional. A selector that chooses target cluster for jobs based on metadata.The selector is evaluated at the time each job is submitted. managedCluster: Optional. A cluster that is managed by the workflow. """ clusterSelector = _messages.MessageField('ClusterSelector', 1) managedCluster = _messages.MessageField('ManagedCluster', 2) class YarnApplication(_messages.Message): r"""A YARN application created by a job. Application information is a subset of <code>org.apache.hadoop.yarn.proto.YarnProtos.ApplicationReportProto</cod e>.Beta Feature: This report is available for testing purposes only. It may be changed before final release. Enums: StateValueValuesEnum: Required. The application state. Fields: name: Required. The application name. progress: Required. The numerical progress of the application, from 1 to 100. state: Required. The application state. trackingUrl: Optional. The HTTP URL of the ApplicationMaster, HistoryServer, or TimelineServer that provides application-specific information. The URL uses the internal hostname, and requires a proxy server for resolution and, possibly, access. """ class StateValueValuesEnum(_messages.Enum): r"""Required. The application state. Values: STATE_UNSPECIFIED: Status is unspecified. NEW: Status is NEW. NEW_SAVING: Status is NEW_SAVING. SUBMITTED: Status is SUBMITTED. ACCEPTED: Status is ACCEPTED. RUNNING: Status is RUNNING. FINISHED: Status is FINISHED. FAILED: Status is FAILED. KILLED: Status is KILLED. """ STATE_UNSPECIFIED = 0 NEW = 1 NEW_SAVING = 2 SUBMITTED = 3 ACCEPTED = 4 RUNNING = 5 FINISHED = 6 FAILED = 7 KILLED = 8 name = _messages.StringField(1) progress = _messages.FloatField(2, variant=_messages.Variant.FLOAT) state = _messages.EnumField('StateValueValuesEnum', 3) trackingUrl = _messages.StringField(4) encoding.AddCustomJsonFieldMapping( StandardQueryParameters, 'f__xgafv', '$.xgafv') encoding.AddCustomJsonEnumMapping( StandardQueryParameters.FXgafvValueValuesEnum, '_1', '1') encoding.AddCustomJsonEnumMapping( StandardQueryParameters.FXgafvValueValuesEnum, '_2', '2')

lib/googlecloudsdk/third_party/apis/dataproc/v1beta2/dataproc_v1beta2_messages.py

from apitools.base.protorpclite import messages as _messages from apitools.base.py import encoding from apitools.base.py import extra_types package = 'dataproc' class AcceleratorConfig(_messages.Message): r"""Specifies the type and number of accelerator cards attached to the instances of an instance group (see GPUs on Compute Engine). Fields: acceleratorCount: The number of the accelerator cards of this type exposed to this instance. acceleratorTypeUri: Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes( /compute/docs/reference/beta/acceleratorTypes)Examples * https://www.googleapis.com/compute/beta/projects/[project_id]/zones /us-east1-a/acceleratorTypes/nvidia-tesla-k80 * projects/[project_id]/zones/us-east1-a/acceleratorTypes/nvidia-tesla-k80 * nvidia-tesla-k80Auto Zone Exception: If you are using the Cloud Dataproc Auto Zone Placement feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-k80. """ acceleratorCount = _messages.IntegerField(1, variant=_messages.Variant.INT32) acceleratorTypeUri = _messages.StringField(2) class AllocationAffinity(_messages.Message): r"""Allocation Affinity for consuming Zonal allocation. Enums: ConsumeAllocationTypeValueValuesEnum: Fields: consumeAllocationType: A ConsumeAllocationTypeValueValuesEnum attribute. key: Corresponds to the label key of Allocation resource. values: Corresponds to the label values of allocation resource. """ class ConsumeAllocationTypeValueValuesEnum(_messages.Enum): r"""ConsumeAllocationTypeValueValuesEnum enum type. Values: TYPE_UNSPECIFIED: <no description> NO_ALLOCATION: Do not consume from any allocated capacity. ANY_ALLOCATION: Consume any allocation available. SPECIFIC_ALLOCATION: Must consume from a specific allocation. Must specify key value fields for specifying the allocations. """ TYPE_UNSPECIFIED = 0 NO_ALLOCATION = 1 ANY_ALLOCATION = 2 SPECIFIC_ALLOCATION = 3 consumeAllocationType = _messages.EnumField('ConsumeAllocationTypeValueValuesEnum', 1) key = _messages.StringField(2) values = _messages.StringField(3, repeated=True) class AutoscalingConfig(_messages.Message): r"""Autoscaling Policy config associated with the cluster. Fields: policyUri: Optional. The autoscaling policy used by the cluster.Only resource names including projectid and location (region) are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/lo cations/[dataproc_region]/autoscalingPolicies/[policy_id] projects/[proj ect_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id]Note that the policy must be in the same project and Cloud Dataproc region. """ policyUri = _messages.StringField(1) class AutoscalingPolicy(_messages.Message): r"""Describes an autoscaling policy for Dataproc cluster autoscaler. Fields: basicAlgorithm: A BasicAutoscalingAlgorithm attribute. id: Required. The policy id.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters. name: Output only. The "resource name" of the policy, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id}. secondaryWorkerConfig: Required. Describes how the autoscaler will operate for secondary workers. workerConfig: Required. Describes how the autoscaler will operate for primary workers. """ basicAlgorithm = _messages.MessageField('BasicAutoscalingAlgorithm', 1) id = _messages.StringField(2) name = _messages.StringField(3) secondaryWorkerConfig = _messages.MessageField('InstanceGroupAutoscalingPolicyConfig', 4) workerConfig = _messages.MessageField('InstanceGroupAutoscalingPolicyConfig', 5) class BasicAutoscalingAlgorithm(_messages.Message): r"""Basic algorithm for autoscaling. Fields: cooldownPeriod: Required. Cooldown time in between scaling. yarnConfig: Required. YARN autoscaling configuration. """ cooldownPeriod = _messages.StringField(1) yarnConfig = _messages.MessageField('BasicYarnAutoscalingConfig', 2) class BasicYarnAutoscalingConfig(_messages.Message): r"""Basic autoscaling configurations for YARN. Fields: gracefulDecommissionTimeout: Optional. Timeout used during an autoscaling event (cluster update) between 0 seconds (no graceful decommission) and 1 day.Default: 0s. scaleDownFactor: Optional. Fraction of suggested decrease in workers to scale down by between 0 and 1. Suggested decrease when scaling down is determined by the amount of average available memory since the last cooldown period.Default: 1.0. scaleDownMinWorkerFraction: Optional. Minimum workers as a fraction of the current cluster size to to scale down by between 0 and 1.Default: 0.0. scaleUpFactor: Required. Fraction of suggested increase in workers to scale up by between 0 and 1. Suggested increase when scaling up is determined by the amount of average pending memory since the last cooldown period. scaleUpMinWorkerFraction: Optional. Minimum workers as a fraction of the current cluster size to to scale up by between 0 and 1.Default: 0.0. """ gracefulDecommissionTimeout = _messages.StringField(1) scaleDownFactor = _messages.FloatField(2) scaleDownMinWorkerFraction = _messages.FloatField(3) scaleUpFactor = _messages.FloatField(4) scaleUpMinWorkerFraction = _messages.FloatField(5) class Binding(_messages.Message): r"""Associates members with a role. Fields: condition: Unimplemented. The condition that is associated with this binding. NOTE: an unsatisfied condition will not allow user access via current binding. Different bindings, including their conditions, are examined independently. members: Specifies the identities requesting access for a Cloud Platform resource. members can have the following values: allUsers: A special identifier that represents anyone who is on the internet; with or without a Google account. allAuthenticatedUsers: A special identifier that represents anyone who is authenticated with a Google account or a service account. user:{emailid}: An email address that represents a specific Google account. For example, <EMAIL> . serviceAccount:{emailid}: An email address that represents a service account. For example, <EMAIL>. group:{emailid}: An email address that represents a Google group. For example, <EMAIL>. domain:{domain}: A Google Apps domain name that represents all the users of that domain. For example, google.com or example.com. role: Role that is assigned to members. For example, roles/viewer, roles/editor, or roles/owner. """ condition = _messages.MessageField('Expr', 1) members = _messages.StringField(2, repeated=True) role = _messages.StringField(3) class CancelJobRequest(_messages.Message): r"""A request to cancel a job.""" class Cluster(_messages.Message): r"""Describes the identifying information, config, and status of a cluster of Compute Engine instances. Messages: LabelsValue: Optional. The labels to associate with this cluster. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a cluster. Fields: clusterName: Required. The cluster name. Cluster names within a project must be unique. Names of deleted clusters can be reused. clusterUuid: Output only. A cluster UUID (Unique Universal Identifier). Cloud Dataproc generates this value when it creates the cluster. config: Required. The cluster config. Note that Cloud Dataproc may set default values, and values may change when clusters are updated. labels: Optional. The labels to associate with this cluster. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a cluster. metrics: Output only. Contains cluster daemon metrics such as HDFS and YARN stats.Beta Feature: This report is available for testing purposes only. It may be changed before final release. projectId: Required. The Google Cloud Platform project ID that the cluster belongs to. status: Output only. Cluster status. statusHistory: Output only. The previous cluster status. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels to associate with this cluster. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a cluster. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) clusterName = _messages.StringField(1) clusterUuid = _messages.StringField(2) config = _messages.MessageField('ClusterConfig', 3) labels = _messages.MessageField('LabelsValue', 4) metrics = _messages.MessageField('ClusterMetrics', 5) projectId = _messages.StringField(6) status = _messages.MessageField('ClusterStatus', 7) statusHistory = _messages.MessageField('ClusterStatus', 8, repeated=True) class ClusterConfig(_messages.Message): r"""The cluster config. Fields: autoscalingConfig: Optional. Autoscaling config for the policy associated with the cluster. Cluster does not autoscale if this field is unset. configBucket: Optional. A Cloud Storage staging bucket used for sharing generated SSH keys and config. If you do not specify a staging bucket, Cloud Dataproc will determine an appropriate Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Google Compute Engine zone where your cluster is deployed, and then it will create and manage this project-level, per-location bucket for you. encryptionConfig: Optional. Encryption settings for the cluster. endpointConfig: Optional. Port/endpoint configuration for this cluster gceClusterConfig: Required. The shared Compute Engine config settings for all instances in a cluster. initializationActions: Optional. Commands to execute on each node after config is completed. By default, executables are run on master and all worker nodes. You can test a node's <code>role</code> metadata to run an executable on a master or worker node, as shown below using curl (you can also use wget): ROLE=$(curl -H Metadata-Flavor:Google http://metadata/computeMetadata/v1beta2/instance/attributes/dataproc- role) if [[ "${ROLE}" == 'Master' ]]; then ... master specific actions ... else ... worker specific actions ... fi lifecycleConfig: Optional. The config setting for auto delete cluster schedule. masterConfig: Optional. The Compute Engine config settings for the master instance in a cluster. secondaryWorkerConfig: Optional. The Compute Engine config settings for additional worker instances in a cluster. securityConfig: Optional. Security related configuration. softwareConfig: Optional. The config settings for software inside the cluster. workerConfig: Optional. The Compute Engine config settings for worker instances in a cluster. """ autoscalingConfig = _messages.MessageField('AutoscalingConfig', 1) configBucket = _messages.StringField(2) encryptionConfig = _messages.MessageField('EncryptionConfig', 3) endpointConfig = _messages.MessageField('EndpointConfig', 4) gceClusterConfig = _messages.MessageField('GceClusterConfig', 5) initializationActions = _messages.MessageField('NodeInitializationAction', 6, repeated=True) lifecycleConfig = _messages.MessageField('LifecycleConfig', 7) masterConfig = _messages.MessageField('InstanceGroupConfig', 8) secondaryWorkerConfig = _messages.MessageField('InstanceGroupConfig', 9) securityConfig = _messages.MessageField('SecurityConfig', 10) softwareConfig = _messages.MessageField('SoftwareConfig', 11) workerConfig = _messages.MessageField('InstanceGroupConfig', 12) class ClusterMetrics(_messages.Message): r"""Contains cluster daemon metrics, such as HDFS and YARN stats.Beta Feature: This report is available for testing purposes only. It may be changed before final release. Messages: HdfsMetricsValue: The HDFS metrics. YarnMetricsValue: The YARN metrics. Fields: hdfsMetrics: The HDFS metrics. yarnMetrics: The YARN metrics. """ @encoding.MapUnrecognizedFields('additionalProperties') class HdfsMetricsValue(_messages.Message): r"""The HDFS metrics. Messages: AdditionalProperty: An additional property for a HdfsMetricsValue object. Fields: additionalProperties: Additional properties of type HdfsMetricsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a HdfsMetricsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.IntegerField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class YarnMetricsValue(_messages.Message): r"""The YARN metrics. Messages: AdditionalProperty: An additional property for a YarnMetricsValue object. Fields: additionalProperties: Additional properties of type YarnMetricsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a YarnMetricsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.IntegerField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) hdfsMetrics = _messages.MessageField('HdfsMetricsValue', 1) yarnMetrics = _messages.MessageField('YarnMetricsValue', 2) class ClusterOperation(_messages.Message): r"""The cluster operation triggered by a workflow. Fields: done: Output only. Indicates the operation is done. error: Output only. Error, if operation failed. operationId: Output only. The id of the cluster operation. """ done = _messages.BooleanField(1) error = _messages.StringField(2) operationId = _messages.StringField(3) class ClusterOperationMetadata(_messages.Message): r"""Metadata describing the operation. Messages: LabelsValue: Output only. Labels associated with the operation Fields: clusterName: Output only. Name of the cluster for the operation. clusterUuid: Output only. Cluster UUID for the operation. description: Output only. Short description of operation. labels: Output only. Labels associated with the operation operationType: Output only. The operation type. status: Output only. Current operation status. statusHistory: Output only. The previous operation status. warnings: Output only. Errors encountered during operation execution. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Output only. Labels associated with the operation Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) clusterName = _messages.StringField(1) clusterUuid = _messages.StringField(2) description = _messages.StringField(3) labels = _messages.MessageField('LabelsValue', 4) operationType = _messages.StringField(5) status = _messages.MessageField('ClusterOperationStatus', 6) statusHistory = _messages.MessageField('ClusterOperationStatus', 7, repeated=True) warnings = _messages.StringField(8, repeated=True) class ClusterOperationStatus(_messages.Message): r"""The status of the operation. Enums: StateValueValuesEnum: Output only. A message containing the operation state. Fields: details: Output only. A message containing any operation metadata details. innerState: Output only. A message containing the detailed operation state. state: Output only. A message containing the operation state. stateStartTime: Output only. The time this state was entered. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. A message containing the operation state. Values: UNKNOWN: Unused. PENDING: The operation has been created. RUNNING: The operation is running. DONE: The operation is done; either cancelled or completed. """ UNKNOWN = 0 PENDING = 1 RUNNING = 2 DONE = 3 details = _messages.StringField(1) innerState = _messages.StringField(2) state = _messages.EnumField('StateValueValuesEnum', 3) stateStartTime = _messages.StringField(4) class ClusterSelector(_messages.Message): r"""A selector that chooses target cluster for jobs based on metadata. Messages: ClusterLabelsValue: Required. The cluster labels. Cluster must have all labels to match. Fields: clusterLabels: Required. The cluster labels. Cluster must have all labels to match. zone: Optional. The zone where workflow process executes. This parameter does not affect the selection of the cluster.If unspecified, the zone of the first cluster matching the selector is used. """ @encoding.MapUnrecognizedFields('additionalProperties') class ClusterLabelsValue(_messages.Message): r"""Required. The cluster labels. Cluster must have all labels to match. Messages: AdditionalProperty: An additional property for a ClusterLabelsValue object. Fields: additionalProperties: Additional properties of type ClusterLabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ClusterLabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) clusterLabels = _messages.MessageField('ClusterLabelsValue', 1) zone = _messages.StringField(2) class ClusterStatus(_messages.Message): r"""The status of a cluster and its instances. Enums: StateValueValuesEnum: Output only. The cluster's state. SubstateValueValuesEnum: Output only. Additional state information that includes status reported by the agent. Fields: detail: Output only. Optional details of cluster's state. state: Output only. The cluster's state. stateStartTime: Output only. Time when this state was entered. substate: Output only. Additional state information that includes status reported by the agent. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The cluster's state. Values: UNKNOWN: The cluster state is unknown. CREATING: The cluster is being created and set up. It is not ready for use. RUNNING: The cluster is currently running and healthy. It is ready for use. ERROR: The cluster encountered an error. It is not ready for use. DELETING: The cluster is being deleted. It cannot be used. UPDATING: The cluster is being updated. It continues to accept and process jobs. """ UNKNOWN = 0 CREATING = 1 RUNNING = 2 ERROR = 3 DELETING = 4 UPDATING = 5 class SubstateValueValuesEnum(_messages.Enum): r"""Output only. Additional state information that includes status reported by the agent. Values: UNSPECIFIED: The cluster substate is unknown. UNHEALTHY: The cluster is known to be in an unhealthy state (for example, critical daemons are not running or HDFS capacity is exhausted).Applies to RUNNING state. STALE_STATUS: The agent-reported status is out of date (may occur if Cloud Dataproc loses communication with Agent).Applies to RUNNING state. """ UNSPECIFIED = 0 UNHEALTHY = 1 STALE_STATUS = 2 detail = _messages.StringField(1) state = _messages.EnumField('StateValueValuesEnum', 2) stateStartTime = _messages.StringField(3) substate = _messages.EnumField('SubstateValueValuesEnum', 4) class DataprocProjectsLocationsAutoscalingPoliciesCreateRequest(_messages.Message): r"""A DataprocProjectsLocationsAutoscalingPoliciesCreateRequest object. Fields: autoscalingPolicy: A AutoscalingPolicy resource to be passed as the request body. parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}. """ autoscalingPolicy = _messages.MessageField('AutoscalingPolicy', 1) parent = _messages.StringField(2, required=True) class DataprocProjectsLocationsAutoscalingPoliciesDeleteRequest(_messages.Message): r"""A DataprocProjectsLocationsAutoscalingPoliciesDeleteRequest object. Fields: name: Required. The "resource name" of the autoscaling policy, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id}. """ name = _messages.StringField(1, required=True) class DataprocProjectsLocationsAutoscalingPoliciesGetRequest(_messages.Message): r"""A DataprocProjectsLocationsAutoscalingPoliciesGetRequest object. Fields: name: Required. The "resource name" of the autoscaling policy, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id}. """ name = _messages.StringField(1, required=True) class DataprocProjectsLocationsAutoscalingPoliciesListRequest(_messages.Message): r"""A DataprocProjectsLocationsAutoscalingPoliciesListRequest object. Fields: pageSize: Optional. The maximum number of results to return in each response. pageToken: Optional. The page token, returned by a previous call, to request the next page of results. parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class DataprocProjectsLocationsWorkflowTemplatesCreateRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesCreateRequest object. Fields: parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} workflowTemplate: A WorkflowTemplate resource to be passed as the request body. """ parent = _messages.StringField(1, required=True) workflowTemplate = _messages.MessageField('WorkflowTemplate', 2) class DataprocProjectsLocationsWorkflowTemplatesDeleteRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesDeleteRequest object. Fields: name: Required. The "resource name" of the workflow template, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/workflowTemplates/{template_id} version: Optional. The version of workflow template to delete. If specified, will only delete the template if the current server version matches specified version. """ name = _messages.StringField(1, required=True) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class DataprocProjectsLocationsWorkflowTemplatesGetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesGetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being requested. See the operation documentation for the appropriate value for this field. """ resource = _messages.StringField(1, required=True) class DataprocProjectsLocationsWorkflowTemplatesGetRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesGetRequest object. Fields: name: Required. The "resource name" of the workflow template, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/workflowTemplates/{template_id} version: Optional. The version of workflow template to retrieve. Only previously instatiated versions can be retrieved.If unspecified, retrieves the current version. """ name = _messages.StringField(1, required=True) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class DataprocProjectsLocationsWorkflowTemplatesInstantiateInlineRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesInstantiateInlineRequest object. Fields: instanceId: Deprecated. Please use request_id field instead. parent: Required. The "resource name" of the workflow template region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} requestId: Optional. A tag that prevents multiple concurrent workflow instances with the same tag from running. This mitigates risk of concurrent instances started due to retries.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The tag must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters. workflowTemplate: A WorkflowTemplate resource to be passed as the request body. """ instanceId = _messages.StringField(1) parent = _messages.StringField(2, required=True) requestId = _messages.StringField(3) workflowTemplate = _messages.MessageField('WorkflowTemplate', 4) class DataprocProjectsLocationsWorkflowTemplatesInstantiateRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesInstantiateRequest object. Fields: instantiateWorkflowTemplateRequest: A InstantiateWorkflowTemplateRequest resource to be passed as the request body. name: Required. The "resource name" of the workflow template, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/workflowTemplates/{template_id} """ instantiateWorkflowTemplateRequest = _messages.MessageField('InstantiateWorkflowTemplateRequest', 1) name = _messages.StringField(2, required=True) class DataprocProjectsLocationsWorkflowTemplatesListRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesListRequest object. Fields: pageSize: Optional. The maximum number of results to return in each response. pageToken: Optional. The page token, returned by a previous call, to request the next page of results. parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class DataprocProjectsLocationsWorkflowTemplatesSetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesSetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being specified. See the operation documentation for the appropriate value for this field. setIamPolicyRequest: A SetIamPolicyRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) setIamPolicyRequest = _messages.MessageField('SetIamPolicyRequest', 2) class DataprocProjectsLocationsWorkflowTemplatesTestIamPermissionsRequest(_messages.Message): r"""A DataprocProjectsLocationsWorkflowTemplatesTestIamPermissionsRequest object. Fields: resource: REQUIRED: The resource for which the policy detail is being requested. See the operation documentation for the appropriate value for this field. testIamPermissionsRequest: A TestIamPermissionsRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) testIamPermissionsRequest = _messages.MessageField('TestIamPermissionsRequest', 2) class DataprocProjectsRegionsAutoscalingPoliciesCreateRequest(_messages.Message): r"""A DataprocProjectsRegionsAutoscalingPoliciesCreateRequest object. Fields: autoscalingPolicy: A AutoscalingPolicy resource to be passed as the request body. parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}. """ autoscalingPolicy = _messages.MessageField('AutoscalingPolicy', 1) parent = _messages.StringField(2, required=True) class DataprocProjectsRegionsAutoscalingPoliciesDeleteRequest(_messages.Message): r"""A DataprocProjectsRegionsAutoscalingPoliciesDeleteRequest object. Fields: name: Required. The "resource name" of the autoscaling policy, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id}. """ name = _messages.StringField(1, required=True) class DataprocProjectsRegionsAutoscalingPoliciesGetRequest(_messages.Message): r"""A DataprocProjectsRegionsAutoscalingPoliciesGetRequest object. Fields: name: Required. The "resource name" of the autoscaling policy, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id}. """ name = _messages.StringField(1, required=True) class DataprocProjectsRegionsAutoscalingPoliciesListRequest(_messages.Message): r"""A DataprocProjectsRegionsAutoscalingPoliciesListRequest object. Fields: pageSize: Optional. The maximum number of results to return in each response. pageToken: Optional. The page token, returned by a previous call, to request the next page of results. parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class DataprocProjectsRegionsClustersCreateRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersCreateRequest object. Fields: cluster: A Cluster resource to be passed as the request body. projectId: Required. The ID of the Google Cloud Platform project that the cluster belongs to. region: Required. The Cloud Dataproc region in which to handle the request. requestId: Optional. A unique id used to identify the request. If the server receives two CreateClusterRequest requests with the same id, then the second request will be ignored and the first google.longrunning.Operation created and stored in the backend is returned.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters. """ cluster = _messages.MessageField('Cluster', 1) projectId = _messages.StringField(2, required=True) region = _messages.StringField(3, required=True) requestId = _messages.StringField(4) class DataprocProjectsRegionsClustersDeleteRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersDeleteRequest object. Fields: clusterName: Required. The cluster name. clusterUuid: Optional. Specifying the cluster_uuid means the RPC should fail (with error NOT_FOUND) if cluster with specified UUID does not exist. projectId: Required. The ID of the Google Cloud Platform project that the cluster belongs to. region: Required. The Cloud Dataproc region in which to handle the request. requestId: Optional. A unique id used to identify the request. If the server receives two DeleteClusterRequest requests with the same id, then the second request will be ignored and the first google.longrunning.Operation created and stored in the backend is returned.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters. """ clusterName = _messages.StringField(1, required=True) clusterUuid = _messages.StringField(2) projectId = _messages.StringField(3, required=True) region = _messages.StringField(4, required=True) requestId = _messages.StringField(5) class DataprocProjectsRegionsClustersDiagnoseRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersDiagnoseRequest object. Fields: clusterName: Required. The cluster name. diagnoseClusterRequest: A DiagnoseClusterRequest resource to be passed as the request body. projectId: Required. The ID of the Google Cloud Platform project that the cluster belongs to. region: Required. The Cloud Dataproc region in which to handle the request. """ clusterName = _messages.StringField(1, required=True) diagnoseClusterRequest = _messages.MessageField('DiagnoseClusterRequest', 2) projectId = _messages.StringField(3, required=True) region = _messages.StringField(4, required=True) class DataprocProjectsRegionsClustersGetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersGetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being requested. See the operation documentation for the appropriate value for this field. """ resource = _messages.StringField(1, required=True) class DataprocProjectsRegionsClustersGetRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersGetRequest object. Fields: clusterName: Required. The cluster name. projectId: Required. The ID of the Google Cloud Platform project that the cluster belongs to. region: Required. The Cloud Dataproc region in which to handle the request. """ clusterName = _messages.StringField(1, required=True) projectId = _messages.StringField(2, required=True) region = _messages.StringField(3, required=True) class DataprocProjectsRegionsClustersListRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersListRequest object. Fields: filter: Optional. A filter constraining the clusters to list. Filters are case-sensitive and have the following syntax:field = value AND field = value ...where field is one of status.state, clusterName, or labels.[KEY], and [KEY] is a label key. value can be * to match all values. status.state can be one of the following: ACTIVE, INACTIVE, CREATING, RUNNING, ERROR, DELETING, or UPDATING. ACTIVE contains the CREATING, UPDATING, and RUNNING states. INACTIVE contains the DELETING and ERROR states. clusterName is the name of the cluster provided at creation time. Only the logical AND operator is supported; space- separated items are treated as having an implicit AND operator.Example filter:status.state = ACTIVE AND clusterName = mycluster AND labels.env = staging AND labels.starred = * pageSize: Optional. The standard List page size. pageToken: Optional. The standard List page token. projectId: Required. The ID of the Google Cloud Platform project that the cluster belongs to. region: Required. The Cloud Dataproc region in which to handle the request. """ filter = _messages.StringField(1) pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32) pageToken = _messages.StringField(3) projectId = _messages.StringField(4, required=True) region = _messages.StringField(5, required=True) class DataprocProjectsRegionsClustersPatchRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersPatchRequest object. Fields: cluster: A Cluster resource to be passed as the request body. clusterName: Required. The cluster name. gracefulDecommissionTimeout: Optional. Timeout for graceful YARN decomissioning. Graceful decommissioning allows removing nodes from the cluster without interrupting jobs in progress. Timeout specifies how long to wait for jobs in progress to finish before forcefully removing nodes (and potentially interrupting jobs). Default timeout is 0 (for forceful decommission), and the maximum allowed timeout is 1 day.Only supported on Dataproc image versions 1.2 and higher. projectId: Required. The ID of the Google Cloud Platform project the cluster belongs to. region: Required. The Cloud Dataproc region in which to handle the request. requestId: Optional. A unique id used to identify the request. If the server receives two UpdateClusterRequest requests with the same id, then the second request will be ignored and the first google.longrunning.Operation created and stored in the backend is returned.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters. updateMask: Required. Specifies the path, relative to Cluster, of the field to update. For example, to change the number of workers in a cluster to 5, the update_mask parameter would be specified as config.worker_config.num_instances, and the PATCH request body would specify the new value, as follows: { "config":{ "workerConfig":{ "numInstances":"5" } } } Similarly, to change the number of preemptible workers in a cluster to 5, the update_mask parameter would be config.secondary_worker_config.num_instances, and the PATCH request body would be set as follows: { "config":{ "secondaryWorkerConfig":{ "numInstances":"5" } } } <strong>Note:</strong> currently only the following fields can be updated: <table> <tr> <td><strong>Mask</strong></td><td><strong>Purpose</strong></td> </tr> <tr> <td>labels</td><td>Updates labels</td> </tr> <tr> <td>config.worker_config.num_instances</td><td>Resize primary worker group</td> </tr> <tr> <td>config.secondary_worker_config.num_instances</td><td>Resize secondary worker group</td> </tr> <tr> <td>config.lifecycle_config.auto_delete_ttl</td><td>Reset MAX TTL duration</td> </tr> <tr> <td>config.lifecycle_config.auto_delete_time</td><td>Update MAX TTL deletion timestamp</td> </tr> <tr> <td>config.lifecycle_config.idle_delete_ttl</td><td>Update Idle TTL duration</td> </tr> </table> """ cluster = _messages.MessageField('Cluster', 1) clusterName = _messages.StringField(2, required=True) gracefulDecommissionTimeout = _messages.StringField(3) projectId = _messages.StringField(4, required=True) region = _messages.StringField(5, required=True) requestId = _messages.StringField(6) updateMask = _messages.StringField(7) class DataprocProjectsRegionsClustersSetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersSetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being specified. See the operation documentation for the appropriate value for this field. setIamPolicyRequest: A SetIamPolicyRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) setIamPolicyRequest = _messages.MessageField('SetIamPolicyRequest', 2) class DataprocProjectsRegionsClustersTestIamPermissionsRequest(_messages.Message): r"""A DataprocProjectsRegionsClustersTestIamPermissionsRequest object. Fields: resource: REQUIRED: The resource for which the policy detail is being requested. See the operation documentation for the appropriate value for this field. testIamPermissionsRequest: A TestIamPermissionsRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) testIamPermissionsRequest = _messages.MessageField('TestIamPermissionsRequest', 2) class DataprocProjectsRegionsJobsCancelRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsCancelRequest object. Fields: cancelJobRequest: A CancelJobRequest resource to be passed as the request body. jobId: Required. The job ID. projectId: Required. The ID of the Google Cloud Platform project that the job belongs to. region: Required. The Cloud Dataproc region in which to handle the request. """ cancelJobRequest = _messages.MessageField('CancelJobRequest', 1) jobId = _messages.StringField(2, required=True) projectId = _messages.StringField(3, required=True) region = _messages.StringField(4, required=True) class DataprocProjectsRegionsJobsDeleteRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsDeleteRequest object. Fields: jobId: Required. The job ID. projectId: Required. The ID of the Google Cloud Platform project that the job belongs to. region: Required. The Cloud Dataproc region in which to handle the request. """ jobId = _messages.StringField(1, required=True) projectId = _messages.StringField(2, required=True) region = _messages.StringField(3, required=True) class DataprocProjectsRegionsJobsGetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsGetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being requested. See the operation documentation for the appropriate value for this field. """ resource = _messages.StringField(1, required=True) class DataprocProjectsRegionsJobsGetRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsGetRequest object. Fields: jobId: Required. The job ID. projectId: Required. The ID of the Google Cloud Platform project that the job belongs to. region: Required. The Cloud Dataproc region in which to handle the request. """ jobId = _messages.StringField(1, required=True) projectId = _messages.StringField(2, required=True) region = _messages.StringField(3, required=True) class DataprocProjectsRegionsJobsListRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsListRequest object. Enums: JobStateMatcherValueValuesEnum: Optional. Specifies enumerated categories of jobs to list. (default = match ALL jobs).If filter is provided, jobStateMatcher will be ignored. Fields: clusterName: Optional. If set, the returned jobs list includes only jobs that were submitted to the named cluster. filter: Optional. A filter constraining the jobs to list. Filters are case-sensitive and have the following syntax:field = value AND field = value ...where field is status.state or labels.[KEY], and [KEY] is a label key. value can be * to match all values. status.state can be either ACTIVE or NON_ACTIVE. Only the logical AND operator is supported; space-separated items are treated as having an implicit AND operator.Example filter:status.state = ACTIVE AND labels.env = staging AND labels.starred = * jobStateMatcher: Optional. Specifies enumerated categories of jobs to list. (default = match ALL jobs).If filter is provided, jobStateMatcher will be ignored. pageSize: Optional. The number of results to return in each response. pageToken: Optional. The page token, returned by a previous call, to request the next page of results. projectId: Required. The ID of the Google Cloud Platform project that the job belongs to. region: Required. The Cloud Dataproc region in which to handle the request. """ class JobStateMatcherValueValuesEnum(_messages.Enum): r"""Optional. Specifies enumerated categories of jobs to list. (default = match ALL jobs).If filter is provided, jobStateMatcher will be ignored. Values: ALL: <no description> ACTIVE: <no description> NON_ACTIVE: <no description> """ ALL = 0 ACTIVE = 1 NON_ACTIVE = 2 clusterName = _messages.StringField(1) filter = _messages.StringField(2) jobStateMatcher = _messages.EnumField('JobStateMatcherValueValuesEnum', 3) pageSize = _messages.IntegerField(4, variant=_messages.Variant.INT32) pageToken = _messages.StringField(5) projectId = _messages.StringField(6, required=True) region = _messages.StringField(7, required=True) class DataprocProjectsRegionsJobsPatchRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsPatchRequest object. Fields: job: A Job resource to be passed as the request body. jobId: Required. The job ID. projectId: Required. The ID of the Google Cloud Platform project that the job belongs to. region: Required. The Cloud Dataproc region in which to handle the request. updateMask: Required. Specifies the path, relative to <code>Job</code>, of the field to update. For example, to update the labels of a Job the <code>update_mask</code> parameter would be specified as <code>labels</code>, and the PATCH request body would specify the new value. <strong>Note:</strong> Currently, <code>labels</code> is the only field that can be updated. """ job = _messages.MessageField('Job', 1) jobId = _messages.StringField(2, required=True) projectId = _messages.StringField(3, required=True) region = _messages.StringField(4, required=True) updateMask = _messages.StringField(5) class DataprocProjectsRegionsJobsSetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsSetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being specified. See the operation documentation for the appropriate value for this field. setIamPolicyRequest: A SetIamPolicyRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) setIamPolicyRequest = _messages.MessageField('SetIamPolicyRequest', 2) class DataprocProjectsRegionsJobsSubmitRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsSubmitRequest object. Fields: projectId: Required. The ID of the Google Cloud Platform project that the job belongs to. region: Required. The Cloud Dataproc region in which to handle the request. submitJobRequest: A SubmitJobRequest resource to be passed as the request body. """ projectId = _messages.StringField(1, required=True) region = _messages.StringField(2, required=True) submitJobRequest = _messages.MessageField('SubmitJobRequest', 3) class DataprocProjectsRegionsJobsTestIamPermissionsRequest(_messages.Message): r"""A DataprocProjectsRegionsJobsTestIamPermissionsRequest object. Fields: resource: REQUIRED: The resource for which the policy detail is being requested. See the operation documentation for the appropriate value for this field. testIamPermissionsRequest: A TestIamPermissionsRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) testIamPermissionsRequest = _messages.MessageField('TestIamPermissionsRequest', 2) class DataprocProjectsRegionsOperationsCancelRequest(_messages.Message): r"""A DataprocProjectsRegionsOperationsCancelRequest object. Fields: name: The name of the operation resource to be cancelled. """ name = _messages.StringField(1, required=True) class DataprocProjectsRegionsOperationsDeleteRequest(_messages.Message): r"""A DataprocProjectsRegionsOperationsDeleteRequest object. Fields: name: The name of the operation resource to be deleted. """ name = _messages.StringField(1, required=True) class DataprocProjectsRegionsOperationsGetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsOperationsGetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being requested. See the operation documentation for the appropriate value for this field. """ resource = _messages.StringField(1, required=True) class DataprocProjectsRegionsOperationsGetRequest(_messages.Message): r"""A DataprocProjectsRegionsOperationsGetRequest object. Fields: name: The name of the operation resource. """ name = _messages.StringField(1, required=True) class DataprocProjectsRegionsOperationsListRequest(_messages.Message): r"""A DataprocProjectsRegionsOperationsListRequest object. Fields: filter: The standard list filter. name: The name of the operation's parent resource. pageSize: The standard list page size. pageToken: The standard list page token. """ filter = _messages.StringField(1) name = _messages.StringField(2, required=True) pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32) pageToken = _messages.StringField(4) class DataprocProjectsRegionsOperationsSetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsOperationsSetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being specified. See the operation documentation for the appropriate value for this field. setIamPolicyRequest: A SetIamPolicyRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) setIamPolicyRequest = _messages.MessageField('SetIamPolicyRequest', 2) class DataprocProjectsRegionsOperationsTestIamPermissionsRequest(_messages.Message): r"""A DataprocProjectsRegionsOperationsTestIamPermissionsRequest object. Fields: resource: REQUIRED: The resource for which the policy detail is being requested. See the operation documentation for the appropriate value for this field. testIamPermissionsRequest: A TestIamPermissionsRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) testIamPermissionsRequest = _messages.MessageField('TestIamPermissionsRequest', 2) class DataprocProjectsRegionsWorkflowTemplatesCreateRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesCreateRequest object. Fields: parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} workflowTemplate: A WorkflowTemplate resource to be passed as the request body. """ parent = _messages.StringField(1, required=True) workflowTemplate = _messages.MessageField('WorkflowTemplate', 2) class DataprocProjectsRegionsWorkflowTemplatesDeleteRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesDeleteRequest object. Fields: name: Required. The "resource name" of the workflow template, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/workflowTemplates/{template_id} version: Optional. The version of workflow template to delete. If specified, will only delete the template if the current server version matches specified version. """ name = _messages.StringField(1, required=True) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class DataprocProjectsRegionsWorkflowTemplatesGetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesGetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being requested. See the operation documentation for the appropriate value for this field. """ resource = _messages.StringField(1, required=True) class DataprocProjectsRegionsWorkflowTemplatesGetRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesGetRequest object. Fields: name: Required. The "resource name" of the workflow template, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/workflowTemplates/{template_id} version: Optional. The version of workflow template to retrieve. Only previously instatiated versions can be retrieved.If unspecified, retrieves the current version. """ name = _messages.StringField(1, required=True) version = _messages.IntegerField(2, variant=_messages.Variant.INT32) class DataprocProjectsRegionsWorkflowTemplatesInstantiateInlineRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesInstantiateInlineRequest object. Fields: instanceId: Deprecated. Please use request_id field instead. parent: Required. The "resource name" of the workflow template region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} requestId: Optional. A tag that prevents multiple concurrent workflow instances with the same tag from running. This mitigates risk of concurrent instances started due to retries.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The tag must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters. workflowTemplate: A WorkflowTemplate resource to be passed as the request body. """ instanceId = _messages.StringField(1) parent = _messages.StringField(2, required=True) requestId = _messages.StringField(3) workflowTemplate = _messages.MessageField('WorkflowTemplate', 4) class DataprocProjectsRegionsWorkflowTemplatesInstantiateRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesInstantiateRequest object. Fields: instantiateWorkflowTemplateRequest: A InstantiateWorkflowTemplateRequest resource to be passed as the request body. name: Required. The "resource name" of the workflow template, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/workflowTemplates/{template_id} """ instantiateWorkflowTemplateRequest = _messages.MessageField('InstantiateWorkflowTemplateRequest', 1) name = _messages.StringField(2, required=True) class DataprocProjectsRegionsWorkflowTemplatesListRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesListRequest object. Fields: pageSize: Optional. The maximum number of results to return in each response. pageToken: Optional. The page token, returned by a previous call, to request the next page of results. parent: Required. The "resource name" of the region, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region} """ pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32) pageToken = _messages.StringField(2) parent = _messages.StringField(3, required=True) class DataprocProjectsRegionsWorkflowTemplatesSetIamPolicyRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesSetIamPolicyRequest object. Fields: resource: REQUIRED: The resource for which the policy is being specified. See the operation documentation for the appropriate value for this field. setIamPolicyRequest: A SetIamPolicyRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) setIamPolicyRequest = _messages.MessageField('SetIamPolicyRequest', 2) class DataprocProjectsRegionsWorkflowTemplatesTestIamPermissionsRequest(_messages.Message): r"""A DataprocProjectsRegionsWorkflowTemplatesTestIamPermissionsRequest object. Fields: resource: REQUIRED: The resource for which the policy detail is being requested. See the operation documentation for the appropriate value for this field. testIamPermissionsRequest: A TestIamPermissionsRequest resource to be passed as the request body. """ resource = _messages.StringField(1, required=True) testIamPermissionsRequest = _messages.MessageField('TestIamPermissionsRequest', 2) class DiagnoseClusterRequest(_messages.Message): r"""A request to collect cluster diagnostic information.""" class DiagnoseClusterResults(_messages.Message): r"""The location of diagnostic output. Fields: outputUri: Output only. The Cloud Storage URI of the diagnostic output. The output report is a plain text file with a summary of collected diagnostics. """ outputUri = _messages.StringField(1) class DiskConfig(_messages.Message): r"""Specifies the config of disk options for a group of VM instances. Fields: bootDiskSizeGb: Optional. Size in GB of the boot disk (default is 500GB). bootDiskType: Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd- standard" (Persistent Disk Hard Disk Drive). numLocalSsds: Optional. Number of attached SSDs, from 0 to 4 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries. """ bootDiskSizeGb = _messages.IntegerField(1, variant=_messages.Variant.INT32) bootDiskType = _messages.StringField(2) numLocalSsds = _messages.IntegerField(3, variant=_messages.Variant.INT32) class Empty(_messages.Message): r"""A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}. """ class EncryptionConfig(_messages.Message): r"""Encryption settings for the cluster. Fields: gcePdKmsKeyName: Optional. The Cloud KMS key name to use for PD disk encryption for all instances in the cluster. """ gcePdKmsKeyName = _messages.StringField(1) class EndpointConfig(_messages.Message): r"""Endpoint config for this cluster Messages: HttpPortsValue: Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true. Fields: enableHttpPortAccess: Optional. If true, enable http access to specific ports on the cluster from external sources. Defaults to false. httpPorts: Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true. """ @encoding.MapUnrecognizedFields('additionalProperties') class HttpPortsValue(_messages.Message): r"""Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true. Messages: AdditionalProperty: An additional property for a HttpPortsValue object. Fields: additionalProperties: Additional properties of type HttpPortsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a HttpPortsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) enableHttpPortAccess = _messages.BooleanField(1) httpPorts = _messages.MessageField('HttpPortsValue', 2) class Expr(_messages.Message): r"""Represents an expression text. Example: title: "User account presence" description: "Determines whether the request has a user account" expression: "size(request.user) > 0" Fields: description: An optional description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI. expression: Textual representation of an expression in Common Expression Language syntax.The application context of the containing message determines which well-known feature set of CEL is supported. location: An optional string indicating the location of the expression for error reporting, e.g. a file name and a position in the file. title: An optional title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression. """ description = _messages.StringField(1) expression = _messages.StringField(2) location = _messages.StringField(3) title = _messages.StringField(4) class GceClusterConfig(_messages.Message): r"""Common config settings for resources of Compute Engine cluster instances, applicable to all instances in the cluster. Messages: MetadataValue: The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs/storing-retrieving- metadata#project_and_instance_metadata)). Fields: allocationAffinity: Allocation Affinity for consuming Zonal allocation. internalIpOnly: Optional. If true, all instances in the cluster will only have internal IP addresses. By default, clusters are not restricted to internal IP addresses, and will have ephemeral external IP addresses assigned to each instance. This internal_ip_only restriction can only be enabled for subnetwork enabled networks, and all off-cluster dependencies must be configured to be accessible without external IP addresses. metadata: The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs /storing-retrieving-metadata#project_and_instance_metadata)). networkUri: Optional. The Compute Engine network to be used for machine communications. Cannot be specified with subnetwork_uri. If neither network_uri nor subnetwork_uri is specified, the "default" network of the project is used, if it exists. Cannot be a "Custom Subnet Network" (see Using Subnetworks for more information).A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/pr ojects/[project_id]/regions/global/default projects/[project_id]/regions/global/default default serviceAccount: Optional. The service account of the instances. Defaults to the default Compute Engine service account. Custom service accounts need permissions equivalent to the following IAM roles: roles/logging.logWriter roles/storage.objectAdmin(see https://cloud.google.com/compute/docs/access/service- accounts#custom_service_accounts for more information). Example: [account_id]@[project_id].iam.gserviceaccount.com serviceAccountScopes: Optional. The URIs of service account scopes to be included in Compute Engine instances. The following base set of scopes is always included: https://www.googleapis.com/auth/cloud.useraccounts.readonly https://www.googleapis.com/auth/devstorage.read_write https://www.googleapis.com/auth/logging.writeIf no scopes are specified, the following defaults are also provided: https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigtable.admin.table https://www.googleapis.com/auth/bigtable.data https://www.googleapis.com/auth/devstorage.full_control subnetworkUri: Optional. The Compute Engine subnetwork to be used for machine communications. Cannot be specified with network_uri.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/regions/us- east1/sub0 projects/[project_id]/regions/us-east1/sub0 sub0 tags: The Compute Engine tags to add to all instances (see Tagging instances). zoneUri: Optional. The zone where the Compute Engine cluster will be located. On a create request, it is required in the "global" region. If omitted in a non-global Cloud Dataproc region, the service will pick a zone in the corresponding Compute Engine region. On a get request, zone will always be present.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone] projects/[project_id]/zones/[zone] us-central1-f """ @encoding.MapUnrecognizedFields('additionalProperties') class MetadataValue(_messages.Message): r"""The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs /storing-retrieving-metadata#project_and_instance_metadata)). Messages: AdditionalProperty: An additional property for a MetadataValue object. Fields: additionalProperties: Additional properties of type MetadataValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a MetadataValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) allocationAffinity = _messages.MessageField('AllocationAffinity', 1) internalIpOnly = _messages.BooleanField(2) metadata = _messages.MessageField('MetadataValue', 3) networkUri = _messages.StringField(4) serviceAccount = _messages.StringField(5) serviceAccountScopes = _messages.StringField(6, repeated=True) subnetworkUri = _messages.StringField(7) tags = _messages.StringField(8, repeated=True) zoneUri = _messages.StringField(9) class GetIamPolicyRequest(_messages.Message): r"""Request message for GetIamPolicy method.""" class HadoopJob(_messages.Message): r"""A Cloud Dataproc job for running Apache Hadoop MapReduce (https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop- mapreduce-client-core/MapReduceTutorial.html) jobs on Apache Hadoop YARN (https://hadoop.apache.org/docs/r2.7.1/hadoop-yarn/hadoop-yarn- site/YARN.html). Messages: PropertiesValue: Optional. A mapping of property names to values, used to configure Hadoop. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site and classes in user code. Fields: archiveUris: Optional. HCFS URIs of archives to be extracted in the working directory of Hadoop drivers and tasks. Supported file types: .jar, .tar, .tar.gz, .tgz, or .zip. args: Optional. The arguments to pass to the driver. Do not include arguments, such as -libjars or -Dfoo=bar, that can be set as job properties, since a collision may occur that causes an incorrect job submission. fileUris: Optional. HCFS (Hadoop Compatible Filesystem) URIs of files to be copied to the working directory of Hadoop drivers and distributed tasks. Useful for naively parallel tasks. jarFileUris: Optional. Jar file URIs to add to the CLASSPATHs of the Hadoop driver and tasks. loggingConfig: Optional. The runtime log config for job execution. mainClass: The name of the driver's main class. The jar file containing the class must be in the default CLASSPATH or specified in jar_file_uris. mainJarFileUri: The HCFS URI of the jar file containing the main class. Examples: 'gs://foo-bucket/analytics-binaries/extract-useful-metrics- mr.jar' 'hdfs:/tmp/test-samples/custom-wordcount.jar' 'file:///home/usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar' properties: Optional. A mapping of property names to values, used to configure Hadoop. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site and classes in user code. """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names to values, used to configure Hadoop. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site and classes in user code. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) archiveUris = _messages.StringField(1, repeated=True) args = _messages.StringField(2, repeated=True) fileUris = _messages.StringField(3, repeated=True) jarFileUris = _messages.StringField(4, repeated=True) loggingConfig = _messages.MessageField('LoggingConfig', 5) mainClass = _messages.StringField(6) mainJarFileUri = _messages.StringField(7) properties = _messages.MessageField('PropertiesValue', 8) class HiveJob(_messages.Message): r"""A Cloud Dataproc job for running Apache Hive (https://hive.apache.org/) queries on YARN. Messages: PropertiesValue: Optional. A mapping of property names and values, used to configure Hive. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/hive/conf/hive-site.xml, and classes in user code. ScriptVariablesValue: Optional. Mapping of query variable names to values (equivalent to the Hive command: SET name="value";). Fields: continueOnFailure: Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries. jarFileUris: Optional. HCFS URIs of jar files to add to the CLASSPATH of the Hive server and Hadoop MapReduce (MR) tasks. Can contain Hive SerDes and UDFs. properties: Optional. A mapping of property names and values, used to configure Hive. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/hive/conf/hive-site.xml, and classes in user code. queryFileUri: The HCFS URI of the script that contains Hive queries. queryList: A list of queries. scriptVariables: Optional. Mapping of query variable names to values (equivalent to the Hive command: SET name="value";). """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names and values, used to configure Hive. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/hive/conf/hive-site.xml, and classes in user code. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class ScriptVariablesValue(_messages.Message): r"""Optional. Mapping of query variable names to values (equivalent to the Hive command: SET name="value";). Messages: AdditionalProperty: An additional property for a ScriptVariablesValue object. Fields: additionalProperties: Additional properties of type ScriptVariablesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ScriptVariablesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) continueOnFailure = _messages.BooleanField(1) jarFileUris = _messages.StringField(2, repeated=True) properties = _messages.MessageField('PropertiesValue', 3) queryFileUri = _messages.StringField(4) queryList = _messages.MessageField('QueryList', 5) scriptVariables = _messages.MessageField('ScriptVariablesValue', 6) class InstanceGroupAutoscalingPolicyConfig(_messages.Message): r"""Configuration for the size bounds of an instance group, including its proportional size to other groups. Fields: maxInstances: Required. Maximum number of instances for this group. Must be >= min_instances. minInstances: Optional. Minimum number of instances for this group.Default for primary workers is 2, default for secondary workers is 0. weight: Optional. Weight for instance group. Determines fraction of total workers in cluster that will be composed of instances from this instance group (e.g. if primary workers have weight 2 and secondary workers have weight 1, then the cluster should have approximately 2 primary workers to each secondary worker. Cluster may not reach these exact weights if constrained by min/max bounds or other autoscaling configurations.Default 1. Note that all groups have equal an equal weight by default, so the cluster will attempt to maintain an equal number of workers in each group within configured size bounds per group. """ maxInstances = _messages.IntegerField(1, variant=_messages.Variant.INT32) minInstances = _messages.IntegerField(2, variant=_messages.Variant.INT32) weight = _messages.IntegerField(3, variant=_messages.Variant.INT32) class InstanceGroupConfig(_messages.Message): r"""Optional. The config settings for Compute Engine resources in an instance group, such as a master or worker group. Fields: accelerators: Optional. The Compute Engine accelerator configuration for these instances.Beta Feature: This feature is still under development. It may be changed before final release. diskConfig: Optional. Disk option config settings. imageUri: Optional. The Compute Engine image resource used for cluster instances. It can be specified or may be inferred from SoftwareConfig.image_version. instanceNames: Output only. The list of instance names. Cloud Dataproc derives the names from cluster_name, num_instances, and the instance group. isPreemptible: Optional. Specifies that this instance group contains preemptible instances. machineTypeUri: Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/us- east1-a/machineTypes/n1-standard-2 projects/[project_id]/zones/us- east1-a/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Cloud Dataproc Auto Zone Placement feature, you must use the short name of the machine type resource, for example, n1-standard-2. managedGroupConfig: Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups. minCpuPlatform: Optional. Specifies the minimum cpu platform for the Instance Group. See Cloud Dataproc→Minimum CPU Platform. numInstances: Optional. The number of VM instances in the instance group. For master instance groups, must be set to 1. """ accelerators = _messages.MessageField('AcceleratorConfig', 1, repeated=True) diskConfig = _messages.MessageField('DiskConfig', 2) imageUri = _messages.StringField(3) instanceNames = _messages.StringField(4, repeated=True) isPreemptible = _messages.BooleanField(5) machineTypeUri = _messages.StringField(6) managedGroupConfig = _messages.MessageField('ManagedGroupConfig', 7) minCpuPlatform = _messages.StringField(8) numInstances = _messages.IntegerField(9, variant=_messages.Variant.INT32) class InstantiateWorkflowTemplateRequest(_messages.Message): r"""A request to instantiate a workflow template. Messages: ParametersValue: Optional. Map from parameter names to values that should be used for those parameters. Values may not exceed 100 characters. Fields: instanceId: Deprecated. Please use request_id field instead. parameters: Optional. Map from parameter names to values that should be used for those parameters. Values may not exceed 100 characters. requestId: Optional. A tag that prevents multiple concurrent workflow instances with the same tag from running. This mitigates risk of concurrent instances started due to retries.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The tag must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters. version: Optional. The version of workflow template to instantiate. If specified, the workflow will be instantiated only if the current version of the workflow template has the supplied version.This option cannot be used to instantiate a previous version of workflow template. """ @encoding.MapUnrecognizedFields('additionalProperties') class ParametersValue(_messages.Message): r"""Optional. Map from parameter names to values that should be used for those parameters. Values may not exceed 100 characters. Messages: AdditionalProperty: An additional property for a ParametersValue object. Fields: additionalProperties: Additional properties of type ParametersValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ParametersValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) instanceId = _messages.StringField(1) parameters = _messages.MessageField('ParametersValue', 2) requestId = _messages.StringField(3) version = _messages.IntegerField(4, variant=_messages.Variant.INT32) class Job(_messages.Message): r"""A Cloud Dataproc job resource. Messages: LabelsValue: Optional. The labels to associate with this job. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a job. Fields: driverControlFilesUri: Output only. If present, the location of miscellaneous control files which may be used as part of job setup and handling. If not present, control files may be placed in the same location as driver_output_uri. driverOutputResourceUri: Output only. A URI pointing to the location of the stdout of the job's driver program. hadoopJob: Job is a Hadoop job. hiveJob: Job is a Hive job. jobUuid: Output only. A UUID that uniquely identifies a job within the project over time. This is in contrast to a user-settable reference.job_id that may be reused over time. labels: Optional. The labels to associate with this job. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a job. pigJob: Job is a Pig job. placement: Required. Job information, including how, when, and where to run the job. prestoJob: Job is a Presto job pysparkJob: Job is a Pyspark job. reference: Optional. The fully qualified reference to the job, which can be used to obtain the equivalent REST path of the job resource. If this property is not specified when a job is created, the server generates a <code>job_id</code>. scheduling: Optional. Job scheduling configuration. sparkJob: Job is a Spark job. sparkRJob: Job is a SparkR job. sparkSqlJob: Job is a SparkSql job. status: Output only. The job status. Additional application-specific status information may be contained in the <code>type_job</code> and <code>yarn_applications</code> fields. statusHistory: Output only. The previous job status. submittedBy: Output only. The email address of the user submitting the job. For jobs submitted on the cluster, the address is <code>username@hostname</code>. yarnApplications: Output only. The collection of YARN applications spun up by this job.Beta Feature: This report is available for testing purposes only. It may be changed before final release. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels to associate with this job. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a job. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) driverControlFilesUri = _messages.StringField(1) driverOutputResourceUri = _messages.StringField(2) hadoopJob = _messages.MessageField('HadoopJob', 3) hiveJob = _messages.MessageField('HiveJob', 4) jobUuid = _messages.StringField(5) labels = _messages.MessageField('LabelsValue', 6) pigJob = _messages.MessageField('PigJob', 7) placement = _messages.MessageField('JobPlacement', 8) prestoJob = _messages.MessageField('PrestoJob', 9) pysparkJob = _messages.MessageField('PySparkJob', 10) reference = _messages.MessageField('JobReference', 11) scheduling = _messages.MessageField('JobScheduling', 12) sparkJob = _messages.MessageField('SparkJob', 13) sparkRJob = _messages.MessageField('SparkRJob', 14) sparkSqlJob = _messages.MessageField('SparkSqlJob', 15) status = _messages.MessageField('JobStatus', 16) statusHistory = _messages.MessageField('JobStatus', 17, repeated=True) submittedBy = _messages.StringField(18) yarnApplications = _messages.MessageField('YarnApplication', 19, repeated=True) class JobPlacement(_messages.Message): r"""Cloud Dataproc job config. Fields: clusterName: Required. The name of the cluster where the job will be submitted. clusterUuid: Output only. A cluster UUID generated by the Cloud Dataproc service when the job is submitted. """ clusterName = _messages.StringField(1) clusterUuid = _messages.StringField(2) class JobReference(_messages.Message): r"""Encapsulates the full scoping used to reference a job. Fields: jobId: Optional. The job ID, which must be unique within the project. The job ID is generated by the server upon job submission or provided by the user as a means to perform retries without creating duplicate jobs. The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), or hyphens (-). The maximum length is 100 characters. projectId: Required. The ID of the Google Cloud Platform project that the job belongs to. """ jobId = _messages.StringField(1) projectId = _messages.StringField(2) class JobScheduling(_messages.Message): r"""Job scheduling options. Fields: maxFailuresPerHour: Optional. Maximum number of times per hour a driver may be restarted as a result of driver terminating with non-zero code before job is reported failed.A job may be reported as thrashing if driver exits with non-zero code 4 times within 10 minute window.Maximum value is 10. """ maxFailuresPerHour = _messages.IntegerField(1, variant=_messages.Variant.INT32) class JobStatus(_messages.Message): r"""Cloud Dataproc job status. Enums: StateValueValuesEnum: Output only. A state message specifying the overall job state. SubstateValueValuesEnum: Output only. Additional state information, which includes status reported by the agent. Fields: details: Output only. Optional job state details, such as an error description if the state is <code>ERROR</code>. state: Output only. A state message specifying the overall job state. stateStartTime: Output only. The time when this state was entered. substate: Output only. Additional state information, which includes status reported by the agent. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. A state message specifying the overall job state. Values: STATE_UNSPECIFIED: The job state is unknown. PENDING: The job is pending; it has been submitted, but is not yet running. SETUP_DONE: Job has been received by the service and completed initial setup; it will soon be submitted to the cluster. RUNNING: The job is running on the cluster. CANCEL_PENDING: A CancelJob request has been received, but is pending. CANCEL_STARTED: Transient in-flight resources have been canceled, and the request to cancel the running job has been issued to the cluster. CANCELLED: The job cancellation was successful. DONE: The job has completed successfully. ERROR: The job has completed, but encountered an error. ATTEMPT_FAILURE: Job attempt has failed. The detail field contains failure details for this attempt.Applies to restartable jobs only. """ STATE_UNSPECIFIED = 0 PENDING = 1 SETUP_DONE = 2 RUNNING = 3 CANCEL_PENDING = 4 CANCEL_STARTED = 5 CANCELLED = 6 DONE = 7 ERROR = 8 ATTEMPT_FAILURE = 9 class SubstateValueValuesEnum(_messages.Enum): r"""Output only. Additional state information, which includes status reported by the agent. Values: UNSPECIFIED: The job substate is unknown. SUBMITTED: The Job is submitted to the agent.Applies to RUNNING state. QUEUED: The Job has been received and is awaiting execution (it may be waiting for a condition to be met). See the "details" field for the reason for the delay.Applies to RUNNING state. STALE_STATUS: The agent-reported status is out of date, which may be caused by a loss of communication between the agent and Cloud Dataproc. If the agent does not send a timely update, the job will fail.Applies to RUNNING state. """ UNSPECIFIED = 0 SUBMITTED = 1 QUEUED = 2 STALE_STATUS = 3 details = _messages.StringField(1) state = _messages.EnumField('StateValueValuesEnum', 2) stateStartTime = _messages.StringField(3) substate = _messages.EnumField('SubstateValueValuesEnum', 4) class KerberosConfig(_messages.Message): r"""Specifies Kerberos related configuration. Fields: crossRealmTrustAdminServer: Optional. The admin server (IP or hostname) for the remote trusted realm in a cross realm trust relationship. crossRealmTrustKdc: Optional. The KDC (IP or hostname) for the remote trusted realm in a cross realm trust relationship. crossRealmTrustRealm: Optional. The remote realm the Dataproc on-cluster KDC will trust, should the user enable cross realm trust. crossRealmTrustSharedPasswordUri: Optional. The GCS uri of a KMS encrypted file containing the shared password between the on-cluster Kerberos realm and the remote trusted realm, in a cross realm trust relationship. enableKerberos: Optional. Flag to indicate whether to Kerberize the cluster. kdcDbKeyUri: Optional. The GCS uri of a KMS encrypted file containing the master key of the KDC database. keyPasswordUri: Optional. The GCS uri of a KMS encrypted file containing the password to the user provided key. For the self-signed certificate, this password is generated by Dataproc. keystorePasswordUri: Optional. The GCS uri of a KMS encrypted file containing the password to the user provided keystore. For the self- signed certificate, this password is generated by Dataproc. keystoreUri: Optional. The GCS uri of the keystore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate. kmsKeyUri: Required. The uri of the KMS key used to encrypt various sensitive files. rootPrincipalPasswordUri: Required. The GCS uri of a KMS encrypted file containing the root principal password. tgtLifetimeHours: Optional. The lifetime of the ticket granting ticket, in hours. If not specified, or user specifies 0, then default value 10 will be used. truststorePasswordUri: Optional. The GCS uri of a KMS encrypted file containing the password to the user provided truststore. For the self- signed certificate, this password is generated by Dataproc. truststoreUri: Optional. The GCS uri of the truststore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate. """ crossRealmTrustAdminServer = _messages.StringField(1) crossRealmTrustKdc = _messages.StringField(2) crossRealmTrustRealm = _messages.StringField(3) crossRealmTrustSharedPasswordUri = _messages.StringField(4) enableKerberos = _messages.BooleanField(5) kdcDbKeyUri = _messages.StringField(6) keyPasswordUri = _messages.StringField(7) keystorePasswordUri = _messages.StringField(8) keystoreUri = _messages.StringField(9) kmsKeyUri = _messages.StringField(10) rootPrincipalPasswordUri = _messages.StringField(11) tgtLifetimeHours = _messages.IntegerField(12, variant=_messages.Variant.INT32) truststorePasswordUri = _messages.StringField(13) truststoreUri = _messages.StringField(14) class LifecycleConfig(_messages.Message): r"""Specifies the cluster auto-delete schedule configuration. Fields: autoDeleteTime: Optional. The time when cluster will be auto-deleted. autoDeleteTtl: Optional. The lifetime duration of cluster. The cluster will be auto-deleted at the end of this period. Valid range: 10m, 14d.Example: "1d", to delete the cluster 1 day after its creation.. idleDeleteTtl: Optional. The duration to keep the cluster alive while idling. Passing this threshold will cause the cluster to be deleted. Valid range: 10m, 14d.Example: "10m", the minimum value, to delete the cluster when it has had no jobs running for 10 minutes. idleStartTime: Output only. The time when cluster became idle (most recent job finished) and became eligible for deletion due to idleness. """ autoDeleteTime = _messages.StringField(1) autoDeleteTtl = _messages.StringField(2) idleDeleteTtl = _messages.StringField(3) idleStartTime = _messages.StringField(4) class ListAutoscalingPoliciesResponse(_messages.Message): r"""A response to a request to list autoscaling policies in a project. Fields: nextPageToken: Output only. This token is included in the response if there are more results to fetch. policies: Output only. Autoscaling policies list. """ nextPageToken = _messages.StringField(1) policies = _messages.MessageField('AutoscalingPolicy', 2, repeated=True) class ListClustersResponse(_messages.Message): r"""The list of all clusters in a project. Fields: clusters: Output only. The clusters in the project. nextPageToken: Output only. This token is included in the response if there are more results to fetch. To fetch additional results, provide this value as the page_token in a subsequent <code>ListClustersRequest</code>. """ clusters = _messages.MessageField('Cluster', 1, repeated=True) nextPageToken = _messages.StringField(2) class ListJobsResponse(_messages.Message): r"""A list of jobs in a project. Fields: jobs: Output only. Jobs list. nextPageToken: Optional. This token is included in the response if there are more results to fetch. To fetch additional results, provide this value as the page_token in a subsequent <code>ListJobsRequest</code>. """ jobs = _messages.MessageField('Job', 1, repeated=True) nextPageToken = _messages.StringField(2) class ListOperationsResponse(_messages.Message): r"""The response message for Operations.ListOperations. Fields: nextPageToken: The standard List next-page token. operations: A list of operations that matches the specified filter in the request. """ nextPageToken = _messages.StringField(1) operations = _messages.MessageField('Operation', 2, repeated=True) class ListWorkflowTemplatesResponse(_messages.Message): r"""A response to a request to list workflow templates in a project. Fields: nextPageToken: Output only. This token is included in the response if there are more results to fetch. To fetch additional results, provide this value as the page_token in a subsequent <code>ListWorkflowTemplatesRequest</code>. templates: Output only. WorkflowTemplates list. """ nextPageToken = _messages.StringField(1) templates = _messages.MessageField('WorkflowTemplate', 2, repeated=True) class LoggingConfig(_messages.Message): r"""The runtime logging config of the job. Messages: DriverLogLevelsValue: The per-package log levels for the driver. This may include "root" package name to configure rootLogger. Examples: 'com.google = FATAL', 'root = INFO', 'org.apache = DEBUG' Fields: driverLogLevels: The per-package log levels for the driver. This may include "root" package name to configure rootLogger. Examples: 'com.google = FATAL', 'root = INFO', 'org.apache = DEBUG' """ @encoding.MapUnrecognizedFields('additionalProperties') class DriverLogLevelsValue(_messages.Message): r"""The per-package log levels for the driver. This may include "root" package name to configure rootLogger. Examples: 'com.google = FATAL', 'root = INFO', 'org.apache = DEBUG' Messages: AdditionalProperty: An additional property for a DriverLogLevelsValue object. Fields: additionalProperties: Additional properties of type DriverLogLevelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a DriverLogLevelsValue object. Enums: ValueValueValuesEnum: Fields: key: Name of the additional property. value: A ValueValueValuesEnum attribute. """ class ValueValueValuesEnum(_messages.Enum): r"""ValueValueValuesEnum enum type. Values: LEVEL_UNSPECIFIED: <no description> ALL: <no description> TRACE: <no description> DEBUG: <no description> INFO: <no description> WARN: <no description> ERROR: <no description> FATAL: <no description> OFF: <no description> """ LEVEL_UNSPECIFIED = 0 ALL = 1 TRACE = 2 DEBUG = 3 INFO = 4 WARN = 5 ERROR = 6 FATAL = 7 OFF = 8 key = _messages.StringField(1) value = _messages.EnumField('ValueValueValuesEnum', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) driverLogLevels = _messages.MessageField('DriverLogLevelsValue', 1) class ManagedCluster(_messages.Message): r"""Cluster that is managed by the workflow. Messages: LabelsValue: Optional. The labels to associate with this cluster.Label keys must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given cluster. Fields: clusterName: Required. The cluster name prefix. A unique cluster name will be formed by appending a random suffix.The name must contain only lower- case letters (a-z), numbers (0-9), and hyphens (-). Must begin with a letter. Cannot begin or end with hyphen. Must consist of between 2 and 35 characters. config: Required. The cluster configuration. labels: Optional. The labels to associate with this cluster.Label keys must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given cluster. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels to associate with this cluster.Label keys must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following PCRE regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given cluster. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) clusterName = _messages.StringField(1) config = _messages.MessageField('ClusterConfig', 2) labels = _messages.MessageField('LabelsValue', 3) class ManagedGroupConfig(_messages.Message): r"""Specifies the resources used to actively manage an instance group. Fields: instanceGroupManagerName: Output only. The name of the Instance Group Manager for this group. instanceTemplateName: Output only. The name of the Instance Template used for the Managed Instance Group. """ instanceGroupManagerName = _messages.StringField(1) instanceTemplateName = _messages.StringField(2) class NodeInitializationAction(_messages.Message): r"""Specifies an executable to run on a fully configured node and a timeout period for executable completion. Fields: executableFile: Required. Cloud Storage URI of executable file. executionTimeout: Optional. Amount of time executable has to complete. Default is 10 minutes. Cluster creation fails with an explanatory error message (the name of the executable that caused the error and the exceeded timeout period) if the executable is not completed at end of the timeout period. """ executableFile = _messages.StringField(1) executionTimeout = _messages.StringField(2) class Operation(_messages.Message): r"""This resource represents a long-running operation that is the result of a network API call. Messages: MetadataValue: Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. ResponseValue: The normal response of the operation in case of success. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse. Fields: done: If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available. error: The error result of the operation in case of failure or cancellation. metadata: Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. name: The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should have the format of operations/some/unique/name. response: The normal response of the operation in case of success. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse. """ @encoding.MapUnrecognizedFields('additionalProperties') class MetadataValue(_messages.Message): r"""Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. Messages: AdditionalProperty: An additional property for a MetadataValue object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. """ class AdditionalProperty(_messages.Message): r"""An additional property for a MetadataValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class ResponseValue(_messages.Message): r"""The normal response of the operation in case of success. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse. Messages: AdditionalProperty: An additional property for a ResponseValue object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. """ class AdditionalProperty(_messages.Message): r"""An additional property for a ResponseValue object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) done = _messages.BooleanField(1) error = _messages.MessageField('Status', 2) metadata = _messages.MessageField('MetadataValue', 3) name = _messages.StringField(4) response = _messages.MessageField('ResponseValue', 5) class OrderedJob(_messages.Message): r"""A job executed by the workflow. Messages: LabelsValue: Optional. The labels to associate with this job.Label keys must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given job. Fields: hadoopJob: Job is a Hadoop job. hiveJob: Job is a Hive job. labels: Optional. The labels to associate with this job.Label keys must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given job. pigJob: Job is a Pig job. prerequisiteStepIds: Optional. The optional list of prerequisite job step_ids. If not specified, the job will start at the beginning of workflow. prestoJob: Job is a Presto job. pysparkJob: Job is a Pyspark job. scheduling: Optional. Job scheduling configuration. sparkJob: Job is a Spark job. sparkRJob: Job is a SparkR job. sparkSqlJob: Job is a SparkSql job. stepId: Required. The step id. The id must be unique among all jobs within the template.The step id is used as prefix for job id, as job goog- dataproc-workflow-step-id label, and in prerequisiteStepIds field from other steps.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels to associate with this job.Label keys must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}{0,62}Label values must be between 1 and 63 characters long, and must conform to the following regular expression: \p{Ll}\p{Lo}\p{N}_-{0,63}No more than 32 labels can be associated with a given job. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) hadoopJob = _messages.MessageField('HadoopJob', 1) hiveJob = _messages.MessageField('HiveJob', 2) labels = _messages.MessageField('LabelsValue', 3) pigJob = _messages.MessageField('PigJob', 4) prerequisiteStepIds = _messages.StringField(5, repeated=True) prestoJob = _messages.MessageField('PrestoJob', 6) pysparkJob = _messages.MessageField('PySparkJob', 7) scheduling = _messages.MessageField('JobScheduling', 8) sparkJob = _messages.MessageField('SparkJob', 9) sparkRJob = _messages.MessageField('SparkRJob', 10) sparkSqlJob = _messages.MessageField('SparkSqlJob', 11) stepId = _messages.StringField(12) class ParameterValidation(_messages.Message): r"""Configuration for parameter validation. Fields: regex: Validation based on regular expressions. values: Validation based on a list of allowed values. """ regex = _messages.MessageField('RegexValidation', 1) values = _messages.MessageField('ValueValidation', 2) class PigJob(_messages.Message): r"""A Cloud Dataproc job for running Apache Pig (https://pig.apache.org/) queries on YARN. Messages: PropertiesValue: Optional. A mapping of property names to values, used to configure Pig. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/pig/conf/pig.properties, and classes in user code. ScriptVariablesValue: Optional. Mapping of query variable names to values (equivalent to the Pig command: name=[value]). Fields: continueOnFailure: Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries. jarFileUris: Optional. HCFS URIs of jar files to add to the CLASSPATH of the Pig Client and Hadoop MapReduce (MR) tasks. Can contain Pig UDFs. loggingConfig: Optional. The runtime log config for job execution. properties: Optional. A mapping of property names to values, used to configure Pig. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/pig/conf/pig.properties, and classes in user code. queryFileUri: The HCFS URI of the script that contains the Pig queries. queryList: A list of queries. scriptVariables: Optional. Mapping of query variable names to values (equivalent to the Pig command: name=[value]). """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names to values, used to configure Pig. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/hadoop/conf/*-site.xml, /etc/pig/conf/pig.properties, and classes in user code. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class ScriptVariablesValue(_messages.Message): r"""Optional. Mapping of query variable names to values (equivalent to the Pig command: name=[value]). Messages: AdditionalProperty: An additional property for a ScriptVariablesValue object. Fields: additionalProperties: Additional properties of type ScriptVariablesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ScriptVariablesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) continueOnFailure = _messages.BooleanField(1) jarFileUris = _messages.StringField(2, repeated=True) loggingConfig = _messages.MessageField('LoggingConfig', 3) properties = _messages.MessageField('PropertiesValue', 4) queryFileUri = _messages.StringField(5) queryList = _messages.MessageField('QueryList', 6) scriptVariables = _messages.MessageField('ScriptVariablesValue', 7) class Policy(_messages.Message): r"""Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources.A Policy consists of a list of bindings. A binding binds a list of members to a role, where the members can be user accounts, Google groups, Google domains, and service accounts. A role is a named list of permissions defined by IAM.JSON Example { "bindings": [ { "role": "roles/owner", "members": [ "user:<EMAIL>", "group:<EMAIL>", "domain:google.com", "serviceAccount:<EMAIL>" ] }, { "role": "roles/viewer", "members": ["user:<EMAIL>"] } ] } YAML Example bindings: - members: - user:<EMAIL> - group:<EMAIL> - domain:google.com - serviceAccount:my-<EMAIL>- <EMAIL> role: roles/owner - members: - user:<EMAIL> role: roles/viewer For a description of IAM and its features, see the IAM developer's guide (https://cloud.google.com/iam/docs). Fields: bindings: Associates a list of members to a role. bindings with no members will result in an error. etag: etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the etag in the read- modify-write cycle to perform policy updates in order to avoid race conditions: An etag is returned in the response to getIamPolicy, and systems are expected to put that etag in the request to setIamPolicy to ensure that their change will be applied to the same version of the policy.If no etag is provided in the call to setIamPolicy, then the existing policy is overwritten blindly. version: Deprecated. """ bindings = _messages.MessageField('Binding', 1, repeated=True) etag = _messages.BytesField(2) version = _messages.IntegerField(3, variant=_messages.Variant.INT32) class PrestoJob(_messages.Message): r"""A Cloud Dataproc job for running Presto (https://prestosql.io/) queries Messages: PropertiesValue: Optional. A mapping of property names to values. Used to set Presto session properties (https://prestodb.io/docs/current/sql/set- session.html) Equivalent to using the --session flag in the Presto CLI Fields: clientTags: Optional. Presto client tags to attach to this query continueOnFailure: Optional. Whether to continue executing queries if a query fails. The default value is false. Setting to true can be useful when executing independent parallel queries. loggingConfig: Optional. The runtime log config for job execution. outputFormat: Optional. The format in which query output will be displayed. See the Presto documentation for supported output formats properties: Optional. A mapping of property names to values. Used to set Presto session properties (https://prestodb.io/docs/current/sql/set- session.html) Equivalent to using the --session flag in the Presto CLI queryFileUri: The HCFS URI of the script that contains SQL queries. queryList: A list of queries. """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names to values. Used to set Presto session properties (https://prestodb.io/docs/current/sql/set-session.html) Equivalent to using the --session flag in the Presto CLI Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) clientTags = _messages.StringField(1, repeated=True) continueOnFailure = _messages.BooleanField(2) loggingConfig = _messages.MessageField('LoggingConfig', 3) outputFormat = _messages.StringField(4) properties = _messages.MessageField('PropertiesValue', 5) queryFileUri = _messages.StringField(6) queryList = _messages.MessageField('QueryList', 7) class PySparkJob(_messages.Message): r"""A Cloud Dataproc job for running Apache PySpark (https://spark.apache.org/docs/0.9.0/python-programming-guide.html) applications on YARN. Messages: PropertiesValue: Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code. Fields: archiveUris: Optional. HCFS URIs of archives to be extracted in the working directory of .jar, .tar, .tar.gz, .tgz, and .zip. args: Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission. fileUris: Optional. HCFS URIs of files to be copied to the working directory of Python drivers and distributed tasks. Useful for naively parallel tasks. jarFileUris: Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Python driver and tasks. loggingConfig: Optional. The runtime log config for job execution. mainPythonFileUri: Required. The HCFS URI of the main Python file to use as the driver. Must be a .py file. properties: Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code. pythonFileUris: Optional. HCFS file URIs of Python files to pass to the PySpark framework. Supported file types: .py, .egg, and .zip. """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf /spark-defaults.conf and classes in user code. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) archiveUris = _messages.StringField(1, repeated=True) args = _messages.StringField(2, repeated=True) fileUris = _messages.StringField(3, repeated=True) jarFileUris = _messages.StringField(4, repeated=True) loggingConfig = _messages.MessageField('LoggingConfig', 5) mainPythonFileUri = _messages.StringField(6) properties = _messages.MessageField('PropertiesValue', 7) pythonFileUris = _messages.StringField(8, repeated=True) class QueryList(_messages.Message): r"""A list of queries to run on a cluster. Fields: queries: Required. The queries to execute. You do not need to terminate a query with a semicolon. Multiple queries can be specified in one string by separating each with a semicolon. Here is an example of an Cloud Dataproc API snippet that uses a QueryList to specify a HiveJob: "hiveJob": { "queryList": { "queries": [ "query1", "query2", "query3;query4", ] } } """ queries = _messages.StringField(1, repeated=True) class RegexValidation(_messages.Message): r"""Validation based on regular expressions. Fields: regexes: Required. RE2 regular expressions used to validate the parameter's value. The value must match the regex in its entirety (substring matches are not sufficient). """ regexes = _messages.StringField(1, repeated=True) class SecurityConfig(_messages.Message): r"""Security related configuration, including encryption, Kerberos, etc. Fields: kerberosConfig: Kerberos related configuration. """ kerberosConfig = _messages.MessageField('KerberosConfig', 1) class SetIamPolicyRequest(_messages.Message): r"""Request message for SetIamPolicy method. Fields: policy: REQUIRED: The complete policy to be applied to the resource. The size of the policy is limited to a few 10s of KB. An empty policy is a valid policy but certain Cloud Platform services (such as Projects) might reject them. """ policy = _messages.MessageField('Policy', 1) class SoftwareConfig(_messages.Message): r"""Specifies the selection and config of software inside the cluster. Enums: OptionalComponentsValueListEntryValuesEnum: Messages: PropertiesValue: Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, such as core:fs.defaultFS. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties. Fields: imageVersion: Optional. The version of software inside the cluster. It must be one of the supported Cloud Dataproc Versions, such as "1.2" (including a subminor version, such as "1.2.29"), or the "preview" version. If unspecified, it defaults to the latest version. optionalComponents: The set of optional components to activate on the cluster. properties: Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, such as core:fs.defaultFS. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties. """ class OptionalComponentsValueListEntryValuesEnum(_messages.Enum): r"""OptionalComponentsValueListEntryValuesEnum enum type. Values: COMPONENT_UNSPECIFIED: <no description> JUPYTER: <no description> HIVE_WEBHCAT: <no description> ZEPPELIN: <no description> ANACONDA: <no description> PRESTO: <no description> KERBEROS: <no description> """ COMPONENT_UNSPECIFIED = 0 JUPYTER = 1 HIVE_WEBHCAT = 2 ZEPPELIN = 3 ANACONDA = 4 PRESTO = 5 KERBEROS = 6 @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, such as core:fs.defaultFS. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) imageVersion = _messages.StringField(1) optionalComponents = _messages.EnumField('OptionalComponentsValueListEntryValuesEnum', 2, repeated=True) properties = _messages.MessageField('PropertiesValue', 3) class SparkJob(_messages.Message): r"""A Cloud Dataproc job for running Apache Spark (http://spark.apache.org/) applications on YARN. Messages: PropertiesValue: Optional. A mapping of property names to values, used to configure Spark. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code. Fields: archiveUris: Optional. HCFS URIs of archives to be extracted in the working directory of Spark drivers and tasks. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip. args: Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission. fileUris: Optional. HCFS URIs of files to be copied to the working directory of Spark drivers and distributed tasks. Useful for naively parallel tasks. jarFileUris: Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Spark driver and tasks. loggingConfig: Optional. The runtime log config for job execution. mainClass: The name of the driver's main class. The jar file that contains the class must be in the default CLASSPATH or specified in jar_file_uris. mainJarFileUri: The HCFS URI of the jar file that contains the main class. properties: Optional. A mapping of property names to values, used to configure Spark. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code. """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names to values, used to configure Spark. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark- defaults.conf and classes in user code. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) archiveUris = _messages.StringField(1, repeated=True) args = _messages.StringField(2, repeated=True) fileUris = _messages.StringField(3, repeated=True) jarFileUris = _messages.StringField(4, repeated=True) loggingConfig = _messages.MessageField('LoggingConfig', 5) mainClass = _messages.StringField(6) mainJarFileUri = _messages.StringField(7) properties = _messages.MessageField('PropertiesValue', 8) class SparkRJob(_messages.Message): r"""A Cloud Dataproc job for running Apache SparkR (https://spark.apache.org/docs/latest/sparkr.html) applications on YARN. Messages: PropertiesValue: Optional. A mapping of property names to values, used to configure SparkR. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code. Fields: archiveUris: Optional. HCFS URIs of archives to be extracted in the working directory of Spark drivers and tasks. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip. args: Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission. fileUris: Optional. HCFS URIs of files to be copied to the working directory of R drivers and distributed tasks. Useful for naively parallel tasks. loggingConfig: Optional. The runtime log config for job execution. mainRFileUri: Required. The HCFS URI of the main R file to use as the driver. Must be a .R file. properties: Optional. A mapping of property names to values, used to configure SparkR. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code. """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names to values, used to configure SparkR. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark- defaults.conf and classes in user code. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) archiveUris = _messages.StringField(1, repeated=True) args = _messages.StringField(2, repeated=True) fileUris = _messages.StringField(3, repeated=True) loggingConfig = _messages.MessageField('LoggingConfig', 4) mainRFileUri = _messages.StringField(5) properties = _messages.MessageField('PropertiesValue', 6) class SparkSqlJob(_messages.Message): r"""A Cloud Dataproc job for running Apache Spark SQL (http://spark.apache.org/sql/) queries. Messages: PropertiesValue: Optional. A mapping of property names to values, used to configure Spark SQL's SparkConf. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. ScriptVariablesValue: Optional. Mapping of query variable names to values (equivalent to the Spark SQL command: SET name="value";). Fields: jarFileUris: Optional. HCFS URIs of jar files to be added to the Spark CLASSPATH. loggingConfig: Optional. The runtime log config for job execution. properties: Optional. A mapping of property names to values, used to configure Spark SQL's SparkConf. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. queryFileUri: The HCFS URI of the script that contains SQL queries. queryList: A list of queries. scriptVariables: Optional. Mapping of query variable names to values (equivalent to the Spark SQL command: SET name="value";). """ @encoding.MapUnrecognizedFields('additionalProperties') class PropertiesValue(_messages.Message): r"""Optional. A mapping of property names to values, used to configure Spark SQL's SparkConf. Properties that conflict with values set by the Cloud Dataproc API may be overwritten. Messages: AdditionalProperty: An additional property for a PropertiesValue object. Fields: additionalProperties: Additional properties of type PropertiesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a PropertiesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) @encoding.MapUnrecognizedFields('additionalProperties') class ScriptVariablesValue(_messages.Message): r"""Optional. Mapping of query variable names to values (equivalent to the Spark SQL command: SET name="value";). Messages: AdditionalProperty: An additional property for a ScriptVariablesValue object. Fields: additionalProperties: Additional properties of type ScriptVariablesValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ScriptVariablesValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) jarFileUris = _messages.StringField(1, repeated=True) loggingConfig = _messages.MessageField('LoggingConfig', 2) properties = _messages.MessageField('PropertiesValue', 3) queryFileUri = _messages.StringField(4) queryList = _messages.MessageField('QueryList', 5) scriptVariables = _messages.MessageField('ScriptVariablesValue', 6) class StandardQueryParameters(_messages.Message): r"""Query parameters accepted by all methods. Enums: FXgafvValueValuesEnum: V1 error format. AltValueValuesEnum: Data format for response. Fields: f__xgafv: V1 error format. access_token: OAuth access token. alt: Data format for response. callback: JSONP fields: Selector specifying which fields to include in a partial response. key: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token. oauth_token: OAuth 2.0 token for the current user. prettyPrint: Returns response with indentations and line breaks. quotaUser: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters. trace: A tracing token of the form "token:<tokenid>" to include in api requests. uploadType: Legacy upload protocol for media (e.g. "media", "multipart"). upload_protocol: Upload protocol for media (e.g. "raw", "multipart"). """ class AltValueValuesEnum(_messages.Enum): r"""Data format for response. Values: json: Responses with Content-Type of application/json media: Media download with context-dependent Content-Type proto: Responses with Content-Type of application/x-protobuf """ json = 0 media = 1 proto = 2 class FXgafvValueValuesEnum(_messages.Enum): r"""V1 error format. Values: _1: v1 error format _2: v2 error format """ _1 = 0 _2 = 1 f__xgafv = _messages.EnumField('FXgafvValueValuesEnum', 1) access_token = _messages.StringField(2) alt = _messages.EnumField('AltValueValuesEnum', 3, default=u'json') callback = _messages.StringField(4) fields = _messages.StringField(5) key = _messages.StringField(6) oauth_token = _messages.StringField(7) prettyPrint = _messages.BooleanField(8, default=True) quotaUser = _messages.StringField(9) trace = _messages.StringField(10) uploadType = _messages.StringField(11) upload_protocol = _messages.StringField(12) class Status(_messages.Message): r"""The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). The error model is designed to be: Simple to use and understand for most users Flexible enough to meet unexpected needsOverviewThe Status message contains three pieces of data: error code, error message, and error details. The error code should be an enum value of google.rpc.Code, but it may accept additional error codes if needed. The error message should be a developer-facing English message that helps developers understand and resolve the error. If a localized user- facing error message is needed, put the localized message in the error details or localize it in the client. The optional error details may contain arbitrary information about the error. There is a predefined set of error detail types in the package google.rpc that can be used for common error conditions.Language mappingThe Status message is the logical representation of the error model, but it is not necessarily the actual wire format. When the Status message is exposed in different client libraries and different wire protocols, it can be mapped differently. For example, it will likely be mapped to some exceptions in Java, but more likely mapped to some error codes in C.Other usesThe error model and the Status message can be used in a variety of environments, either with or without APIs, to provide a consistent developer experience across different environments.Example uses of this error model include: Partial errors. If a service needs to return partial errors to the client, it may embed the Status in the normal response to indicate the partial errors. Workflow errors. A typical workflow has multiple steps. Each step may have a Status message for error reporting. Batch operations. If a client uses batch request and batch response, the Status message should be used directly inside batch response, one for each error sub-response. Asynchronous operations. If an API call embeds asynchronous operation results in its response, the status of those operations should be represented directly using the Status message. Logging. If some API errors are stored in logs, the message Status could be used directly after any stripping needed for security/privacy reasons. Messages: DetailsValueListEntry: A DetailsValueListEntry object. Fields: code: The status code, which should be an enum value of google.rpc.Code. details: A list of messages that carry the error details. There is a common set of message types for APIs to use. message: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client. """ @encoding.MapUnrecognizedFields('additionalProperties') class DetailsValueListEntry(_messages.Message): r"""A DetailsValueListEntry object. Messages: AdditionalProperty: An additional property for a DetailsValueListEntry object. Fields: additionalProperties: Properties of the object. Contains field @type with type URL. """ class AdditionalProperty(_messages.Message): r"""An additional property for a DetailsValueListEntry object. Fields: key: Name of the additional property. value: A extra_types.JsonValue attribute. """ key = _messages.StringField(1) value = _messages.MessageField('extra_types.JsonValue', 2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) code = _messages.IntegerField(1, variant=_messages.Variant.INT32) details = _messages.MessageField('DetailsValueListEntry', 2, repeated=True) message = _messages.StringField(3) class SubmitJobRequest(_messages.Message): r"""A request to submit a job. Fields: job: Required. The job resource. requestId: Optional. A unique id used to identify the request. If the server receives two SubmitJobRequest requests with the same id, then the second request will be ignored and the first Job created and stored in the backend is returned.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters. """ job = _messages.MessageField('Job', 1) requestId = _messages.StringField(2) class TemplateParameter(_messages.Message): r"""A configurable parameter that replaces one or more fields in the template. Parameterizable fields: - Labels - File uris - Job properties - Job arguments - Script variables - Main class (in HadoopJob and SparkJob) - Zone (in ClusterSelector) Fields: description: Optional. Brief description of the parameter. Must not exceed 1024 characters. fields: Required. Paths to all fields that the parameter replaces. A field is allowed to appear in at most one parameter's list of field paths.A field path is similar in syntax to a google.protobuf.FieldMask. For example, a field path that references the zone field of a workflow template's cluster selector would be specified as placement.clusterSelector.zone.Also, field paths can reference fields using the following syntax: Values in maps can be referenced by key: labels'key' placement.clusterSelector.clusterLabels'key' placement.managedCluster.labels'key' placement.clusterSelector.clusterLabels'key' jobs'step-id'.labels'key' Jobs in the jobs list can be referenced by step-id: jobs'step- id'.hadoopJob.mainJarFileUri jobs'step-id'.hiveJob.queryFileUri jobs 'step-id'.pySparkJob.mainPythonFileUri jobs'step- id'.hadoopJob.jarFileUris0 jobs'step-id'.hadoopJob.archiveUris0 jobs 'step-id'.hadoopJob.fileUris0 jobs'step-id'.pySparkJob.pythonFileUris0 Items in repeated fields can be referenced by a zero-based index: jobs 'step-id'.sparkJob.args0 Other examples: jobs'step- id'.hadoopJob.properties'key' jobs'step-id'.hadoopJob.args0 jobs'step- id'.hiveJob.scriptVariables'key' jobs'step-id'.hadoopJob.mainJarFileUri placement.clusterSelector.zoneIt may not be possible to parameterize maps and repeated fields in their entirety since only individual map values and individual items in repeated fields can be referenced. For example, the following field paths are invalid: placement.clusterSelector.clusterLabels jobs'step-id'.sparkJob.args name: Required. Parameter name. The parameter name is used as the key, and paired with the parameter value, which are passed to the template when the template is instantiated. The name must contain only capital letters (A-Z), numbers (0-9), and underscores (_), and must not start with a number. The maximum length is 40 characters. validation: Optional. Validation rules to be applied to this parameter's value. """ description = _messages.StringField(1) fields = _messages.StringField(2, repeated=True) name = _messages.StringField(3) validation = _messages.MessageField('ParameterValidation', 4) class TestIamPermissionsRequest(_messages.Message): r"""Request message for TestIamPermissions method. Fields: permissions: The set of permissions to check for the resource. Permissions with wildcards (such as '*' or 'storage.*') are not allowed. For more information see IAM Overview (https://cloud.google.com/iam/docs/overview#permissions). """ permissions = _messages.StringField(1, repeated=True) class TestIamPermissionsResponse(_messages.Message): r"""Response message for TestIamPermissions method. Fields: permissions: A subset of TestPermissionsRequest.permissions that the caller is allowed. """ permissions = _messages.StringField(1, repeated=True) class ValueValidation(_messages.Message): r"""Validation based on a list of allowed values. Fields: values: Required. List of allowed values for the parameter. """ values = _messages.StringField(1, repeated=True) class WorkflowGraph(_messages.Message): r"""The workflow graph. Fields: nodes: Output only. The workflow nodes. """ nodes = _messages.MessageField('WorkflowNode', 1, repeated=True) class WorkflowMetadata(_messages.Message): r"""A Cloud Dataproc workflow template resource. Enums: StateValueValuesEnum: Output only. The workflow state. Messages: ParametersValue: Map from parameter names to values that were used for those parameters. Fields: clusterName: Output only. The name of the target cluster. clusterUuid: Output only. The UUID of target cluster. createCluster: Output only. The create cluster operation metadata. deleteCluster: Output only. The delete cluster operation metadata. endTime: Output only. Workflow end time. graph: Output only. The workflow graph. parameters: Map from parameter names to values that were used for those parameters. startTime: Output only. Workflow start time. state: Output only. The workflow state. template: Output only. The "resource name" of the template. version: Output only. The version of template at the time of workflow instantiation. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The workflow state. Values: UNKNOWN: Unused. PENDING: The operation has been created. RUNNING: The operation is running. DONE: The operation is done; either cancelled or completed. """ UNKNOWN = 0 PENDING = 1 RUNNING = 2 DONE = 3 @encoding.MapUnrecognizedFields('additionalProperties') class ParametersValue(_messages.Message): r"""Map from parameter names to values that were used for those parameters. Messages: AdditionalProperty: An additional property for a ParametersValue object. Fields: additionalProperties: Additional properties of type ParametersValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a ParametersValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) clusterName = _messages.StringField(1) clusterUuid = _messages.StringField(2) createCluster = _messages.MessageField('ClusterOperation', 3) deleteCluster = _messages.MessageField('ClusterOperation', 4) endTime = _messages.StringField(5) graph = _messages.MessageField('WorkflowGraph', 6) parameters = _messages.MessageField('ParametersValue', 7) startTime = _messages.StringField(8) state = _messages.EnumField('StateValueValuesEnum', 9) template = _messages.StringField(10) version = _messages.IntegerField(11, variant=_messages.Variant.INT32) class WorkflowNode(_messages.Message): r"""The workflow node. Enums: StateValueValuesEnum: Output only. The node state. Fields: error: Output only. The error detail. jobId: Output only. The job id; populated after the node enters RUNNING state. prerequisiteStepIds: Output only. Node's prerequisite nodes. state: Output only. The node state. stepId: Output only. The name of the node. """ class StateValueValuesEnum(_messages.Enum): r"""Output only. The node state. Values: NODE_STATUS_UNSPECIFIED: State is unspecified. BLOCKED: The node is awaiting prerequisite node to finish. RUNNABLE: The node is runnable but not running. RUNNING: The node is running. COMPLETED: The node completed successfully. FAILED: The node failed. A node can be marked FAILED because its ancestor or peer failed. """ NODE_STATUS_UNSPECIFIED = 0 BLOCKED = 1 RUNNABLE = 2 RUNNING = 3 COMPLETED = 4 FAILED = 5 error = _messages.StringField(1) jobId = _messages.StringField(2) prerequisiteStepIds = _messages.StringField(3, repeated=True) state = _messages.EnumField('StateValueValuesEnum', 4) stepId = _messages.StringField(5) class WorkflowTemplate(_messages.Message): r"""A Cloud Dataproc workflow template resource. Messages: LabelsValue: Optional. The labels to associate with this template. These labels will be propagated to all jobs and clusters created by the workflow instance.Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).No more than 32 labels can be associated with a template. Fields: createTime: Output only. The time template was created. id: Required. The template id.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.. jobs: Required. The Directed Acyclic Graph of Jobs to submit. labels: Optional. The labels to associate with this template. These labels will be propagated to all jobs and clusters created by the workflow instance.Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).No more than 32 labels can be associated with a template. name: Output only. The "resource name" of the template, as described in https://cloud.google.com/apis/design/resource_names of the form projects/{project_id}/regions/{region}/workflowTemplates/{template_id} parameters: Optional. Template parameters whose values are substituted into the template. Values for parameters must be provided when the template is instantiated. placement: Required. WorkflowTemplate scheduling information. updateTime: Output only. The time template was last updated. version: Optional. Used to perform a consistent read-modify-write.This field should be left blank for a CreateWorkflowTemplate request. It is required for an UpdateWorkflowTemplate request, and must match the current server version. A typical update template flow would fetch the current template with a GetWorkflowTemplate request, which will return the current template with the version field filled in with the current server version. The user updates other fields in the template, then returns it as part of the UpdateWorkflowTemplate request. """ @encoding.MapUnrecognizedFields('additionalProperties') class LabelsValue(_messages.Message): r"""Optional. The labels to associate with this template. These labels will be propagated to all jobs and clusters created by the workflow instance.Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt).No more than 32 labels can be associated with a template. Messages: AdditionalProperty: An additional property for a LabelsValue object. Fields: additionalProperties: Additional properties of type LabelsValue """ class AdditionalProperty(_messages.Message): r"""An additional property for a LabelsValue object. Fields: key: Name of the additional property. value: A string attribute. """ key = _messages.StringField(1) value = _messages.StringField(2) additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True) createTime = _messages.StringField(1) id = _messages.StringField(2) jobs = _messages.MessageField('OrderedJob', 3, repeated=True) labels = _messages.MessageField('LabelsValue', 4) name = _messages.StringField(5) parameters = _messages.MessageField('TemplateParameter', 6, repeated=True) placement = _messages.MessageField('WorkflowTemplatePlacement', 7) updateTime = _messages.StringField(8) version = _messages.IntegerField(9, variant=_messages.Variant.INT32) class WorkflowTemplatePlacement(_messages.Message): r"""Specifies workflow execution target.Either managed_cluster or cluster_selector is required. Fields: clusterSelector: Optional. A selector that chooses target cluster for jobs based on metadata.The selector is evaluated at the time each job is submitted. managedCluster: Optional. A cluster that is managed by the workflow. """ clusterSelector = _messages.MessageField('ClusterSelector', 1) managedCluster = _messages.MessageField('ManagedCluster', 2) class YarnApplication(_messages.Message): r"""A YARN application created by a job. Application information is a subset of <code>org.apache.hadoop.yarn.proto.YarnProtos.ApplicationReportProto</cod e>.Beta Feature: This report is available for testing purposes only. It may be changed before final release. Enums: StateValueValuesEnum: Required. The application state. Fields: name: Required. The application name. progress: Required. The numerical progress of the application, from 1 to 100. state: Required. The application state. trackingUrl: Optional. The HTTP URL of the ApplicationMaster, HistoryServer, or TimelineServer that provides application-specific information. The URL uses the internal hostname, and requires a proxy server for resolution and, possibly, access. """ class StateValueValuesEnum(_messages.Enum): r"""Required. The application state. Values: STATE_UNSPECIFIED: Status is unspecified. NEW: Status is NEW. NEW_SAVING: Status is NEW_SAVING. SUBMITTED: Status is SUBMITTED. ACCEPTED: Status is ACCEPTED. RUNNING: Status is RUNNING. FINISHED: Status is FINISHED. FAILED: Status is FAILED. KILLED: Status is KILLED. """ STATE_UNSPECIFIED = 0 NEW = 1 NEW_SAVING = 2 SUBMITTED = 3 ACCEPTED = 4 RUNNING = 5 FINISHED = 6 FAILED = 7 KILLED = 8 name = _messages.StringField(1) progress = _messages.FloatField(2, variant=_messages.Variant.FLOAT) state = _messages.EnumField('StateValueValuesEnum', 3) trackingUrl = _messages.StringField(4) encoding.AddCustomJsonFieldMapping( StandardQueryParameters, 'f__xgafv', '$.xgafv') encoding.AddCustomJsonEnumMapping( StandardQueryParameters.FXgafvValueValuesEnum, '_1', '1') encoding.AddCustomJsonEnumMapping( StandardQueryParameters.FXgafvValueValuesEnum, '_2', '2')

0.912602

0.257859

import models import util.validation from database import db_txn from linkr import db from util.exception import * @db_txn def add_link(alias, outgoing_url, password=<PASSWORD>, user_id=None, require_recaptcha=False): """ Add a new link to the database after performing necessary input validation. :param alias: The link alias. :param outgoing_url: The associated outgoing URL. :param password: Plain-text password associated with this link, if applicable. :param user_id: ID of the user to associate with this link, if applicable. :param require_recaptcha: True to require ReCAPTCHA for accessing this link; False otherwise. :return: An instance of models.Link representing the new entry. :raises InvalidAliasException: If the alias is invalid. :raises ReservedAliasException: If the alias is reserved. :raises InvalidURLException: If the outgoing URL is invalid. :raises UnavailableAliasException: If the alias already exists in the database. """ if not util.validation.is_alias_valid(alias): raise InvalidAliasException('Alias `{alias}` is not URL safe'.format(alias=alias)) if util.validation.is_alias_reserved(alias): raise ReservedAliasException('Alias `{alias}` is reserved'.format(alias=alias)) if not util.validation.is_url_valid(outgoing_url): raise InvalidURLException('URL `{url}` is not a valid URL'.format(url=outgoing_url)) if models.Link.query.filter_by(alias=alias).scalar(): raise UnavailableAliasException('Alias `{alias}` already exists'.format(alias=alias)) new_link = models.Link( alias=alias, outgoing_url=outgoing_url, password=password, user_id=user_id, require_recaptcha=require_recaptcha, ) db.session.add(new_link) return new_link @db_txn def edit_link(link_id, alias=None, outgoing_url=None): """ Edit an existing link's details. :param link_id: The ID of the link to edit. :param alias: The new alias of the link, or None to leave it unchanged. :param outgoing_url: The new outgoing URL of the link, or None to leave it unchanged. :return: The models.Link instance representing the modified link object. :raises InvalidAliasException: If the alias is invalid. :raises InvalidURLException: If the outgoing URL is invalid. :raises NonexistentLinkException: If no link exists with the provided link ID. """ to_modify = get_link_by_id(link_id) if not to_modify: raise NonexistentLinkException('No link exists with link ID `{link_id}`'.format( link_id=link_id, )) if alias and not util.validation.is_alias_valid(alias): raise InvalidAliasException('Alias `{alias}` is not URL safe'.format(alias=alias)) if alias and util.validation.is_alias_reserved(alias): raise ReservedAliasException('Alias `{alias}` is reserved'.format(alias=alias)) if outgoing_url and not util.validation.is_url_valid(outgoing_url): raise InvalidURLException('URL `{url}` is not a valid URL'.format(url=outgoing_url)) to_modify.edit(alias=alias, outgoing_url=outgoing_url) db.session.add(to_modify) return to_modify @db_txn def update_link_password(link_id, password): """ Update a link's password. This method allows both adding a password to a previously non-password-protected link, changing the password on a password-protected link, and removing the password from a password-protected link. :param link_id: ID of the link for which the password should be updated. :param password: The <PASSWORD>. :return: The models.Link instance representing the modified Link object. """ to_modify = get_link_by_id(link_id) if not to_modify: raise NonexistentLinkException('No link exists with link ID `{link_id}`'.format( link_id=link_id, )) to_modify.update_password(password) db.session.add(to_modify) return to_modify @db_txn def delete_link(link_id): """ Delete a link from the database, if it exists. :param link_id: The link ID to delete. :return: The models.Link instance representing the deleted entry. :raises NonexistentLinkException: If the link ID does not exist. """ to_delete = models.Link.query.filter_by(link_id=link_id) if not to_delete.scalar(): raise NonexistentLinkException('Link ID `{link_id}` does not exist.'.format( link_id=link_id, )) to_delete.delete(synchronize_session='fetch') return to_delete @db_txn def add_link_hit(link_id, remote_ip, referer, user_agent): """ Add a new link hit. :param link_id: ID of the accessed link. :param remote_ip: The remote IP address of the client. :param referer: The referer of the hit. :param user_agent: The client's user agent string. :return: An instance of models.LinkHit representing the added entry. :raises NonexistentLinkException: If the link ID does not exist. """ associated_link = models.Link.query.filter_by(link_id=link_id) if not associated_link.scalar(): raise NonexistentLinkException('Link ID `{link_id}` does not exist.'.format( link_id=link_id, )) new_link_hit = models.LinkHit( link_id=link_id, remote_ip=remote_ip, referer=referer, user_agent=user_agent, ) db.session.add(new_link_hit) return new_link_hit def get_link_hits_by_id(link_id, page_num=0, num_per_page=100): """ Retrieve paginated listing of link hits for a particular link ID. :param link_id: The link ID whose hits should be retrieved. :param page_num: The page number to use in the pagination, zero-indexed. :param num_per_page: The number of hits to retrieve per page. :return: A list of models.LinkHit instances describing link hits, ordered by timestamp (most recent first). """ return models.LinkHit.query.filter_by( link_id=link_id, ).order_by( models.LinkHit.hit_id.desc() ).offset( page_num * num_per_page ).limit( num_per_page ).all() def get_link_by_id(link_id): """ Retrieve a link by its ID. :param link_id: The ID of the link. :return: The models.Link entry, or None if nonexistent. """ return models.Link.query.filter_by(link_id=link_id).first() def get_link_by_alias(alias): """ Retrieve a link by its alias. :param alias: The alias of the link. :return: The models.Link entry, or None if nonexistent. """ return models.Link.query.filter_by(alias=alias).first() def get_links_like_alias(alias, page_num=0, num_per_page=100): """ Retrieve links whose aliases contain the input. :param alias: A substring of an actual alias. :param page_num: The page number to use in the pagination, zero-indexed. :param num_per_page: The number of links to retrieve per page. :return: All models.Link instances whose aliases is a superstring of the input. """ return models.Link.query.filter( models.Link.alias.like('%{alias}%'.format(alias=alias)) ).offset( page_num * num_per_page ).limit( num_per_page ).all() def get_links_for_user(user_id, page_num=0, num_per_page=100): """ Retrieve a paginated listing of all links created by a user. :param user_id: The ID of the user for which links should be retrieved. :param page_num: The page number to use in the pagination, zero-indexed. :param num_per_page: The number of links to retrieve per page. :return: A list of models.Link objects describing the links created by the specified user. """ if not models.User.query.filter_by(user_id=user_id).scalar(): raise NonexistentUserException('No user exists with user_id `{user_id}`'.format( user_id=user_id, )) return models.Link.query.filter_by( user_id=user_id ).order_by( models.Link.link_id.asc() ).offset( page_num * num_per_page ).limit( num_per_page ).all() def get_recent_links(page_num=0, num_per_page=100): """ Retrieve paginated listing of recently created links. :param page_num: The page number to use in the pagination, zero-indexed. :param num_per_page: The number of links to retrieve per page. :return: A list of models.Link instances describing recent links, ordered by timestamp (most recent first). """ return models.Link.query.order_by( models.Link.link_id.desc() ).offset( page_num * num_per_page ).limit( num_per_page ).all()

database/link.py

import models import util.validation from database import db_txn from linkr import db from util.exception import * @db_txn def add_link(alias, outgoing_url, password=<PASSWORD>, user_id=None, require_recaptcha=False): """ Add a new link to the database after performing necessary input validation. :param alias: The link alias. :param outgoing_url: The associated outgoing URL. :param password: Plain-text password associated with this link, if applicable. :param user_id: ID of the user to associate with this link, if applicable. :param require_recaptcha: True to require ReCAPTCHA for accessing this link; False otherwise. :return: An instance of models.Link representing the new entry. :raises InvalidAliasException: If the alias is invalid. :raises ReservedAliasException: If the alias is reserved. :raises InvalidURLException: If the outgoing URL is invalid. :raises UnavailableAliasException: If the alias already exists in the database. """ if not util.validation.is_alias_valid(alias): raise InvalidAliasException('Alias `{alias}` is not URL safe'.format(alias=alias)) if util.validation.is_alias_reserved(alias): raise ReservedAliasException('Alias `{alias}` is reserved'.format(alias=alias)) if not util.validation.is_url_valid(outgoing_url): raise InvalidURLException('URL `{url}` is not a valid URL'.format(url=outgoing_url)) if models.Link.query.filter_by(alias=alias).scalar(): raise UnavailableAliasException('Alias `{alias}` already exists'.format(alias=alias)) new_link = models.Link( alias=alias, outgoing_url=outgoing_url, password=password, user_id=user_id, require_recaptcha=require_recaptcha, ) db.session.add(new_link) return new_link @db_txn def edit_link(link_id, alias=None, outgoing_url=None): """ Edit an existing link's details. :param link_id: The ID of the link to edit. :param alias: The new alias of the link, or None to leave it unchanged. :param outgoing_url: The new outgoing URL of the link, or None to leave it unchanged. :return: The models.Link instance representing the modified link object. :raises InvalidAliasException: If the alias is invalid. :raises InvalidURLException: If the outgoing URL is invalid. :raises NonexistentLinkException: If no link exists with the provided link ID. """ to_modify = get_link_by_id(link_id) if not to_modify: raise NonexistentLinkException('No link exists with link ID `{link_id}`'.format( link_id=link_id, )) if alias and not util.validation.is_alias_valid(alias): raise InvalidAliasException('Alias `{alias}` is not URL safe'.format(alias=alias)) if alias and util.validation.is_alias_reserved(alias): raise ReservedAliasException('Alias `{alias}` is reserved'.format(alias=alias)) if outgoing_url and not util.validation.is_url_valid(outgoing_url): raise InvalidURLException('URL `{url}` is not a valid URL'.format(url=outgoing_url)) to_modify.edit(alias=alias, outgoing_url=outgoing_url) db.session.add(to_modify) return to_modify @db_txn def update_link_password(link_id, password): """ Update a link's password. This method allows both adding a password to a previously non-password-protected link, changing the password on a password-protected link, and removing the password from a password-protected link. :param link_id: ID of the link for which the password should be updated. :param password: The <PASSWORD>. :return: The models.Link instance representing the modified Link object. """ to_modify = get_link_by_id(link_id) if not to_modify: raise NonexistentLinkException('No link exists with link ID `{link_id}`'.format( link_id=link_id, )) to_modify.update_password(password) db.session.add(to_modify) return to_modify @db_txn def delete_link(link_id): """ Delete a link from the database, if it exists. :param link_id: The link ID to delete. :return: The models.Link instance representing the deleted entry. :raises NonexistentLinkException: If the link ID does not exist. """ to_delete = models.Link.query.filter_by(link_id=link_id) if not to_delete.scalar(): raise NonexistentLinkException('Link ID `{link_id}` does not exist.'.format( link_id=link_id, )) to_delete.delete(synchronize_session='fetch') return to_delete @db_txn def add_link_hit(link_id, remote_ip, referer, user_agent): """ Add a new link hit. :param link_id: ID of the accessed link. :param remote_ip: The remote IP address of the client. :param referer: The referer of the hit. :param user_agent: The client's user agent string. :return: An instance of models.LinkHit representing the added entry. :raises NonexistentLinkException: If the link ID does not exist. """ associated_link = models.Link.query.filter_by(link_id=link_id) if not associated_link.scalar(): raise NonexistentLinkException('Link ID `{link_id}` does not exist.'.format( link_id=link_id, )) new_link_hit = models.LinkHit( link_id=link_id, remote_ip=remote_ip, referer=referer, user_agent=user_agent, ) db.session.add(new_link_hit) return new_link_hit def get_link_hits_by_id(link_id, page_num=0, num_per_page=100): """ Retrieve paginated listing of link hits for a particular link ID. :param link_id: The link ID whose hits should be retrieved. :param page_num: The page number to use in the pagination, zero-indexed. :param num_per_page: The number of hits to retrieve per page. :return: A list of models.LinkHit instances describing link hits, ordered by timestamp (most recent first). """ return models.LinkHit.query.filter_by( link_id=link_id, ).order_by( models.LinkHit.hit_id.desc() ).offset( page_num * num_per_page ).limit( num_per_page ).all() def get_link_by_id(link_id): """ Retrieve a link by its ID. :param link_id: The ID of the link. :return: The models.Link entry, or None if nonexistent. """ return models.Link.query.filter_by(link_id=link_id).first() def get_link_by_alias(alias): """ Retrieve a link by its alias. :param alias: The alias of the link. :return: The models.Link entry, or None if nonexistent. """ return models.Link.query.filter_by(alias=alias).first() def get_links_like_alias(alias, page_num=0, num_per_page=100): """ Retrieve links whose aliases contain the input. :param alias: A substring of an actual alias. :param page_num: The page number to use in the pagination, zero-indexed. :param num_per_page: The number of links to retrieve per page. :return: All models.Link instances whose aliases is a superstring of the input. """ return models.Link.query.filter( models.Link.alias.like('%{alias}%'.format(alias=alias)) ).offset( page_num * num_per_page ).limit( num_per_page ).all() def get_links_for_user(user_id, page_num=0, num_per_page=100): """ Retrieve a paginated listing of all links created by a user. :param user_id: The ID of the user for which links should be retrieved. :param page_num: The page number to use in the pagination, zero-indexed. :param num_per_page: The number of links to retrieve per page. :return: A list of models.Link objects describing the links created by the specified user. """ if not models.User.query.filter_by(user_id=user_id).scalar(): raise NonexistentUserException('No user exists with user_id `{user_id}`'.format( user_id=user_id, )) return models.Link.query.filter_by( user_id=user_id ).order_by( models.Link.link_id.asc() ).offset( page_num * num_per_page ).limit( num_per_page ).all() def get_recent_links(page_num=0, num_per_page=100): """ Retrieve paginated listing of recently created links. :param page_num: The page number to use in the pagination, zero-indexed. :param num_per_page: The number of links to retrieve per page. :return: A list of models.Link instances describing recent links, ordered by timestamp (most recent first). """ return models.Link.query.order_by( models.Link.link_id.desc() ).offset( page_num * num_per_page ).limit( num_per_page ).all()

0.75401

0.15704

import os from tqdm import tqdm import torch import torch.nn as nn import torch.optim as optim import torch.backends.cudnn as cudnn class PairFM(nn.Module): def __init__(self, user_num, item_num, factors=84, epochs=20, lr=0.001, reg_1=0., reg_2=0., loss_type='BPR', gpuid='0', early_stop=True): """ Pair-wise FM Recommender Class Parameters ---------- user_num : int, the number of users item_num : int, the number of items factors : int, the number of latent factor epochs : int, number of training epochs lr : float, learning rate reg_1 : float, first-order regularization term reg_2 : float, second-order regularization term loss_type : str, loss function type gpuid : str, GPU ID early_stop : bool, whether to activate early stop mechanism """ super(PairFM, self).__init__() os.environ['CUDA_VISIBLE_DEVICES'] = gpuid cudnn.benchmark = True self.epochs = epochs self.lr = lr self.reg_1 = reg_1 self.reg_2 = reg_2 self.embed_user = nn.Embedding(user_num, factors) self.embed_item = nn.Embedding(item_num, factors) self.u_bias = nn.Embedding(user_num, 1) self.i_bias = nn.Embedding(item_num, 1) self.bias_ = nn.Parameter(torch.tensor([0.0])) # init weight nn.init.normal_(self.embed_user.weight, std=0.01) nn.init.normal_(self.embed_item.weight, std=0.01) nn.init.constant_(self.u_bias.weight, 0.0) nn.init.constant_(self.i_bias.weight, 0.0) self.loss_type = loss_type self.early_stop = early_stop def forward(self, u, i, j): user = self.embed_user(u) item_i = self.embed_item(i) item_j = self.embed_item(j) # inner product part pred_i = (user * item_i).sum(dim=-1, keepdim=True) pred_j = (user * item_j).sum(dim=-1, keepdim=True) # add bias pred_i += self.u_bias(u) + self.i_bias(i) + self.bias_ pred_j += self.u_bias(u) + self.i_bias(j) + self.bias_ return pred_i.view(-1), pred_j.view(-1) def fit(self, train_loader): if torch.cuda.is_available(): self.cuda() else: self.cpu() optimizer = optim.SGD(self.parameters(), lr=self.lr) last_loss = 0. for epoch in range(1, self.epochs + 1): self.train() current_loss = 0. # set process bar display pbar = tqdm(train_loader) pbar.set_description(f'[Epoch {epoch:03d}]') for user, item_i, item_j, label in pbar: if torch.cuda.is_available(): user = user.cuda() item_i = item_i.cuda() item_j = item_j.cuda() label = label.cuda() else: user = user.cpu() item_i = item_i.cpu() item_j = item_j.cpu() label = label.cpu() self.zero_grad() pred_i, pred_j = self.forward(user, item_i, item_j) if self.loss_type == 'BPR': loss = -(pred_i - pred_j).sigmoid().log().sum() elif self.loss_type == 'HL': loss = torch.clamp(1 - (pred_i - pred_j) * label, min=0).sum() elif self.loss_type == 'TL': loss = (pred_j - pred_i).sigmoid().mean() + pred_j.pow(2).sigmoid().mean() else: raise ValueError(f'Invalid loss type: {self.loss_type}') loss += self.reg_1 * (self.embed_item.weight.norm(p=1) + self.embed_user.weight.norm(p=1)) loss += self.reg_2 * (self.embed_item.weight.norm() + self.embed_user.weight.norm()) if torch.isnan(loss): raise ValueError(f'Loss=Nan or Infinity: current settings does not fit the recommender') loss.backward() optimizer.step() pbar.set_postfix(loss=loss.item()) current_loss += loss.item() self.eval() delta_loss = float(current_loss - last_loss) if (abs(delta_loss) < 1e-5) and self.early_stop: print('Satisfy early stop mechanism') break else: last_loss = current_loss def predict(self, u, i): pred_i, _ = self.forward(u, i, i) return pred_i.cpu()

daisy/model/pair/FMRecommender.py

import os from tqdm import tqdm import torch import torch.nn as nn import torch.optim as optim import torch.backends.cudnn as cudnn class PairFM(nn.Module): def __init__(self, user_num, item_num, factors=84, epochs=20, lr=0.001, reg_1=0., reg_2=0., loss_type='BPR', gpuid='0', early_stop=True): """ Pair-wise FM Recommender Class Parameters ---------- user_num : int, the number of users item_num : int, the number of items factors : int, the number of latent factor epochs : int, number of training epochs lr : float, learning rate reg_1 : float, first-order regularization term reg_2 : float, second-order regularization term loss_type : str, loss function type gpuid : str, GPU ID early_stop : bool, whether to activate early stop mechanism """ super(PairFM, self).__init__() os.environ['CUDA_VISIBLE_DEVICES'] = gpuid cudnn.benchmark = True self.epochs = epochs self.lr = lr self.reg_1 = reg_1 self.reg_2 = reg_2 self.embed_user = nn.Embedding(user_num, factors) self.embed_item = nn.Embedding(item_num, factors) self.u_bias = nn.Embedding(user_num, 1) self.i_bias = nn.Embedding(item_num, 1) self.bias_ = nn.Parameter(torch.tensor([0.0])) # init weight nn.init.normal_(self.embed_user.weight, std=0.01) nn.init.normal_(self.embed_item.weight, std=0.01) nn.init.constant_(self.u_bias.weight, 0.0) nn.init.constant_(self.i_bias.weight, 0.0) self.loss_type = loss_type self.early_stop = early_stop def forward(self, u, i, j): user = self.embed_user(u) item_i = self.embed_item(i) item_j = self.embed_item(j) # inner product part pred_i = (user * item_i).sum(dim=-1, keepdim=True) pred_j = (user * item_j).sum(dim=-1, keepdim=True) # add bias pred_i += self.u_bias(u) + self.i_bias(i) + self.bias_ pred_j += self.u_bias(u) + self.i_bias(j) + self.bias_ return pred_i.view(-1), pred_j.view(-1) def fit(self, train_loader): if torch.cuda.is_available(): self.cuda() else: self.cpu() optimizer = optim.SGD(self.parameters(), lr=self.lr) last_loss = 0. for epoch in range(1, self.epochs + 1): self.train() current_loss = 0. # set process bar display pbar = tqdm(train_loader) pbar.set_description(f'[Epoch {epoch:03d}]') for user, item_i, item_j, label in pbar: if torch.cuda.is_available(): user = user.cuda() item_i = item_i.cuda() item_j = item_j.cuda() label = label.cuda() else: user = user.cpu() item_i = item_i.cpu() item_j = item_j.cpu() label = label.cpu() self.zero_grad() pred_i, pred_j = self.forward(user, item_i, item_j) if self.loss_type == 'BPR': loss = -(pred_i - pred_j).sigmoid().log().sum() elif self.loss_type == 'HL': loss = torch.clamp(1 - (pred_i - pred_j) * label, min=0).sum() elif self.loss_type == 'TL': loss = (pred_j - pred_i).sigmoid().mean() + pred_j.pow(2).sigmoid().mean() else: raise ValueError(f'Invalid loss type: {self.loss_type}') loss += self.reg_1 * (self.embed_item.weight.norm(p=1) + self.embed_user.weight.norm(p=1)) loss += self.reg_2 * (self.embed_item.weight.norm() + self.embed_user.weight.norm()) if torch.isnan(loss): raise ValueError(f'Loss=Nan or Infinity: current settings does not fit the recommender') loss.backward() optimizer.step() pbar.set_postfix(loss=loss.item()) current_loss += loss.item() self.eval() delta_loss = float(current_loss - last_loss) if (abs(delta_loss) < 1e-5) and self.early_stop: print('Satisfy early stop mechanism') break else: last_loss = current_loss def predict(self, u, i): pred_i, _ = self.forward(u, i, i) return pred_i.cpu()

0.908193

0.314051

import os import unittest from armi.nuclearDataIO.cccc import isotxs from armi.utils import plotting from armi.reactor.tests import test_reactors from armi.tests import ISOAA_PATH, TEST_ROOT from armi.reactor.flags import Flags from armi.utils.directoryChangers import TemporaryDirectoryChanger class TestPlotting(unittest.TestCase): """ Test and demonstrate some plotting capabilities of ARMI. Notes ----- These tests don't do a great job of making sure the plot appears correctly, but they do check that the lines of code run, and that an image is produced, and demonstrate how they are meant to be called. """ # Change to False when you want to inspect the plots. Change back please. removeFiles = True @classmethod def setUpClass(cls): cls.o, cls.r = test_reactors.loadTestReactor() def test_plotDepthMap(self): # indirectly tests plot face map # set some params to visualize for i, b in enumerate(self.o.r.core.getBlocks()): b.p.percentBu = i / 100 fName = plotting.plotBlockDepthMap( self.r.core, param="percentBu", fName="depthMapPlot.png", depthIndex=2 ) self._checkExists(fName) def test_plotAssemblyTypes(self): plotting.plotAssemblyTypes( self.r.core.parent.blueprints, "coreAssemblyTypes1.png" ) self._checkExists("coreAssemblyTypes1.png") def test_plotBlockFlux(self): try: xslib = isotxs.readBinary(ISOAA_PATH) self.r.core.lib = xslib blockList = self.r.core.getBlocks() for _, b in enumerate(blockList): b.p.mgFlux = range(33) plotting.plotBlockFlux(self.r.core, fName="flux.png", bList=blockList) self.assertTrue(os.path.exists("flux.png")) plotting.plotBlockFlux( self.r.core, fName="peak.png", bList=blockList, peak=True ) self.assertTrue(os.path.exists("peak.png")) plotting.plotBlockFlux( self.r.core, fName="bList2.png", bList=blockList, bList2=blockList, ) self.assertTrue(os.path.exists("bList2.png")) # can't test adjoint at the moment, testBlock doesn't like to .getMgFlux(adjoint=True) finally: os.remove("flux.txt") # secondarily created during the call. os.remove("flux.png") # created during the call. os.remove("peak.txt") # csecondarily reated during the call. os.remove("peak.png") # created during the call. os.remove("bList2.txt") # secondarily created during the call. os.remove("bList2.png") # created during the call. def test_plotHexBlock(self): with TemporaryDirectoryChanger(): first_fuel_block = self.r.core.getFirstBlock(Flags.FUEL) first_fuel_block.autoCreateSpatialGrids() plotting.plotBlockDiagram(first_fuel_block, "blockDiagram23.svg", True) self.assertTrue(os.path.exists("blockDiagram23.svg")) def test_plotCartesianBlock(self): from armi import settings from armi.reactor import blueprints, reactors with TemporaryDirectoryChanger(): cs = settings.Settings( os.path.join(TEST_ROOT, "tutorials", "c5g7-settings.yaml") ) blueprint = blueprints.loadFromCs(cs) _ = reactors.factory(cs, blueprint) for name, bDesign in blueprint.blockDesigns.items(): b = bDesign.construct(cs, blueprint, 0, 1, 1, "AA", {}) plotting.plotBlockDiagram(b, "{}.svg".format(name), True) self.assertTrue(os.path.exists("uo2.svg")) self.assertTrue(os.path.exists("mox.svg")) def _checkExists(self, fName): self.assertTrue(os.path.exists(fName)) if self.removeFiles: os.remove(fName) if __name__ == "__main__": unittest.main()

armi/utils/tests/test_plotting.py

import os import unittest from armi.nuclearDataIO.cccc import isotxs from armi.utils import plotting from armi.reactor.tests import test_reactors from armi.tests import ISOAA_PATH, TEST_ROOT from armi.reactor.flags import Flags from armi.utils.directoryChangers import TemporaryDirectoryChanger class TestPlotting(unittest.TestCase): """ Test and demonstrate some plotting capabilities of ARMI. Notes ----- These tests don't do a great job of making sure the plot appears correctly, but they do check that the lines of code run, and that an image is produced, and demonstrate how they are meant to be called. """ # Change to False when you want to inspect the plots. Change back please. removeFiles = True @classmethod def setUpClass(cls): cls.o, cls.r = test_reactors.loadTestReactor() def test_plotDepthMap(self): # indirectly tests plot face map # set some params to visualize for i, b in enumerate(self.o.r.core.getBlocks()): b.p.percentBu = i / 100 fName = plotting.plotBlockDepthMap( self.r.core, param="percentBu", fName="depthMapPlot.png", depthIndex=2 ) self._checkExists(fName) def test_plotAssemblyTypes(self): plotting.plotAssemblyTypes( self.r.core.parent.blueprints, "coreAssemblyTypes1.png" ) self._checkExists("coreAssemblyTypes1.png") def test_plotBlockFlux(self): try: xslib = isotxs.readBinary(ISOAA_PATH) self.r.core.lib = xslib blockList = self.r.core.getBlocks() for _, b in enumerate(blockList): b.p.mgFlux = range(33) plotting.plotBlockFlux(self.r.core, fName="flux.png", bList=blockList) self.assertTrue(os.path.exists("flux.png")) plotting.plotBlockFlux( self.r.core, fName="peak.png", bList=blockList, peak=True ) self.assertTrue(os.path.exists("peak.png")) plotting.plotBlockFlux( self.r.core, fName="bList2.png", bList=blockList, bList2=blockList, ) self.assertTrue(os.path.exists("bList2.png")) # can't test adjoint at the moment, testBlock doesn't like to .getMgFlux(adjoint=True) finally: os.remove("flux.txt") # secondarily created during the call. os.remove("flux.png") # created during the call. os.remove("peak.txt") # csecondarily reated during the call. os.remove("peak.png") # created during the call. os.remove("bList2.txt") # secondarily created during the call. os.remove("bList2.png") # created during the call. def test_plotHexBlock(self): with TemporaryDirectoryChanger(): first_fuel_block = self.r.core.getFirstBlock(Flags.FUEL) first_fuel_block.autoCreateSpatialGrids() plotting.plotBlockDiagram(first_fuel_block, "blockDiagram23.svg", True) self.assertTrue(os.path.exists("blockDiagram23.svg")) def test_plotCartesianBlock(self): from armi import settings from armi.reactor import blueprints, reactors with TemporaryDirectoryChanger(): cs = settings.Settings( os.path.join(TEST_ROOT, "tutorials", "c5g7-settings.yaml") ) blueprint = blueprints.loadFromCs(cs) _ = reactors.factory(cs, blueprint) for name, bDesign in blueprint.blockDesigns.items(): b = bDesign.construct(cs, blueprint, 0, 1, 1, "AA", {}) plotting.plotBlockDiagram(b, "{}.svg".format(name), True) self.assertTrue(os.path.exists("uo2.svg")) self.assertTrue(os.path.exists("mox.svg")) def _checkExists(self, fName): self.assertTrue(os.path.exists(fName)) if self.removeFiles: os.remove(fName) if __name__ == "__main__": unittest.main()

0.626696

0.48621

import os import shutil import unittest import tempfile from telemetry.core import util from telemetry.internal.backends.chrome import crx_id class CrxIdUnittest(unittest.TestCase): CRX_ID_DIR = util.GetUnittestDataDir() PACKED_CRX = os.path.join(CRX_ID_DIR, 'jebgalgnebhfojomionfpkfelancnnkf.crx') PACKED_APP_ID = 'jebgalgnebhfojomionfpkfelancnnkf' PACKED_HASH_BYTES = \ '{0x94, 0x16, 0x0b, 0x6d, 0x41, 0x75, 0xe9, 0xec,' \ ' 0x8e, 0xd5, 0xfa, 0x54, 0xb0, 0xd2, 0xdd, 0xa5,' \ ' 0x6e, 0x05, 0x6b, 0xe8, 0x73, 0x47, 0xf6, 0xc4,' \ ' 0x11, 0x9f, 0xbc, 0xb3, 0x09, 0xb3, 0x5b, 0x40}' UNPACKED_APP_ID = 'cbcdidchbppangcjoddlpdjlenngjldk' UNPACKED_HASH_BYTES = \ '{0x21, 0x23, 0x83, 0x27, 0x1f, 0xf0, 0xd6, 0x29,' \ ' 0xe3, 0x3b, 0xf3, 0x9b, 0x4d, 0xd6, 0x9b, 0x3a,' \ ' 0xff, 0x7d, 0x6b, 0xc4, 0x78, 0x30, 0x47, 0xa6,' \ ' 0x23, 0x12, 0x72, 0x84, 0x9b, 0x9a, 0xf6, 0x3c}' def testPackedHashAppId(self): """ Test the output generated for a canned, packed CRX. """ self.assertEqual(crx_id.GetCRXAppID(self.PACKED_CRX), self.PACKED_APP_ID) self.assertEqual(crx_id.GetCRXHash(self.PACKED_CRX), self.PACKED_HASH_BYTES) def testUnpackedHashAppId(self): """ Test the output generated for a canned, unpacked extension. """ unpacked_test_manifest_path = os.path.join( self.CRX_ID_DIR, 'manifest_with_key.json') temp_unpacked_crx = tempfile.mkdtemp() shutil.copy2(unpacked_test_manifest_path, os.path.join(temp_unpacked_crx, 'manifest.json')) self.assertEqual(crx_id.GetCRXAppID(temp_unpacked_crx), self.UNPACKED_APP_ID) self.assertEqual(crx_id.GetCRXHash(temp_unpacked_crx), self.UNPACKED_HASH_BYTES) self.assertTrue(crx_id.HasPublicKey(temp_unpacked_crx)) shutil.rmtree(temp_unpacked_crx) def testFromFilePath(self): """ Test calculation of extension id from file paths. """ self.assertEqual(crx_id.GetCRXAppID('/tmp/temp_extension', from_file_path=True), 'ajbbicncdkdlchpjplgjaglppbcbmaji') def testFromWindowsPath(self): self.assertEqual(crx_id.GetCRXAppID(r'D:\Documents\chrome\test_extension', from_file_path=True, is_win_path=True), 'fegemedmbnhglnecjgbdhekaghkccplm') # Test drive letter normalization. k_win_path_id = 'aiinlcdagjihibappcdnnhcccdokjlaf' self.assertEqual(crx_id.GetCRXAppID(r'c:\temp_extension', from_file_path=True, is_win_path=True), k_win_path_id) self.assertEqual(crx_id.GetCRXAppID(r'C:\temp_extension', from_file_path=True, is_win_path=True), k_win_path_id)

telemetry/telemetry/internal/backends/chrome/crx_id_unittest.py

import os import shutil import unittest import tempfile from telemetry.core import util from telemetry.internal.backends.chrome import crx_id class CrxIdUnittest(unittest.TestCase): CRX_ID_DIR = util.GetUnittestDataDir() PACKED_CRX = os.path.join(CRX_ID_DIR, 'jebgalgnebhfojomionfpkfelancnnkf.crx') PACKED_APP_ID = 'jebgalgnebhfojomionfpkfelancnnkf' PACKED_HASH_BYTES = \ '{0x94, 0x16, 0x0b, 0x6d, 0x41, 0x75, 0xe9, 0xec,' \ ' 0x8e, 0xd5, 0xfa, 0x54, 0xb0, 0xd2, 0xdd, 0xa5,' \ ' 0x6e, 0x05, 0x6b, 0xe8, 0x73, 0x47, 0xf6, 0xc4,' \ ' 0x11, 0x9f, 0xbc, 0xb3, 0x09, 0xb3, 0x5b, 0x40}' UNPACKED_APP_ID = 'cbcdidchbppangcjoddlpdjlenngjldk' UNPACKED_HASH_BYTES = \ '{0x21, 0x23, 0x83, 0x27, 0x1f, 0xf0, 0xd6, 0x29,' \ ' 0xe3, 0x3b, 0xf3, 0x9b, 0x4d, 0xd6, 0x9b, 0x3a,' \ ' 0xff, 0x7d, 0x6b, 0xc4, 0x78, 0x30, 0x47, 0xa6,' \ ' 0x23, 0x12, 0x72, 0x84, 0x9b, 0x9a, 0xf6, 0x3c}' def testPackedHashAppId(self): """ Test the output generated for a canned, packed CRX. """ self.assertEqual(crx_id.GetCRXAppID(self.PACKED_CRX), self.PACKED_APP_ID) self.assertEqual(crx_id.GetCRXHash(self.PACKED_CRX), self.PACKED_HASH_BYTES) def testUnpackedHashAppId(self): """ Test the output generated for a canned, unpacked extension. """ unpacked_test_manifest_path = os.path.join( self.CRX_ID_DIR, 'manifest_with_key.json') temp_unpacked_crx = tempfile.mkdtemp() shutil.copy2(unpacked_test_manifest_path, os.path.join(temp_unpacked_crx, 'manifest.json')) self.assertEqual(crx_id.GetCRXAppID(temp_unpacked_crx), self.UNPACKED_APP_ID) self.assertEqual(crx_id.GetCRXHash(temp_unpacked_crx), self.UNPACKED_HASH_BYTES) self.assertTrue(crx_id.HasPublicKey(temp_unpacked_crx)) shutil.rmtree(temp_unpacked_crx) def testFromFilePath(self): """ Test calculation of extension id from file paths. """ self.assertEqual(crx_id.GetCRXAppID('/tmp/temp_extension', from_file_path=True), 'ajbbicncdkdlchpjplgjaglppbcbmaji') def testFromWindowsPath(self): self.assertEqual(crx_id.GetCRXAppID(r'D:\Documents\chrome\test_extension', from_file_path=True, is_win_path=True), 'fegemedmbnhglnecjgbdhekaghkccplm') # Test drive letter normalization. k_win_path_id = 'aiinlcdagjihibappcdnnhcccdokjlaf' self.assertEqual(crx_id.GetCRXAppID(r'c:\temp_extension', from_file_path=True, is_win_path=True), k_win_path_id) self.assertEqual(crx_id.GetCRXAppID(r'C:\temp_extension', from_file_path=True, is_win_path=True), k_win_path_id)

0.255715

0.243238

import base64 import json import random from pathlib import Path import imagehash import numpy as np from raymon.profiling.extractors import SimpleExtractor class FixedSubpatchSimilarity(SimpleExtractor): _attrs = ["patch", "refs"] _patch_keys = ["x0", "y0", "<KEY>"] def __init__(self, patch, refs=None, nrefs=10, idfr=None): """[summary] Args: patch ([int], optional): [description]. The x0, y0, x1, y1 of the patch to look at. refs ([np.array], optional): [description]. References of what the patch should look like """ self._nrefs = None self._patch = None self._refs = None self._idfr = None self.patch = patch self.nrefs = nrefs self.refs = refs self.idfr = idfr """ PROPERTIES """ @property def patch(self): return self._patch @patch.setter def patch(self, value): if isinstance(value, dict): self._patch = {key: value[key] for key in self._patch_keys} elif isinstance(value, list) and len(value) == 4: self._patch = {key: value[i] for i, key in enumerate(self._patch_keys)} else: raise ValueError(f"patch must be a dict or list, not {type(value)}") # make sure the correct keys are there print(f"Patch set to: {self._patch} for {self}") @property def refs(self): return self._refs @refs.setter def refs(self, value): if value is None: self._refs = None return if not (isinstance(value, list) and len(value) == self.nrefs): raise ValueError(f"refs should be a list of length {self.nrefs}") parsed_refs = [] for ref in value: if isinstance(ref, imagehash.ImageHash): parsed_refs.append(ref) elif isinstance(ref, str): parsed_refs.append(imagehash.hex_to_hash(ref)) else: raise ValueError(f"refs should either be str or ImageHash, not {type(ref)}") self._refs = parsed_refs @property def nrefs(self): return self._nrefs @nrefs.setter def nrefs(self, value): value = int(value) if not (isinstance(value, int) and value > 0): self._nrefs = None raise ValueError(f"nrefs should be a an int > 0") self._nrefs = value @property def idfr(self): return self._idfr @idfr.setter def idfr(self, value): self._idfr = str(value) """Feature extractor""" def extract(self, data): phash = self._extract(data) dist = min(abs(ref - phash) for ref in self.refs) return dist def _extract(self, data): patch = [self.patch["x0"], self.patch["y0"], self.patch["x1"], self.patch["y1"]] crop = data.crop(box=patch) phash = imagehash.phash(crop) return phash """Serializable interface """ def to_jcr(self): data = { "patch": self.patch, "refs": [str(ref) for ref in self.refs] if self.refs is not None else None, "nrefs": self.nrefs, } state = {"class": self.class2str(), "state": data} return state @classmethod def from_jcr(cls, jcr): patch, refs, nrefs, idfr = None, None, None, None if "patch" in jcr: patch = jcr["patch"] if "nrefs" in jcr: nrefs = jcr["nrefs"] if "refs" in jcr: refs = jcr["refs"] if "idfr" in jcr: refs = jcr["idfr"] return cls(patch=patch, refs=refs, nrefs=nrefs, idfr=idfr) """Buildable interface""" def build(self, data): refs = [] chosen_samples = random.choices(data, k=self.nrefs) for sample in chosen_samples: ref = self._extract(sample) refs.append(ref) self.refs = refs def is_built(self): return self.refs is not None and len(self.refs) == self.nrefs def __str__(self): return f"{self.class2str()} ({self.idfr})"

raymon/profiling/extractors/vision/similarity.py

import base64 import json import random from pathlib import Path import imagehash import numpy as np from raymon.profiling.extractors import SimpleExtractor class FixedSubpatchSimilarity(SimpleExtractor): _attrs = ["patch", "refs"] _patch_keys = ["x0", "y0", "<KEY>"] def __init__(self, patch, refs=None, nrefs=10, idfr=None): """[summary] Args: patch ([int], optional): [description]. The x0, y0, x1, y1 of the patch to look at. refs ([np.array], optional): [description]. References of what the patch should look like """ self._nrefs = None self._patch = None self._refs = None self._idfr = None self.patch = patch self.nrefs = nrefs self.refs = refs self.idfr = idfr """ PROPERTIES """ @property def patch(self): return self._patch @patch.setter def patch(self, value): if isinstance(value, dict): self._patch = {key: value[key] for key in self._patch_keys} elif isinstance(value, list) and len(value) == 4: self._patch = {key: value[i] for i, key in enumerate(self._patch_keys)} else: raise ValueError(f"patch must be a dict or list, not {type(value)}") # make sure the correct keys are there print(f"Patch set to: {self._patch} for {self}") @property def refs(self): return self._refs @refs.setter def refs(self, value): if value is None: self._refs = None return if not (isinstance(value, list) and len(value) == self.nrefs): raise ValueError(f"refs should be a list of length {self.nrefs}") parsed_refs = [] for ref in value: if isinstance(ref, imagehash.ImageHash): parsed_refs.append(ref) elif isinstance(ref, str): parsed_refs.append(imagehash.hex_to_hash(ref)) else: raise ValueError(f"refs should either be str or ImageHash, not {type(ref)}") self._refs = parsed_refs @property def nrefs(self): return self._nrefs @nrefs.setter def nrefs(self, value): value = int(value) if not (isinstance(value, int) and value > 0): self._nrefs = None raise ValueError(f"nrefs should be a an int > 0") self._nrefs = value @property def idfr(self): return self._idfr @idfr.setter def idfr(self, value): self._idfr = str(value) """Feature extractor""" def extract(self, data): phash = self._extract(data) dist = min(abs(ref - phash) for ref in self.refs) return dist def _extract(self, data): patch = [self.patch["x0"], self.patch["y0"], self.patch["x1"], self.patch["y1"]] crop = data.crop(box=patch) phash = imagehash.phash(crop) return phash """Serializable interface """ def to_jcr(self): data = { "patch": self.patch, "refs": [str(ref) for ref in self.refs] if self.refs is not None else None, "nrefs": self.nrefs, } state = {"class": self.class2str(), "state": data} return state @classmethod def from_jcr(cls, jcr): patch, refs, nrefs, idfr = None, None, None, None if "patch" in jcr: patch = jcr["patch"] if "nrefs" in jcr: nrefs = jcr["nrefs"] if "refs" in jcr: refs = jcr["refs"] if "idfr" in jcr: refs = jcr["idfr"] return cls(patch=patch, refs=refs, nrefs=nrefs, idfr=idfr) """Buildable interface""" def build(self, data): refs = [] chosen_samples = random.choices(data, k=self.nrefs) for sample in chosen_samples: ref = self._extract(sample) refs.append(ref) self.refs = refs def is_built(self): return self.refs is not None and len(self.refs) == self.nrefs def __str__(self): return f"{self.class2str()} ({self.idfr})"

0.737158

0.224491

from dataclasses import dataclass from datetime import date, timedelta, datetime from typing import Iterable, Union, List import pandas as pd import numpy as np @dataclass class DateRange: start_date: date end_date: date def __init__(self, start_date, end_date): self.start_date = safe_convert_to_date(start_date) self.end_date = safe_convert_to_date(end_date) def intersection(self, other): new_start = max(self.start_date, other.start_date) new_end = min(self.end_date, other.end_date) if new_start < new_end: return DateRange(new_start, new_end) def date_range(self) -> np.array: return pd.date_range(self.start_date, self.end_date, freq="D").date def total_days(self) -> int: return np.timedelta64( self.end_date - self.start_date).astype( 'timedelta64[D]').astype(np.float32) def split_into_years(self) -> List: duration = self.end_date - self.start_date num_years = duration / timedelta(days=365) if num_years <= 1: return [self] else: end_date = self.end_date new_date_ranges = [] for year_back in range(np.ceil(num_years).astype(int)): start_date = end_date - timedelta(days=365) if start_date < self.start_date: start_date = self.start_date new_date_ranges.append(DateRange(start_date, end_date)) end_date = start_date return new_date_ranges def get_date_range_list(dates: Iterable[date]) -> List[DateRange]: if not dates: return [] dates = np.array(dates) dates = np.sort(dates) dates_diff = np.diff(dates) location_of_gaps = np.where(dates_diff > timedelta(days=1))[0] index_of_last_date = len(dates) - 1 location_of_gaps = list(location_of_gaps) location_of_gaps.append(index_of_last_date) start_i = 0 date_range_list = [] for end_i in location_of_gaps: date_range = DateRange( start_date=dates[start_i], end_date=dates[end_i]) date_range_list.append(date_range) start_i = end_i + 1 return date_range_list def safe_convert_to_date(dt: Union[datetime, date, str]) -> date: if isinstance(dt, str): dt = pd.Timestamp(dt) if isinstance(dt, datetime): return dt.date() if isinstance(dt, date): return dt def merge_date_ranges_to_years( date_ranges: Iterable[DateRange]) -> List[DateRange]: """ Args: date_ranges: List of DateRanges, in ascending chronological order. Returns: List of DateRanges, each representing a year, in descending order. """ if not date_ranges: return [] # Split multi-year date ranges date_ranges_split = [] for date_range in date_ranges[::-1]: date_ranges_split.extend(date_range.split_into_years()) years_to_download = [] for date_range in date_ranges_split: if years_to_download: intersection = date_range.intersection(years_to_download[-1]) if intersection == date_range: # date_range falls within the last year to retrieve, # so we can ignore this date_range continue elif intersection is None: # No overlap date_to = date_range.end_date else: # Overlap date_to = intersection.start_date else: date_to = date_range.end_date date_from = date_to - timedelta(days=365) years_to_download.append(DateRange(date_from, date_to)) return years_to_download

pvoutput/daterange.py

from dataclasses import dataclass from datetime import date, timedelta, datetime from typing import Iterable, Union, List import pandas as pd import numpy as np @dataclass class DateRange: start_date: date end_date: date def __init__(self, start_date, end_date): self.start_date = safe_convert_to_date(start_date) self.end_date = safe_convert_to_date(end_date) def intersection(self, other): new_start = max(self.start_date, other.start_date) new_end = min(self.end_date, other.end_date) if new_start < new_end: return DateRange(new_start, new_end) def date_range(self) -> np.array: return pd.date_range(self.start_date, self.end_date, freq="D").date def total_days(self) -> int: return np.timedelta64( self.end_date - self.start_date).astype( 'timedelta64[D]').astype(np.float32) def split_into_years(self) -> List: duration = self.end_date - self.start_date num_years = duration / timedelta(days=365) if num_years <= 1: return [self] else: end_date = self.end_date new_date_ranges = [] for year_back in range(np.ceil(num_years).astype(int)): start_date = end_date - timedelta(days=365) if start_date < self.start_date: start_date = self.start_date new_date_ranges.append(DateRange(start_date, end_date)) end_date = start_date return new_date_ranges def get_date_range_list(dates: Iterable[date]) -> List[DateRange]: if not dates: return [] dates = np.array(dates) dates = np.sort(dates) dates_diff = np.diff(dates) location_of_gaps = np.where(dates_diff > timedelta(days=1))[0] index_of_last_date = len(dates) - 1 location_of_gaps = list(location_of_gaps) location_of_gaps.append(index_of_last_date) start_i = 0 date_range_list = [] for end_i in location_of_gaps: date_range = DateRange( start_date=dates[start_i], end_date=dates[end_i]) date_range_list.append(date_range) start_i = end_i + 1 return date_range_list def safe_convert_to_date(dt: Union[datetime, date, str]) -> date: if isinstance(dt, str): dt = pd.Timestamp(dt) if isinstance(dt, datetime): return dt.date() if isinstance(dt, date): return dt def merge_date_ranges_to_years( date_ranges: Iterable[DateRange]) -> List[DateRange]: """ Args: date_ranges: List of DateRanges, in ascending chronological order. Returns: List of DateRanges, each representing a year, in descending order. """ if not date_ranges: return [] # Split multi-year date ranges date_ranges_split = [] for date_range in date_ranges[::-1]: date_ranges_split.extend(date_range.split_into_years()) years_to_download = [] for date_range in date_ranges_split: if years_to_download: intersection = date_range.intersection(years_to_download[-1]) if intersection == date_range: # date_range falls within the last year to retrieve, # so we can ignore this date_range continue elif intersection is None: # No overlap date_to = date_range.end_date else: # Overlap date_to = intersection.start_date else: date_to = date_range.end_date date_from = date_to - timedelta(days=365) years_to_download.append(DateRange(date_from, date_to)) return years_to_download

0.844537

0.314656

import os import mock from oslo_serialization import jsonutils from watcher.common import context from watcher.common import exception from watcher.common import nova_helper from watcher.common import service as watcher_service from watcher.decision_engine.model import element from watcher.decision_engine.model.notification import nova as novanotification from watcher.tests import base as base_test from watcher.tests.decision_engine.model import faker_cluster_state from watcher.tests.decision_engine.model.notification import fake_managers class NotificationTestCase(base_test.TestCase): @staticmethod def load_message(filename): cwd = os.path.abspath(os.path.dirname(__file__)) data_folder = os.path.join(cwd, "data") with open(os.path.join(data_folder, filename), 'rb') as json_file: json_data = jsonutils.load(json_file) return json_data class TestReceiveNovaNotifications(NotificationTestCase): FAKE_METADATA = {'message_id': None, 'timestamp': None} FAKE_NOTIFICATIONS = { 'instance.create.end': 'instance-create-end.json', 'instance.lock': 'instance-lock.json', 'instance.unlock': 'instance-unlock.json', 'instance.pause.end': 'instance-pause-end.json', 'instance.power_off.end': 'instance-power_off-end.json', 'instance.power_on.end': 'instance-power_on-end.json', 'instance.resize_confirm.end': 'instance-resize_confirm-end.json', 'instance.restore.end': 'instance-restore-end.json', 'instance.resume.end': 'instance-resume-end.json', 'instance.shelve.end': 'instance-shelve-end.json', 'instance.shutdown.end': 'instance-shutdown-end.json', 'instance.suspend.end': 'instance-suspend-end.json', 'instance.unpause.end': 'instance-unpause-end.json', 'instance.unrescue.end': 'instance-unrescue-end.json', 'instance.unshelve.end': 'instance-unshelve-end.json', 'instance.rebuild.end': 'instance-rebuild-end.json', 'instance.rescue.end': 'instance-rescue-end.json', 'instance.update': 'instance-update.json', 'instance.live_migration_force_complete.end': 'instance-live_migration_force_complete-end.json', 'instance.live_migration_post_dest.end': 'instance-live_migration_post_dest-end.json', 'instance.delete.end': 'instance-delete-end.json', 'instance.soft_delete.end': 'instance-soft_delete-end.json', 'service.create': 'service-create.json', 'service.delete': 'service-delete.json', 'service.update': 'service-update.json', } def setUp(self): super(TestReceiveNovaNotifications, self).setUp() p_from_dict = mock.patch.object(context.RequestContext, 'from_dict') m_from_dict = p_from_dict.start() m_from_dict.return_value = self.context self.addCleanup(p_from_dict.stop) p_heartbeat = mock.patch.object( watcher_service.ServiceHeartbeat, "send_beat") self.m_heartbeat = p_heartbeat.start() self.addCleanup(p_heartbeat.stop) @mock.patch.object(novanotification.VersionedNotification, 'info') def test_receive_nova_notifications(self, m_info): de_service = watcher_service.Service(fake_managers.FakeManager) n_dicts = novanotification.VersionedNotification.notification_mapping for n_type in n_dicts.keys(): n_json = self.FAKE_NOTIFICATIONS[n_type] message = self.load_message(n_json) expected_message = message['payload'] publisher_id = message['publisher_id'] incoming = mock.Mock(ctxt=self.context.to_dict(), message=message) de_service.notification_handler.dispatcher.dispatch(incoming) m_info.assert_called_with( self.context, publisher_id, n_type, expected_message, self.FAKE_METADATA) class TestNovaNotifications(NotificationTestCase): FAKE_METADATA = {'message_id': None, 'timestamp': None} def setUp(self): super(TestNovaNotifications, self).setUp() # fake cluster self.fake_cdmc = faker_cluster_state.FakerModelCollector() def test_nova_service_update(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) node0_uuid = 'Node_0' node0 = compute_model.get_node_by_uuid(node0_uuid) message = self.load_message('scenario3_service-update-disabled.json') self.assertEqual('hostname_0', node0.hostname) self.assertEqual(element.ServiceState.ONLINE.value, node0.state) self.assertEqual(element.ServiceState.ENABLED.value, node0.status) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual('Node_0', node0.hostname) self.assertEqual(element.ServiceState.OFFLINE.value, node0.state) self.assertEqual(element.ServiceState.DISABLED.value, node0.status) message = self.load_message('scenario3_service-update-enabled.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual('Node_0', node0.hostname) self.assertEqual(element.ServiceState.ONLINE.value, node0.state) self.assertEqual(element.ServiceState.ENABLED.value, node0.status) @mock.patch.object(nova_helper, "NovaHelper") def test_nova_service_create(self, m_nova_helper_cls): m_get_compute_node_by_hostname = mock.Mock( side_effect=lambda uuid: mock.Mock( name='m_get_compute_node_by_hostname', id=3, hypervisor_hostname="host2", state='up', status='enabled', uuid=uuid, memory_mb=7777, vcpus=42, free_disk_gb=974, local_gb=1337)) m_nova_helper_cls.return_value = mock.Mock( get_compute_node_by_hostname=m_get_compute_node_by_hostname, name='m_nova_helper') compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) new_node_uuid = 'host2' self.assertRaises( exception.ComputeNodeNotFound, compute_model.get_node_by_uuid, new_node_uuid) message = self.load_message('service-create.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) new_node = compute_model.get_node_by_uuid(new_node_uuid) self.assertEqual('host2', new_node.hostname) self.assertEqual(element.ServiceState.ONLINE.value, new_node.state) self.assertEqual(element.ServiceState.ENABLED.value, new_node.status) def test_nova_service_delete(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) node0_uuid = 'Node_0' # Before self.assertTrue(compute_model.get_node_by_uuid(node0_uuid)) message = self.load_message('service-delete.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) # After self.assertRaises( exception.ComputeNodeNotFound, compute_model.get_node_by_uuid, node0_uuid) def test_nova_instance_update(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-update.json') self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.PAUSED.value, instance0.state) @mock.patch.object(nova_helper, "NovaHelper") def test_nova_instance_update_notfound_still_creates( self, m_nova_helper_cls): m_get_compute_node_by_hostname = mock.Mock( side_effect=lambda uuid: mock.Mock( name='m_get_compute_node_by_hostname', id=3, hypervisor_hostname="Node_2", state='up', status='enabled', uuid=uuid, memory_mb=7777, vcpus=42, free_disk_gb=974, local_gb=1337)) m_nova_helper_cls.return_value = mock.Mock( get_compute_node_by_hostname=m_get_compute_node_by_hostname, name='m_nova_helper') compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '9966d6bd-a45c-4e1c-9d57-3054899a3ec7' message = self.load_message('scenario3_notfound_instance-update.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) instance0 = compute_model.get_instance_by_uuid(instance0_uuid) self.assertEqual(element.InstanceState.PAUSED.value, instance0.state) self.assertEqual(1, instance0.vcpus) self.assertEqual(1, instance0.disk_capacity) self.assertEqual(512, instance0.memory) m_get_compute_node_by_hostname.assert_called_once_with('Node_2') node_2 = compute_model.get_node_by_uuid('Node_2') self.assertEqual(7777, node_2.memory) self.assertEqual(42, node_2.vcpus) self.assertEqual(974, node_2.disk) self.assertEqual(1337, node_2.disk_capacity) @mock.patch.object(nova_helper, "NovaHelper") def test_instance_update_node_notfound_set_unmapped( self, m_nova_helper_cls): m_get_compute_node_by_hostname = mock.Mock( side_effect=exception.ComputeNodeNotFound(name="TEST")) m_nova_helper_cls.return_value = mock.Mock( get_compute_node_by_hostname=m_get_compute_node_by_hostname, name='m_nova_helper') compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '9966d6bd-a45c-4e1c-9d57-3054899a3ec7' message = self.load_message( 'scenario3_notfound_instance-update.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) instance0 = compute_model.get_instance_by_uuid(instance0_uuid) self.assertEqual(element.InstanceState.PAUSED.value, instance0.state) self.assertEqual(1, instance0.vcpus) self.assertEqual(1, instance0.disk) self.assertEqual(1, instance0.disk_capacity) self.assertEqual(512, instance0.memory) m_get_compute_node_by_hostname.assert_any_call('Node_2') self.assertRaises( exception.ComputeNodeNotFound, compute_model.get_node_by_uuid, 'Node_2') def test_nova_instance_create(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = 'c03c0bf9-f46e-4e4f-93f1-817568567ee2' self.assertRaises( exception.InstanceNotFound, compute_model.get_instance_by_uuid, instance0_uuid) message = self.load_message('instance-create-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) instance0 = compute_model.get_instance_by_uuid(instance0_uuid) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) self.assertEqual(1, instance0.vcpus) self.assertEqual(1, instance0.disk_capacity) self.assertEqual(512, instance0.memory) def test_nova_instance_delete_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' # Before self.assertTrue(compute_model.get_instance_by_uuid(instance0_uuid)) message = self.load_message('instance-delete-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) # After self.assertRaises( exception.InstanceNotFound, compute_model.get_instance_by_uuid, instance0_uuid) def test_nova_instance_soft_delete_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' # Before self.assertTrue(compute_model.get_instance_by_uuid(instance0_uuid)) message = self.load_message('instance-soft_delete-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) # After self.assertRaises( exception.InstanceNotFound, compute_model.get_instance_by_uuid, instance0_uuid) def test_live_migrated_force_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_0', node.uuid) message = self.load_message( 'instance-live_migration_force_complete-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_1', node.uuid) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_live_migrated_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_0', node.uuid) message = self.load_message( 'instance-live_migration_post_dest-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_1', node.uuid) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_nova_instance_lock(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-lock.json') self.assertFalse(instance0.locked) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertTrue(instance0.locked) message = self.load_message('instance-unlock.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertFalse(instance0.locked) def test_nova_instance_pause(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-pause-end.json') self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.PAUSED.value, instance0.state) message = self.load_message('instance-unpause-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_nova_instance_power_on_off(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-power_off-end.json') self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.STOPPED.value, instance0.state) message = self.load_message('instance-power_on-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_instance_rebuild_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_0', node.uuid) message = self.load_message('instance-rebuild-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_1', node.uuid) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_nova_instance_rescue(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-rescue-end.json') self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.RESCUED.value, instance0.state) message = self.load_message('instance-unrescue-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_instance_resize_confirm_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_0', node.uuid) message = self.load_message( 'instance-resize_confirm-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_1', node.uuid) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_nova_instance_restore_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-restore-end.json') instance0.state = element.InstanceState.ERROR.value self.assertEqual(element.InstanceState.ERROR.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_nova_instance_resume_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-resume-end.json') instance0.state = element.InstanceState.ERROR.value self.assertEqual(element.InstanceState.ERROR.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_nova_instance_shelve(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-shelve-end.json') self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.SHELVED.value, instance0.state) message = self.load_message('instance-unshelve-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_nova_instance_shutdown_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-shutdown-end.json') self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.STOPPED.value, instance0.state) def test_nova_instance_suspend_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-suspend-end.json') self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual( element.InstanceState.SUSPENDED.value, instance0.state)

python-watcher-2.0.0/watcher/tests/decision_engine/model/notification/test_nova_notifications.py

import os import mock from oslo_serialization import jsonutils from watcher.common import context from watcher.common import exception from watcher.common import nova_helper from watcher.common import service as watcher_service from watcher.decision_engine.model import element from watcher.decision_engine.model.notification import nova as novanotification from watcher.tests import base as base_test from watcher.tests.decision_engine.model import faker_cluster_state from watcher.tests.decision_engine.model.notification import fake_managers class NotificationTestCase(base_test.TestCase): @staticmethod def load_message(filename): cwd = os.path.abspath(os.path.dirname(__file__)) data_folder = os.path.join(cwd, "data") with open(os.path.join(data_folder, filename), 'rb') as json_file: json_data = jsonutils.load(json_file) return json_data class TestReceiveNovaNotifications(NotificationTestCase): FAKE_METADATA = {'message_id': None, 'timestamp': None} FAKE_NOTIFICATIONS = { 'instance.create.end': 'instance-create-end.json', 'instance.lock': 'instance-lock.json', 'instance.unlock': 'instance-unlock.json', 'instance.pause.end': 'instance-pause-end.json', 'instance.power_off.end': 'instance-power_off-end.json', 'instance.power_on.end': 'instance-power_on-end.json', 'instance.resize_confirm.end': 'instance-resize_confirm-end.json', 'instance.restore.end': 'instance-restore-end.json', 'instance.resume.end': 'instance-resume-end.json', 'instance.shelve.end': 'instance-shelve-end.json', 'instance.shutdown.end': 'instance-shutdown-end.json', 'instance.suspend.end': 'instance-suspend-end.json', 'instance.unpause.end': 'instance-unpause-end.json', 'instance.unrescue.end': 'instance-unrescue-end.json', 'instance.unshelve.end': 'instance-unshelve-end.json', 'instance.rebuild.end': 'instance-rebuild-end.json', 'instance.rescue.end': 'instance-rescue-end.json', 'instance.update': 'instance-update.json', 'instance.live_migration_force_complete.end': 'instance-live_migration_force_complete-end.json', 'instance.live_migration_post_dest.end': 'instance-live_migration_post_dest-end.json', 'instance.delete.end': 'instance-delete-end.json', 'instance.soft_delete.end': 'instance-soft_delete-end.json', 'service.create': 'service-create.json', 'service.delete': 'service-delete.json', 'service.update': 'service-update.json', } def setUp(self): super(TestReceiveNovaNotifications, self).setUp() p_from_dict = mock.patch.object(context.RequestContext, 'from_dict') m_from_dict = p_from_dict.start() m_from_dict.return_value = self.context self.addCleanup(p_from_dict.stop) p_heartbeat = mock.patch.object( watcher_service.ServiceHeartbeat, "send_beat") self.m_heartbeat = p_heartbeat.start() self.addCleanup(p_heartbeat.stop) @mock.patch.object(novanotification.VersionedNotification, 'info') def test_receive_nova_notifications(self, m_info): de_service = watcher_service.Service(fake_managers.FakeManager) n_dicts = novanotification.VersionedNotification.notification_mapping for n_type in n_dicts.keys(): n_json = self.FAKE_NOTIFICATIONS[n_type] message = self.load_message(n_json) expected_message = message['payload'] publisher_id = message['publisher_id'] incoming = mock.Mock(ctxt=self.context.to_dict(), message=message) de_service.notification_handler.dispatcher.dispatch(incoming) m_info.assert_called_with( self.context, publisher_id, n_type, expected_message, self.FAKE_METADATA) class TestNovaNotifications(NotificationTestCase): FAKE_METADATA = {'message_id': None, 'timestamp': None} def setUp(self): super(TestNovaNotifications, self).setUp() # fake cluster self.fake_cdmc = faker_cluster_state.FakerModelCollector() def test_nova_service_update(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) node0_uuid = 'Node_0' node0 = compute_model.get_node_by_uuid(node0_uuid) message = self.load_message('scenario3_service-update-disabled.json') self.assertEqual('hostname_0', node0.hostname) self.assertEqual(element.ServiceState.ONLINE.value, node0.state) self.assertEqual(element.ServiceState.ENABLED.value, node0.status) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual('Node_0', node0.hostname) self.assertEqual(element.ServiceState.OFFLINE.value, node0.state) self.assertEqual(element.ServiceState.DISABLED.value, node0.status) message = self.load_message('scenario3_service-update-enabled.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual('Node_0', node0.hostname) self.assertEqual(element.ServiceState.ONLINE.value, node0.state) self.assertEqual(element.ServiceState.ENABLED.value, node0.status) @mock.patch.object(nova_helper, "NovaHelper") def test_nova_service_create(self, m_nova_helper_cls): m_get_compute_node_by_hostname = mock.Mock( side_effect=lambda uuid: mock.Mock( name='m_get_compute_node_by_hostname', id=3, hypervisor_hostname="host2", state='up', status='enabled', uuid=uuid, memory_mb=7777, vcpus=42, free_disk_gb=974, local_gb=1337)) m_nova_helper_cls.return_value = mock.Mock( get_compute_node_by_hostname=m_get_compute_node_by_hostname, name='m_nova_helper') compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) new_node_uuid = 'host2' self.assertRaises( exception.ComputeNodeNotFound, compute_model.get_node_by_uuid, new_node_uuid) message = self.load_message('service-create.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) new_node = compute_model.get_node_by_uuid(new_node_uuid) self.assertEqual('host2', new_node.hostname) self.assertEqual(element.ServiceState.ONLINE.value, new_node.state) self.assertEqual(element.ServiceState.ENABLED.value, new_node.status) def test_nova_service_delete(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) node0_uuid = 'Node_0' # Before self.assertTrue(compute_model.get_node_by_uuid(node0_uuid)) message = self.load_message('service-delete.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) # After self.assertRaises( exception.ComputeNodeNotFound, compute_model.get_node_by_uuid, node0_uuid) def test_nova_instance_update(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-update.json') self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.PAUSED.value, instance0.state) @mock.patch.object(nova_helper, "NovaHelper") def test_nova_instance_update_notfound_still_creates( self, m_nova_helper_cls): m_get_compute_node_by_hostname = mock.Mock( side_effect=lambda uuid: mock.Mock( name='m_get_compute_node_by_hostname', id=3, hypervisor_hostname="Node_2", state='up', status='enabled', uuid=uuid, memory_mb=7777, vcpus=42, free_disk_gb=974, local_gb=1337)) m_nova_helper_cls.return_value = mock.Mock( get_compute_node_by_hostname=m_get_compute_node_by_hostname, name='m_nova_helper') compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '9966d6bd-a45c-4e1c-9d57-3054899a3ec7' message = self.load_message('scenario3_notfound_instance-update.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) instance0 = compute_model.get_instance_by_uuid(instance0_uuid) self.assertEqual(element.InstanceState.PAUSED.value, instance0.state) self.assertEqual(1, instance0.vcpus) self.assertEqual(1, instance0.disk_capacity) self.assertEqual(512, instance0.memory) m_get_compute_node_by_hostname.assert_called_once_with('Node_2') node_2 = compute_model.get_node_by_uuid('Node_2') self.assertEqual(7777, node_2.memory) self.assertEqual(42, node_2.vcpus) self.assertEqual(974, node_2.disk) self.assertEqual(1337, node_2.disk_capacity) @mock.patch.object(nova_helper, "NovaHelper") def test_instance_update_node_notfound_set_unmapped( self, m_nova_helper_cls): m_get_compute_node_by_hostname = mock.Mock( side_effect=exception.ComputeNodeNotFound(name="TEST")) m_nova_helper_cls.return_value = mock.Mock( get_compute_node_by_hostname=m_get_compute_node_by_hostname, name='m_nova_helper') compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '9966d6bd-a45c-4e1c-9d57-3054899a3ec7' message = self.load_message( 'scenario3_notfound_instance-update.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) instance0 = compute_model.get_instance_by_uuid(instance0_uuid) self.assertEqual(element.InstanceState.PAUSED.value, instance0.state) self.assertEqual(1, instance0.vcpus) self.assertEqual(1, instance0.disk) self.assertEqual(1, instance0.disk_capacity) self.assertEqual(512, instance0.memory) m_get_compute_node_by_hostname.assert_any_call('Node_2') self.assertRaises( exception.ComputeNodeNotFound, compute_model.get_node_by_uuid, 'Node_2') def test_nova_instance_create(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = 'c03c0bf9-f46e-4e4f-93f1-817568567ee2' self.assertRaises( exception.InstanceNotFound, compute_model.get_instance_by_uuid, instance0_uuid) message = self.load_message('instance-create-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) instance0 = compute_model.get_instance_by_uuid(instance0_uuid) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) self.assertEqual(1, instance0.vcpus) self.assertEqual(1, instance0.disk_capacity) self.assertEqual(512, instance0.memory) def test_nova_instance_delete_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' # Before self.assertTrue(compute_model.get_instance_by_uuid(instance0_uuid)) message = self.load_message('instance-delete-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) # After self.assertRaises( exception.InstanceNotFound, compute_model.get_instance_by_uuid, instance0_uuid) def test_nova_instance_soft_delete_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' # Before self.assertTrue(compute_model.get_instance_by_uuid(instance0_uuid)) message = self.load_message('instance-soft_delete-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) # After self.assertRaises( exception.InstanceNotFound, compute_model.get_instance_by_uuid, instance0_uuid) def test_live_migrated_force_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_0', node.uuid) message = self.load_message( 'instance-live_migration_force_complete-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_1', node.uuid) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_live_migrated_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_0', node.uuid) message = self.load_message( 'instance-live_migration_post_dest-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_1', node.uuid) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_nova_instance_lock(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-lock.json') self.assertFalse(instance0.locked) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertTrue(instance0.locked) message = self.load_message('instance-unlock.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertFalse(instance0.locked) def test_nova_instance_pause(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-pause-end.json') self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.PAUSED.value, instance0.state) message = self.load_message('instance-unpause-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_nova_instance_power_on_off(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-power_off-end.json') self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.STOPPED.value, instance0.state) message = self.load_message('instance-power_on-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_instance_rebuild_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_0', node.uuid) message = self.load_message('instance-rebuild-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_1', node.uuid) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_nova_instance_rescue(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-rescue-end.json') self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.RESCUED.value, instance0.state) message = self.load_message('instance-unrescue-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_instance_resize_confirm_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_0', node.uuid) message = self.load_message( 'instance-resize_confirm-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) node = compute_model.get_node_by_instance_uuid(instance0_uuid) self.assertEqual('Node_1', node.uuid) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_nova_instance_restore_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-restore-end.json') instance0.state = element.InstanceState.ERROR.value self.assertEqual(element.InstanceState.ERROR.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_nova_instance_resume_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-resume-end.json') instance0.state = element.InstanceState.ERROR.value self.assertEqual(element.InstanceState.ERROR.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_nova_instance_shelve(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-shelve-end.json') self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.SHELVED.value, instance0.state) message = self.load_message('instance-unshelve-end.json') handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) def test_nova_instance_shutdown_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-shutdown-end.json') self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual(element.InstanceState.STOPPED.value, instance0.state) def test_nova_instance_suspend_end(self): compute_model = self.fake_cdmc.generate_scenario_3_with_2_nodes() self.fake_cdmc.cluster_data_model = compute_model handler = novanotification.VersionedNotification(self.fake_cdmc) instance0_uuid = '73b09e16-35b7-4922-804e-e8f5d9b740fc' instance0 = compute_model.get_instance_by_uuid(instance0_uuid) message = self.load_message('instance-suspend-end.json') self.assertEqual(element.InstanceState.ACTIVE.value, instance0.state) handler.info( ctxt=self.context, publisher_id=message['publisher_id'], event_type=message['event_type'], payload=message['payload'], metadata=self.FAKE_METADATA, ) self.assertEqual( element.InstanceState.SUSPENDED.value, instance0.state)

0.496338

0.096748

import pandas as pd import numpy as np from sklearn.linear_model import RidgeCV from sklearn import metrics from utils.constants import Maps # Pandas options for better printing pd.set_option('display.max_columns', 500) pd.set_option('display.max_rows', 1000) pd.set_option('display.width', 1000) # Read in our scored maps generated in the map_score script map_scores = pd.read_csv('results/scored_maps.csv') # Limit our results to 2021 maps map_scores = map_scores[map_scores['season'] == 2021] # Duplicate and mirror our map scores so that we can get a row for each team on attack and defense on each map map_scores_swapped = map_scores.copy(deep=True) map_scores_swapped['team_one_score'] = map_scores['team_two_score'] map_scores_swapped['team_two_score'] = map_scores['team_one_score'] map_scores_swapped['team_one_name'] = map_scores['team_two_name'] map_scores_swapped['team_two_name'] = map_scores['team_one_name'] map_scores = pd.concat([map_scores_swapped, map_scores]) map_scores = map_scores.dropna() players_per_map = pd.read_csv('results/players_per_map.csv') full_frame = map_scores \ .merge(players_per_map, left_on=['match_id', 'map_name', 'team_one_name'], right_on=['match_id', 'map_name', 'team']) \ .merge(players_per_map, left_on=['match_id', 'map_name', 'team_two_name'], right_on=['match_id', 'map_name', 'team'], suffixes=('_team_one', '_team_two')) full_frame = full_frame[ ['team_one_score', 'player1_team_one', 'player2_team_one', 'player3_team_one', 'player4_team_one', 'player5_team_one', 'player6_team_one', 'player1_team_two', 'player2_team_two', 'player3_team_two', 'player4_team_two', 'player5_team_two', 'player6_team_two']] players = list(full_frame[ ['player1_team_one', 'player2_team_one', 'player3_team_one', 'player4_team_one', 'player5_team_one', 'player6_team_one', 'player1_team_two', 'player2_team_two', 'player3_team_two', 'player4_team_two', 'player5_team_two', 'player6_team_two']].stack().unique()) players.sort() # Convert an input row into a row of our sparse matrix def map_players(row_in, players): t1_players = [row_in[0], row_in[1], row_in[2], row_in[3], row_in[4], row_in[5]] t2_players = [row_in[6], row_in[7], row_in[8], row_in[9], row_in[10], row_in[11]] row_out = np.zeros([len(players) * 2]) for p in t1_players: row_out[players.index(p)] = 1 for p in t2_players: row_out[players.index(p) + len(players)] = -1 return row_out # Take in our input data, convert the teams into our sparse design matrix and the map scores into our target column def extract_X_Y(frame): stints_x_base = frame[['player1_team_one', 'player2_team_one', 'player3_team_one', 'player4_team_one', 'player5_team_one', 'player6_team_one', 'player1_team_two', 'player2_team_two', 'player3_team_two', 'player4_team_two', 'player5_team_two', 'player6_team_two']].values stint_X_rows = np.apply_along_axis(map_players, 1, stints_x_base, players) stint_Y_rows = frame[['team_one_score']].values return stint_X_rows, stint_Y_rows # Convert lambda value to alpha needed for ridge CV def lambda_to_alpha(lambda_value, samples): return (lambda_value * samples) / 2.0 # Convert RidgeCV alpha back into a lambda value def alpha_to_lambda(alpha_value, samples): return (alpha_value * 2.0) / samples # Calculate Regularized Map Score Added def calculate_rmts(stint_X_rows, stint_Y_rows): # We will perform cross validation across a number of different lambdas lambdas = [.01, 0.025, .05, 0.075, .1, .125, .15, .175, .2, .225, .25] # convert the lambdas into alpha values alphas = [lambda_to_alpha(l, stint_X_rows.shape[0]) for l in lambdas] # Create our ridge CV model clf = RidgeCV(alphas=alphas, cv=5, fit_intercept=True, normalize=False) # Fit our data model = clf.fit(stint_X_rows, stint_Y_rows) # extract our teams, and coefficients and combine them into a single matrix (20 x 3) team_arr = np.transpose(np.array(players).reshape(1, len(players))) coef_array_attack = np.transpose(model.coef_[:, 0:len(players)]) coef_array_def = np.transpose(model.coef_[:, len(players):]) team_coef_arr = np.concatenate([team_arr, coef_array_attack, coef_array_def], axis=1) # build a dataframe from our matrix rmts = pd.DataFrame(team_coef_arr) intercept = model.intercept_[0] # Rename columns to include the current map type attack_str = 'rmsa attack' defend_str = 'rmsa defend' rmts.columns = ['player', attack_str, defend_str] rmts[attack_str] = rmts[attack_str].astype(float) rmts[defend_str] = rmts[defend_str].astype(float) # Calculate a total RMSA rmts['rmsa'] = rmts[attack_str] + rmts[defend_str] rmts['intercept'] = intercept # Generate a couple of error statistics lambda_picked = alpha_to_lambda(model.alpha_, stint_X_rows.shape[0]) print('r^2: ', model.score(stint_X_rows, stint_Y_rows)) print('lambda: ', lambda_picked) print('intercept: ', intercept) pred = model.predict(stint_X_rows) print('MAE: ', metrics.mean_absolute_error(stint_Y_rows, pred)) print('MSE: ', metrics.mean_squared_error(stint_Y_rows, pred)) rmts = rmts.sort_values(by='rmsa', ascending=False) return rmts x, y = extract_X_Y(full_frame) rmsa = calculate_rmts(x, y) rmsa['rank'] = rmsa['rmsa'].rank(ascending=False) rmsa = rmsa[['rank', 'player', 'rmsa', 'rmsa attack', 'rmsa defend']] print(rmsa) rmsa.to_csv('results/player_rating.csv', index=False)

player_rating.py

import pandas as pd import numpy as np from sklearn.linear_model import RidgeCV from sklearn import metrics from utils.constants import Maps # Pandas options for better printing pd.set_option('display.max_columns', 500) pd.set_option('display.max_rows', 1000) pd.set_option('display.width', 1000) # Read in our scored maps generated in the map_score script map_scores = pd.read_csv('results/scored_maps.csv') # Limit our results to 2021 maps map_scores = map_scores[map_scores['season'] == 2021] # Duplicate and mirror our map scores so that we can get a row for each team on attack and defense on each map map_scores_swapped = map_scores.copy(deep=True) map_scores_swapped['team_one_score'] = map_scores['team_two_score'] map_scores_swapped['team_two_score'] = map_scores['team_one_score'] map_scores_swapped['team_one_name'] = map_scores['team_two_name'] map_scores_swapped['team_two_name'] = map_scores['team_one_name'] map_scores = pd.concat([map_scores_swapped, map_scores]) map_scores = map_scores.dropna() players_per_map = pd.read_csv('results/players_per_map.csv') full_frame = map_scores \ .merge(players_per_map, left_on=['match_id', 'map_name', 'team_one_name'], right_on=['match_id', 'map_name', 'team']) \ .merge(players_per_map, left_on=['match_id', 'map_name', 'team_two_name'], right_on=['match_id', 'map_name', 'team'], suffixes=('_team_one', '_team_two')) full_frame = full_frame[ ['team_one_score', 'player1_team_one', 'player2_team_one', 'player3_team_one', 'player4_team_one', 'player5_team_one', 'player6_team_one', 'player1_team_two', 'player2_team_two', 'player3_team_two', 'player4_team_two', 'player5_team_two', 'player6_team_two']] players = list(full_frame[ ['player1_team_one', 'player2_team_one', 'player3_team_one', 'player4_team_one', 'player5_team_one', 'player6_team_one', 'player1_team_two', 'player2_team_two', 'player3_team_two', 'player4_team_two', 'player5_team_two', 'player6_team_two']].stack().unique()) players.sort() # Convert an input row into a row of our sparse matrix def map_players(row_in, players): t1_players = [row_in[0], row_in[1], row_in[2], row_in[3], row_in[4], row_in[5]] t2_players = [row_in[6], row_in[7], row_in[8], row_in[9], row_in[10], row_in[11]] row_out = np.zeros([len(players) * 2]) for p in t1_players: row_out[players.index(p)] = 1 for p in t2_players: row_out[players.index(p) + len(players)] = -1 return row_out # Take in our input data, convert the teams into our sparse design matrix and the map scores into our target column def extract_X_Y(frame): stints_x_base = frame[['player1_team_one', 'player2_team_one', 'player3_team_one', 'player4_team_one', 'player5_team_one', 'player6_team_one', 'player1_team_two', 'player2_team_two', 'player3_team_two', 'player4_team_two', 'player5_team_two', 'player6_team_two']].values stint_X_rows = np.apply_along_axis(map_players, 1, stints_x_base, players) stint_Y_rows = frame[['team_one_score']].values return stint_X_rows, stint_Y_rows # Convert lambda value to alpha needed for ridge CV def lambda_to_alpha(lambda_value, samples): return (lambda_value * samples) / 2.0 # Convert RidgeCV alpha back into a lambda value def alpha_to_lambda(alpha_value, samples): return (alpha_value * 2.0) / samples # Calculate Regularized Map Score Added def calculate_rmts(stint_X_rows, stint_Y_rows): # We will perform cross validation across a number of different lambdas lambdas = [.01, 0.025, .05, 0.075, .1, .125, .15, .175, .2, .225, .25] # convert the lambdas into alpha values alphas = [lambda_to_alpha(l, stint_X_rows.shape[0]) for l in lambdas] # Create our ridge CV model clf = RidgeCV(alphas=alphas, cv=5, fit_intercept=True, normalize=False) # Fit our data model = clf.fit(stint_X_rows, stint_Y_rows) # extract our teams, and coefficients and combine them into a single matrix (20 x 3) team_arr = np.transpose(np.array(players).reshape(1, len(players))) coef_array_attack = np.transpose(model.coef_[:, 0:len(players)]) coef_array_def = np.transpose(model.coef_[:, len(players):]) team_coef_arr = np.concatenate([team_arr, coef_array_attack, coef_array_def], axis=1) # build a dataframe from our matrix rmts = pd.DataFrame(team_coef_arr) intercept = model.intercept_[0] # Rename columns to include the current map type attack_str = 'rmsa attack' defend_str = 'rmsa defend' rmts.columns = ['player', attack_str, defend_str] rmts[attack_str] = rmts[attack_str].astype(float) rmts[defend_str] = rmts[defend_str].astype(float) # Calculate a total RMSA rmts['rmsa'] = rmts[attack_str] + rmts[defend_str] rmts['intercept'] = intercept # Generate a couple of error statistics lambda_picked = alpha_to_lambda(model.alpha_, stint_X_rows.shape[0]) print('r^2: ', model.score(stint_X_rows, stint_Y_rows)) print('lambda: ', lambda_picked) print('intercept: ', intercept) pred = model.predict(stint_X_rows) print('MAE: ', metrics.mean_absolute_error(stint_Y_rows, pred)) print('MSE: ', metrics.mean_squared_error(stint_Y_rows, pred)) rmts = rmts.sort_values(by='rmsa', ascending=False) return rmts x, y = extract_X_Y(full_frame) rmsa = calculate_rmts(x, y) rmsa['rank'] = rmsa['rmsa'].rank(ascending=False) rmsa = rmsa[['rank', 'player', 'rmsa', 'rmsa attack', 'rmsa defend']] print(rmsa) rmsa.to_csv('results/player_rating.csv', index=False)

0.804291

0.371906

import sqlite3 from models.sl_logger import SlLogger logger = SlLogger.get_logger(__name__) class UserModel: def __init__(self, _id, username, password, pswd_hint): self.id = _id self.username = username self.password = password self.pswd_hint = pswd_hint @classmethod def create_connection(cls, db_file): connection = None try: connection = sqlite3.connect(db_file) logger.info('Connection to ' + db_file + ' is successful') except Exception as e: logger.error(e) return connection @classmethod def find_by_username(cls, username): connection = UserModel.create_connection('data.db') if connection: cursor = connection.cursor() query = "SELECT * FROM users WHERE username=?" logger.info("Querying users table in data.db on username field") result = cursor.execute(query, (username,)) row = result.fetchone() if row: user = cls(*row) else: user = None connection.close() return user @classmethod def find_by_id(cls, _id): connection = UserModel.create_connection('data.db') if connection: cursor = connection.cursor() query = "SELECT * FROM users WHERE id=?" logger.info("Querying users table in data.db on id field") result = cursor.execute(query, (_id,)) row = result.fetchone() if row: user = cls(*row) else: user = None connection.close() return user @classmethod def get_password_by_username(cls, username, password): connection = UserModel.create_connection('data.db') if connection: cursor = connection.cursor() query = "SELECT " + password + " FROM users WHERE username=?" logger.info("Querying users table in data.db on username field to get " + password) result = cursor.execute(query, (username,)) row = result.fetchone()[0] connection.close() return row @classmethod def user_exists(cls, username): connection = UserModel.create_connection('data.db') if connection: cursor = connection.cursor() query = "SELECT COUNT(*) FROM users WHERE username=?" logger.info("Querying users table in data.db to check if username exists") result = cursor.execute(query, (username,)) row = result.fetchone()[0] connection.close() return row @classmethod def update_password(cls, username, newPassword): connection = UserModel.create_connection('data.db') if connection: cursor = connection.cursor() query = "UPDATE users SET password=? WHERE username=?" logger.info("Updating user table with new hashed password") cursor.execute(query, (newPassword, username)) connection.commit() connection.close()

models/user.py

import sqlite3 from models.sl_logger import SlLogger logger = SlLogger.get_logger(__name__) class UserModel: def __init__(self, _id, username, password, pswd_hint): self.id = _id self.username = username self.password = password self.pswd_hint = pswd_hint @classmethod def create_connection(cls, db_file): connection = None try: connection = sqlite3.connect(db_file) logger.info('Connection to ' + db_file + ' is successful') except Exception as e: logger.error(e) return connection @classmethod def find_by_username(cls, username): connection = UserModel.create_connection('data.db') if connection: cursor = connection.cursor() query = "SELECT * FROM users WHERE username=?" logger.info("Querying users table in data.db on username field") result = cursor.execute(query, (username,)) row = result.fetchone() if row: user = cls(*row) else: user = None connection.close() return user @classmethod def find_by_id(cls, _id): connection = UserModel.create_connection('data.db') if connection: cursor = connection.cursor() query = "SELECT * FROM users WHERE id=?" logger.info("Querying users table in data.db on id field") result = cursor.execute(query, (_id,)) row = result.fetchone() if row: user = cls(*row) else: user = None connection.close() return user @classmethod def get_password_by_username(cls, username, password): connection = UserModel.create_connection('data.db') if connection: cursor = connection.cursor() query = "SELECT " + password + " FROM users WHERE username=?" logger.info("Querying users table in data.db on username field to get " + password) result = cursor.execute(query, (username,)) row = result.fetchone()[0] connection.close() return row @classmethod def user_exists(cls, username): connection = UserModel.create_connection('data.db') if connection: cursor = connection.cursor() query = "SELECT COUNT(*) FROM users WHERE username=?" logger.info("Querying users table in data.db to check if username exists") result = cursor.execute(query, (username,)) row = result.fetchone()[0] connection.close() return row @classmethod def update_password(cls, username, newPassword): connection = UserModel.create_connection('data.db') if connection: cursor = connection.cursor() query = "UPDATE users SET password=? WHERE username=?" logger.info("Updating user table with new hashed password") cursor.execute(query, (newPassword, username)) connection.commit() connection.close()

0.376623

0.063628

import psycopg2 def read_db(product_id, track_order): try: connection = psycopg2.connect(database = 'tracklist', user = 'allex', password = '', host = '172.16.17.32', port = '5432') except psycopg2.Error as err: print('An error occurred while trying to connect to the database') else: print('Connection to the database was successful!') cursor = connection.cursor() cursor.execute(f""" select link_sample, cd, track_order, song_title, artists, duration from test.track_list where product_id='{product_id}' and track_order='{track_order}'; """) record = cursor.fetchone() if record is not None: print('record found: ', record) return record else: print('record not found') return None def update_local_server(product_code, product_id, table_string): try: connection2 = psycopg2.connect(database = 'tracklist', user = 'allex', password = '', host = '172.16.17.32', port = '5432') except psycopg2.Error as err: print('An error occurred while trying to connect to the database') else: print('Connection to the database was successful!') def sanitize_single_line_string(initial_string): string = str(initial_string) if string is not None: return string.replace("'", "''") if string.find( "'") != -1 else string else: return string cursor = connection2.cursor() cursor.execute(f""" insert into test.import_table(product_code, product_id, description) values ( '{product_code}', '{product_id}', '{sanitize_single_line_string(table_string)}' ); """) connection2.commit() connection2.close()

read_db.py

import psycopg2 def read_db(product_id, track_order): try: connection = psycopg2.connect(database = 'tracklist', user = 'allex', password = '', host = '172.16.17.32', port = '5432') except psycopg2.Error as err: print('An error occurred while trying to connect to the database') else: print('Connection to the database was successful!') cursor = connection.cursor() cursor.execute(f""" select link_sample, cd, track_order, song_title, artists, duration from test.track_list where product_id='{product_id}' and track_order='{track_order}'; """) record = cursor.fetchone() if record is not None: print('record found: ', record) return record else: print('record not found') return None def update_local_server(product_code, product_id, table_string): try: connection2 = psycopg2.connect(database = 'tracklist', user = 'allex', password = '', host = '172.16.17.32', port = '5432') except psycopg2.Error as err: print('An error occurred while trying to connect to the database') else: print('Connection to the database was successful!') def sanitize_single_line_string(initial_string): string = str(initial_string) if string is not None: return string.replace("'", "''") if string.find( "'") != -1 else string else: return string cursor = connection2.cursor() cursor.execute(f""" insert into test.import_table(product_code, product_id, description) values ( '{product_code}', '{product_id}', '{sanitize_single_line_string(table_string)}' ); """) connection2.commit() connection2.close()

0.244183

0.094093

from __future__ import print_function, division from warnings import warn import pandas as pd import numpy as np import pickle import copy from nilmtk.utils import find_nearest from nilmtk.feature_detectors import cluster from nilmtk.disaggregate import Disaggregator from nilmtk.datastore import HDFDataStore # Fix the seed for repeatability of experiments SEED = 42 np.random.seed(SEED) class CombinatorialOptimisation(Disaggregator): """1 dimensional combinatorial optimisation NILM algorithm. Attributes ---------- model : list of dicts Each dict has these keys: states : list of ints (the power (Watts) used in different states) training_metadata : ElecMeter or MeterGroup object used for training this set of states. We need this information because we need the appliance type (and perhaps some other metadata) for each model. state_combinations : 2D array Each column is an appliance. Each row is a possible combination of power demand values e.g. [[0, 0, 0, 0], [0, 0, 0, 100], [0, 0, 50, 0], [0, 0, 50, 100], ...] MIN_CHUNK_LENGTH : int """ def __init__(self): self.model = [] self.state_combinations = None self.MIN_CHUNK_LENGTH = 100 self.MODEL_NAME = 'CO' def train(self, metergroup, num_states_dict=None, **load_kwargs): """Train using 1D CO. Places the learnt model in the `model` attribute. Parameters ---------- metergroup : a nilmtk.MeterGroup object num_states_dict : dict **load_kwargs : keyword arguments passed to `meter.power_series()` Notes ----- * only uses first chunk for each meter (TODO: handle all chunks). """ if num_states_dict is None: num_states_dict = {} if self.model: raise RuntimeError( "This implementation of Combinatorial Optimisation" " does not support multiple calls to `train`.") num_meters = len(metergroup.meters) if num_meters > 12: max_num_clusters = 2 else: max_num_clusters = 3 for i, meter in enumerate(metergroup.submeters().meters): print("Training model for submeter '{}'".format(meter)) power_series = meter.power_series(**load_kwargs) chunk = next(power_series) num_total_states = num_states_dict.get(meter) if num_total_states is not None: num_on_states = num_total_states - 1 else: num_on_states = None self.train_on_chunk(chunk, meter, max_num_clusters, num_on_states) # Check to see if there are any more chunks. # TODO handle multiple chunks per appliance. try: next(power_series) except StopIteration: pass else: warn("The current implementation of CombinatorialOptimisation" " can only handle a single chunk. But there are multiple" " chunks available. So have only trained on the" " first chunk!") print("Done training!") def train_on_chunk(self, chunk, meter, max_num_clusters, num_on_states): # Check if we've already trained on this meter meters_in_model = [d['training_metadata'] for d in self.model] if meter in meters_in_model: raise RuntimeError( "Meter {} is already in model!" " Can't train twice on the same meter!" .format(meter)) states = cluster(chunk, max_num_clusters, num_on_states) self.model.append({ 'states': states, 'training_metadata': meter}) def _set_state_combinations_if_necessary(self): """Get centroids""" # If we import sklearn at the top of the file then auto doc fails. if (self.state_combinations is None or self.state_combinations.shape[1] != len(self.model)): from sklearn.utils.extmath import cartesian centroids = [model['states'] for model in self.model] self.state_combinations = cartesian(centroids) def disaggregate(self, mains, output_datastore,**load_kwargs): '''Disaggregate mains according to the model learnt previously. Parameters ---------- mains : nilmtk.ElecMeter or nilmtk.MeterGroup output_datastore : instance of nilmtk.DataStore subclass For storing power predictions from disaggregation algorithm. sample_period : number, optional The desired sample period in seconds. Set to 60 by default. sections : TimeFrameGroup, optional Set to mains.good_sections() by default. **load_kwargs : key word arguments Passed to `mains.power_series(**kwargs)` ''' load_kwargs = self._pre_disaggregation_checks(load_kwargs) load_kwargs.setdefault('sample_period', 60) load_kwargs.setdefault('sections', mains.good_sections()) timeframes = [] building_path = '/building{}'.format(mains.building()) mains_data_location = building_path + '/elec/meter1' data_is_available = False for chunk in mains.power_series(**load_kwargs): # Check that chunk is sensible size if len(chunk) < self.MIN_CHUNK_LENGTH: continue # Record metadata timeframes.append(chunk.timeframe) measurement = chunk.name appliance_powers = self.disaggregate_chunk(chunk) for i, model in enumerate(self.model): appliance_power = appliance_powers.iloc[:, i] if len(appliance_power) == 0: continue data_is_available = True cols = pd.MultiIndex.from_tuples([chunk.name]) meter_instance = model['training_metadata'].instance() df = pd.DataFrame( appliance_power.values, index=appliance_power.index, columns=cols) key = '{}/elec/meter{}'.format(building_path, meter_instance) output_datastore.append(key, df) # Copy mains data to disag output mains_df = pd.DataFrame(chunk, columns=cols) output_datastore.append(key=mains_data_location, value=mains_df) if data_is_available: self._save_metadata_for_disaggregation( output_datastore=output_datastore, sample_period=load_kwargs['sample_period'], measurement=measurement, timeframes=timeframes, building=mains.building(), meters=[d['training_metadata'] for d in self.model] ) def disaggregate_chunk(self, mains): """In-memory disaggregation. Parameters ---------- mains : pd.Series Returns ------- appliance_powers : pd.DataFrame where each column represents a disaggregated appliance. Column names are the integer index into `self.model` for the appliance in question. """ if not self.model: raise RuntimeError( "The model needs to be instantiated before" " calling `disaggregate`. The model" " can be instantiated by running `train`.") if len(mains) < self.MIN_CHUNK_LENGTH: raise RuntimeError("Chunk is too short.") # Because CombinatorialOptimisation could have been trained using # either train() or train_on_chunk(), we must # set state_combinations here. self._set_state_combinations_if_necessary() """ # Add vampire power to the model if vampire_power is None: vampire_power = get_vampire_power(mains) if vampire_power > 0: print("Including vampire_power = {} watts to model..." .format(vampire_power)) n_rows = self.state_combinations.shape[0] vampire_power_array = np.zeros((n_rows, 1)) + vampire_power state_combinations = np.hstack( (self.state_combinations, vampire_power_array)) else: state_combinations = self.state_combinations """ state_combinations = self.state_combinations summed_power_of_each_combination = np.sum(state_combinations, axis=1) # summed_power_of_each_combination is now an array where each # value is the total power demand for each combination of states. # Start disaggregation indices_of_state_combinations, residual_power = find_nearest( summed_power_of_each_combination, mains.values) appliance_powers_dict = {} for i, model in enumerate(self.model): print("Estimating power demand for '{}'" .format(model['training_metadata'])) predicted_power = state_combinations[ indices_of_state_combinations, i].flatten() column = pd.Series(predicted_power, index=mains.index, name=i) appliance_powers_dict[self.model[i]['training_metadata']] = column appliance_powers = pd.DataFrame(appliance_powers_dict, dtype='float32') return appliance_powers def import_model(self, filename): with open(filename, 'rb') as in_file: imported_model = pickle.load(in_file) self.model = imported_model.model # Recreate datastores from filenames for pair in self.model: store_filename = pair['training_metadata'].store pair['training_metadata'].store = HDFDataStore(store_filename) self.state_combinations = imported_model.state_combinations self.MIN_CHUNK_LENGTH = imported_model.MIN_CHUNK_LENGTH def export_model(self, filename): # Can't pickle datastore, so convert to filenames original_stores = [] for pair in self.model: original_store = pair['training_metadata'].store original_stores.append(original_store) pair['training_metadata'].store = original_store.store.filename try: with open(filename, 'wb') as out_file: pickle.dump(self, out_file) finally: # Restore the stores even if the pickling fails for original_store, pair in zip(original_stores, self.model): pair['training_metadata'].store = original_store

nilmtk/disaggregate/combinatorial_optimisation.py

from __future__ import print_function, division from warnings import warn import pandas as pd import numpy as np import pickle import copy from nilmtk.utils import find_nearest from nilmtk.feature_detectors import cluster from nilmtk.disaggregate import Disaggregator from nilmtk.datastore import HDFDataStore # Fix the seed for repeatability of experiments SEED = 42 np.random.seed(SEED) class CombinatorialOptimisation(Disaggregator): """1 dimensional combinatorial optimisation NILM algorithm. Attributes ---------- model : list of dicts Each dict has these keys: states : list of ints (the power (Watts) used in different states) training_metadata : ElecMeter or MeterGroup object used for training this set of states. We need this information because we need the appliance type (and perhaps some other metadata) for each model. state_combinations : 2D array Each column is an appliance. Each row is a possible combination of power demand values e.g. [[0, 0, 0, 0], [0, 0, 0, 100], [0, 0, 50, 0], [0, 0, 50, 100], ...] MIN_CHUNK_LENGTH : int """ def __init__(self): self.model = [] self.state_combinations = None self.MIN_CHUNK_LENGTH = 100 self.MODEL_NAME = 'CO' def train(self, metergroup, num_states_dict=None, **load_kwargs): """Train using 1D CO. Places the learnt model in the `model` attribute. Parameters ---------- metergroup : a nilmtk.MeterGroup object num_states_dict : dict **load_kwargs : keyword arguments passed to `meter.power_series()` Notes ----- * only uses first chunk for each meter (TODO: handle all chunks). """ if num_states_dict is None: num_states_dict = {} if self.model: raise RuntimeError( "This implementation of Combinatorial Optimisation" " does not support multiple calls to `train`.") num_meters = len(metergroup.meters) if num_meters > 12: max_num_clusters = 2 else: max_num_clusters = 3 for i, meter in enumerate(metergroup.submeters().meters): print("Training model for submeter '{}'".format(meter)) power_series = meter.power_series(**load_kwargs) chunk = next(power_series) num_total_states = num_states_dict.get(meter) if num_total_states is not None: num_on_states = num_total_states - 1 else: num_on_states = None self.train_on_chunk(chunk, meter, max_num_clusters, num_on_states) # Check to see if there are any more chunks. # TODO handle multiple chunks per appliance. try: next(power_series) except StopIteration: pass else: warn("The current implementation of CombinatorialOptimisation" " can only handle a single chunk. But there are multiple" " chunks available. So have only trained on the" " first chunk!") print("Done training!") def train_on_chunk(self, chunk, meter, max_num_clusters, num_on_states): # Check if we've already trained on this meter meters_in_model = [d['training_metadata'] for d in self.model] if meter in meters_in_model: raise RuntimeError( "Meter {} is already in model!" " Can't train twice on the same meter!" .format(meter)) states = cluster(chunk, max_num_clusters, num_on_states) self.model.append({ 'states': states, 'training_metadata': meter}) def _set_state_combinations_if_necessary(self): """Get centroids""" # If we import sklearn at the top of the file then auto doc fails. if (self.state_combinations is None or self.state_combinations.shape[1] != len(self.model)): from sklearn.utils.extmath import cartesian centroids = [model['states'] for model in self.model] self.state_combinations = cartesian(centroids) def disaggregate(self, mains, output_datastore,**load_kwargs): '''Disaggregate mains according to the model learnt previously. Parameters ---------- mains : nilmtk.ElecMeter or nilmtk.MeterGroup output_datastore : instance of nilmtk.DataStore subclass For storing power predictions from disaggregation algorithm. sample_period : number, optional The desired sample period in seconds. Set to 60 by default. sections : TimeFrameGroup, optional Set to mains.good_sections() by default. **load_kwargs : key word arguments Passed to `mains.power_series(**kwargs)` ''' load_kwargs = self._pre_disaggregation_checks(load_kwargs) load_kwargs.setdefault('sample_period', 60) load_kwargs.setdefault('sections', mains.good_sections()) timeframes = [] building_path = '/building{}'.format(mains.building()) mains_data_location = building_path + '/elec/meter1' data_is_available = False for chunk in mains.power_series(**load_kwargs): # Check that chunk is sensible size if len(chunk) < self.MIN_CHUNK_LENGTH: continue # Record metadata timeframes.append(chunk.timeframe) measurement = chunk.name appliance_powers = self.disaggregate_chunk(chunk) for i, model in enumerate(self.model): appliance_power = appliance_powers.iloc[:, i] if len(appliance_power) == 0: continue data_is_available = True cols = pd.MultiIndex.from_tuples([chunk.name]) meter_instance = model['training_metadata'].instance() df = pd.DataFrame( appliance_power.values, index=appliance_power.index, columns=cols) key = '{}/elec/meter{}'.format(building_path, meter_instance) output_datastore.append(key, df) # Copy mains data to disag output mains_df = pd.DataFrame(chunk, columns=cols) output_datastore.append(key=mains_data_location, value=mains_df) if data_is_available: self._save_metadata_for_disaggregation( output_datastore=output_datastore, sample_period=load_kwargs['sample_period'], measurement=measurement, timeframes=timeframes, building=mains.building(), meters=[d['training_metadata'] for d in self.model] ) def disaggregate_chunk(self, mains): """In-memory disaggregation. Parameters ---------- mains : pd.Series Returns ------- appliance_powers : pd.DataFrame where each column represents a disaggregated appliance. Column names are the integer index into `self.model` for the appliance in question. """ if not self.model: raise RuntimeError( "The model needs to be instantiated before" " calling `disaggregate`. The model" " can be instantiated by running `train`.") if len(mains) < self.MIN_CHUNK_LENGTH: raise RuntimeError("Chunk is too short.") # Because CombinatorialOptimisation could have been trained using # either train() or train_on_chunk(), we must # set state_combinations here. self._set_state_combinations_if_necessary() """ # Add vampire power to the model if vampire_power is None: vampire_power = get_vampire_power(mains) if vampire_power > 0: print("Including vampire_power = {} watts to model..." .format(vampire_power)) n_rows = self.state_combinations.shape[0] vampire_power_array = np.zeros((n_rows, 1)) + vampire_power state_combinations = np.hstack( (self.state_combinations, vampire_power_array)) else: state_combinations = self.state_combinations """ state_combinations = self.state_combinations summed_power_of_each_combination = np.sum(state_combinations, axis=1) # summed_power_of_each_combination is now an array where each # value is the total power demand for each combination of states. # Start disaggregation indices_of_state_combinations, residual_power = find_nearest( summed_power_of_each_combination, mains.values) appliance_powers_dict = {} for i, model in enumerate(self.model): print("Estimating power demand for '{}'" .format(model['training_metadata'])) predicted_power = state_combinations[ indices_of_state_combinations, i].flatten() column = pd.Series(predicted_power, index=mains.index, name=i) appliance_powers_dict[self.model[i]['training_metadata']] = column appliance_powers = pd.DataFrame(appliance_powers_dict, dtype='float32') return appliance_powers def import_model(self, filename): with open(filename, 'rb') as in_file: imported_model = pickle.load(in_file) self.model = imported_model.model # Recreate datastores from filenames for pair in self.model: store_filename = pair['training_metadata'].store pair['training_metadata'].store = HDFDataStore(store_filename) self.state_combinations = imported_model.state_combinations self.MIN_CHUNK_LENGTH = imported_model.MIN_CHUNK_LENGTH def export_model(self, filename): # Can't pickle datastore, so convert to filenames original_stores = [] for pair in self.model: original_store = pair['training_metadata'].store original_stores.append(original_store) pair['training_metadata'].store = original_store.store.filename try: with open(filename, 'wb') as out_file: pickle.dump(self, out_file) finally: # Restore the stores even if the pickling fails for original_store, pair in zip(original_stores, self.model): pair['training_metadata'].store = original_store

0.739705

0.482307

import contextlib import tensorflow as tf import tensorflow_gan as tfgan sn_gettr = tfgan.features.spectral_normalization_custom_getter def snconv2d(input_, output_dim, k_h=3, k_w=3, d_h=2, d_w=2, training=True, name='snconv2d'): """Creates a 2d conv-layer with Spectral Norm applied to the weights. Args: input_: 4D input tensor (batch size, height, width, channel). output_dim: Number of features in the output layer. k_h: The height of the convolutional kernel. k_w: The width of the convolutional kernel. d_h: The height stride of the convolutional kernel. d_w: The width stride of the convolutional kernel. training: If `True`, add the spectral norm assign ops. name: The name of the variable scope. Returns: conv: The normalized tensor. """ with tf.compat.v1.variable_scope( name, custom_getter=sn_gettr(training=training)): return tf.layers.conv2d( input_, filters=output_dim, kernel_size=(k_h, k_w), strides=(d_h, d_w), padding='same', activation=None, use_bias=True, kernel_initializer=tf.contrib.layers.xavier_initializer(), bias_initializer=tf.compat.v1.initializers.zeros(), name=name) def snlinear(x, output_size, bias_start=0.0, training=True, name='snlinear'): """Creates a linear layer with Spectral Normalization applied. Args: x: 2D input tensor (batch size, features). output_size: Integer number of features in output of layer. bias_start: Float to which bias parameters are initialized. training: If `True`, add the spectral norm assign ops. name: Optional, variable scope to put the layer's parameters into. Returns: The normalized output tensor of the linear layer. """ with tf.compat.v1.variable_scope( name, custom_getter=sn_gettr(training=training)): return tf.layers.dense( x, output_size, activation=None, use_bias=True, kernel_initializer=tf.contrib.layers.xavier_initializer(), bias_initializer=tf.compat.v1.initializers.constant(bias_start)) def sn_embedding(x, number_classes, embedding_size, training=True, name='snembedding'): """Creates an embedding lookup with Spectral Normalization applied. Args: x: 1D input tensor (batch size, ). number_classes: The number of classes. embedding_size: The length of the embeddding vector for each class. training: If `True`, add the spectral norm assign ops. name: Optional, variable scope to put the layer's parameters into Returns: The output tensor (batch size, embedding_size). """ with tf.compat.v1.variable_scope(name): embedding_map = tf.compat.v1.get_variable( name='embedding_map', shape=[number_classes, embedding_size], initializer=tf.contrib.layers.xavier_initializer()) embedding_map_bar_transpose = tfgan.features.spectral_normalize( tf.transpose(a=embedding_map), training=training) embedding_map_bar = tf.transpose(a=embedding_map_bar_transpose) return tf.nn.embedding_lookup(params=embedding_map_bar, ids=x) class ConditionalBatchNorm(object): """Conditional Batch Normalization. The same as normal Batch Normalization, but there is a different (gamma, beta) pair for each possible category. For each class, it has a specific gamma and beta as normalization variable. """ # TODO(augustusodena) Merge conditional batch norm with batch norm. # TODO(augustusodena) Use more sophisticated FilM layer here. # TODO(augustusodena) Why does this need to be a class? def __init__(self, num_categories, name='conditional_batch_norm'): """Inits the object. This is just a setter. Args: num_categories: Integer number of classes (and gamma, beta pairs). name: String name to be used for scoping. Returns: Initialized object. """ with tf.compat.v1.variable_scope(name): self.name = name self.num_categories = num_categories def __call__(self, inputs, labels): """Adds Conditional Batch norm to the TF Graph. Args: inputs: Tensor of inputs (e.g. images). labels: Tensor of labels - same first dimension as inputs. Returns: Output tensor. """ inputs = tf.convert_to_tensor(value=inputs) inputs_shape = inputs.get_shape() params_shape = inputs_shape[-1:] axis = [0, 1, 2] shape = tf.TensorShape([self.num_categories]).concatenate(params_shape) with tf.compat.v1.variable_scope(self.name): self.gamma = tf.compat.v1.get_variable( 'gamma', shape, initializer=tf.compat.v1.initializers.ones()) self.beta = tf.compat.v1.get_variable( 'beta', shape, initializer=tf.compat.v1.initializers.zeros()) beta = tf.gather(self.beta, labels) beta = tf.expand_dims(tf.expand_dims(beta, 1), 1) gamma = tf.gather(self.gamma, labels) gamma = tf.expand_dims(tf.expand_dims(gamma, 1), 1) mean, variance = tf.nn.moments(x=inputs, axes=axis, keepdims=True) outputs = tf.nn.batch_normalization( inputs, mean, variance, beta, gamma, variance_epsilon=1e-5) outputs.set_shape(inputs_shape) return outputs class BatchNorm(object): """Batch Normalization. This is just vanilla batch normalization. """ def __init__(self, name='batch_norm'): """Inits the object. This is just a setter. Args: name: String name to be used for scoping. Returns: Initialized object. """ with tf.compat.v1.variable_scope(name): self.name = name def __call__(self, inputs): """Adds Batch Norm to the TF Graph. Args: inputs: Tensor of inputs (e.g. images). Returns: Output tensor. """ inputs = tf.convert_to_tensor(value=inputs) inputs_shape = inputs.get_shape().as_list() params_shape = inputs_shape[-1] axis = [0, 1, 2] shape = tf.TensorShape([params_shape]) with tf.compat.v1.variable_scope(self.name): self.gamma = tf.compat.v1.get_variable( 'gamma', shape, initializer=tf.compat.v1.initializers.ones()) self.beta = tf.compat.v1.get_variable( 'beta', shape, initializer=tf.compat.v1.initializers.zeros()) beta = self.beta gamma = self.gamma mean, variance = tf.nn.moments(x=inputs, axes=axis, keepdims=True) outputs = tf.nn.batch_normalization( inputs, mean, variance, beta, gamma, variance_epsilon=1e-5) outputs.set_shape(inputs_shape) return outputs def sn_conv1x1(x, output_dim, training=True, name='sn_conv1x1'): """Builds graph for a spectrally normalized 1 by 1 convolution. This is used in the context of non-local networks to reduce channel count for strictly computational reasons. Args: x: A 4-D tensorflow tensor. output_dim: An integer representing desired channel count in the output. training: If `True`, add the spectral norm assign ops. name: String to pass to the variable scope context. Returns: A new volume with the same batch, height, and width as the input. """ with tf.compat.v1.variable_scope( name, custom_getter=sn_gettr(training=training)): w = tf.compat.v1.get_variable( 'weights', [1, 1, x.get_shape()[-1], output_dim], initializer=tf.contrib.layers.xavier_initializer()) conv = tf.nn.conv2d( input=x, filters=w, strides=[1, 1, 1, 1], padding='SAME') return conv def sn_non_local_block_sim(x, training=True, name='sn_nonlocal'): """Builds graph for the self-attention block. This is one third of the tricks from the SAGAN paper. Args: x: A 4-D tensorflow tensor. training: If `True`, add the spectral norm assign ops. name: String to pass to the variable scope context. Returns: A new volume with self-attention having been applied. """ with tf.compat.v1.variable_scope(name): _, h, w, num_channels = x.shape.as_list() location_num = h * w downsampled_num = location_num // 4 # theta path theta = sn_conv1x1(x, num_channels // 8, training, 'sn_conv_theta') theta = tf.reshape( theta, [-1, location_num, num_channels // 8]) # phi path phi = sn_conv1x1(x, num_channels // 8, training, 'sn_conv_phi') phi = tf.layers.max_pooling2d(inputs=phi, pool_size=[2, 2], strides=2) phi = tf.reshape( phi, [-1, downsampled_num, num_channels // 8]) attn = tf.matmul(theta, phi, transpose_b=True) attn = tf.nn.softmax(attn) # g path g = sn_conv1x1(x, num_channels // 2, training, 'sn_conv_g') g = tf.layers.max_pooling2d(inputs=g, pool_size=[2, 2], strides=2) g = tf.reshape( g, [-1, downsampled_num, num_channels // 2]) attn_g = tf.matmul(attn, g) attn_g = tf.reshape(attn_g, [-1, h, w, num_channels // 2]) sigma = tf.compat.v1.get_variable( 'sigma_ratio', [], initializer=tf.compat.v1.initializers.constant(0.0)) attn_g = sn_conv1x1(attn_g, num_channels, training, 'sn_conv_attn') return x + sigma * attn_g @contextlib.contextmanager def variables_on_gpu0(): """Put variables on GPU.""" old_fn = tf.compat.v1.get_variable def new_fn(*args, **kwargs): with tf.device('/gpu:0'): return old_fn(*args, **kwargs) tf.compat.v1.get_variable = new_fn yield tf.compat.v1.get_variable = old_fn def avg_grads(tower_grads): """Calculate the average gradient for each shared variable across all towers. Note that this function provides a synchronization point across all towers. Args: tower_grads: List of lists of (gradient, variable) tuples. The outer list is over individual gradients. The inner list is over the gradient calculation for each tower. Returns: List of pairs of (gradient, variable) where the gradient has been averaged across all towers. """ average_grads = [] for grad_and_vars in zip(*tower_grads): # Note that each grad_and_vars looks like the following: # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) grads = [] for g, _ in grad_and_vars: # Add 0 dimension to the gradients to represent the tower. expanded_g = tf.expand_dims(g, 0) # Append on a 'tower' dimension which we will average over below. grads.append(expanded_g) # Average over the 'tower' dimension. grad = tf.concat(grads, 0) grad = tf.reduce_mean(input_tensor=grad, axis=0) # Keep in mind that the Variables are redundant because they are shared # across towers. So .. we will just return the first tower's pointer to # the Variable. v = grad_and_vars[0][1] grad_and_var = (grad, v) average_grads.append(grad_and_var) return average_grads

galaxy2galaxy/layers/spectral_ops.py

import contextlib import tensorflow as tf import tensorflow_gan as tfgan sn_gettr = tfgan.features.spectral_normalization_custom_getter def snconv2d(input_, output_dim, k_h=3, k_w=3, d_h=2, d_w=2, training=True, name='snconv2d'): """Creates a 2d conv-layer with Spectral Norm applied to the weights. Args: input_: 4D input tensor (batch size, height, width, channel). output_dim: Number of features in the output layer. k_h: The height of the convolutional kernel. k_w: The width of the convolutional kernel. d_h: The height stride of the convolutional kernel. d_w: The width stride of the convolutional kernel. training: If `True`, add the spectral norm assign ops. name: The name of the variable scope. Returns: conv: The normalized tensor. """ with tf.compat.v1.variable_scope( name, custom_getter=sn_gettr(training=training)): return tf.layers.conv2d( input_, filters=output_dim, kernel_size=(k_h, k_w), strides=(d_h, d_w), padding='same', activation=None, use_bias=True, kernel_initializer=tf.contrib.layers.xavier_initializer(), bias_initializer=tf.compat.v1.initializers.zeros(), name=name) def snlinear(x, output_size, bias_start=0.0, training=True, name='snlinear'): """Creates a linear layer with Spectral Normalization applied. Args: x: 2D input tensor (batch size, features). output_size: Integer number of features in output of layer. bias_start: Float to which bias parameters are initialized. training: If `True`, add the spectral norm assign ops. name: Optional, variable scope to put the layer's parameters into. Returns: The normalized output tensor of the linear layer. """ with tf.compat.v1.variable_scope( name, custom_getter=sn_gettr(training=training)): return tf.layers.dense( x, output_size, activation=None, use_bias=True, kernel_initializer=tf.contrib.layers.xavier_initializer(), bias_initializer=tf.compat.v1.initializers.constant(bias_start)) def sn_embedding(x, number_classes, embedding_size, training=True, name='snembedding'): """Creates an embedding lookup with Spectral Normalization applied. Args: x: 1D input tensor (batch size, ). number_classes: The number of classes. embedding_size: The length of the embeddding vector for each class. training: If `True`, add the spectral norm assign ops. name: Optional, variable scope to put the layer's parameters into Returns: The output tensor (batch size, embedding_size). """ with tf.compat.v1.variable_scope(name): embedding_map = tf.compat.v1.get_variable( name='embedding_map', shape=[number_classes, embedding_size], initializer=tf.contrib.layers.xavier_initializer()) embedding_map_bar_transpose = tfgan.features.spectral_normalize( tf.transpose(a=embedding_map), training=training) embedding_map_bar = tf.transpose(a=embedding_map_bar_transpose) return tf.nn.embedding_lookup(params=embedding_map_bar, ids=x) class ConditionalBatchNorm(object): """Conditional Batch Normalization. The same as normal Batch Normalization, but there is a different (gamma, beta) pair for each possible category. For each class, it has a specific gamma and beta as normalization variable. """ # TODO(augustusodena) Merge conditional batch norm with batch norm. # TODO(augustusodena) Use more sophisticated FilM layer here. # TODO(augustusodena) Why does this need to be a class? def __init__(self, num_categories, name='conditional_batch_norm'): """Inits the object. This is just a setter. Args: num_categories: Integer number of classes (and gamma, beta pairs). name: String name to be used for scoping. Returns: Initialized object. """ with tf.compat.v1.variable_scope(name): self.name = name self.num_categories = num_categories def __call__(self, inputs, labels): """Adds Conditional Batch norm to the TF Graph. Args: inputs: Tensor of inputs (e.g. images). labels: Tensor of labels - same first dimension as inputs. Returns: Output tensor. """ inputs = tf.convert_to_tensor(value=inputs) inputs_shape = inputs.get_shape() params_shape = inputs_shape[-1:] axis = [0, 1, 2] shape = tf.TensorShape([self.num_categories]).concatenate(params_shape) with tf.compat.v1.variable_scope(self.name): self.gamma = tf.compat.v1.get_variable( 'gamma', shape, initializer=tf.compat.v1.initializers.ones()) self.beta = tf.compat.v1.get_variable( 'beta', shape, initializer=tf.compat.v1.initializers.zeros()) beta = tf.gather(self.beta, labels) beta = tf.expand_dims(tf.expand_dims(beta, 1), 1) gamma = tf.gather(self.gamma, labels) gamma = tf.expand_dims(tf.expand_dims(gamma, 1), 1) mean, variance = tf.nn.moments(x=inputs, axes=axis, keepdims=True) outputs = tf.nn.batch_normalization( inputs, mean, variance, beta, gamma, variance_epsilon=1e-5) outputs.set_shape(inputs_shape) return outputs class BatchNorm(object): """Batch Normalization. This is just vanilla batch normalization. """ def __init__(self, name='batch_norm'): """Inits the object. This is just a setter. Args: name: String name to be used for scoping. Returns: Initialized object. """ with tf.compat.v1.variable_scope(name): self.name = name def __call__(self, inputs): """Adds Batch Norm to the TF Graph. Args: inputs: Tensor of inputs (e.g. images). Returns: Output tensor. """ inputs = tf.convert_to_tensor(value=inputs) inputs_shape = inputs.get_shape().as_list() params_shape = inputs_shape[-1] axis = [0, 1, 2] shape = tf.TensorShape([params_shape]) with tf.compat.v1.variable_scope(self.name): self.gamma = tf.compat.v1.get_variable( 'gamma', shape, initializer=tf.compat.v1.initializers.ones()) self.beta = tf.compat.v1.get_variable( 'beta', shape, initializer=tf.compat.v1.initializers.zeros()) beta = self.beta gamma = self.gamma mean, variance = tf.nn.moments(x=inputs, axes=axis, keepdims=True) outputs = tf.nn.batch_normalization( inputs, mean, variance, beta, gamma, variance_epsilon=1e-5) outputs.set_shape(inputs_shape) return outputs def sn_conv1x1(x, output_dim, training=True, name='sn_conv1x1'): """Builds graph for a spectrally normalized 1 by 1 convolution. This is used in the context of non-local networks to reduce channel count for strictly computational reasons. Args: x: A 4-D tensorflow tensor. output_dim: An integer representing desired channel count in the output. training: If `True`, add the spectral norm assign ops. name: String to pass to the variable scope context. Returns: A new volume with the same batch, height, and width as the input. """ with tf.compat.v1.variable_scope( name, custom_getter=sn_gettr(training=training)): w = tf.compat.v1.get_variable( 'weights', [1, 1, x.get_shape()[-1], output_dim], initializer=tf.contrib.layers.xavier_initializer()) conv = tf.nn.conv2d( input=x, filters=w, strides=[1, 1, 1, 1], padding='SAME') return conv def sn_non_local_block_sim(x, training=True, name='sn_nonlocal'): """Builds graph for the self-attention block. This is one third of the tricks from the SAGAN paper. Args: x: A 4-D tensorflow tensor. training: If `True`, add the spectral norm assign ops. name: String to pass to the variable scope context. Returns: A new volume with self-attention having been applied. """ with tf.compat.v1.variable_scope(name): _, h, w, num_channels = x.shape.as_list() location_num = h * w downsampled_num = location_num // 4 # theta path theta = sn_conv1x1(x, num_channels // 8, training, 'sn_conv_theta') theta = tf.reshape( theta, [-1, location_num, num_channels // 8]) # phi path phi = sn_conv1x1(x, num_channels // 8, training, 'sn_conv_phi') phi = tf.layers.max_pooling2d(inputs=phi, pool_size=[2, 2], strides=2) phi = tf.reshape( phi, [-1, downsampled_num, num_channels // 8]) attn = tf.matmul(theta, phi, transpose_b=True) attn = tf.nn.softmax(attn) # g path g = sn_conv1x1(x, num_channels // 2, training, 'sn_conv_g') g = tf.layers.max_pooling2d(inputs=g, pool_size=[2, 2], strides=2) g = tf.reshape( g, [-1, downsampled_num, num_channels // 2]) attn_g = tf.matmul(attn, g) attn_g = tf.reshape(attn_g, [-1, h, w, num_channels // 2]) sigma = tf.compat.v1.get_variable( 'sigma_ratio', [], initializer=tf.compat.v1.initializers.constant(0.0)) attn_g = sn_conv1x1(attn_g, num_channels, training, 'sn_conv_attn') return x + sigma * attn_g @contextlib.contextmanager def variables_on_gpu0(): """Put variables on GPU.""" old_fn = tf.compat.v1.get_variable def new_fn(*args, **kwargs): with tf.device('/gpu:0'): return old_fn(*args, **kwargs) tf.compat.v1.get_variable = new_fn yield tf.compat.v1.get_variable = old_fn def avg_grads(tower_grads): """Calculate the average gradient for each shared variable across all towers. Note that this function provides a synchronization point across all towers. Args: tower_grads: List of lists of (gradient, variable) tuples. The outer list is over individual gradients. The inner list is over the gradient calculation for each tower. Returns: List of pairs of (gradient, variable) where the gradient has been averaged across all towers. """ average_grads = [] for grad_and_vars in zip(*tower_grads): # Note that each grad_and_vars looks like the following: # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) grads = [] for g, _ in grad_and_vars: # Add 0 dimension to the gradients to represent the tower. expanded_g = tf.expand_dims(g, 0) # Append on a 'tower' dimension which we will average over below. grads.append(expanded_g) # Average over the 'tower' dimension. grad = tf.concat(grads, 0) grad = tf.reduce_mean(input_tensor=grad, axis=0) # Keep in mind that the Variables are redundant because they are shared # across towers. So .. we will just return the first tower's pointer to # the Variable. v = grad_and_vars[0][1] grad_and_var = (grad, v) average_grads.append(grad_and_var) return average_grads

0.917342

0.671982

from __future__ import annotations import json import os import re from dataclasses import dataclass from datetime import datetime from enum import Enum from typing import Any, Collection, Optional from dotenv import load_dotenv from .logger import set_log from .util import datetime_parser, open_query, postgres_date log = set_log(__name__) PostData = dict[str, Collection[str]] # key_map = {"outer1": {"inner11", "inner12}, "outer2": {"inner21"}} KeyMap = dict[str, set[str]] ListInnerResponse = dict[str, list[dict[str, dict[str, str]]]] DictInnerResponse = dict[str, dict[str, Any]] DuneRecord = dict[str, str] # pylint: disable=too-few-public-methods # TODO - use namedtuple for MetaData and QueryResults class MetaData: """The standard information returned from the Dune API as `query_results`""" id: str job_id: str error: Optional[str] runtime: int generated_at: datetime columns: list[str] def __init__(self, obj: str): """ Constructor method :param obj: input should have the following form Example input: { 'id': '3158cc2c-5ed1-4779-b523-eeb9c3b34b21', 'job_id': '093e440d-66ce-4c00-81ec-2406f0403bc0', 'error': None, 'runtime': 0, 'generated_at': '2022-03-19T07:11:37.344998+00:00', 'columns': ['number', 'size', 'time', 'block_hash', 'tx_fees'], '__typename': 'query_results' } """ self.__dict__ = json.loads(obj, object_hook=datetime_parser) class QueryResults: """Class containing the Data results of a Dune Select Query""" meta: Optional[MetaData] data: list[DuneRecord] def __init__(self, data: ListInnerResponse): assert data.keys() == { "query_results", "get_result_by_job_id", "query_errors", }, f"invalid keys {data.keys()}" assert len(data["query_results"]) == 1, f"Unexpected query_results {data}" # Could wrap meta conversion into a try-catch, since we don't really need it. # But, I can't think of a broad enough exception that won't trip up the liner. self.meta = MetaData(json.dumps(data["query_results"][0])) self.data = [rec["data"] for rec in data["get_result_by_job_id"]] class Network(Enum): """Enum for supported EVM networks""" SOLANA = 1 MAINNET = 4 GCHAIN = 6 POLYGON = 7 OPTIMISM_V1 = 8 BINANCE = 9 OPTIMISM_V2 = 10 def __str__(self) -> str: result = super.__str__(self) if self == Network.SOLANA: result = "Solana" elif self == Network.MAINNET: result = "Ethereum Mainnet" elif self == Network.GCHAIN: result = "Gnosis Chain" elif self == Network.POLYGON: result = "Polygon" elif self == Network.OPTIMISM_V1: result = "Optimism (OVM 1.0)" elif self == Network.OPTIMISM_V2: result = "Optimism (OVM 2.0)" elif self == Network.BINANCE: result = "Binance Smart Chain" return result @classmethod def from_string(cls, network_str: str) -> Network: """ Attempts to parse network name from string. returns None is no match """ patterns = { r"(.*)mainnet": cls.MAINNET, r"g(.*)chain": cls.GCHAIN, r"solana": cls.SOLANA, r"poly": cls.POLYGON, r"optimism(.*)1": cls.OPTIMISM_V1, r"optimism(.*)2": cls.OPTIMISM_V2, r"bsc": cls.BINANCE, r"binance": cls.BINANCE, } for pattern, network in patterns.items(): if re.match(pattern, network_str, re.IGNORECASE): return network raise ValueError(f"could not parse Network from '{network_str}'") class ParameterType(Enum): """ Enum of the 4 distinct dune parameter types """ TEXT = "text" NUMBER = "number" DATE = "datetime" @classmethod def from_string(cls, type_str: str) -> ParameterType: """ Attempts to parse Parameter from string. returns None is no match """ patterns = { r"text": cls.TEXT, r"number": cls.NUMBER, r"date": cls.DATE, } for pattern, network in patterns.items(): if re.match(pattern, type_str, re.IGNORECASE): return network raise ValueError(f"could not parse Network from '{type_str}'") class QueryParameter: """Class whose instances are Dune Compatible Query Parameters""" def __init__( self, name: str, parameter_type: ParameterType, value: Any, ): self.key: str = name self.type: ParameterType = parameter_type self.value = value def __eq__(self, other: object) -> bool: if not isinstance(other, QueryParameter): return NotImplemented return all( [ self.key == other.key, self.value == other.value, self.type.value == other.type.value, ] ) @classmethod def text_type(cls, name: str, value: str) -> QueryParameter: """Constructs a Query parameter of type text""" return cls(name, ParameterType.TEXT, value) @classmethod def number_type(cls, name: str, value: int | float) -> QueryParameter: """Constructs a Query parameter of type number""" return cls(name, ParameterType.NUMBER, value) @classmethod def date_type(cls, name: str, value: datetime | str) -> QueryParameter: """ Constructs a Query parameter of type date. For convenience, we allow proper datetime type, or string """ if isinstance(value, str): value = postgres_date(value) return cls(name, ParameterType.DATE, value) def _value_str(self) -> str: if self.type in (ParameterType.TEXT, ParameterType.NUMBER): return str(self.value) if self.type == ParameterType.DATE: # This is the postgres string format of timestamptz return str(self.value.strftime("%Y-%m-%d %H:%M:%S")) raise TypeError(f"Type {self.type} not recognized!") def to_dict(self) -> dict[str, str]: """Converts QueryParameter into string json format accepted by Dune API""" results = { "key": self.key, "type": self.type.value, "value": self._value_str(), } return results @classmethod def from_dict(cls, obj: dict[str, Any]) -> QueryParameter: """ Constructs Query Parameters from json. TODO - this could probably be done similar to the __init__ method of MetaData """ name, value = obj["key"], obj["value"] p_type = ParameterType.from_string(obj["type"]) if p_type == ParameterType.DATE: return cls.date_type(name, value) if p_type == ParameterType.TEXT: assert isinstance(value, str) return cls.text_type(name, value) if p_type == ParameterType.NUMBER: if isinstance(value, str): value = float(value) if "." in value else int(value) return cls.number_type(name, value) raise ValueError(f"Could not parse Query parameter from {obj}") def __str__(self) -> str: return ( f"QueryParameter(" f"name: {self.key}, " f"value: {self.value}, " f"type: {self.type.value})" ) @dataclass class Post: """Holds query json and response validation details""" data: PostData key_map: KeyMap @dataclass class DashboardTile: """ A slightly different arrangement of data that is essentially equivalent to a Query Acts as an intermediary type when composing queries from json """ name: str description: str select_file: str query_id: int network: Network parameters: list[QueryParameter] base_file: Optional[str] @classmethod def from_dict(cls, obj: dict[str, Any], path: str) -> DashboardTile: """Constructs Record from Dune Data as string dict""" return cls( name=obj.get("name", "untitled"), description=obj.get("description", ""), select_file="/".join([path, obj["query_file"]]), network=Network.from_string(obj["network"]), query_id=int(obj["id"]), parameters=[QueryParameter.from_dict(p) for p in obj.get("parameters", [])], base_file=obj.get("requires"), ) def build_query(self) -> str: """Constructs a query from base file and select file attributes""" if self.base_file is not None: components = map(open_query, [self.base_file, self.select_file]) return "\n".join(list(components)) return open_query(self.select_file) @dataclass class DuneQuery: """Contains all the relevant data necessary to initiate a Dune Query""" name: str description: str raw_sql: str network: Network parameters: list[QueryParameter] query_id: int def __hash__(self) -> int: return hash(self.query_id) def __eq__(self, other: object) -> bool: if not isinstance(other, DuneQuery): return NotImplemented equality_conditions = [ self.name == other.name, self.description == other.description, self.raw_sql == other.raw_sql, self.network.value == other.network.value, self.query_id == other.query_id, self.parameters == other.parameters, ] log.debug(f"Equality Conditions: {equality_conditions}") return all(equality_conditions) @classmethod def from_environment( cls, raw_sql: str, network: Network, description: str = "", parameters: Optional[list[QueryParameter]] = None, name: Optional[str] = None, ) -> DuneQuery: """Constructs a query using the Universal Query ID provided in env file.""" load_dotenv() return cls( raw_sql=raw_sql, description=description, network=network, parameters=parameters if parameters is not None else [], name=name if name else "untitled", query_id=int(os.environ["DUNE_QUERY_ID"]), ) @classmethod def from_tile(cls, tile: DashboardTile) -> DuneQuery: """Constructs Dune Query from DashboardTile object""" return cls( name=tile.name, description=tile.description, raw_sql=tile.build_query(), network=tile.network, parameters=tile.parameters, query_id=tile.query_id, ) def _request_parameters(self) -> list[dict[str, str]]: return [p.to_dict() for p in self.parameters] def upsert_query_post(self) -> Post: """Returns json data for a post of type UpsertQuery""" object_data: dict[str, Any] = { "id": self.query_id, "schedule": None, "dataset_id": self.network.value, "name": self.name, "query": self.raw_sql, "user_id": 84, "description": self.description, "is_archived": False, "is_temp": False, "tags": [], "parameters": self._request_parameters(), "visualizations": { "data": [], "on_conflict": { "constraint": "visualizations_pkey", "update_columns": ["name", "options"], }, }, } key_map = { "insert_queries_one": { "id", "dataset_id", "name", "description", "query", "is_private", "is_temp", "is_archived", "created_at", "updated_at", "schedule", "tags", "parameters", "visualizations", "forked_query", "user", "query_favorite_count_all", "favorite_queries", } } return Post( data={ "operationName": "UpsertQuery", "variables": { "object": object_data, "on_conflict": { "constraint": "queries_pkey", "update_columns": [ "dataset_id", "name", "description", "query", "schedule", "is_archived", "is_temp", "tags", "parameters", ], }, "session_id": 0, # must be an int, but value is irrelevant }, "query": """ mutation UpsertQuery( $session_id: Int! $object: queries_insert_input! $on_conflict: queries_on_conflict! $favs_last_24h: Boolean! = false $favs_last_7d: Boolean! = false $favs_last_30d: Boolean! = false $favs_all_time: Boolean! = true ) { insert_queries_one(object: $object, on_conflict: $on_conflict) { ...Query favorite_queries(where: { user_id: { _eq: $session_id } }, limit: 1) { created_at } } } fragment Query on queries { ...BaseQuery ...QueryVisualizations ...QueryForked ...QueryUsers ...QueryFavorites } fragment BaseQuery on queries { id dataset_id name description query is_private is_temp is_archived created_at updated_at schedule tags parameters } fragment QueryVisualizations on queries { visualizations { id type name options created_at } } fragment QueryForked on queries { forked_query { id name user { name } } } fragment QueryUsers on queries { user { ...User } } fragment User on users { id name profile_image_url } fragment QueryFavorites on queries { query_favorite_count_all @include(if: $favs_all_time) { favorite_count } query_favorite_count_last_24h @include(if: $favs_last_24h) { favorite_count } query_favorite_count_last_7d @include(if: $favs_last_7d) { favorite_count } query_favorite_count_last_30d @include(if: $favs_last_30d) { favorite_count } } """, }, key_map=key_map, ) @staticmethod def find_result_by_job(job_id: str) -> Post: """Returns json data for a post of type FindResultDataByResult""" query = """ query FindResultDataByJob($job_id: uuid!) { query_results(where: {job_id: {_eq: $job_id}, error: {_is_null: true}}) { id job_id runtime generated_at columns } query_errors(where: {job_id: {_eq: $job_id}}) { id job_id runtime message metadata type generated_at } get_result_by_job_id(args: {want_job_id: $job_id}) { data } } """ return Post( data={ "operationName": "FindResultDataByJob", "variables": {"job_id": job_id}, "query": query, }, key_map={ "query_results": { "id", "job_id", "runtime", "generated_at", "columns", }, "query_errors": { "id", "job_id", "runtime", "message", "metadata", "type", "generated_at", }, "get_result_by_job_id": {"data"}, }, ) @staticmethod def get_queue_position(job_id: str) -> Post: """Returns json data for a post of type GetQueuePosition This is meant to determine when query execution has completed. """ query = """ query GetQueuePosition($job_id: uuid!) { view_queue_positions(where: {id: {_eq: $job_id}}) { pos } jobs_by_pk(id: $job_id) { id user_id category created_at locked_until } } """ return Post( data={ "operationName": "GetQueuePosition", "variables": {"job_id": job_id}, "query": query, }, key_map={"data": {"view_queue_positions", "jobs_by_pk"}}, ) def execute_query_post(self) -> Post: """Returns json data for a post of type ExecuteQuery""" query = """ mutation ExecuteQuery($query_id: Int!, $parameters: [Parameter!]!) { execute_query(query_id: $query_id, parameters: $parameters) { job_id } } """ return Post( data={ "operationName": "ExecuteQuery", "variables": { "query_id": self.query_id, "parameters": [p.to_dict() for p in self.parameters], }, "query": query, }, key_map={"execute_query": {"job_id"}}, )

src/duneapi/types.py

from __future__ import annotations import json import os import re from dataclasses import dataclass from datetime import datetime from enum import Enum from typing import Any, Collection, Optional from dotenv import load_dotenv from .logger import set_log from .util import datetime_parser, open_query, postgres_date log = set_log(__name__) PostData = dict[str, Collection[str]] # key_map = {"outer1": {"inner11", "inner12}, "outer2": {"inner21"}} KeyMap = dict[str, set[str]] ListInnerResponse = dict[str, list[dict[str, dict[str, str]]]] DictInnerResponse = dict[str, dict[str, Any]] DuneRecord = dict[str, str] # pylint: disable=too-few-public-methods # TODO - use namedtuple for MetaData and QueryResults class MetaData: """The standard information returned from the Dune API as `query_results`""" id: str job_id: str error: Optional[str] runtime: int generated_at: datetime columns: list[str] def __init__(self, obj: str): """ Constructor method :param obj: input should have the following form Example input: { 'id': '3158cc2c-5ed1-4779-b523-eeb9c3b34b21', 'job_id': '093e440d-66ce-4c00-81ec-2406f0403bc0', 'error': None, 'runtime': 0, 'generated_at': '2022-03-19T07:11:37.344998+00:00', 'columns': ['number', 'size', 'time', 'block_hash', 'tx_fees'], '__typename': 'query_results' } """ self.__dict__ = json.loads(obj, object_hook=datetime_parser) class QueryResults: """Class containing the Data results of a Dune Select Query""" meta: Optional[MetaData] data: list[DuneRecord] def __init__(self, data: ListInnerResponse): assert data.keys() == { "query_results", "get_result_by_job_id", "query_errors", }, f"invalid keys {data.keys()}" assert len(data["query_results"]) == 1, f"Unexpected query_results {data}" # Could wrap meta conversion into a try-catch, since we don't really need it. # But, I can't think of a broad enough exception that won't trip up the liner. self.meta = MetaData(json.dumps(data["query_results"][0])) self.data = [rec["data"] for rec in data["get_result_by_job_id"]] class Network(Enum): """Enum for supported EVM networks""" SOLANA = 1 MAINNET = 4 GCHAIN = 6 POLYGON = 7 OPTIMISM_V1 = 8 BINANCE = 9 OPTIMISM_V2 = 10 def __str__(self) -> str: result = super.__str__(self) if self == Network.SOLANA: result = "Solana" elif self == Network.MAINNET: result = "Ethereum Mainnet" elif self == Network.GCHAIN: result = "Gnosis Chain" elif self == Network.POLYGON: result = "Polygon" elif self == Network.OPTIMISM_V1: result = "Optimism (OVM 1.0)" elif self == Network.OPTIMISM_V2: result = "Optimism (OVM 2.0)" elif self == Network.BINANCE: result = "Binance Smart Chain" return result @classmethod def from_string(cls, network_str: str) -> Network: """ Attempts to parse network name from string. returns None is no match """ patterns = { r"(.*)mainnet": cls.MAINNET, r"g(.*)chain": cls.GCHAIN, r"solana": cls.SOLANA, r"poly": cls.POLYGON, r"optimism(.*)1": cls.OPTIMISM_V1, r"optimism(.*)2": cls.OPTIMISM_V2, r"bsc": cls.BINANCE, r"binance": cls.BINANCE, } for pattern, network in patterns.items(): if re.match(pattern, network_str, re.IGNORECASE): return network raise ValueError(f"could not parse Network from '{network_str}'") class ParameterType(Enum): """ Enum of the 4 distinct dune parameter types """ TEXT = "text" NUMBER = "number" DATE = "datetime" @classmethod def from_string(cls, type_str: str) -> ParameterType: """ Attempts to parse Parameter from string. returns None is no match """ patterns = { r"text": cls.TEXT, r"number": cls.NUMBER, r"date": cls.DATE, } for pattern, network in patterns.items(): if re.match(pattern, type_str, re.IGNORECASE): return network raise ValueError(f"could not parse Network from '{type_str}'") class QueryParameter: """Class whose instances are Dune Compatible Query Parameters""" def __init__( self, name: str, parameter_type: ParameterType, value: Any, ): self.key: str = name self.type: ParameterType = parameter_type self.value = value def __eq__(self, other: object) -> bool: if not isinstance(other, QueryParameter): return NotImplemented return all( [ self.key == other.key, self.value == other.value, self.type.value == other.type.value, ] ) @classmethod def text_type(cls, name: str, value: str) -> QueryParameter: """Constructs a Query parameter of type text""" return cls(name, ParameterType.TEXT, value) @classmethod def number_type(cls, name: str, value: int | float) -> QueryParameter: """Constructs a Query parameter of type number""" return cls(name, ParameterType.NUMBER, value) @classmethod def date_type(cls, name: str, value: datetime | str) -> QueryParameter: """ Constructs a Query parameter of type date. For convenience, we allow proper datetime type, or string """ if isinstance(value, str): value = postgres_date(value) return cls(name, ParameterType.DATE, value) def _value_str(self) -> str: if self.type in (ParameterType.TEXT, ParameterType.NUMBER): return str(self.value) if self.type == ParameterType.DATE: # This is the postgres string format of timestamptz return str(self.value.strftime("%Y-%m-%d %H:%M:%S")) raise TypeError(f"Type {self.type} not recognized!") def to_dict(self) -> dict[str, str]: """Converts QueryParameter into string json format accepted by Dune API""" results = { "key": self.key, "type": self.type.value, "value": self._value_str(), } return results @classmethod def from_dict(cls, obj: dict[str, Any]) -> QueryParameter: """ Constructs Query Parameters from json. TODO - this could probably be done similar to the __init__ method of MetaData """ name, value = obj["key"], obj["value"] p_type = ParameterType.from_string(obj["type"]) if p_type == ParameterType.DATE: return cls.date_type(name, value) if p_type == ParameterType.TEXT: assert isinstance(value, str) return cls.text_type(name, value) if p_type == ParameterType.NUMBER: if isinstance(value, str): value = float(value) if "." in value else int(value) return cls.number_type(name, value) raise ValueError(f"Could not parse Query parameter from {obj}") def __str__(self) -> str: return ( f"QueryParameter(" f"name: {self.key}, " f"value: {self.value}, " f"type: {self.type.value})" ) @dataclass class Post: """Holds query json and response validation details""" data: PostData key_map: KeyMap @dataclass class DashboardTile: """ A slightly different arrangement of data that is essentially equivalent to a Query Acts as an intermediary type when composing queries from json """ name: str description: str select_file: str query_id: int network: Network parameters: list[QueryParameter] base_file: Optional[str] @classmethod def from_dict(cls, obj: dict[str, Any], path: str) -> DashboardTile: """Constructs Record from Dune Data as string dict""" return cls( name=obj.get("name", "untitled"), description=obj.get("description", ""), select_file="/".join([path, obj["query_file"]]), network=Network.from_string(obj["network"]), query_id=int(obj["id"]), parameters=[QueryParameter.from_dict(p) for p in obj.get("parameters", [])], base_file=obj.get("requires"), ) def build_query(self) -> str: """Constructs a query from base file and select file attributes""" if self.base_file is not None: components = map(open_query, [self.base_file, self.select_file]) return "\n".join(list(components)) return open_query(self.select_file) @dataclass class DuneQuery: """Contains all the relevant data necessary to initiate a Dune Query""" name: str description: str raw_sql: str network: Network parameters: list[QueryParameter] query_id: int def __hash__(self) -> int: return hash(self.query_id) def __eq__(self, other: object) -> bool: if not isinstance(other, DuneQuery): return NotImplemented equality_conditions = [ self.name == other.name, self.description == other.description, self.raw_sql == other.raw_sql, self.network.value == other.network.value, self.query_id == other.query_id, self.parameters == other.parameters, ] log.debug(f"Equality Conditions: {equality_conditions}") return all(equality_conditions) @classmethod def from_environment( cls, raw_sql: str, network: Network, description: str = "", parameters: Optional[list[QueryParameter]] = None, name: Optional[str] = None, ) -> DuneQuery: """Constructs a query using the Universal Query ID provided in env file.""" load_dotenv() return cls( raw_sql=raw_sql, description=description, network=network, parameters=parameters if parameters is not None else [], name=name if name else "untitled", query_id=int(os.environ["DUNE_QUERY_ID"]), ) @classmethod def from_tile(cls, tile: DashboardTile) -> DuneQuery: """Constructs Dune Query from DashboardTile object""" return cls( name=tile.name, description=tile.description, raw_sql=tile.build_query(), network=tile.network, parameters=tile.parameters, query_id=tile.query_id, ) def _request_parameters(self) -> list[dict[str, str]]: return [p.to_dict() for p in self.parameters] def upsert_query_post(self) -> Post: """Returns json data for a post of type UpsertQuery""" object_data: dict[str, Any] = { "id": self.query_id, "schedule": None, "dataset_id": self.network.value, "name": self.name, "query": self.raw_sql, "user_id": 84, "description": self.description, "is_archived": False, "is_temp": False, "tags": [], "parameters": self._request_parameters(), "visualizations": { "data": [], "on_conflict": { "constraint": "visualizations_pkey", "update_columns": ["name", "options"], }, }, } key_map = { "insert_queries_one": { "id", "dataset_id", "name", "description", "query", "is_private", "is_temp", "is_archived", "created_at", "updated_at", "schedule", "tags", "parameters", "visualizations", "forked_query", "user", "query_favorite_count_all", "favorite_queries", } } return Post( data={ "operationName": "UpsertQuery", "variables": { "object": object_data, "on_conflict": { "constraint": "queries_pkey", "update_columns": [ "dataset_id", "name", "description", "query", "schedule", "is_archived", "is_temp", "tags", "parameters", ], }, "session_id": 0, # must be an int, but value is irrelevant }, "query": """ mutation UpsertQuery( $session_id: Int! $object: queries_insert_input! $on_conflict: queries_on_conflict! $favs_last_24h: Boolean! = false $favs_last_7d: Boolean! = false $favs_last_30d: Boolean! = false $favs_all_time: Boolean! = true ) { insert_queries_one(object: $object, on_conflict: $on_conflict) { ...Query favorite_queries(where: { user_id: { _eq: $session_id } }, limit: 1) { created_at } } } fragment Query on queries { ...BaseQuery ...QueryVisualizations ...QueryForked ...QueryUsers ...QueryFavorites } fragment BaseQuery on queries { id dataset_id name description query is_private is_temp is_archived created_at updated_at schedule tags parameters } fragment QueryVisualizations on queries { visualizations { id type name options created_at } } fragment QueryForked on queries { forked_query { id name user { name } } } fragment QueryUsers on queries { user { ...User } } fragment User on users { id name profile_image_url } fragment QueryFavorites on queries { query_favorite_count_all @include(if: $favs_all_time) { favorite_count } query_favorite_count_last_24h @include(if: $favs_last_24h) { favorite_count } query_favorite_count_last_7d @include(if: $favs_last_7d) { favorite_count } query_favorite_count_last_30d @include(if: $favs_last_30d) { favorite_count } } """, }, key_map=key_map, ) @staticmethod def find_result_by_job(job_id: str) -> Post: """Returns json data for a post of type FindResultDataByResult""" query = """ query FindResultDataByJob($job_id: uuid!) { query_results(where: {job_id: {_eq: $job_id}, error: {_is_null: true}}) { id job_id runtime generated_at columns } query_errors(where: {job_id: {_eq: $job_id}}) { id job_id runtime message metadata type generated_at } get_result_by_job_id(args: {want_job_id: $job_id}) { data } } """ return Post( data={ "operationName": "FindResultDataByJob", "variables": {"job_id": job_id}, "query": query, }, key_map={ "query_results": { "id", "job_id", "runtime", "generated_at", "columns", }, "query_errors": { "id", "job_id", "runtime", "message", "metadata", "type", "generated_at", }, "get_result_by_job_id": {"data"}, }, ) @staticmethod def get_queue_position(job_id: str) -> Post: """Returns json data for a post of type GetQueuePosition This is meant to determine when query execution has completed. """ query = """ query GetQueuePosition($job_id: uuid!) { view_queue_positions(where: {id: {_eq: $job_id}}) { pos } jobs_by_pk(id: $job_id) { id user_id category created_at locked_until } } """ return Post( data={ "operationName": "GetQueuePosition", "variables": {"job_id": job_id}, "query": query, }, key_map={"data": {"view_queue_positions", "jobs_by_pk"}}, ) def execute_query_post(self) -> Post: """Returns json data for a post of type ExecuteQuery""" query = """ mutation ExecuteQuery($query_id: Int!, $parameters: [Parameter!]!) { execute_query(query_id: $query_id, parameters: $parameters) { job_id } } """ return Post( data={ "operationName": "ExecuteQuery", "variables": { "query_id": self.query_id, "parameters": [p.to_dict() for p in self.parameters], }, "query": query, }, key_map={"execute_query": {"job_id"}}, )

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import asyncio from FIREX.utils import admin_cmd, edit_or_reply, sudo_cmd from userbot import * from userbot.cmdhelp import CmdHelp DEFAULTUSER = str(ALIVE_NAME) if ALIVE_NAME else "ν2.ο" @bot.on(admin_cmd(pattern="think$")) @bot.on(sudo_cmd(pattern="think$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 28) event = await edit_or_reply(event, "ThInKiNg...") animation_chars = [ "THINKING", "THI&K#N₹", "T+IN@I?G", "¿H$NK∆NG", "¶H×NK&N*", "NGITHKIN", "T+I#K@₹G", "THINKING", "THI&K#N₹", "T+IN@I?G", "¿H$NK∆NG", "¶H×NK&N*", "NGITHKIN", "T+I#K@₹G", "THINKING", "THI&K#N₹", "T+IN@I?G", "¿H$NK∆NG", "¶H×NK&N*", "NGITHKIN", "T+I#K@₹G", "THINKING", "THI&K#N₹", "T+IN@I?G", "¿H$NK∆NG", "¶H×NK&N*", "NGITHKIN", "T+I#K@₹G", "THINKING", "THI&K#N₹", "T+IN@I?G", "¿H$NK∆NG", "¶H×NK&N*", "NGITHKIN", "T+I#K@₹G", "THINKING... 🤔", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 36]) @bot.on(admin_cmd(pattern=f"snakes$", outgoing=True)) @bot.on(sudo_cmd(pattern="snakes$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 27) event = await edit_or_reply(event, "Sssss...Snake...") animation_chars = [ "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◻️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◻️◻️️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◻️◻️◻️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "‎◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◼️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◻️◻️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◼️◼️◻️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◼️◼️◻️◻️\n◻️◼️◼️◻️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◼️◼️◻️◻️\n◻️◼️◻️◻️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◼️◼️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◻️◼️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◼️◻️◼️◻️\n◻️◻️◻️◻️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 27]) @bot.on(admin_cmd(pattern=f"humans$", outgoing=True)) @bot.on(sudo_cmd(pattern="humans$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 16) event = await edit_or_reply(event, "Tarzan The Man!!") animation_chars = [ "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛🚗\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛🚗⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛🚗⬛⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛🚗⬛⬛⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛🚗⬛⬛⬛⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛🚗⬛⬛⬛⬛⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n🚗⬛⬛⬛⬛⬛⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬜⬜⬜😊⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛😊⬛⬛⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬛⬛⬜⬛⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛😊⬛⬛⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬛⬛⬜⬛⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n⬛⬛⬜⬛⬛⬜⬛\n⬛⬛⬜⬛⬛⬛⬛\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛😊⬛⬛⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬛⬛⬜⬛⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n⬛⬜⬛⬛⬛⬜⬛\n⬛⬛⬛⬛⬛⬛⬛\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬜⬛😊⬛⬜⬛\n⬛⬛⬜⬜⬜⬛⬛\n⬛⬛⬛⬜⬛⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n⬛⬜⬛⬛⬛⬜⬛\n⬛⬛⬛⬛⬛⬛⬛\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛😊⬛⬛⬛\n⬛⬛⬜⬜⬜⬛⬛\n⬛⬜⬛⬜⬛⬜⬛\n⬛⬛⬜⬛⬜⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬜⬜⬜😊⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 16]) @bot.on(admin_cmd(pattern=f"mcs$", outgoing=True)) @bot.on(sudo_cmd(pattern="mcs$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 28) event = await edit_or_reply(event, "Huh??") animation_chars = [ "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◻️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◻️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◻️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◻️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◻️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◻️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◻️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◻️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◻️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◻️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◻️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◻️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◻️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◻️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◻️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◻️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◻️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◻️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◻️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◻️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◻️◼️◻️◼️\n◼️◼️◼️◼️◼️\n◼️◻️◻️◻️◼️\n◼️◼️◼️◼️◼️", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 28]) @bot.on(admin_cmd(pattern="viruses$")) @bot.on(sudo_cmd(pattern="viruses$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 30) event = await edit_or_reply(event, "Giving this sir/miss Virus!!") animation_chars = [ "🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️◼️◼️️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️◼️◼️◼️🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "‎◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️◼️", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️◼️◼️", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️◼️\n◼️🔴🔵🌕♓♎⛎◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️🔴🔵🌕♓♎⛎◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️\n◼️◼️◼️◼️\n◼️◼️◼️◼️\n◼️◼️◼️◼️", "◼️◼️◼️\n◼️◼️◼️\n◼️◼️◼️", "◼️◼️\n◼️◼️", "◼️", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 30]) @bot.on(admin_cmd(pattern=f"raping$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"raping$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 30) event = await edit_or_reply(event, "Rappppeeeee.....Shhh!!!") animation_chars = [ "**r**", "**ra**", "**rap**", "**rape**", "**rape_**", "**rape_t**", "**rape_tr**", "**rape_tra**", "**rape_trai**", "**rape_train**", "**ape_train🚅**", "**pe_train🚅🚃🚃**", "**e_train🚅🚃🚃🚃**", "**_train🚅🚃🚃🚃🚃**", "**train🚅🚃🚃🚃🚃🚃**", "**rain🚅🚃🚃🚃🚃🚃🚃**", "**ain🚅🚃🚃🚃🚃🚃🚃🚃**", "**in🚅🚃🚃🚃🚃🚃🚃🚃🚃**", "**n🚅🚃🚃🚃🚃🚃🚃🚃🚃🚃**", "🚅🚃🚃🚃🚃🚃🚃🚃🚃🚃", "🚃🚃🚃🚃🚃🚃🚃🚃🚃", "🚃🚃🚃🚃🚃🚃🚃🚃", "🚃🚃🚃🚃🚃🚃🚃", "🚃🚃🚃🚃🚃🚃", "🚃🚃🚃🚃🚃", "🚃🚃🚃🚃", "🚃🚃🚃", "🚃🚃", "🚃", "**rApEd**", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 30]) @bot.on(admin_cmd(pattern=f"nikl$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"nikl$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 6) event = await edit_or_reply(event, "Nikal Lavde Lassun..") animation_chars = [ "`⠀⠀⠀⣠⣶⡾⠏⠉⠙⠳⢦⡀⠀⠀⠀⢠⠞⠉⠙⠲⡀⠀\n ⠀⣴⠿⠏⠀⠀⠀⠀⠀ ⢳⡀⠀⡏⠀⠀⠀ ⠀⢷\n⢠⣟⣋⡀⢀⣀⣀⡀⠀⣀⡀⣧⠀⢸⠀⠀⠀ ⠀ ⡇\n⢸⣯⡭⠁⠸⣛⣟⠆⡴⣻⡲⣿ ⣸ Nikal ⡇\n ⣟⣿⡭⠀⠀⠀⠀⠀⢱⠀⠀ ⣿ ⢹⠀ ⡇\n ⠙⢿⣯⠄⠀⠀⠀__⠀⠀⡿ ⠀⡇⠀⠀⠀⠀ ⡼\n⠀⠀⠀⠹⣶⠆⠀⠀⠀⠀⠀⡴⠃⠀ ⠘⠤⣄⣠⠞⠀\n⠀⠀⠀⠀⢸⣷⡦⢤⡤⢤⣞⣁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀\n⠀⢀⣤⣴⣿⣏⠁⠀⠀⠸⣏⢯⣷⣖⣦⡀⠀⠀⠀⠀⠀⠀\n⢀⣾⣽⣿⣿⣿⣿⠛⢲⣶⣾⢉⡷⣿⣿⠵⣿⠀⠀⠀⠀⠀⠀\n⣼⣿⠍⠉⣿⡭⠉⠙⢺⣇⣼⡏⠀⠀ ⠀⣄⢸⠀⠀⠀⠀⠀⠀`", "`⠀⠀⠀⣠⣶⡾⠏⠉⠙⠳⢦⡀⠀⠀⠀⢠⠞⠉⠙⠲⡀⠀\n ⠀⣴⠿⠏⠀⠀⠀⠀⠀ ⠀⢳⡀⠀⡏⠀⠀⠀ ⠀⢷\n⢠⣟⣋⡀⢀⣀⣀⡀⠀⣀⡀⣧⠀⢸⠀⠀⠀ ⡇\n⢸⣯⡭⠁⠸⣛⣟⠆⡴⣻⡲⣿ ⣸ Lavde ⡇\n ⣟⣿⡭⠀⠀⠀⠀⠀⢱⠀⠀ ⣿ ⢹⠀ ⡇\n ⠙⢿⣯⠄⠀⠀|__|⠀⠀⡿ ⠀⡇⠀⠀⠀⠀ ⡼\n⠀⠀⠀⠹⣶⠆⠀⠀⠀⠀⠀⡴⠃⠀ ⠘⠤⣄⣠⠞⠀\n⠀⠀⠀⠀⢸⣷⡦⢤⡤⢤⣞⣁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀\n⠀⢀⣤⣴⣿⣏⠁⠀⠀⠸⣏⢯⣷⣖⣦⡀⠀⠀⠀⠀⠀⠀\n⢀⣾⣽⣿⣿⣿⣿⠛⢲⣶⣾⢉⡷⣿⣿⠵⣿⠀⠀⠀⠀⠀⠀\n⣼⣿⠍⠉⣿⡭⠉⠙⢺⣇⣼⡏⠀⠀ ⠀⣄⢸⠀⠀⠀⠀⠀⠀`", "`⠀⠀⠀⣠⣶⡾⠏⠉⠙⠳⢦⡀⠀⠀⠀⢠⠞⠉⠙⠲⡀⠀\n ⠀⣴⠿⠏⠀⠀ ⠀⢳⡀⠀⡏⠀⠀ ⠀⢷\n⢠⣟⣋⡀⢀⣀⣀⡀⠀⣀⡀⣧⠀⢸⠀⠀⠀⠀ ⡇\n⢸⣯⡭⠁⠸⣛⣟⠆⡴⣻⡲⣿ ⣸ Pehli ⡇\n ⣟⣿⡭⠀⠀⠀⠀⠀⢱⠀⠀ ⣿ ⢹⠀ ⡇\n ⠙⢿⣯⠄⠀⠀(P)⠀⠀⡿ ⠀⡇⠀⠀⠀⠀ ⡼\n⠀⠀⠀⠹⣶⠆⠀⠀⠀⠀⠀⡴⠃⠀ ⠘⠤⣄⣠⠞⠀\n⠀⠀⠀⠀⢸⣷⡦⢤⡤⢤⣞⣁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀\n⠀⢀⣤⣴⣿⣏⠁⠀⠀⠸⣏⢯⣷⣖⣦⡀⠀⠀⠀⠀⠀⠀\n⢀⣾⣽⣿⣿⣿⣿⠛⢲⣶⣾⢉⡷⣿⣿⠵⣿⠀⠀⠀⠀⠀⠀\n⣼⣿⠍⠉⣿⡭⠉⠙⢺⣇⣼⡏⠀⠀ ⠀⣄⢸⠀⠀⠀⠀⠀⠀`", "`⠀⠀⠀⣠⣶⡾⠏⠉⠙⠳⢦⡀⠀⠀⠀⢠⠞⠉⠙⠲⡀⠀\n ⠀⣴⠿⠏⠀⠀ ⠀⢳⡀⠀⡏⠀⠀ ⠀⢷\n⢠⣟⣋⡀⢀⣀⣀⡀⠀⣀⡀⣧⠀⢸⠀ ⠀ ⡇\n⢸⣯⡭⠁⠸⣛⣟⠆⡴⣻⡲⣿ ⣸ Fursat ⡇\n ⣟⣿⡭⠀⠀⠀⠀⠀⢱⠀ ⣿ ⢹⠀ ⡇\n ⠙⢿⣯⠄⠀⠀⠀__ ⠀⠀⡿ ⠀⡇⠀⠀⠀⠀ ⡼\n⠀⠀⠀⠹⣶⠆⠀⠀⠀⠀⠀⡴⠃⠀ ⠘⠤⣄⣠⠞⠀\n⠀⠀⠀⠀⢸⣷⡦⢤⡤⢤⣞⣁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀\n⠀⢀⣤⣴⣿⣏⠁⠀⠀⠸⣏⢯⣷⣖⣦⡀⠀⠀⠀⠀⠀⠀\n⢀⣾⣽⣿⣿⣿⣿⠛⢲⣶⣾⢉⡷⣿⣿⠵⣿⠀⠀⠀⠀⠀⠀\n⣼⣿⠍⠉⣿⡭⠉⠙⢺⣇⣼⡏⠀⠀ ⠀⣄⢸⠀⠀⠀⠀⠀⠀`", "`⠀⠀⠀⣠⣶⡾⠏⠉⠙⠳⢦⡀⠀⠀⠀⢠⠞⠉⠙⠲⡀⠀\n ⠀⣴⠿⠏⠀⠀⠀⠀⠀ ⢳⡀⠀⡏⠀⠀ ⠀⢷\n⢠⣟⣋⡀⢀⣀⣀⡀⠀⣀⡀⣧⠀⢸⠀⠀ ⠀ ⡇\n⢸⣯⡭⠁⠸⣛⣟⠆⡴⣻⡲⣿ ⣸ Meeee ⡇\n ⣟⣿⡭⠀⠀⠀⠀⠀⢱⠀⠀ ⣿ ⢹⠀ ⡇\n ⠙⢿⣯⠄⠀⠀|__| ⠀⡿ ⠀⡇⠀⠀⠀⠀ ⡼\n⠀⠀⠀⠹⣶⠆⠀⠀⠀⠀⠀⡴⠃⠀ ⠘⠤⣄⣠⠞⠀\n⠀⠀⠀⠀⢸⣷⡦⢤⡤⢤⣞⣁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀\n⠀⢀⣤⣴⣿⣏⠁⠀⠀⠸⣏⢯⣷⣖⣦⡀⠀⠀⠀⠀⠀⠀\n⢀⣾⣽⣿⣿⣿⣿⠛⢲⣶⣾⢉⡷⣿⣿⠵⣿⠀⠀⠀⠀⠀⠀\n⣼⣿⠍⠉⣿⡭⠉⠙⢺⣇⣼⡏⠀⠀ ⠀⣄⢸⠀⠀⠀⠀⠀⠀`", "`⠀⠀⠀⣠⣶⡾⠏⠉⠙⠳⢦⡀⠀⠀⠀⢠⠞⠉⠙⠲⡀⠀\n ⠀⣴⠿⠏⠀⠀⠀⠀⠀ ⠀⢳⡀⠀⡏⠀⠀ ⠀⢷\n⢠⣟⣋⡀⢀⣀⣀⡀⠀⣀⡀⣧⠀⢸⠀ ⠀ ⡇\n⢸⣯⡭⠁⠸⣛⣟⠆⡴⣻⡲⣿ ⣸ Nikal ⡇\n ⣟⣿⡭⠀⠀⠀⠀⠀⢱⠀ ⣿ ⢹⠀ ⡇\n ⠙⢿⣯⠄⠀⠀lodu⠀⠀⡿ ⠀⡇⠀⠀⠀⠀ ⡼\n⠀⠀⠀⠹⣶⠆⠀⠀⠀⠀⠀⡴⠃⠀ ⠘⠤⣄⣠⠞⠀\n⠀⠀⠀⠀⢸⣷⡦⢤⡤⢤⣞⣁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀\n⠀⢀⣤⣴⣿⣏⠁⠀⠀⠸⣏⢯⣷⣖⣦⡀⠀⠀⠀⠀⠀⠀\n⢀⣾⣽⣿⣿⣿⣿⠛⢲⣶⣾⢉⡷⣿⣿⠵⣿⠀⠀⠀⠀⠀⠀\n⣼⣿⠍⠉⣿⡭⠉⠙⢺⣇⣼⡏⠀⠀ ⠀⣄⢸⠀⠀⠀⠀⠀⠀`", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 6]) @bot.on(admin_cmd(pattern=f"musics$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"musics$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1.5 animation_ttl = range(0, 11) event = await edit_or_reply(event, "Booty..Booty..Booty shake...Kan chil gye na?😂") animation_chars = [ "⬤⬤⬤ 81% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:00** ▱▱▱▱▱▱▱▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `▶️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀ **⠀Device: Nokia 1100**", "⬤⬤⬤ 81% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:01** ▰▱▱▱▱▱▱▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀ **⠀Device: Nokia 1100**", "⬤⬤⬤ 81% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀ **Now Playing: Shape of u**\n\n**00:02** ▰▰▱▱▱▱▱▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤⬤ 81% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:03** ▰▰▰▱▱▱▱▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤◯ 80% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:04** ▰▰▰▰▱▱▱▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤◯ 80% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:05** ▰▰▰▰▱▱▱▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤◯ 80% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:06** ▰▰▰▰▰▰▱▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤◯ 80% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:07** ▰▰▰▰▰▰▰▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤◯ 80% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:08** ▰▰▰▰▰▰▰▰▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤◯ 80% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:09** ▰▰▰▰▰▰▰▰▰▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤◯ 80% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:10** ▰▰▰▰▰▰▰▰▰▰ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏺️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"squeeze$", outgoing=True)) @bot.on(sudo_cmd(pattern="squeeze$", allow_sudo=True)) async def _(event): if event.fwd_from: return event = await edit_or_reply( event, "╔═══════════════════╗ \n \n╚═══════════════════╝" ) await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n \t░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n ░ \t░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n ░ ░ ░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n ░ ░ ░ ░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n ░ ░ ░ ░ ░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit( "╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝" ) await asyncio.sleep(1) await event.edit( "╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝" ) await asyncio.sleep(1) await event.edit( "╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝" ) await asyncio.sleep(1) await event.edit( "╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝" ) await asyncio.sleep(1) await event.edit( "╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝" ) await asyncio.sleep(1) await event.edit( "╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝" ) await asyncio.sleep(6) @bot.on(admin_cmd(pattern=f"loadings$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"loadings$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 20) event = await edit_or_reply(event, "▯") animation_chars = ["▮", "▯", "▬", "▭", "‎"] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 4]) @bot.on(admin_cmd(pattern=f"squares$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"squares$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 20) event = await edit_or_reply(event, "◨") animation_chars = ["◧", "◨", "◧", "◨", ""] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 4]) @bot.on(admin_cmd(pattern=f"ups$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"ups$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 20) event = await edit_or_reply(event, "╻") animation_chars = ["╹", "╻", "╹", "╻", "‎"] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 4]) @bot.on(admin_cmd(pattern=f"rounds$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"rounds$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 20) event = await edit_or_reply(event, "Round...") animation_chars = ["⚫", "⬤", "●", "∘", "‎"] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 4]) @bot.on(admin_cmd(pattern=f"hearts$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"hearts$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 20) event = await edit_or_reply(event, "❤️") animation_chars = ["🖤", "❤️", "🖤", "❤️", "‎"] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 4]) @bot.on(admin_cmd(pattern=f"anime$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"anime$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 20) event = await edit_or_reply(event, "😢") animation_chars = [ "😁", "😧", "😡", "😢", "‎**Repo of eviral**", "😁", "😧", "😡", "😢", "__**[Good to see you Guys....]**__(github.com/Teameviral/FIREX)", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 10]) @bot.on(admin_cmd(pattern=f"fnls$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"fnls$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 2 animation_ttl = range(0, 6) event = await edit_or_reply(event, "Hey There....") animation_chars = ["😁🏿", "😁🏾", "😁🏽", "😁🏼", "‎😁", "**Good to see you Firand....**"] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 6]) @bot.on(admin_cmd(pattern=f"monkeys$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"monkeys$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 2 animation_ttl = range(0, 12) event = await edit_or_reply(event, "Hey There....") animation_chars = ["🐵", "🙉", "🙈", "🙊", "🖕‎🐵🖕", "**Good to see you Firand....**"] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 6]) @bot.on(admin_cmd(pattern=f"herbers$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"herbers$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 2 animation_ttl = range(0, 10) event = await edit_or_reply(event, "Power On......") animation_chars = [ "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 10%\n\n ●○○○○○○○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 5.9%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 8.13GB\n **🔹used:** 33.77GB\n **🔹total:** 60.0GB\n \n ●●●●●●●○○○\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 158.98GB\n **🔹recv:** 146.27GB\n **🔹sent_packets:** 84518799\n **🔹recv_packets:** 159720314\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 30%\n\n ●●●○○○○○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 20.4%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 7.18GB\n **🔹used:** 28.26GB\n **🔹total:** 60.0GB\n \n ●●●●●●●●●●\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 146.27GB\n **🔹recv:** 124.33GB\n **🔹sent_packets:** 54635686\n **🔹recv_packets:** 143565654\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 60%\n\n ●●●●●●○○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 60.9%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 6.52GB\n **🔹used:** 35.78GB\n **🔹total:** 60.0GB\n \n ●●●○○○○○○○\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 124.33GB\n **🔹recv:** 162.48GB\n **🔹sent_packets:** 25655655\n **🔹recv_packets:** 165289456\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 100%\n\n ●●●●●●●●●●\n\n **🔹cpu core**\n **🔹core_usage:** 100.0%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 9.81GB\n **🔹used:** 30.11GB\n **🔹total:** 60.0GB\n \n ●●●●●●●●●●\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 162.48GB\n **🔹recv:** 175.75GB\n **🔹sent_packets:** 56565435\n **🔹recv_packets:** 135345655\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 70%\n\n ●●●●●●●○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 80.4%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 5.76GB\n **🔹used:** 29.35GB\n **🔹total:** 60.0GB\n \n ●●●●●●●○○○\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 175.75GB\n **🔹recv:** 118.55GB\n **🔹sent_packets:** 36547698\n **🔹recv_packets:** 185466554\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 60%\n\n ●●●●●●○○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 62.9%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 8.23GB\n **🔹used:** 33.32GB\n **🔹total:** 60.0GB\n \n ●●●●●●○○○○\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 118.55GB\n **🔹recv:** 168.65GB\n **🔹sent_packets:** 24786554\n **🔹recv_packets:** 156745865\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 30%\n\n ●●●○○○○○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 30.6%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 9.75GB\n **🔹used:** 36.54GB\n **🔹total:** 60.0GB\n \n ●●●●●●●●●●\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 168.65GB\n **🔹recv:** 128.35GB\n **🔹sent_packets:** 56565435\n **🔹recv_packets:** 1475823589\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 10%\n\n ●○○○○○○○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 10.2%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 10.20GB\n **🔹used:** 25.40GB\n **🔹total:** 60.0GB\n \n ●●●●●●○○○○\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 128.35GB\n **🔹recv:** 108.31GB\n **🔹sent_packets:** 54635686\n **🔹recv_packets:** 157865426\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 100%\n\n ●●●●●●●●●●\n\n **🔹cpu core**\n\n **🔹core_usage:** 100.0%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 5.25GB\n **🔹used:** 31.14GB\n **🔹total:** 60.0GB\n \n ●●●●●●●●●●\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 108.31GB\n **🔹recv:** 167.17GB\n **🔹sent_packets:** 84518799\n **🔹recv_packets:** 124575356\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 70%\n\n ●●●●●●●○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 76.2%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 8.01GB\n **🔹used:** 33.27GB\n **🔹total:** 60.0GB\n \n ●●●○○○○○○○\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 167.17GB\n **🔹recv:** 158.98GB\n **🔹sent_packets:** 36547698\n **🔹recv_packets:** 165455856\n\n\n**===================**\n", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 10]) @bot.on(admin_cmd(pattern=f"hands$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"hands$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 13) event = await edit_or_reply(event, "🖐️") animation_chars = [ "👈", "👉", "☝️", "👆", "🖕", "👇", "✌️", "🤞", "🖖", "🤘", "🤙", "🖐️", "👌", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 13]) @bot.on(admin_cmd(pattern=f"gsgs$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"gsgs$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 12) event = await edit_or_reply(event, "ContDown....") animation_chars = [ "🔟", "9️⃣", "8️⃣", "7️⃣", "6️⃣", "5️⃣", "4️⃣", "3️⃣", "2️⃣", "1️⃣", "0️⃣", "🆘", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 12]) @bot.on(admin_cmd(pattern=r"thearts$", outgoing=True)) @bot.on(sudo_cmd(pattern=r"thearts$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 0.3 animation_ttl = range(0, 54) event = await edit_or_reply(event, "🖤") animation_chars = [ "❤️", "🧡", "💛", "💚", "💙", "💜", "🖤", "💘", "💝", "❤️", "🧡", "💛", "💚", "💙", "💜", "🖤", "💘", "💝", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 18]) @bot.on(admin_cmd(pattern="unoobs$")) @bot.on(sudo_cmd(pattern="unoobs$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 9) event = await edit_or_reply(event, "U nOoB") animation_chars = [ "EvErYbOdY", "iZ", "BiGGeSt", "NoOoB", "uNtiL", "YoU", "aRriVe", "😈", "EvErYbOdY iZ BiGGeSt NoOoB uNtiL YoU aRriVe 😈", ] for i in animation_ttl: await event.edit(animation_chars[i % 9]) await asyncio.sleep(animation_interval) @bot.on(admin_cmd(pattern="menoobs$")) @bot.on(sudo_cmd(pattern="menoobs$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 9) event = await edit_or_reply(event, "mE nOoB") animation_chars = [ "EvErYbOdY", "iZ", "BiGGeSt", "NoOoB", "uNtiL", "i", "aRriVe", "😈", "EvErYbOdY iZ BiGGeSt NoOoB uNtiL i aRriVe 😈", ] for i in animation_ttl: await event.edit(animation_chars[i % 9]) await asyncio.sleep(animation_interval) @bot.on(admin_cmd(pattern="upros$")) @bot.on(sudo_cmd(pattern="upros$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 8) event = await edit_or_reply(event, "U pRo") animation_chars = [ "EvErYbOdY", "iZ", "PeRu", "uNtiL", "YoU", "aRriVe", "😈", "EvErYbOdY iZ PeRu uNtiL YoU aRriVe 😈", ] for i in animation_ttl: await event.edit(animation_chars[i % 8]) await asyncio.sleep(animation_interval) @bot.on(admin_cmd(pattern="mepros$")) @bot.on(sudo_cmd(pattern="mepros$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 8) event = await edit_or_reply(event, "mE pRo") animation_chars = [ "EvErYbOdY", "iZ", "PeRu", "uNtiL", "i", "aRriVe", "😈", "EvErYbOdY iZ PeRu uNtiL i aRriVe 😈", ] for i in animation_ttl: await event.edit(animation_chars[i % 8]) await asyncio.sleep(animation_interval) @bot.on(admin_cmd(pattern=f"quickheals$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"quickheals$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 3 animation_ttl = range(0, 11) event = await edit_or_reply(event, "QuickHeaml") animation_chars = [ "`Downloading File..`", "`File Downloaded....`", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 0%\n▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 4%\n█▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 8%\n██▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 20%\n█████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 36%\n█████████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 52%\n█████████████▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 84%\n█████████████████████▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 100%\n█████████████████████████ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nTask: 01 of 01 Files Scanned...\n\nResult: No Virus Found...`", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"sqhs$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"sqhs$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 3 animation_ttl = range(0, 11) event = await edit_or_reply(event, "SQH") animation_chars = [ "`Downloading File..`", "`File Downloaded....`", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 0%\n▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 4%\n█▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 8%\n██▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 20%\n█████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 36%\n█████████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 52%\n█████████████▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 84%\n█████████████████████▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 100%\n█████████████████████████ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nTask: 01 of 01 Files Scanned...\n\nResult: No Virus Found...`", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"medkit$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"medkit$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 3 animation_ttl = range(0, 11) event = await edit_or_reply(event, "Injecting Virus") animation_chars = [ "`Downloading File..`", "`File Downloaded....`", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 0%\n▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 4%\n█▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 8%\n██▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 20%\n█████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 36%\n█████████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 52%\n█████████████▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 84%\n█████████████████████▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 100%\n█████████████████████████ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nTask: 01 of 01 Files Scanned...\n\nResult: ⚠️Virus Found⚠️\nMore Info: Torzan, Spyware, Adware`", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"macoss$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"macoss$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 11) event = await edit_or_reply(event, "Now My Phone Become Mac(Apple Laptop)") animation_chars = [ "`Connecting To Hackintosh...`", "`Initiating Hackintosh Login.`", "`Loading Hackintosh... 0%\n▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Hackintosh... 3%\n█▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Hackintosh... 9%\n██▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Hackintosh... 23%\n█████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Hackintosh... 39%\n█████████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Hackintosh... 69%\n█████████████▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Hackintosh... 89%\n█████████████████████▒▒▒▒ `", "`Loading Hackintosh... 100%\n█████████████████████████ `", "`Welcome...\n\nStock OS: Symbian OS\nCurrent OS: Hackintosh`\n\n**My PC Specs:**\n\n **CPU:** __2.9GHz Intel Core i9-8950HK (hexa-core, 12MB cache, up to 4.8GHz)__\n\n**Graphics:** __Nvidia GeForce GTX 1080 OC (8GB GDDR5X)__\n\n**RAM:** __32GB DDR4 (2,666MHz)__\n\n**Screen:** __17.3-inch, QHD (2,560 x 1,440) 120Hz G-Sync__\n\n**Storage:** __512GB PCIe SSD, 1TB HDD (7,200 rpm)__\n\n**Ports:** __2 x USB 3.0, 1 x USB-C 3.0, 1 x USB-C (Thunderbolt 3), HDMI, mini DisplayPort, Ethernet, headphone jack, microphone jack__\n\n**Connectivity:** __Killer 1550 802.11ac Wi-Fi, Bluetooth 5.0__\n\n**Camera:** __Alienware FHD camera, Tobii IR Eye-tracking with Windows hello__\n\n**Size:** __16.7 x 13.1 x 1.18 inches (42.4 x 33.2 x 2.99cm; W x D x H)__", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"window$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"window$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 11) event = await edit_or_reply(event, "Now My Phone Become Windows") animation_chars = [ "`Connecting To Windows 10...`", "`Initiating Windows 10 Login.`", "`Loading Windows 10... 0%\n▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Windows 10... 3%\n█▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Windows 10... 9%\n██▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Windows 10... 23%\n█████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Windows 10... 39%\n█████████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Windows 10... 69%\n█████████████▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Windows 10... 89%\n█████████████████████▒▒▒▒ `", "`Loading Windows 10... 100%\n█████████████████████████ `", "`Welcome...\n\nStock OS: Symbian OS\nCurrent OS: Windows 10`\n\n**My PC Specs:**\n\n **CPU:** __3.4GHz ryzen 9 5950x (16-core,32 threads 64MB cache, up to 4.9GHz)__\n\n**Graphics:** __Nvidia GeForce RTX 3090 OC (24GB GDDR6X)__\n\n**RAM:** __64GB DDR4 (4000MHz)__\n\n**Screen:** __17.3-inch, UHD (3840 x 2160) 144Hz Hdr G-Sync__\n\n**Storage:** __512GB nvme gen 4 SSD, 5 TB HDD (7,200 rpm)__\n\n**Ports:** __2 x USB 3.1, 1 x USB-C 3.0, 1 x USB-C (Thunderbolt 3), 2 HDMI2.0, mini DisplayPort, Ethernet, headphone jack, microphone jack__\n\n**Connectivity:** __Killer 1550 802.11ac Wi-Fi, Bluetooth 5.0__\n\n**Camera:** __Alienware FHD camera, Tobii IR Eye-tracking with Windows hello__\n\n**Size:** __16.7 x 13.1 x 1.18 inches (42.4 x 33.2 x 2.99cm; W x D x H)__", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"linuxs$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"linuxs$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 0.5 animation_ttl = range(0, 11) event = await edit_or_reply(event, "Now My Phone Become Linux") animation_chars = [ "`Connecting To Linux...`", "`Initiating Linux Login.`", "`Loading Linux... 0%\n▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Linux... 3%\n█▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Linux... 9%\n██▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Linux... 23%\n█████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Linux... 39%\n█████████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Linux... 69%\n█████████████▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Linux... 89%\n█████████████████████▒▒▒▒ `", "`Loading Linux... 100%\n█████████████████████████ `", "`Welcome...\n\nStock OS: Symbian OS\nCurrent OS: Linux`\n\n**My PC Specs:**\n\n **CPU:** __2.9GHz Intel Core i9-8950HK (hexa-core, 12MB cache, up to 4.8GHz)__\n\n**Graphics:** __Nvidia GeForce GTX 1080 OC (8GB GDDR5X)__\n\n**RAM:** __32GB DDR4 (2,666MHz)__\n\n**Screen:** __17.3-inch, QHD (2,560 x 1,440) 120Hz G-Sync__\n\n**Storage:** __512GB PCIe SSD, 1TB HDD (7,200 rpm)__\n\n**Ports:** __2 x USB 3.0, 1 x USB-C 3.0, 1 x USB-C (Thunderbolt 3), HDMI, mini DisplayPort, Ethernet, headphone jack, microphone jack__\n\n**Connectivity:** __Killer 1550 802.11ac Wi-Fi, Bluetooth 5.0__\n\n**Camera:** __Alienware FHD camera, Tobii IR Eye-tracking with Windows hello__\n\n**Size:** __16.7 x 13.1 x 1.18 inches (42.4 x 33.2 x 2.99cm; W x D x H)__", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"stocks$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"stocks$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 0.5 animation_ttl = range(0, 11) event = await edit_or_reply(event, "Stocks") animation_chars = [ "`Connecting To Symbian OS...`", "`Initiating Symbian OS Login.`", "`Loading Symbian OS... 0%\n█████████████████████████ `", "`Loading Symbian OS... 3%\n█████████████████████▒▒▒▒ `", "`Loading Symbian OS... 9%\n█████████████▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Symbian OS... 23%\n█████████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Symbian OS... 39%\n█████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Symbian OS... 69%\n██▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Symbian OS... 89%\n█▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Symbian OS... 100%\n▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Welcome...\n\nStock OS: Symbian OS\nCurrent OS: Symbian OS`\n\n**My PC Specs:**\n\n **CPU:** __2.9GHz Intel Core i9-8950HK (hexa-core, 12MB cache, up to 4.8GHz)__\n\n**Graphics:** __Nvidia GeForce GTX 1080 OC (8GB GDDR5X)__\n\n**RAM:** __32GB DDR4 (2,666MHz)__\n\n**Screen:** __17.3-inch, QHD (2,560 x 1,440) 120Hz G-Sync__\n\n**Storage:** __512GB PCIe SSD, 1TB HDD (7,200 rpm)__\n\n**Ports:** __2 x USB 3.0, 1 x USB-C 3.0, 1 x USB-C (Thunderbolt 3), HDMI, mini DisplayPort, Ethernet, headphone jack, microphone jack__\n\n**Connectivity:** __Killer 1550 802.11ac Wi-Fi, Bluetooth 5.0__\n\n**Camera:** __Alienware FHD camera, Tobii IR Eye-tracking with Windows hello__\n\n**Size:** __16.7 x 13.1 x 1.18 inches (42.4 x 33.2 x 2.99cm; W x D x H)__", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"oss$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"oss$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 0.1 animation_ttl = range(0, 7) event = await edit_or_reply(event, "OS") animation_chars = [ "`Scanning OS...`", "`Scanning OS......`", "__Current Loaded OS: Symbian OS__\n\n**To Boot Other OS, Use The Following Trigger:**\n☑️ `.macos`\n☑️ `.windows`\n☑️ `.linux`\n☑️ `.stock`", "__Current Loaded OS: Symbian OS__\n\n**To Boot Other OS, Use The Following Trigger:**\n✅ `.macos`\n☑️ `.windows`\n☑️ `.linux`\n☑️ `.stock`", "__Current Loaded OS: Symbian OS__\n\n**To Boot Other OS, Use The Following Trigger:**\n✅ `.macos`\n✅ `.windows`\n☑️ `.linux`\n☑️ `.stock`", "__Current Loaded OS: Symbian OS__\n\n**To Boot Other OS, Use The Following Trigger:**\n✅ `.macos`\n✅ `.windows`\n✅ `.linux`\n☑️ `.stock`", "__Current Loaded OS: Symbian OS__\n\n**To Boot Other OS, Use The Following Trigger:**\n✅ `.macos`\n✅ `.windows`\n✅ `.linux`\n✅ `.stock`\n\nDeveloped By: @Eviral", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 7]) CmdHelp("animation").add_command("eye", None, "Use And see").add_command( "think", None, "Use and see" ).add_command("snakes", None, "Use and see").add_command( "humans", None, "Use and see" ).add_command( "mcs", None, "Use and see" ).add_command( "viruses", None, "Use and see" ).add_command( "raping", None, "Use and see" ).add_command( "nikl", None, "Use and see" ).add_command( "musics", None, "Use and see" ).add_command( "squeeze", None, "Use and see" ).add_command( "loadings", None, "use and see" ).add_command( "squares", None, "Use and see" ).add_command( "ups", None, "Use and see" ).add_command( "rounds", None, "use and see" ).add_command( "hearts", None, "Use And See" ).add_command( "animes", None, "Use And See" ).add_command( "fmls", None, "Use And See" ).add_command( "monkeys", None, "Use and see" ).add_command( "herbers", None, "Use and see" ).add_command( "hands", None, "Use and see" ).add_command( "gsgs", None, "Use and see" ).add_command( "unoobs", None, "Use and see" ).add_command( "menoobs", None, "Use and see" ).add_command( "upros", None, "Use and see" ).add_command( "mepros", None, "Use and see" ).add_command( "thearts", None, "Use and see" ).add_command( "quickheal", None, "use and see" ).add_command( "sqhs", None, "Use and see" ).add_command( "medkit", None, "Use and see" ).add_command( "macos", None, "use and see" ).add_command( "window", None, "Use And See" ).add_command( "linuxs", None, "Use And See" ).add_command( "stocks", None, "Use And See" ).add_command( "oss", None, "Use And See" ).add_type( "Official" ).add_info( "Its Very Useful Module too much just use these u must(might) have fun" ).add_warning( "Harmless Module✅" ).add_type( "Addons" ).add()

userbot/plugins/Xtra_Plugin/animations.py

import asyncio from FIREX.utils import admin_cmd, edit_or_reply, sudo_cmd from userbot import * from userbot.cmdhelp import CmdHelp DEFAULTUSER = str(ALIVE_NAME) if ALIVE_NAME else "ν2.ο" @bot.on(admin_cmd(pattern="think$")) @bot.on(sudo_cmd(pattern="think$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 28) event = await edit_or_reply(event, "ThInKiNg...") animation_chars = [ "THINKING", "THI&K#N₹", "T+IN@I?G", "¿H$NK∆NG", "¶H×NK&N*", "NGITHKIN", "T+I#K@₹G", "THINKING", "THI&K#N₹", "T+IN@I?G", "¿H$NK∆NG", "¶H×NK&N*", "NGITHKIN", "T+I#K@₹G", "THINKING", "THI&K#N₹", "T+IN@I?G", "¿H$NK∆NG", "¶H×NK&N*", "NGITHKIN", "T+I#K@₹G", "THINKING", "THI&K#N₹", "T+IN@I?G", "¿H$NK∆NG", "¶H×NK&N*", "NGITHKIN", "T+I#K@₹G", "THINKING", "THI&K#N₹", "T+IN@I?G", "¿H$NK∆NG", "¶H×NK&N*", "NGITHKIN", "T+I#K@₹G", "THINKING... 🤔", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 36]) @bot.on(admin_cmd(pattern=f"snakes$", outgoing=True)) @bot.on(sudo_cmd(pattern="snakes$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 27) event = await edit_or_reply(event, "Sssss...Snake...") animation_chars = [ "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◻️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◻️◻️️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◻️◻️◻️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "‎◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◼️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◻️◻️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◼️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◼️◼️◼️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◼️◼️◻️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◼️◼️◻️◻️\n◻️◼️◼️◻️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◼️◼️◻️◻️\n◻️◼️◻️◻️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◼️◼️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◻️◼️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️\n◻️◻️◻️◻️◻️", "◻️◻️◻️◻️◻️\n◻️◼️◻️◼️◻️\n◻️◻️◻️◻️◻️\n◻️◼️◼️◼️◻️\n◻️◻️◻️◻️◻️", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 27]) @bot.on(admin_cmd(pattern=f"humans$", outgoing=True)) @bot.on(sudo_cmd(pattern="humans$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 16) event = await edit_or_reply(event, "Tarzan The Man!!") animation_chars = [ "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛🚗\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛🚗⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛🚗⬛⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛🚗⬛⬛⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛🚗⬛⬛⬛⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛🚗⬛⬛⬛⬛⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n🚗⬛⬛⬛⬛⬛⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬜⬜⬜⬜⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬜⬜⬜😊⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛😊⬛⬛⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬛⬛⬜⬛⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛😊⬛⬛⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬛⬛⬜⬛⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n⬛⬛⬜⬛⬛⬜⬛\n⬛⬛⬜⬛⬛⬛⬛\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛😊⬛⬛⬛\n⬛⬜⬜⬜⬜⬜⬛\n⬛⬛⬛⬜⬛⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n⬛⬜⬛⬛⬛⬜⬛\n⬛⬛⬛⬛⬛⬛⬛\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬜⬛😊⬛⬜⬛\n⬛⬛⬜⬜⬜⬛⬛\n⬛⬛⬛⬜⬛⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n⬛⬜⬛⬛⬛⬜⬛\n⬛⬛⬛⬛⬛⬛⬛\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛😊⬛⬛⬛\n⬛⬛⬜⬜⬜⬛⬛\n⬛⬜⬛⬜⬛⬜⬛\n⬛⬛⬜⬛⬜⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n⬛⬛⬜⬛⬜⬛⬛\n🔲🔲🔲🔲🔲🔲🔲", "⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬛⬛⬛⬛⬛⬛⬛\n⬜⬜⬜😊⬜⬜⬜\n⬜⬜⬜⬜⬜⬜⬜\n🔲🔲🔲🔲🔲🔲🔲", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 16]) @bot.on(admin_cmd(pattern=f"mcs$", outgoing=True)) @bot.on(sudo_cmd(pattern="mcs$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 28) event = await edit_or_reply(event, "Huh??") animation_chars = [ "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◻️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◻️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◻️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◻️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◻️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◻️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◻️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◻️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◻️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◻️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◻️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◻️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◻️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◻️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◻️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◻️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◻️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◻️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◻️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◻️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◻️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◻️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◻️◼️◻️◼️\n◼️◼️◼️◼️◼️\n◼️◻️◻️◻️◼️\n◼️◼️◼️◼️◼️", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 28]) @bot.on(admin_cmd(pattern="viruses$")) @bot.on(sudo_cmd(pattern="viruses$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 30) event = await edit_or_reply(event, "Giving this sir/miss Virus!!") animation_chars = [ "🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️◼️◼️️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️◼️◼️◼️🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "‎◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️◼️", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️◼️◼️", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️◼️\n◼️🔴🔵🌕♓♎⛎◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️🔴🔵🌕♓♎⛎🔴🔵🌕♓♎⛎◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️🔴🔵🌕♓♎⛎◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️\n◼️◼️◼️◼️◼️", "◼️◼️◼️◼️\n◼️◼️◼️◼️\n◼️◼️◼️◼️\n◼️◼️◼️◼️", "◼️◼️◼️\n◼️◼️◼️\n◼️◼️◼️", "◼️◼️\n◼️◼️", "◼️", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 30]) @bot.on(admin_cmd(pattern=f"raping$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"raping$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 30) event = await edit_or_reply(event, "Rappppeeeee.....Shhh!!!") animation_chars = [ "**r**", "**ra**", "**rap**", "**rape**", "**rape_**", "**rape_t**", "**rape_tr**", "**rape_tra**", "**rape_trai**", "**rape_train**", "**ape_train🚅**", "**pe_train🚅🚃🚃**", "**e_train🚅🚃🚃🚃**", "**_train🚅🚃🚃🚃🚃**", "**train🚅🚃🚃🚃🚃🚃**", "**rain🚅🚃🚃🚃🚃🚃🚃**", "**ain🚅🚃🚃🚃🚃🚃🚃🚃**", "**in🚅🚃🚃🚃🚃🚃🚃🚃🚃**", "**n🚅🚃🚃🚃🚃🚃🚃🚃🚃🚃**", "🚅🚃🚃🚃🚃🚃🚃🚃🚃🚃", "🚃🚃🚃🚃🚃🚃🚃🚃🚃", "🚃🚃🚃🚃🚃🚃🚃🚃", "🚃🚃🚃🚃🚃🚃🚃", "🚃🚃🚃🚃🚃🚃", "🚃🚃🚃🚃🚃", "🚃🚃🚃🚃", "🚃🚃🚃", "🚃🚃", "🚃", "**rApEd**", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 30]) @bot.on(admin_cmd(pattern=f"nikl$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"nikl$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 6) event = await edit_or_reply(event, "Nikal Lavde Lassun..") animation_chars = [ "`⠀⠀⠀⣠⣶⡾⠏⠉⠙⠳⢦⡀⠀⠀⠀⢠⠞⠉⠙⠲⡀⠀\n ⠀⣴⠿⠏⠀⠀⠀⠀⠀ ⢳⡀⠀⡏⠀⠀⠀ ⠀⢷\n⢠⣟⣋⡀⢀⣀⣀⡀⠀⣀⡀⣧⠀⢸⠀⠀⠀ ⠀ ⡇\n⢸⣯⡭⠁⠸⣛⣟⠆⡴⣻⡲⣿ ⣸ Nikal ⡇\n ⣟⣿⡭⠀⠀⠀⠀⠀⢱⠀⠀ ⣿ ⢹⠀ ⡇\n ⠙⢿⣯⠄⠀⠀⠀__⠀⠀⡿ ⠀⡇⠀⠀⠀⠀ ⡼\n⠀⠀⠀⠹⣶⠆⠀⠀⠀⠀⠀⡴⠃⠀ ⠘⠤⣄⣠⠞⠀\n⠀⠀⠀⠀⢸⣷⡦⢤⡤⢤⣞⣁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀\n⠀⢀⣤⣴⣿⣏⠁⠀⠀⠸⣏⢯⣷⣖⣦⡀⠀⠀⠀⠀⠀⠀\n⢀⣾⣽⣿⣿⣿⣿⠛⢲⣶⣾⢉⡷⣿⣿⠵⣿⠀⠀⠀⠀⠀⠀\n⣼⣿⠍⠉⣿⡭⠉⠙⢺⣇⣼⡏⠀⠀ ⠀⣄⢸⠀⠀⠀⠀⠀⠀`", "`⠀⠀⠀⣠⣶⡾⠏⠉⠙⠳⢦⡀⠀⠀⠀⢠⠞⠉⠙⠲⡀⠀\n ⠀⣴⠿⠏⠀⠀⠀⠀⠀ ⠀⢳⡀⠀⡏⠀⠀⠀ ⠀⢷\n⢠⣟⣋⡀⢀⣀⣀⡀⠀⣀⡀⣧⠀⢸⠀⠀⠀ ⡇\n⢸⣯⡭⠁⠸⣛⣟⠆⡴⣻⡲⣿ ⣸ Lavde ⡇\n ⣟⣿⡭⠀⠀⠀⠀⠀⢱⠀⠀ ⣿ ⢹⠀ ⡇\n ⠙⢿⣯⠄⠀⠀|__|⠀⠀⡿ ⠀⡇⠀⠀⠀⠀ ⡼\n⠀⠀⠀⠹⣶⠆⠀⠀⠀⠀⠀⡴⠃⠀ ⠘⠤⣄⣠⠞⠀\n⠀⠀⠀⠀⢸⣷⡦⢤⡤⢤⣞⣁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀\n⠀⢀⣤⣴⣿⣏⠁⠀⠀⠸⣏⢯⣷⣖⣦⡀⠀⠀⠀⠀⠀⠀\n⢀⣾⣽⣿⣿⣿⣿⠛⢲⣶⣾⢉⡷⣿⣿⠵⣿⠀⠀⠀⠀⠀⠀\n⣼⣿⠍⠉⣿⡭⠉⠙⢺⣇⣼⡏⠀⠀ ⠀⣄⢸⠀⠀⠀⠀⠀⠀`", "`⠀⠀⠀⣠⣶⡾⠏⠉⠙⠳⢦⡀⠀⠀⠀⢠⠞⠉⠙⠲⡀⠀\n ⠀⣴⠿⠏⠀⠀ ⠀⢳⡀⠀⡏⠀⠀ ⠀⢷\n⢠⣟⣋⡀⢀⣀⣀⡀⠀⣀⡀⣧⠀⢸⠀⠀⠀⠀ ⡇\n⢸⣯⡭⠁⠸⣛⣟⠆⡴⣻⡲⣿ ⣸ Pehli ⡇\n ⣟⣿⡭⠀⠀⠀⠀⠀⢱⠀⠀ ⣿ ⢹⠀ ⡇\n ⠙⢿⣯⠄⠀⠀(P)⠀⠀⡿ ⠀⡇⠀⠀⠀⠀ ⡼\n⠀⠀⠀⠹⣶⠆⠀⠀⠀⠀⠀⡴⠃⠀ ⠘⠤⣄⣠⠞⠀\n⠀⠀⠀⠀⢸⣷⡦⢤⡤⢤⣞⣁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀\n⠀⢀⣤⣴⣿⣏⠁⠀⠀⠸⣏⢯⣷⣖⣦⡀⠀⠀⠀⠀⠀⠀\n⢀⣾⣽⣿⣿⣿⣿⠛⢲⣶⣾⢉⡷⣿⣿⠵⣿⠀⠀⠀⠀⠀⠀\n⣼⣿⠍⠉⣿⡭⠉⠙⢺⣇⣼⡏⠀⠀ ⠀⣄⢸⠀⠀⠀⠀⠀⠀`", "`⠀⠀⠀⣠⣶⡾⠏⠉⠙⠳⢦⡀⠀⠀⠀⢠⠞⠉⠙⠲⡀⠀\n ⠀⣴⠿⠏⠀⠀ ⠀⢳⡀⠀⡏⠀⠀ ⠀⢷\n⢠⣟⣋⡀⢀⣀⣀⡀⠀⣀⡀⣧⠀⢸⠀ ⠀ ⡇\n⢸⣯⡭⠁⠸⣛⣟⠆⡴⣻⡲⣿ ⣸ Fursat ⡇\n ⣟⣿⡭⠀⠀⠀⠀⠀⢱⠀ ⣿ ⢹⠀ ⡇\n ⠙⢿⣯⠄⠀⠀⠀__ ⠀⠀⡿ ⠀⡇⠀⠀⠀⠀ ⡼\n⠀⠀⠀⠹⣶⠆⠀⠀⠀⠀⠀⡴⠃⠀ ⠘⠤⣄⣠⠞⠀\n⠀⠀⠀⠀⢸⣷⡦⢤⡤⢤⣞⣁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀\n⠀⢀⣤⣴⣿⣏⠁⠀⠀⠸⣏⢯⣷⣖⣦⡀⠀⠀⠀⠀⠀⠀\n⢀⣾⣽⣿⣿⣿⣿⠛⢲⣶⣾⢉⡷⣿⣿⠵⣿⠀⠀⠀⠀⠀⠀\n⣼⣿⠍⠉⣿⡭⠉⠙⢺⣇⣼⡏⠀⠀ ⠀⣄⢸⠀⠀⠀⠀⠀⠀`", "`⠀⠀⠀⣠⣶⡾⠏⠉⠙⠳⢦⡀⠀⠀⠀⢠⠞⠉⠙⠲⡀⠀\n ⠀⣴⠿⠏⠀⠀⠀⠀⠀ ⢳⡀⠀⡏⠀⠀ ⠀⢷\n⢠⣟⣋⡀⢀⣀⣀⡀⠀⣀⡀⣧⠀⢸⠀⠀ ⠀ ⡇\n⢸⣯⡭⠁⠸⣛⣟⠆⡴⣻⡲⣿ ⣸ Meeee ⡇\n ⣟⣿⡭⠀⠀⠀⠀⠀⢱⠀⠀ ⣿ ⢹⠀ ⡇\n ⠙⢿⣯⠄⠀⠀|__| ⠀⡿ ⠀⡇⠀⠀⠀⠀ ⡼\n⠀⠀⠀⠹⣶⠆⠀⠀⠀⠀⠀⡴⠃⠀ ⠘⠤⣄⣠⠞⠀\n⠀⠀⠀⠀⢸⣷⡦⢤⡤⢤⣞⣁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀\n⠀⢀⣤⣴⣿⣏⠁⠀⠀⠸⣏⢯⣷⣖⣦⡀⠀⠀⠀⠀⠀⠀\n⢀⣾⣽⣿⣿⣿⣿⠛⢲⣶⣾⢉⡷⣿⣿⠵⣿⠀⠀⠀⠀⠀⠀\n⣼⣿⠍⠉⣿⡭⠉⠙⢺⣇⣼⡏⠀⠀ ⠀⣄⢸⠀⠀⠀⠀⠀⠀`", "`⠀⠀⠀⣠⣶⡾⠏⠉⠙⠳⢦⡀⠀⠀⠀⢠⠞⠉⠙⠲⡀⠀\n ⠀⣴⠿⠏⠀⠀⠀⠀⠀ ⠀⢳⡀⠀⡏⠀⠀ ⠀⢷\n⢠⣟⣋⡀⢀⣀⣀⡀⠀⣀⡀⣧⠀⢸⠀ ⠀ ⡇\n⢸⣯⡭⠁⠸⣛⣟⠆⡴⣻⡲⣿ ⣸ Nikal ⡇\n ⣟⣿⡭⠀⠀⠀⠀⠀⢱⠀ ⣿ ⢹⠀ ⡇\n ⠙⢿⣯⠄⠀⠀lodu⠀⠀⡿ ⠀⡇⠀⠀⠀⠀ ⡼\n⠀⠀⠀⠹⣶⠆⠀⠀⠀⠀⠀⡴⠃⠀ ⠘⠤⣄⣠⠞⠀\n⠀⠀⠀⠀⢸⣷⡦⢤⡤⢤⣞⣁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀\n⠀⢀⣤⣴⣿⣏⠁⠀⠀⠸⣏⢯⣷⣖⣦⡀⠀⠀⠀⠀⠀⠀\n⢀⣾⣽⣿⣿⣿⣿⠛⢲⣶⣾⢉⡷⣿⣿⠵⣿⠀⠀⠀⠀⠀⠀\n⣼⣿⠍⠉⣿⡭⠉⠙⢺⣇⣼⡏⠀⠀ ⠀⣄⢸⠀⠀⠀⠀⠀⠀`", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 6]) @bot.on(admin_cmd(pattern=f"musics$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"musics$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1.5 animation_ttl = range(0, 11) event = await edit_or_reply(event, "Booty..Booty..Booty shake...Kan chil gye na?😂") animation_chars = [ "⬤⬤⬤ 81% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:00** ▱▱▱▱▱▱▱▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `▶️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀ **⠀Device: Nokia 1100**", "⬤⬤⬤ 81% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:01** ▰▱▱▱▱▱▱▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀ **⠀Device: Nokia 1100**", "⬤⬤⬤ 81% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀ **Now Playing: Shape of u**\n\n**00:02** ▰▰▱▱▱▱▱▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤⬤ 81% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:03** ▰▰▰▱▱▱▱▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤◯ 80% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:04** ▰▰▰▰▱▱▱▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤◯ 80% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:05** ▰▰▰▰▱▱▱▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤◯ 80% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:06** ▰▰▰▰▰▰▱▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤◯ 80% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:07** ▰▰▰▰▰▰▰▱▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤◯ 80% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:08** ▰▰▰▰▰▰▰▰▱▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤◯ 80% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:09** ▰▰▰▰▰▰▰▰▰▱ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏸️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", "⬤⬤◯ 80% ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀`✖️`\n\n⠀⠀⠀⠀⠀[MadBoi Music Player](tg://user?id=1732236209)\n\n⠀⠀⠀⠀**Now Playing: Shape of u**\n\n**00:10** ▰▰▰▰▰▰▰▰▰▰ **00:10**\n\n⠀⠀⠀⠀⠀`🔂` `⏮️` `⏪️` `⏺️` `⏩️` `⏭️`\n\n**⠀Next Song:** __Alan Walker - Alone.__\n\n⠀⠀⠀⠀**⠀Device: Nokia 1100**", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"squeeze$", outgoing=True)) @bot.on(sudo_cmd(pattern="squeeze$", allow_sudo=True)) async def _(event): if event.fwd_from: return event = await edit_or_reply( event, "╔═══════════════════╗ \n \n╚═══════════════════╝" ) await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n \t░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n ░ \t░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n ░ ░ ░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n ░ ░ ░ ░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n ░ ░ ░ ░ ░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit("╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝") await asyncio.sleep(1) await event.edit( "╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝" ) await asyncio.sleep(1) await event.edit( "╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝" ) await asyncio.sleep(1) await event.edit( "╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝" ) await asyncio.sleep(1) await event.edit( "╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝" ) await asyncio.sleep(1) await event.edit( "╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝" ) await asyncio.sleep(1) await event.edit( "╔═══════════════════╗ \n ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ \n╚═══════════════════╝" ) await asyncio.sleep(6) @bot.on(admin_cmd(pattern=f"loadings$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"loadings$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 20) event = await edit_or_reply(event, "▯") animation_chars = ["▮", "▯", "▬", "▭", "‎"] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 4]) @bot.on(admin_cmd(pattern=f"squares$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"squares$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 20) event = await edit_or_reply(event, "◨") animation_chars = ["◧", "◨", "◧", "◨", ""] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 4]) @bot.on(admin_cmd(pattern=f"ups$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"ups$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 20) event = await edit_or_reply(event, "╻") animation_chars = ["╹", "╻", "╹", "╻", "‎"] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 4]) @bot.on(admin_cmd(pattern=f"rounds$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"rounds$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 20) event = await edit_or_reply(event, "Round...") animation_chars = ["⚫", "⬤", "●", "∘", "‎"] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 4]) @bot.on(admin_cmd(pattern=f"hearts$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"hearts$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 20) event = await edit_or_reply(event, "❤️") animation_chars = ["🖤", "❤️", "🖤", "❤️", "‎"] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 4]) @bot.on(admin_cmd(pattern=f"anime$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"anime$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 20) event = await edit_or_reply(event, "😢") animation_chars = [ "😁", "😧", "😡", "😢", "‎**Repo of eviral**", "😁", "😧", "😡", "😢", "__**[Good to see you Guys....]**__(github.com/Teameviral/FIREX)", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 10]) @bot.on(admin_cmd(pattern=f"fnls$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"fnls$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 2 animation_ttl = range(0, 6) event = await edit_or_reply(event, "Hey There....") animation_chars = ["😁🏿", "😁🏾", "😁🏽", "😁🏼", "‎😁", "**Good to see you Firand....**"] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 6]) @bot.on(admin_cmd(pattern=f"monkeys$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"monkeys$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 2 animation_ttl = range(0, 12) event = await edit_or_reply(event, "Hey There....") animation_chars = ["🐵", "🙉", "🙈", "🙊", "🖕‎🐵🖕", "**Good to see you Firand....**"] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 6]) @bot.on(admin_cmd(pattern=f"herbers$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"herbers$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 2 animation_ttl = range(0, 10) event = await edit_or_reply(event, "Power On......") animation_chars = [ "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 10%\n\n ●○○○○○○○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 5.9%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 8.13GB\n **🔹used:** 33.77GB\n **🔹total:** 60.0GB\n \n ●●●●●●●○○○\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 158.98GB\n **🔹recv:** 146.27GB\n **🔹sent_packets:** 84518799\n **🔹recv_packets:** 159720314\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 30%\n\n ●●●○○○○○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 20.4%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 7.18GB\n **🔹used:** 28.26GB\n **🔹total:** 60.0GB\n \n ●●●●●●●●●●\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 146.27GB\n **🔹recv:** 124.33GB\n **🔹sent_packets:** 54635686\n **🔹recv_packets:** 143565654\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 60%\n\n ●●●●●●○○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 60.9%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 6.52GB\n **🔹used:** 35.78GB\n **🔹total:** 60.0GB\n \n ●●●○○○○○○○\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 124.33GB\n **🔹recv:** 162.48GB\n **🔹sent_packets:** 25655655\n **🔹recv_packets:** 165289456\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 100%\n\n ●●●●●●●●●●\n\n **🔹cpu core**\n **🔹core_usage:** 100.0%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 9.81GB\n **🔹used:** 30.11GB\n **🔹total:** 60.0GB\n \n ●●●●●●●●●●\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 162.48GB\n **🔹recv:** 175.75GB\n **🔹sent_packets:** 56565435\n **🔹recv_packets:** 135345655\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 70%\n\n ●●●●●●●○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 80.4%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 5.76GB\n **🔹used:** 29.35GB\n **🔹total:** 60.0GB\n \n ●●●●●●●○○○\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 175.75GB\n **🔹recv:** 118.55GB\n **🔹sent_packets:** 36547698\n **🔹recv_packets:** 185466554\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 60%\n\n ●●●●●●○○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 62.9%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 8.23GB\n **🔹used:** 33.32GB\n **🔹total:** 60.0GB\n \n ●●●●●●○○○○\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 118.55GB\n **🔹recv:** 168.65GB\n **🔹sent_packets:** 24786554\n **🔹recv_packets:** 156745865\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 30%\n\n ●●●○○○○○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 30.6%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 9.75GB\n **🔹used:** 36.54GB\n **🔹total:** 60.0GB\n \n ●●●●●●●●●●\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 168.65GB\n **🔹recv:** 128.35GB\n **🔹sent_packets:** 56565435\n **🔹recv_packets:** 1475823589\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 10%\n\n ●○○○○○○○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 10.2%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 10.20GB\n **🔹used:** 25.40GB\n **🔹total:** 60.0GB\n \n ●●●●●●○○○○\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 128.35GB\n **🔹recv:** 108.31GB\n **🔹sent_packets:** 54635686\n **🔹recv_packets:** 157865426\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 100%\n\n ●●●●●●●●●●\n\n **🔹cpu core**\n\n **🔹core_usage:** 100.0%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 5.25GB\n **🔹used:** 31.14GB\n **🔹total:** 60.0GB\n \n ●●●●●●●●●●\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 108.31GB\n **🔹recv:** 167.17GB\n **🔹sent_packets:** 84518799\n **🔹recv_packets:** 124575356\n\n\n**===================**\n", "**===================**\n **Server Details** \n**===================**\n\n\n**=>>> CPU <<<=**\n\n **🔹current_freq:** 2500.09MHz\n **🔹total_usage:** 70%\n\n ●●●●●●●○○○\n\n **🔹cpu core**\n\n **🔹core_usage:** 76.2%\n **🔹current_freq:** 2500.09MHz\n |██████████▉ |\n \n**=>>> RAM <<<=**\n\n **🔹free:** 8.01GB\n **🔹used:** 33.27GB\n **🔹total:** 60.0GB\n \n ●●●○○○○○○○\n\n\n**=>>> DISK <<<=**\n\n **🔹free:** 224.12GB\n **🔹used:** 131.84GB\n **🔹total:** 375.02GB\n **🔹usage:** 37.0%\n\n |████▍ |\n\n\n**=>>> NETWORK <<<=**\n\n **🔹sent:** 167.17GB\n **🔹recv:** 158.98GB\n **🔹sent_packets:** 36547698\n **🔹recv_packets:** 165455856\n\n\n**===================**\n", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 10]) @bot.on(admin_cmd(pattern=f"hands$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"hands$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 13) event = await edit_or_reply(event, "🖐️") animation_chars = [ "👈", "👉", "☝️", "👆", "🖕", "👇", "✌️", "🤞", "🖖", "🤘", "🤙", "🖐️", "👌", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 13]) @bot.on(admin_cmd(pattern=f"gsgs$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"gsgs$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 12) event = await edit_or_reply(event, "ContDown....") animation_chars = [ "🔟", "9️⃣", "8️⃣", "7️⃣", "6️⃣", "5️⃣", "4️⃣", "3️⃣", "2️⃣", "1️⃣", "0️⃣", "🆘", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 12]) @bot.on(admin_cmd(pattern=r"thearts$", outgoing=True)) @bot.on(sudo_cmd(pattern=r"thearts$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 0.3 animation_ttl = range(0, 54) event = await edit_or_reply(event, "🖤") animation_chars = [ "❤️", "🧡", "💛", "💚", "💙", "💜", "🖤", "💘", "💝", "❤️", "🧡", "💛", "💚", "💙", "💜", "🖤", "💘", "💝", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 18]) @bot.on(admin_cmd(pattern="unoobs$")) @bot.on(sudo_cmd(pattern="unoobs$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 9) event = await edit_or_reply(event, "U nOoB") animation_chars = [ "EvErYbOdY", "iZ", "BiGGeSt", "NoOoB", "uNtiL", "YoU", "aRriVe", "😈", "EvErYbOdY iZ BiGGeSt NoOoB uNtiL YoU aRriVe 😈", ] for i in animation_ttl: await event.edit(animation_chars[i % 9]) await asyncio.sleep(animation_interval) @bot.on(admin_cmd(pattern="menoobs$")) @bot.on(sudo_cmd(pattern="menoobs$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 9) event = await edit_or_reply(event, "mE nOoB") animation_chars = [ "EvErYbOdY", "iZ", "BiGGeSt", "NoOoB", "uNtiL", "i", "aRriVe", "😈", "EvErYbOdY iZ BiGGeSt NoOoB uNtiL i aRriVe 😈", ] for i in animation_ttl: await event.edit(animation_chars[i % 9]) await asyncio.sleep(animation_interval) @bot.on(admin_cmd(pattern="upros$")) @bot.on(sudo_cmd(pattern="upros$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 8) event = await edit_or_reply(event, "U pRo") animation_chars = [ "EvErYbOdY", "iZ", "PeRu", "uNtiL", "YoU", "aRriVe", "😈", "EvErYbOdY iZ PeRu uNtiL YoU aRriVe 😈", ] for i in animation_ttl: await event.edit(animation_chars[i % 8]) await asyncio.sleep(animation_interval) @bot.on(admin_cmd(pattern="mepros$")) @bot.on(sudo_cmd(pattern="mepros$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 8) event = await edit_or_reply(event, "mE pRo") animation_chars = [ "EvErYbOdY", "iZ", "PeRu", "uNtiL", "i", "aRriVe", "😈", "EvErYbOdY iZ PeRu uNtiL i aRriVe 😈", ] for i in animation_ttl: await event.edit(animation_chars[i % 8]) await asyncio.sleep(animation_interval) @bot.on(admin_cmd(pattern=f"quickheals$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"quickheals$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 3 animation_ttl = range(0, 11) event = await edit_or_reply(event, "QuickHeaml") animation_chars = [ "`Downloading File..`", "`File Downloaded....`", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 0%\n▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 4%\n█▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 8%\n██▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 20%\n█████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 36%\n█████████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 52%\n█████████████▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 84%\n█████████████████████▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nFile Scanned... 100%\n█████████████████████████ `", "`Quick Heal Total Security Checkup\n\n\nSubscription: Pru User\nValid Until: 31/12/2099\n\nTask: 01 of 01 Files Scanned...\n\nResult: No Virus Found...`", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"sqhs$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"sqhs$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 3 animation_ttl = range(0, 11) event = await edit_or_reply(event, "SQH") animation_chars = [ "`Downloading File..`", "`File Downloaded....`", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 0%\n▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 4%\n█▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 8%\n██▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 20%\n█████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 36%\n█████████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 52%\n█████████████▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 84%\n█████████████████████▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 100%\n█████████████████████████ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nTask: 01 of 01 Files Scanned...\n\nResult: No Virus Found...`", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"medkit$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"medkit$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 3 animation_ttl = range(0, 11) event = await edit_or_reply(event, "Injecting Virus") animation_chars = [ "`Downloading File..`", "`File Downloaded....`", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 0%\n▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 4%\n█▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 8%\n██▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 20%\n█████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 36%\n█████████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 52%\n█████████████▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 84%\n█████████████████████▒▒▒▒ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nFile Scanned... 100%\n█████████████████████████ `", "`Quick Heal Total Security Checkup\n\n\nSubscription:` {DEFAULTUSER} `\nValid Until: 31/12/2099\n\nTask: 01 of 01 Files Scanned...\n\nResult: ⚠️Virus Found⚠️\nMore Info: Torzan, Spyware, Adware`", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"macoss$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"macoss$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 11) event = await edit_or_reply(event, "Now My Phone Become Mac(Apple Laptop)") animation_chars = [ "`Connecting To Hackintosh...`", "`Initiating Hackintosh Login.`", "`Loading Hackintosh... 0%\n▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Hackintosh... 3%\n█▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Hackintosh... 9%\n██▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Hackintosh... 23%\n█████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Hackintosh... 39%\n█████████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Hackintosh... 69%\n█████████████▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Hackintosh... 89%\n█████████████████████▒▒▒▒ `", "`Loading Hackintosh... 100%\n█████████████████████████ `", "`Welcome...\n\nStock OS: Symbian OS\nCurrent OS: Hackintosh`\n\n**My PC Specs:**\n\n **CPU:** __2.9GHz Intel Core i9-8950HK (hexa-core, 12MB cache, up to 4.8GHz)__\n\n**Graphics:** __Nvidia GeForce GTX 1080 OC (8GB GDDR5X)__\n\n**RAM:** __32GB DDR4 (2,666MHz)__\n\n**Screen:** __17.3-inch, QHD (2,560 x 1,440) 120Hz G-Sync__\n\n**Storage:** __512GB PCIe SSD, 1TB HDD (7,200 rpm)__\n\n**Ports:** __2 x USB 3.0, 1 x USB-C 3.0, 1 x USB-C (Thunderbolt 3), HDMI, mini DisplayPort, Ethernet, headphone jack, microphone jack__\n\n**Connectivity:** __Killer 1550 802.11ac Wi-Fi, Bluetooth 5.0__\n\n**Camera:** __Alienware FHD camera, Tobii IR Eye-tracking with Windows hello__\n\n**Size:** __16.7 x 13.1 x 1.18 inches (42.4 x 33.2 x 2.99cm; W x D x H)__", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"window$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"window$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 1 animation_ttl = range(0, 11) event = await edit_or_reply(event, "Now My Phone Become Windows") animation_chars = [ "`Connecting To Windows 10...`", "`Initiating Windows 10 Login.`", "`Loading Windows 10... 0%\n▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Windows 10... 3%\n█▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Windows 10... 9%\n██▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Windows 10... 23%\n█████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Windows 10... 39%\n█████████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Windows 10... 69%\n█████████████▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Windows 10... 89%\n█████████████████████▒▒▒▒ `", "`Loading Windows 10... 100%\n█████████████████████████ `", "`Welcome...\n\nStock OS: Symbian OS\nCurrent OS: Windows 10`\n\n**My PC Specs:**\n\n **CPU:** __3.4GHz ryzen 9 5950x (16-core,32 threads 64MB cache, up to 4.9GHz)__\n\n**Graphics:** __Nvidia GeForce RTX 3090 OC (24GB GDDR6X)__\n\n**RAM:** __64GB DDR4 (4000MHz)__\n\n**Screen:** __17.3-inch, UHD (3840 x 2160) 144Hz Hdr G-Sync__\n\n**Storage:** __512GB nvme gen 4 SSD, 5 TB HDD (7,200 rpm)__\n\n**Ports:** __2 x USB 3.1, 1 x USB-C 3.0, 1 x USB-C (Thunderbolt 3), 2 HDMI2.0, mini DisplayPort, Ethernet, headphone jack, microphone jack__\n\n**Connectivity:** __Killer 1550 802.11ac Wi-Fi, Bluetooth 5.0__\n\n**Camera:** __Alienware FHD camera, Tobii IR Eye-tracking with Windows hello__\n\n**Size:** __16.7 x 13.1 x 1.18 inches (42.4 x 33.2 x 2.99cm; W x D x H)__", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"linuxs$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"linuxs$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 0.5 animation_ttl = range(0, 11) event = await edit_or_reply(event, "Now My Phone Become Linux") animation_chars = [ "`Connecting To Linux...`", "`Initiating Linux Login.`", "`Loading Linux... 0%\n▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Linux... 3%\n█▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Linux... 9%\n██▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Linux... 23%\n█████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Linux... 39%\n█████████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Linux... 69%\n█████████████▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Linux... 89%\n█████████████████████▒▒▒▒ `", "`Loading Linux... 100%\n█████████████████████████ `", "`Welcome...\n\nStock OS: Symbian OS\nCurrent OS: Linux`\n\n**My PC Specs:**\n\n **CPU:** __2.9GHz Intel Core i9-8950HK (hexa-core, 12MB cache, up to 4.8GHz)__\n\n**Graphics:** __Nvidia GeForce GTX 1080 OC (8GB GDDR5X)__\n\n**RAM:** __32GB DDR4 (2,666MHz)__\n\n**Screen:** __17.3-inch, QHD (2,560 x 1,440) 120Hz G-Sync__\n\n**Storage:** __512GB PCIe SSD, 1TB HDD (7,200 rpm)__\n\n**Ports:** __2 x USB 3.0, 1 x USB-C 3.0, 1 x USB-C (Thunderbolt 3), HDMI, mini DisplayPort, Ethernet, headphone jack, microphone jack__\n\n**Connectivity:** __Killer 1550 802.11ac Wi-Fi, Bluetooth 5.0__\n\n**Camera:** __Alienware FHD camera, Tobii IR Eye-tracking with Windows hello__\n\n**Size:** __16.7 x 13.1 x 1.18 inches (42.4 x 33.2 x 2.99cm; W x D x H)__", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"stocks$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"stocks$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 0.5 animation_ttl = range(0, 11) event = await edit_or_reply(event, "Stocks") animation_chars = [ "`Connecting To Symbian OS...`", "`Initiating Symbian OS Login.`", "`Loading Symbian OS... 0%\n█████████████████████████ `", "`Loading Symbian OS... 3%\n█████████████████████▒▒▒▒ `", "`Loading Symbian OS... 9%\n█████████████▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Symbian OS... 23%\n█████████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Symbian OS... 39%\n█████▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Symbian OS... 69%\n██▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Symbian OS... 89%\n█▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Loading Symbian OS... 100%\n▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ `", "`Welcome...\n\nStock OS: Symbian OS\nCurrent OS: Symbian OS`\n\n**My PC Specs:**\n\n **CPU:** __2.9GHz Intel Core i9-8950HK (hexa-core, 12MB cache, up to 4.8GHz)__\n\n**Graphics:** __Nvidia GeForce GTX 1080 OC (8GB GDDR5X)__\n\n**RAM:** __32GB DDR4 (2,666MHz)__\n\n**Screen:** __17.3-inch, QHD (2,560 x 1,440) 120Hz G-Sync__\n\n**Storage:** __512GB PCIe SSD, 1TB HDD (7,200 rpm)__\n\n**Ports:** __2 x USB 3.0, 1 x USB-C 3.0, 1 x USB-C (Thunderbolt 3), HDMI, mini DisplayPort, Ethernet, headphone jack, microphone jack__\n\n**Connectivity:** __Killer 1550 802.11ac Wi-Fi, Bluetooth 5.0__\n\n**Camera:** __Alienware FHD camera, Tobii IR Eye-tracking with Windows hello__\n\n**Size:** __16.7 x 13.1 x 1.18 inches (42.4 x 33.2 x 2.99cm; W x D x H)__", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 11]) @bot.on(admin_cmd(pattern=f"oss$", outgoing=True)) @bot.on(sudo_cmd(pattern=f"oss$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 0.1 animation_ttl = range(0, 7) event = await edit_or_reply(event, "OS") animation_chars = [ "`Scanning OS...`", "`Scanning OS......`", "__Current Loaded OS: Symbian OS__\n\n**To Boot Other OS, Use The Following Trigger:**\n☑️ `.macos`\n☑️ `.windows`\n☑️ `.linux`\n☑️ `.stock`", "__Current Loaded OS: Symbian OS__\n\n**To Boot Other OS, Use The Following Trigger:**\n✅ `.macos`\n☑️ `.windows`\n☑️ `.linux`\n☑️ `.stock`", "__Current Loaded OS: Symbian OS__\n\n**To Boot Other OS, Use The Following Trigger:**\n✅ `.macos`\n✅ `.windows`\n☑️ `.linux`\n☑️ `.stock`", "__Current Loaded OS: Symbian OS__\n\n**To Boot Other OS, Use The Following Trigger:**\n✅ `.macos`\n✅ `.windows`\n✅ `.linux`\n☑️ `.stock`", "__Current Loaded OS: Symbian OS__\n\n**To Boot Other OS, Use The Following Trigger:**\n✅ `.macos`\n✅ `.windows`\n✅ `.linux`\n✅ `.stock`\n\nDeveloped By: @Eviral", ] for i in animation_ttl: await asyncio.sleep(animation_interval) await event.edit(animation_chars[i % 7]) CmdHelp("animation").add_command("eye", None, "Use And see").add_command( "think", None, "Use and see" ).add_command("snakes", None, "Use and see").add_command( "humans", None, "Use and see" ).add_command( "mcs", None, "Use and see" ).add_command( "viruses", None, "Use and see" ).add_command( "raping", None, "Use and see" ).add_command( "nikl", None, "Use and see" ).add_command( "musics", None, "Use and see" ).add_command( "squeeze", None, "Use and see" ).add_command( "loadings", None, "use and see" ).add_command( "squares", None, "Use and see" ).add_command( "ups", None, "Use and see" ).add_command( "rounds", None, "use and see" ).add_command( "hearts", None, "Use And See" ).add_command( "animes", None, "Use And See" ).add_command( "fmls", None, "Use And See" ).add_command( "monkeys", None, "Use and see" ).add_command( "herbers", None, "Use and see" ).add_command( "hands", None, "Use and see" ).add_command( "gsgs", None, "Use and see" ).add_command( "unoobs", None, "Use and see" ).add_command( "menoobs", None, "Use and see" ).add_command( "upros", None, "Use and see" ).add_command( "mepros", None, "Use and see" ).add_command( "thearts", None, "Use and see" ).add_command( "quickheal", None, "use and see" ).add_command( "sqhs", None, "Use and see" ).add_command( "medkit", None, "Use and see" ).add_command( "macos", None, "use and see" ).add_command( "window", None, "Use And See" ).add_command( "linuxs", None, "Use And See" ).add_command( "stocks", None, "Use And See" ).add_command( "oss", None, "Use And See" ).add_type( "Official" ).add_info( "Its Very Useful Module too much just use these u must(might) have fun" ).add_warning( "Harmless Module✅" ).add_type( "Addons" ).add()

0.17774

0.065276

import unittest from domain_scoring.domain_value_transformer import NaiveTransformer, SMALLER from util.lists import all_pairs class NaiveDomainValueTransformerTest(unittest.TestCase): def setUp(self): self.transformer = NaiveTransformer() self.oracle = self._create_oracle(range(6)) def test_transform(self): self.assertEqual([(1, 0.5), (2, 1.0)], self.transformer.transform([(1, 2)], [SMALLER])) def test_spread_domain_value(self): self.assertEqual( [(1, 0.5), (2, 1.0)], self.transformer._spread_domain_value([1, 2])) self.assertEqual( [(1, 0.25), (2, 0.5), (3, .75), (4, 1.0)], self.transformer._spread_domain_value([1, 2, 3, 4])) self.assertEqual( [(1, 0.5), (2, 1.0)], self.transformer._spread_domain_value([1, 2])) self.assertEqual( [(1, 1.0)], self.transformer._spread_domain_value([1])) def test_order_pairs(self): self.assertEqual([0, 1, 2, 3, 4, 5], self.transformer._order_pairs(self.oracle, [5, 3, 1, 0, 2, 4])) self.assertEqual([1, 2, 3, 4, 5], self.transformer._order_pairs(self.oracle, [5, 3, 1, 2, 4])) def test_merge(self): self.assertEqual([1, 2, 3, 4, 5], self.transformer._merge(self.oracle, [1, 2, 3], [4, 5])) self.assertEqual([1, 2, 3], self.transformer._merge(self.oracle, [3], [1, 2])) self.assertEqual([0, 1, 2, 3, 4, 5], self.transformer._merge(self.oracle, [1, 4, 5], [0, 2, 3])) self.assertEqual([0, 1, 2, 3, 4, 5], self.transformer._merge(self.oracle, [], [0, 1, 2, 3, 4, 5])) self.assertEqual([0, 1, 2, 3, 4, 5], self.transformer._merge(self.oracle, [0, 1, 2, 3, 4, 5], [])) def test_extract_metapaths(self): pairs = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)] self.assertEqual([1, 2, 3, 4], self.transformer._extract_metapaths(pairs)) def _create_oracle(self, elements): all_elements = all_pairs(elements) return dict(zip(all_elements, [SMALLER] * len(all_elements))) if __name__ == '__main__': unittest.main()

tests/domain_scoring/domain_value_transformer_test.py

import unittest from domain_scoring.domain_value_transformer import NaiveTransformer, SMALLER from util.lists import all_pairs class NaiveDomainValueTransformerTest(unittest.TestCase): def setUp(self): self.transformer = NaiveTransformer() self.oracle = self._create_oracle(range(6)) def test_transform(self): self.assertEqual([(1, 0.5), (2, 1.0)], self.transformer.transform([(1, 2)], [SMALLER])) def test_spread_domain_value(self): self.assertEqual( [(1, 0.5), (2, 1.0)], self.transformer._spread_domain_value([1, 2])) self.assertEqual( [(1, 0.25), (2, 0.5), (3, .75), (4, 1.0)], self.transformer._spread_domain_value([1, 2, 3, 4])) self.assertEqual( [(1, 0.5), (2, 1.0)], self.transformer._spread_domain_value([1, 2])) self.assertEqual( [(1, 1.0)], self.transformer._spread_domain_value([1])) def test_order_pairs(self): self.assertEqual([0, 1, 2, 3, 4, 5], self.transformer._order_pairs(self.oracle, [5, 3, 1, 0, 2, 4])) self.assertEqual([1, 2, 3, 4, 5], self.transformer._order_pairs(self.oracle, [5, 3, 1, 2, 4])) def test_merge(self): self.assertEqual([1, 2, 3, 4, 5], self.transformer._merge(self.oracle, [1, 2, 3], [4, 5])) self.assertEqual([1, 2, 3], self.transformer._merge(self.oracle, [3], [1, 2])) self.assertEqual([0, 1, 2, 3, 4, 5], self.transformer._merge(self.oracle, [1, 4, 5], [0, 2, 3])) self.assertEqual([0, 1, 2, 3, 4, 5], self.transformer._merge(self.oracle, [], [0, 1, 2, 3, 4, 5])) self.assertEqual([0, 1, 2, 3, 4, 5], self.transformer._merge(self.oracle, [0, 1, 2, 3, 4, 5], [])) def test_extract_metapaths(self): pairs = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)] self.assertEqual([1, 2, 3, 4], self.transformer._extract_metapaths(pairs)) def _create_oracle(self, elements): all_elements = all_pairs(elements) return dict(zip(all_elements, [SMALLER] * len(all_elements))) if __name__ == '__main__': unittest.main()

0.582135

0.645092

__all__ = ["index", "modules", "custom_doc_links", "git_url"] index = {"CheckpointingNotSupported": "00_callbacks.ipynb", "GradientCheckpointing": "00_callbacks.ipynb", "Singleton": "00_utils.ipynb", "str_to_type": "00_utils.ipynb", "print_versions": "00_utils.ipynb", "set_seed": "00_utils.ipynb", "PreCalculatedLoss": "00_utils.ipynb", "PreCalculatedCrossEntropyLoss": "00_utils.ipynb", "PreCalculatedBCELoss": "00_utils.ipynb", "PreCalculatedMSELoss": "00_utils.ipynb", "MultiTargetLoss": "00_utils.ipynb", "get_hf_objects": "01_text-utils.ipynb", "BlurrText": "01_text-utils.ipynb", "Preprocessor": "11_text-data-core.ipynb", "ClassificationPreprocessor": "11_text-data-core.ipynb", "TextInput": "11_text-data-core.ipynb", "BatchTokenizeTransform": "11_text-data-core.ipynb", "BatchDecodeTransform": "11_text-data-core.ipynb", "blurr_sort_func": "11_text-data-core.ipynb", "TextBlock": "11_text-data-core.ipynb", "get_blurr_tfm": "11_text-data-core.ipynb", "first_blurr_tfm": "11_text-data-core.ipynb", "TextBatchCreator": "11_text-data-core.ipynb", "TextDataLoader": "11_text-data-core.ipynb", "preproc_hf_dataset": "11_text-data-core.ipynb", "blurr_splitter": "11_text-modeling-core.ipynb", "BaseModelWrapper": "11_text-modeling-core.ipynb", "BaseModelCallback": "11_text-modeling-core.ipynb", "Learner.blurr_predict": "11_text-modeling-core.ipynb", "Learner.blurr_generate": "11_text-modeling-core.ipynb", "Blearner": "11_text-modeling-core.ipynb", "BlearnerForSequenceClassification": "11_text-modeling-core.ipynb", "LMPreprocessor": "12_text-data-language-modeling.ipynb", "LMType": "12_text-data-language-modeling.ipynb", "BaseLMStrategy": "12_text-data-language-modeling.ipynb", "CausalLMStrategy": "12_text-data-language-modeling.ipynb", "BertMLMStrategy": "12_text-data-language-modeling.ipynb", "CausalLMTextInput": "12_text-data-language-modeling.ipynb", "MLMTextInput": "12_text-data-language-modeling.ipynb", "LMBatchTokenizeTransform": "12_text-data-language-modeling.ipynb", "LMMetricsCallback": "12_text-modeling-language-modeling.ipynb", "Learner.blurr_fill_mask": "12_text-modeling-language-modeling.ipynb", "BlearnerForLM": "12_text-modeling-language-modeling.ipynb", "TokenClassPreprocessor": "13_text-data-token-classification.ipynb", "BaseLabelingStrategy": "13_text-data-token-classification.ipynb", "OnlyFirstTokenLabelingStrategy": "13_text-data-token-classification.ipynb", "SameLabelLabelingStrategy": "13_text-data-token-classification.ipynb", "BILabelingStrategy": "13_text-data-token-classification.ipynb", "get_token_labels_from_input_ids": "13_text-data-token-classification.ipynb", "get_word_labels_from_token_labels": "13_text-data-token-classification.ipynb", "TokenTensorCategory": "13_text-data-token-classification.ipynb", "TokenCategorize": "13_text-data-token-classification.ipynb", "TokenCategoryBlock": "13_text-data-token-classification.ipynb", "TokenClassTextInput": "13_text-data-token-classification.ipynb", "TokenClassBatchTokenizeTransform": "13_text-data-token-classification.ipynb", "calculate_token_class_metrics": "13_text-modeling-token-classification.ipynb", "TokenClassMetricsCallback": "13_text-modeling-token-classification.ipynb", "TokenAggregationStrategies": "13_text-modeling-token-classification.ipynb", "Learner.blurr_predict_tokens": "13_text-modeling-token-classification.ipynb", "BlearnerForTokenClassification": "13_text-modeling-token-classification.ipynb", "QAPreprocessor": "14_text-data-question-answering.ipynb", "QATextInput": "14_text-data-question-answering.ipynb", "QABatchTokenizeTransform": "14_text-data-question-answering.ipynb", "squad_metric": "14_text-modeling-question-answering.ipynb", "QAModelCallback": "14_text-modeling-question-answering.ipynb", "QAMetricsCallback": "14_text-modeling-question-answering.ipynb", "compute_qa_metrics": "14_text-modeling-question-answering.ipynb", "PreCalculatedQALoss": "14_text-modeling-question-answering.ipynb", "Learner.blurr_predict_answers": "14_text-modeling-question-answering.ipynb", "BlearnerForQuestionAnswering": "14_text-modeling-question-answering.ipynb", "Seq2SeqPreprocessor": "20_text-data-seq2seq-core.ipynb", "Seq2SeqTextInput": "20_text-data-seq2seq-core.ipynb", "Seq2SeqBatchTokenizeTransform": "20_text-data-seq2seq-core.ipynb", "Seq2SeqBatchDecodeTransform": "20_text-data-seq2seq-core.ipynb", "default_text_gen_kwargs": "20_text-data-seq2seq-core.ipynb", "Seq2SeqTextBlock": "20_text-data-seq2seq-core.ipynb", "blurr_seq2seq_splitter": "20_text-modeling-seq2seq-core.ipynb", "Seq2SeqMetricsCallback": "20_text-modeling-seq2seq-core.ipynb", "SummarizationPreprocessor": "21_text-data-seq2seq-summarization.ipynb", "Learner.blurr_summarize": "21_text-modeling-seq2seq-summarization.ipynb", "BlearnerForSummarization": "21_text-modeling-seq2seq-summarization.ipynb", "TranslationPreprocessor": "22_text-data-seq2seq-translation.ipynb", "Learner.blurr_translate": "22_text-modeling-seq2seq-translation.ipynb", "BlearnerForTranslation": "22_text-modeling-seq2seq-translation.ipynb"} modules = ["callbacks.py", "utils.py", "text/callbacks.py", "text/utils.py", "text/data/core.py", "text/modeling/core.py", "text/data/language_modeling.py", "text/modeling/language_modeling.py", "text/data/token_classification.py", "text/modeling/token_classification.py", "text/data/question_answering.py", "text/modeling/question_answering.py", "text/data/seq2seq/core.py", "text/modeling/seq2seq/core.py", "text/data/seq2seq/summarization.py", "text/modeling/seq2seq/summarization.py", "text/data/seq2seq/translation.py", "text/modeling/seq2seq/translation.py", "examples/text/high_level_api.py", "examples/text/glue.py", "examples/text/glue_low_level_api.py", "examples/text/multilabel_classification.py", "examples/text/causal_lm_gpt2.py"] doc_url = "https://ohmeow.github.io/blurr/" git_url = "https://github.com/ohmeow/blurr/tree/master/" def custom_doc_links(name): return None

blurr/_nbdev.py

__all__ = ["index", "modules", "custom_doc_links", "git_url"] index = {"CheckpointingNotSupported": "00_callbacks.ipynb", "GradientCheckpointing": "00_callbacks.ipynb", "Singleton": "00_utils.ipynb", "str_to_type": "00_utils.ipynb", "print_versions": "00_utils.ipynb", "set_seed": "00_utils.ipynb", "PreCalculatedLoss": "00_utils.ipynb", "PreCalculatedCrossEntropyLoss": "00_utils.ipynb", "PreCalculatedBCELoss": "00_utils.ipynb", "PreCalculatedMSELoss": "00_utils.ipynb", "MultiTargetLoss": "00_utils.ipynb", "get_hf_objects": "01_text-utils.ipynb", "BlurrText": "01_text-utils.ipynb", "Preprocessor": "11_text-data-core.ipynb", "ClassificationPreprocessor": "11_text-data-core.ipynb", "TextInput": "11_text-data-core.ipynb", "BatchTokenizeTransform": "11_text-data-core.ipynb", "BatchDecodeTransform": "11_text-data-core.ipynb", "blurr_sort_func": "11_text-data-core.ipynb", "TextBlock": "11_text-data-core.ipynb", "get_blurr_tfm": "11_text-data-core.ipynb", "first_blurr_tfm": "11_text-data-core.ipynb", "TextBatchCreator": "11_text-data-core.ipynb", "TextDataLoader": "11_text-data-core.ipynb", "preproc_hf_dataset": "11_text-data-core.ipynb", "blurr_splitter": "11_text-modeling-core.ipynb", "BaseModelWrapper": "11_text-modeling-core.ipynb", "BaseModelCallback": "11_text-modeling-core.ipynb", "Learner.blurr_predict": "11_text-modeling-core.ipynb", "Learner.blurr_generate": "11_text-modeling-core.ipynb", "Blearner": "11_text-modeling-core.ipynb", "BlearnerForSequenceClassification": "11_text-modeling-core.ipynb", "LMPreprocessor": "12_text-data-language-modeling.ipynb", "LMType": "12_text-data-language-modeling.ipynb", "BaseLMStrategy": "12_text-data-language-modeling.ipynb", "CausalLMStrategy": "12_text-data-language-modeling.ipynb", "BertMLMStrategy": "12_text-data-language-modeling.ipynb", "CausalLMTextInput": "12_text-data-language-modeling.ipynb", "MLMTextInput": "12_text-data-language-modeling.ipynb", "LMBatchTokenizeTransform": "12_text-data-language-modeling.ipynb", "LMMetricsCallback": "12_text-modeling-language-modeling.ipynb", "Learner.blurr_fill_mask": "12_text-modeling-language-modeling.ipynb", "BlearnerForLM": "12_text-modeling-language-modeling.ipynb", "TokenClassPreprocessor": "13_text-data-token-classification.ipynb", "BaseLabelingStrategy": "13_text-data-token-classification.ipynb", "OnlyFirstTokenLabelingStrategy": "13_text-data-token-classification.ipynb", "SameLabelLabelingStrategy": "13_text-data-token-classification.ipynb", "BILabelingStrategy": "13_text-data-token-classification.ipynb", "get_token_labels_from_input_ids": "13_text-data-token-classification.ipynb", "get_word_labels_from_token_labels": "13_text-data-token-classification.ipynb", "TokenTensorCategory": "13_text-data-token-classification.ipynb", "TokenCategorize": "13_text-data-token-classification.ipynb", "TokenCategoryBlock": "13_text-data-token-classification.ipynb", "TokenClassTextInput": "13_text-data-token-classification.ipynb", "TokenClassBatchTokenizeTransform": "13_text-data-token-classification.ipynb", "calculate_token_class_metrics": "13_text-modeling-token-classification.ipynb", "TokenClassMetricsCallback": "13_text-modeling-token-classification.ipynb", "TokenAggregationStrategies": "13_text-modeling-token-classification.ipynb", "Learner.blurr_predict_tokens": "13_text-modeling-token-classification.ipynb", "BlearnerForTokenClassification": "13_text-modeling-token-classification.ipynb", "QAPreprocessor": "14_text-data-question-answering.ipynb", "QATextInput": "14_text-data-question-answering.ipynb", "QABatchTokenizeTransform": "14_text-data-question-answering.ipynb", "squad_metric": "14_text-modeling-question-answering.ipynb", "QAModelCallback": "14_text-modeling-question-answering.ipynb", "QAMetricsCallback": "14_text-modeling-question-answering.ipynb", "compute_qa_metrics": "14_text-modeling-question-answering.ipynb", "PreCalculatedQALoss": "14_text-modeling-question-answering.ipynb", "Learner.blurr_predict_answers": "14_text-modeling-question-answering.ipynb", "BlearnerForQuestionAnswering": "14_text-modeling-question-answering.ipynb", "Seq2SeqPreprocessor": "20_text-data-seq2seq-core.ipynb", "Seq2SeqTextInput": "20_text-data-seq2seq-core.ipynb", "Seq2SeqBatchTokenizeTransform": "20_text-data-seq2seq-core.ipynb", "Seq2SeqBatchDecodeTransform": "20_text-data-seq2seq-core.ipynb", "default_text_gen_kwargs": "20_text-data-seq2seq-core.ipynb", "Seq2SeqTextBlock": "20_text-data-seq2seq-core.ipynb", "blurr_seq2seq_splitter": "20_text-modeling-seq2seq-core.ipynb", "Seq2SeqMetricsCallback": "20_text-modeling-seq2seq-core.ipynb", "SummarizationPreprocessor": "21_text-data-seq2seq-summarization.ipynb", "Learner.blurr_summarize": "21_text-modeling-seq2seq-summarization.ipynb", "BlearnerForSummarization": "21_text-modeling-seq2seq-summarization.ipynb", "TranslationPreprocessor": "22_text-data-seq2seq-translation.ipynb", "Learner.blurr_translate": "22_text-modeling-seq2seq-translation.ipynb", "BlearnerForTranslation": "22_text-modeling-seq2seq-translation.ipynb"} modules = ["callbacks.py", "utils.py", "text/callbacks.py", "text/utils.py", "text/data/core.py", "text/modeling/core.py", "text/data/language_modeling.py", "text/modeling/language_modeling.py", "text/data/token_classification.py", "text/modeling/token_classification.py", "text/data/question_answering.py", "text/modeling/question_answering.py", "text/data/seq2seq/core.py", "text/modeling/seq2seq/core.py", "text/data/seq2seq/summarization.py", "text/modeling/seq2seq/summarization.py", "text/data/seq2seq/translation.py", "text/modeling/seq2seq/translation.py", "examples/text/high_level_api.py", "examples/text/glue.py", "examples/text/glue_low_level_api.py", "examples/text/multilabel_classification.py", "examples/text/causal_lm_gpt2.py"] doc_url = "https://ohmeow.github.io/blurr/" git_url = "https://github.com/ohmeow/blurr/tree/master/" def custom_doc_links(name): return None

0.547948

0.411702

import math import pandas as pd import os class BaseGeoCalculations(): @staticmethod def __getLength(lon_max, lon_min): return lon_max - lon_min @staticmethod def __getWidth(lat_max, lat_min): return lat_max - lat_min @staticmethod # Returns a tuple with the side length and width in grades. def __getLengthWidth(lon_max, lon_min, lat_max, lat_min): side_length = BaseGeoCalculations.__getLength(lon_max, lon_min) side_width = BaseGeoCalculations.__getWidth(lat_max, lat_min) return (side_length, side_width) @staticmethod def getCenter(lon_max, lon_min, lat_max, lat_min): side_length, side_width = BaseGeoCalculations.__getLengthWidth( lon_max, lon_min, lat_max, lat_min) lon_center = side_length/2 + lon_min lat_center = side_length/2 + lat_min return {"lon_center": lon_center, "lat_center": lat_center} @staticmethod def addBuffer(lon_max, lon_min, lat_max, lat_min): # Calculates new vertices for the map adding a buffer of 1/5 of the length side_length, side_width = BaseGeoCalculations.__getLengthWidth( lon_max, lon_min, lat_max, lat_min) old_vertices = [lon_max, lon_min, lat_max, lat_min] buffer_x = side_length/5 buffer_y = side_width/5 new_lon_max = lon_max + buffer_x new_lon_min = lon_min - buffer_x new_lat_max = lat_max + buffer_y new_lat_min = lat_min - buffer_y return {"lon_max": new_lon_max, "lon_min": new_lon_min, "lat_max": new_lat_max, "lat_min": new_lat_min} class MapParameters(BaseGeoCalculations): directory = os.getcwd() path_csv = os.path.join(directory, "ACExpansion", "utils", "AC_american_records_csv.csv") data = pd.read_csv(path_csv) def __init__(self, country): super().__init__() self.country = country self.data_country = self.filter_data_by_country() def filter_data_by_country(self): data_country = MapParameters.data[ MapParameters.data.country==self.country] return data_country def getVertices(self): lon_max = self.data_country["longitude"].max() lon_min = self.data_country["longitude"].min() lat_max = self.data_country["latitude"].max() lat_min = self.data_country["latitude"].min() return {"lon_max":lon_max, "lon_min":lon_min, "lat_max":lat_max, "lat_min":lat_min} def getMapParameters(self): vertices = self.getVertices() buffer_coord = self.addBuffer(**vertices) map_center = self.getCenter(**buffer_coord) return {**map_center, **buffer_coord, "country": self.country}

AcridotheresCristatellusExpansion/ACExpansion/utils/utils_get_map_parameters.py

import math import pandas as pd import os class BaseGeoCalculations(): @staticmethod def __getLength(lon_max, lon_min): return lon_max - lon_min @staticmethod def __getWidth(lat_max, lat_min): return lat_max - lat_min @staticmethod # Returns a tuple with the side length and width in grades. def __getLengthWidth(lon_max, lon_min, lat_max, lat_min): side_length = BaseGeoCalculations.__getLength(lon_max, lon_min) side_width = BaseGeoCalculations.__getWidth(lat_max, lat_min) return (side_length, side_width) @staticmethod def getCenter(lon_max, lon_min, lat_max, lat_min): side_length, side_width = BaseGeoCalculations.__getLengthWidth( lon_max, lon_min, lat_max, lat_min) lon_center = side_length/2 + lon_min lat_center = side_length/2 + lat_min return {"lon_center": lon_center, "lat_center": lat_center} @staticmethod def addBuffer(lon_max, lon_min, lat_max, lat_min): # Calculates new vertices for the map adding a buffer of 1/5 of the length side_length, side_width = BaseGeoCalculations.__getLengthWidth( lon_max, lon_min, lat_max, lat_min) old_vertices = [lon_max, lon_min, lat_max, lat_min] buffer_x = side_length/5 buffer_y = side_width/5 new_lon_max = lon_max + buffer_x new_lon_min = lon_min - buffer_x new_lat_max = lat_max + buffer_y new_lat_min = lat_min - buffer_y return {"lon_max": new_lon_max, "lon_min": new_lon_min, "lat_max": new_lat_max, "lat_min": new_lat_min} class MapParameters(BaseGeoCalculations): directory = os.getcwd() path_csv = os.path.join(directory, "ACExpansion", "utils", "AC_american_records_csv.csv") data = pd.read_csv(path_csv) def __init__(self, country): super().__init__() self.country = country self.data_country = self.filter_data_by_country() def filter_data_by_country(self): data_country = MapParameters.data[ MapParameters.data.country==self.country] return data_country def getVertices(self): lon_max = self.data_country["longitude"].max() lon_min = self.data_country["longitude"].min() lat_max = self.data_country["latitude"].max() lat_min = self.data_country["latitude"].min() return {"lon_max":lon_max, "lon_min":lon_min, "lat_max":lat_max, "lat_min":lat_min} def getMapParameters(self): vertices = self.getVertices() buffer_coord = self.addBuffer(**vertices) map_center = self.getCenter(**buffer_coord) return {**map_center, **buffer_coord, "country": self.country}

0.72952

0.193471

import sys import importlib def _get_deps_info(): """Overview of the installed version of main dependencies Returns ------- deps_info: dict version information on relevant Python libraries """ deps = [ "pip", "setuptools", "imblearn", "sklearn", "numpy", "scipy", "Cython", "pandas", "keras", "tensorflow", "joblib", ] def get_version(module): return module.__version__ deps_info = {} for modname in deps: try: if modname in sys.modules: mod = sys.modules[modname] else: mod = importlib.import_module(modname) ver = get_version(mod) deps_info[modname] = ver except ImportError: deps_info[modname] = None return deps_info def show_versions(github=False): """Print debugging information. .. versionadded:: 0.5 Parameters ---------- github : bool, If true, wrap system info with GitHub markup. """ from sklearn.utils._show_versions import _get_sys_info _sys_info = _get_sys_info() _deps_info = _get_deps_info() _github_markup = ( "<details>" "<summary>System, Dependency Information</summary>\n\n" "**System Information**\n\n" "{0}\n" "**Python Dependencies**\n\n" "{1}\n" "</details>" ) if github: _sys_markup = "" _deps_markup = "" for k, stat in _sys_info.items(): _sys_markup += f"* {k:<10}: `{stat}`\n" for k, stat in _deps_info.items(): _deps_markup += f"* {k:<10}: `{stat}`\n" print(_github_markup.format(_sys_markup, _deps_markup)) else: print("\nSystem:") for k, stat in _sys_info.items(): print(f"{k:>11}: {stat}") print("\nPython dependencies:") for k, stat in _deps_info.items(): print(f"{k:>11}: {stat}")

imblearn/utils/_show_versions.py

import sys import importlib def _get_deps_info(): """Overview of the installed version of main dependencies Returns ------- deps_info: dict version information on relevant Python libraries """ deps = [ "pip", "setuptools", "imblearn", "sklearn", "numpy", "scipy", "Cython", "pandas", "keras", "tensorflow", "joblib", ] def get_version(module): return module.__version__ deps_info = {} for modname in deps: try: if modname in sys.modules: mod = sys.modules[modname] else: mod = importlib.import_module(modname) ver = get_version(mod) deps_info[modname] = ver except ImportError: deps_info[modname] = None return deps_info def show_versions(github=False): """Print debugging information. .. versionadded:: 0.5 Parameters ---------- github : bool, If true, wrap system info with GitHub markup. """ from sklearn.utils._show_versions import _get_sys_info _sys_info = _get_sys_info() _deps_info = _get_deps_info() _github_markup = ( "<details>" "<summary>System, Dependency Information</summary>\n\n" "**System Information**\n\n" "{0}\n" "**Python Dependencies**\n\n" "{1}\n" "</details>" ) if github: _sys_markup = "" _deps_markup = "" for k, stat in _sys_info.items(): _sys_markup += f"* {k:<10}: `{stat}`\n" for k, stat in _deps_info.items(): _deps_markup += f"* {k:<10}: `{stat}`\n" print(_github_markup.format(_sys_markup, _deps_markup)) else: print("\nSystem:") for k, stat in _sys_info.items(): print(f"{k:>11}: {stat}") print("\nPython dependencies:") for k, stat in _deps_info.items(): print(f"{k:>11}: {stat}")

0.437583

0.208139

import os import numpy as np import pandas as pd from sklearn.preprocessing import LabelEncoder import keras from framesandoflow import files2frames, images_normalize class FramesGenerator(keras.utils.Sequence): """ Read and yields video frames/optical flow for Keras.model.fit_generator """ def __init__(self, sPath, nBatchSize, nFrames, nHeight, nWidth, nChannels, liClassesFull=None, bShuffle=True): """ Assume directory structure: ... / sPath / class / videoname / frames.jpg """ self.nBatchSize = nBatchSize self.nFrames = nFrames self.nHeight = nHeight self.nWidth = nWidth self.nChannels = nChannels self.tuXshape = (nFrames, nHeight, nWidth, nChannels) self.bShuffle = bShuffle # retrieve all videos = frame directories a = [] f = os.listdir(sPath) for item in f: inner = sPath + "/" + item inner_path = os.listdir(inner) for item2 in inner_path: l = inner + "/" + item2 a.append(l) self.dfVideos = pd.DataFrame(sorted(a), columns=["sFrameDir"]) self.nSamples = len(self.dfVideos) if self.nSamples == 0: raise ValueError("Found no frame directories files in " + sPath) print("Detected %d samples in %s ..." % (self.nSamples, sPath)) # extract class labels from path seLabels = self.dfVideos.sFrameDir.apply(lambda s: s.split("/")[-2]) self.dfVideos.loc[:, "sLabel"] = seLabels # extract list of unique classes from all detected labels self.liClasses = sorted(list(self.dfVideos.sLabel.unique())) self.nClasses = len(self.liClasses) # encode labels trLabelEncoder = LabelEncoder() # creates instace of LabelEncoder() trLabelEncoder.fit(self.liClasses) # encodes label into class value from 0 to nclasses-1 self.dfVideos.loc[:, "nLabel"] = trLabelEncoder.transform( self.dfVideos.sLabel) # convert textual labels into numerical encoded labels # inverse transform does the opposite self.on_epoch_end() return def __len__(self): """ Denotes the number of batches per epoch """ return int(np.ceil(self.nSamples / self.nBatchSize)) def on_epoch_end(self): """ Updates indexes after each epoch """ self.indexes = np.arange(self.nSamples) if self.bShuffle == True: np.random.shuffle(self.indexes) def __getitem__(self, nStep): """ Generate one batch of data """ # Generate indexes of the batch indexes = self.indexes[nStep * self.nBatchSize:(nStep + 1) * self.nBatchSize] # get batch of videos dfVideosBatch = self.dfVideos.loc[indexes, :] nBatchSize = len(dfVideosBatch) # initialize arrays arX = np.empty((nBatchSize,) + self.tuXshape, dtype=float) arY = np.empty((nBatchSize), dtype=int) # Generate data for i in range(nBatchSize): # generate data for single video(frames) arX[i,], arY[i] = self.__data_generation(dfVideosBatch.iloc[i, :]) # onehot the labels return arX, keras.utils.to_categorical(arY, num_classes=self.nClasses) def __data_generation(self, seVideo): """ Returns frames for 1 video, including normalizing & preprocessing """ # Get the frames from disc ar_nFrames = files2frames(seVideo.sFrameDir) # only use the first nChannels (typically 3, but maybe 2 for optical flow) ar_nFrames = ar_nFrames[..., 0:self.nChannels] ar_fFrames = images_normalize(ar_nFrames, self.nFrames, self.nHeight, self.nWidth, bRescale=True) return ar_fFrames, seVideo.nLabel def data_generation(self, seVideo): return self.__data_generation(seVideo) class VideoClasses(): """ Loads the video classes (incl descriptions) from a csv file """ def __init__(self, sClassFile: str): self.dfClass = pd.read_csv(sClassFile) self.dfClass = self.dfClass.sort_values("sClass").reset_index(drop=True) self.liClasses = list(self.dfClass.sClass) self.nClasses = len(self.dfClass) print("Loaded %d classes from %s" % (self.nClasses, sClassFile)) return

datagenerator.py

import os import numpy as np import pandas as pd from sklearn.preprocessing import LabelEncoder import keras from framesandoflow import files2frames, images_normalize class FramesGenerator(keras.utils.Sequence): """ Read and yields video frames/optical flow for Keras.model.fit_generator """ def __init__(self, sPath, nBatchSize, nFrames, nHeight, nWidth, nChannels, liClassesFull=None, bShuffle=True): """ Assume directory structure: ... / sPath / class / videoname / frames.jpg """ self.nBatchSize = nBatchSize self.nFrames = nFrames self.nHeight = nHeight self.nWidth = nWidth self.nChannels = nChannels self.tuXshape = (nFrames, nHeight, nWidth, nChannels) self.bShuffle = bShuffle # retrieve all videos = frame directories a = [] f = os.listdir(sPath) for item in f: inner = sPath + "/" + item inner_path = os.listdir(inner) for item2 in inner_path: l = inner + "/" + item2 a.append(l) self.dfVideos = pd.DataFrame(sorted(a), columns=["sFrameDir"]) self.nSamples = len(self.dfVideos) if self.nSamples == 0: raise ValueError("Found no frame directories files in " + sPath) print("Detected %d samples in %s ..." % (self.nSamples, sPath)) # extract class labels from path seLabels = self.dfVideos.sFrameDir.apply(lambda s: s.split("/")[-2]) self.dfVideos.loc[:, "sLabel"] = seLabels # extract list of unique classes from all detected labels self.liClasses = sorted(list(self.dfVideos.sLabel.unique())) self.nClasses = len(self.liClasses) # encode labels trLabelEncoder = LabelEncoder() # creates instace of LabelEncoder() trLabelEncoder.fit(self.liClasses) # encodes label into class value from 0 to nclasses-1 self.dfVideos.loc[:, "nLabel"] = trLabelEncoder.transform( self.dfVideos.sLabel) # convert textual labels into numerical encoded labels # inverse transform does the opposite self.on_epoch_end() return def __len__(self): """ Denotes the number of batches per epoch """ return int(np.ceil(self.nSamples / self.nBatchSize)) def on_epoch_end(self): """ Updates indexes after each epoch """ self.indexes = np.arange(self.nSamples) if self.bShuffle == True: np.random.shuffle(self.indexes) def __getitem__(self, nStep): """ Generate one batch of data """ # Generate indexes of the batch indexes = self.indexes[nStep * self.nBatchSize:(nStep + 1) * self.nBatchSize] # get batch of videos dfVideosBatch = self.dfVideos.loc[indexes, :] nBatchSize = len(dfVideosBatch) # initialize arrays arX = np.empty((nBatchSize,) + self.tuXshape, dtype=float) arY = np.empty((nBatchSize), dtype=int) # Generate data for i in range(nBatchSize): # generate data for single video(frames) arX[i,], arY[i] = self.__data_generation(dfVideosBatch.iloc[i, :]) # onehot the labels return arX, keras.utils.to_categorical(arY, num_classes=self.nClasses) def __data_generation(self, seVideo): """ Returns frames for 1 video, including normalizing & preprocessing """ # Get the frames from disc ar_nFrames = files2frames(seVideo.sFrameDir) # only use the first nChannels (typically 3, but maybe 2 for optical flow) ar_nFrames = ar_nFrames[..., 0:self.nChannels] ar_fFrames = images_normalize(ar_nFrames, self.nFrames, self.nHeight, self.nWidth, bRescale=True) return ar_fFrames, seVideo.nLabel def data_generation(self, seVideo): return self.__data_generation(seVideo) class VideoClasses(): """ Loads the video classes (incl descriptions) from a csv file """ def __init__(self, sClassFile: str): self.dfClass = pd.read_csv(sClassFile) self.dfClass = self.dfClass.sort_values("sClass").reset_index(drop=True) self.liClasses = list(self.dfClass.sClass) self.nClasses = len(self.dfClass) print("Loaded %d classes from %s" % (self.nClasses, sClassFile)) return

0.735262

0.29726

from south.utils import datetime_utils as datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Changing field 'GeoLocation.lat' db.alter_column(u'faver_geolocation', 'lat', self.gf('django.db.models.fields.CharField')(max_length=15)) # Changing field 'GeoLocation.long' db.alter_column(u'faver_geolocation', 'long', self.gf('django.db.models.fields.CharField')(max_length=15)) def backwards(self, orm): # Changing field 'GeoLocation.lat' db.alter_column(u'faver_geolocation', 'lat', self.gf('django.db.models.fields.CharField')(max_length=10)) # Changing field 'GeoLocation.long' db.alter_column(u'faver_geolocation', 'long', self.gf('django.db.models.fields.CharField')(max_length=10)) models = { u'faver.geolocation': { 'Meta': {'object_name': 'GeoLocation'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'lat': ('django.db.models.fields.CharField', [], {'max_length': '15'}), 'long': ('django.db.models.fields.CharField', [], {'max_length': '15'}) }, u'faver.route': { 'Meta': {'object_name': 'Route'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'nr': ('django.db.models.fields.IntegerField', [], {}), 'stops': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['faver.StationStop']", 'symmetrical': 'False'}) }, u'faver.station': { 'Meta': {'object_name': 'Station'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'rtec_id': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}) }, u'faver.stationstop': { 'Meta': {'object_name': 'StationStop'}, 'day_type': ('django.db.models.fields.IntegerField', [], {}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'location': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['faver.GeoLocation']", 'null': 'True', 'blank': 'True'}), 'order_nr': ('django.db.models.fields.IntegerField', [], {}), 'station': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['faver.Station']"}) }, u'faver.stoptime': { 'Meta': {'object_name': 'StopTime'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'station': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'stopstime'", 'null': 'True', 'to': u"orm['faver.StationStop']"}), 'time': ('django.db.models.fields.DateTimeField', [], {}) } } complete_apps = ['faver']

trolly/faver/migrations/0007_auto__chg_field_geolocation_lat__chg_field_geolocation_long.py

from south.utils import datetime_utils as datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Changing field 'GeoLocation.lat' db.alter_column(u'faver_geolocation', 'lat', self.gf('django.db.models.fields.CharField')(max_length=15)) # Changing field 'GeoLocation.long' db.alter_column(u'faver_geolocation', 'long', self.gf('django.db.models.fields.CharField')(max_length=15)) def backwards(self, orm): # Changing field 'GeoLocation.lat' db.alter_column(u'faver_geolocation', 'lat', self.gf('django.db.models.fields.CharField')(max_length=10)) # Changing field 'GeoLocation.long' db.alter_column(u'faver_geolocation', 'long', self.gf('django.db.models.fields.CharField')(max_length=10)) models = { u'faver.geolocation': { 'Meta': {'object_name': 'GeoLocation'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'lat': ('django.db.models.fields.CharField', [], {'max_length': '15'}), 'long': ('django.db.models.fields.CharField', [], {'max_length': '15'}) }, u'faver.route': { 'Meta': {'object_name': 'Route'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'nr': ('django.db.models.fields.IntegerField', [], {}), 'stops': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['faver.StationStop']", 'symmetrical': 'False'}) }, u'faver.station': { 'Meta': {'object_name': 'Station'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'rtec_id': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}) }, u'faver.stationstop': { 'Meta': {'object_name': 'StationStop'}, 'day_type': ('django.db.models.fields.IntegerField', [], {}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'location': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['faver.GeoLocation']", 'null': 'True', 'blank': 'True'}), 'order_nr': ('django.db.models.fields.IntegerField', [], {}), 'station': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['faver.Station']"}) }, u'faver.stoptime': { 'Meta': {'object_name': 'StopTime'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'station': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'stopstime'", 'null': 'True', 'to': u"orm['faver.StationStop']"}), 'time': ('django.db.models.fields.DateTimeField', [], {}) } } complete_apps = ['faver']

0.502686

0.090574

import base64 import json from typing import Any, Dict, List from google.protobuf.json_format import Parse, ParseDict from cosmpy.common.rest_client import RestClient from cosmpy.protos.cosmos.crypto.secp256k1.keys_pb2 import ( # noqa: F401 # pylint: disable=unused-import PubKey as ProtoPubKey, ) from cosmpy.protos.cosmos.tx.v1beta1.service_pb2 import ( BroadcastTxRequest, BroadcastTxResponse, GetTxRequest, GetTxResponse, GetTxsEventRequest, GetTxsEventResponse, SimulateRequest, SimulateResponse, ) from cosmpy.protos.cosmwasm.wasm.v1beta1.tx_pb2 import ( # noqa: F401 # pylint: disable=unused-import MsgExecuteContract, MsgInstantiateContract, MsgStoreCode, ) from cosmpy.tx.interface import TxInterface # Unused imports are required to make sure that related types get generated - Parse and ParseDict fail without them class TxRestClient(TxInterface): """Tx REST client.""" API_URL = "/cosmos/tx/v1beta1" def __init__(self, rest_client: RestClient) -> None: """ Create a Tx rest client :param rest_client: RestClient api """ self.rest_client = rest_client def Simulate(self, request: SimulateRequest) -> SimulateResponse: """ Simulate executing a transaction to estimate gas usage. :param request: SimulateRequest :return: SimulateResponse """ response = self.rest_client.get( f"{self.API_URL}/simulate", ) return Parse(response, SimulateResponse()) def GetTx(self, request: GetTxRequest) -> GetTxResponse: """ GetTx fetches a tx by hash. :param request: GetTxRequest :return: GetTxResponse """ response = self.rest_client.get(f"{self.API_URL}/txs/{request.hash}") # JSON in JSON in case of CosmWasm messages workaround dict_response = json.loads(response) self._fix_messages(dict_response["tx"]["body"]["messages"]) self._fix_messages(dict_response["tx_response"]["tx"]["body"]["messages"]) return ParseDict(dict_response, GetTxResponse()) def BroadcastTx(self, request: BroadcastTxRequest) -> BroadcastTxResponse: """ BroadcastTx broadcast transaction. :param request: BroadcastTxRequest :return: BroadcastTxResponse """ response = self.rest_client.post(f"{self.API_URL}/txs", request) return Parse(response, BroadcastTxResponse()) def GetTxsEvent(self, request: GetTxsEventRequest) -> GetTxsEventResponse: """ GetTxsEvent fetches txs by event. :param request: GetTxsEventRequest :return: GetTxsEventResponse """ response = self.rest_client.get(f"{self.API_URL}/txs", request) # JSON in JSON in case of CosmWasm messages workaround dict_response = json.loads(response) for tx in dict_response["txs"]: self._fix_messages(tx["body"]["messages"]) for tx_response in dict_response["tx_responses"]: self._fix_messages(tx_response["tx"]["body"]["messages"]) return ParseDict(dict_response, GetTxsEventResponse()) @staticmethod def _fix_messages(messages: List[Dict[str, Any]]): """ Fix for REST api response in case of CosmWasm messages contains dict instead of base64 encoded string :param messages: List of message in Tx response """ for message in messages: if message["@type"] == "/cosmwasm.wasm.v1.MsgInstantiateContract": message["msg"] = base64.b64encode( json.dumps(message["msg"]).encode("UTF8") ).decode() if message["@type"] == "/cosmwasm.wasm.v1.MsgExecuteContract": message["msg"] = base64.b64encode( json.dumps(message["msg"]).encode("UTF8") ).decode()

cosmpy/tx/rest_client.py

import base64 import json from typing import Any, Dict, List from google.protobuf.json_format import Parse, ParseDict from cosmpy.common.rest_client import RestClient from cosmpy.protos.cosmos.crypto.secp256k1.keys_pb2 import ( # noqa: F401 # pylint: disable=unused-import PubKey as ProtoPubKey, ) from cosmpy.protos.cosmos.tx.v1beta1.service_pb2 import ( BroadcastTxRequest, BroadcastTxResponse, GetTxRequest, GetTxResponse, GetTxsEventRequest, GetTxsEventResponse, SimulateRequest, SimulateResponse, ) from cosmpy.protos.cosmwasm.wasm.v1beta1.tx_pb2 import ( # noqa: F401 # pylint: disable=unused-import MsgExecuteContract, MsgInstantiateContract, MsgStoreCode, ) from cosmpy.tx.interface import TxInterface # Unused imports are required to make sure that related types get generated - Parse and ParseDict fail without them class TxRestClient(TxInterface): """Tx REST client.""" API_URL = "/cosmos/tx/v1beta1" def __init__(self, rest_client: RestClient) -> None: """ Create a Tx rest client :param rest_client: RestClient api """ self.rest_client = rest_client def Simulate(self, request: SimulateRequest) -> SimulateResponse: """ Simulate executing a transaction to estimate gas usage. :param request: SimulateRequest :return: SimulateResponse """ response = self.rest_client.get( f"{self.API_URL}/simulate", ) return Parse(response, SimulateResponse()) def GetTx(self, request: GetTxRequest) -> GetTxResponse: """ GetTx fetches a tx by hash. :param request: GetTxRequest :return: GetTxResponse """ response = self.rest_client.get(f"{self.API_URL}/txs/{request.hash}") # JSON in JSON in case of CosmWasm messages workaround dict_response = json.loads(response) self._fix_messages(dict_response["tx"]["body"]["messages"]) self._fix_messages(dict_response["tx_response"]["tx"]["body"]["messages"]) return ParseDict(dict_response, GetTxResponse()) def BroadcastTx(self, request: BroadcastTxRequest) -> BroadcastTxResponse: """ BroadcastTx broadcast transaction. :param request: BroadcastTxRequest :return: BroadcastTxResponse """ response = self.rest_client.post(f"{self.API_URL}/txs", request) return Parse(response, BroadcastTxResponse()) def GetTxsEvent(self, request: GetTxsEventRequest) -> GetTxsEventResponse: """ GetTxsEvent fetches txs by event. :param request: GetTxsEventRequest :return: GetTxsEventResponse """ response = self.rest_client.get(f"{self.API_URL}/txs", request) # JSON in JSON in case of CosmWasm messages workaround dict_response = json.loads(response) for tx in dict_response["txs"]: self._fix_messages(tx["body"]["messages"]) for tx_response in dict_response["tx_responses"]: self._fix_messages(tx_response["tx"]["body"]["messages"]) return ParseDict(dict_response, GetTxsEventResponse()) @staticmethod def _fix_messages(messages: List[Dict[str, Any]]): """ Fix for REST api response in case of CosmWasm messages contains dict instead of base64 encoded string :param messages: List of message in Tx response """ for message in messages: if message["@type"] == "/cosmwasm.wasm.v1.MsgInstantiateContract": message["msg"] = base64.b64encode( json.dumps(message["msg"]).encode("UTF8") ).decode() if message["@type"] == "/cosmwasm.wasm.v1.MsgExecuteContract": message["msg"] = base64.b64encode( json.dumps(message["msg"]).encode("UTF8") ).decode()

0.782579

0.077204

import os import tempfile import unittest import logging from pyidf import ValidationLevel import pyidf from pyidf.idf import IDF from pyidf.hvac_design_objects import SizingSystem log = logging.getLogger(__name__) class TestSizingSystem(unittest.TestCase): def setUp(self): self.fd, self.path = tempfile.mkstemp() def tearDown(self): os.remove(self.path) def test_create_sizingsystem(self): pyidf.validation_level = ValidationLevel.error obj = SizingSystem() # object-list var_airloop_name = "object-list|AirLoop Name" obj.airloop_name = var_airloop_name # alpha var_type_of_load_to_size_on = "Sensible" obj.type_of_load_to_size_on = var_type_of_load_to_size_on # real var_design_outdoor_air_flow_rate = 0.0 obj.design_outdoor_air_flow_rate = var_design_outdoor_air_flow_rate # real var_central_heating_maximum_system_air_flow_ratio = 0.5 obj.central_heating_maximum_system_air_flow_ratio = var_central_heating_maximum_system_air_flow_ratio # real var_preheat_design_temperature = 5.5 obj.preheat_design_temperature = var_preheat_design_temperature # real var_preheat_design_humidity_ratio = 6.6 obj.preheat_design_humidity_ratio = var_preheat_design_humidity_ratio # real var_precool_design_temperature = 7.7 obj.precool_design_temperature = var_precool_design_temperature # real var_precool_design_humidity_ratio = 8.8 obj.precool_design_humidity_ratio = var_precool_design_humidity_ratio # real var_central_cooling_design_supply_air_temperature = 9.9 obj.central_cooling_design_supply_air_temperature = var_central_cooling_design_supply_air_temperature # real var_central_heating_design_supply_air_temperature = 10.1 obj.central_heating_design_supply_air_temperature = var_central_heating_design_supply_air_temperature # alpha var_type_of_zone_sum_to_use = "Coincident" obj.type_of_zone_sum_to_use = var_type_of_zone_sum_to_use # alpha var_a_100_outdoor_air_in_cooling = "Yes" obj.a_100_outdoor_air_in_cooling = var_a_100_outdoor_air_in_cooling # alpha var_a_100_outdoor_air_in_heating = "Yes" obj.a_100_outdoor_air_in_heating = var_a_100_outdoor_air_in_heating # real var_central_cooling_design_supply_air_humidity_ratio = 14.14 obj.central_cooling_design_supply_air_humidity_ratio = var_central_cooling_design_supply_air_humidity_ratio # real var_central_heating_design_supply_air_humidity_ratio = 15.15 obj.central_heating_design_supply_air_humidity_ratio = var_central_heating_design_supply_air_humidity_ratio # alpha var_cooling_supply_air_flow_rate_method = "Flow/System" obj.cooling_supply_air_flow_rate_method = var_cooling_supply_air_flow_rate_method # real var_cooling_supply_air_flow_rate = 0.0 obj.cooling_supply_air_flow_rate = var_cooling_supply_air_flow_rate # real var_cooling_supply_air_flow_rate_per_floor_area = 0.0 obj.cooling_supply_air_flow_rate_per_floor_area = var_cooling_supply_air_flow_rate_per_floor_area # real var_cooling_fraction_of_autosized_cooling_supply_air_flow_rate = 0.0 obj.cooling_fraction_of_autosized_cooling_supply_air_flow_rate = var_cooling_fraction_of_autosized_cooling_supply_air_flow_rate # real var_cooling_supply_air_flow_rate_per_unit_cooling_capacity = 0.0 obj.cooling_supply_air_flow_rate_per_unit_cooling_capacity = var_cooling_supply_air_flow_rate_per_unit_cooling_capacity # alpha var_heating_supply_air_flow_rate_method = "Flow/System" obj.heating_supply_air_flow_rate_method = var_heating_supply_air_flow_rate_method # real var_heating_supply_air_flow_rate = 0.0 obj.heating_supply_air_flow_rate = var_heating_supply_air_flow_rate # real var_heating_supply_air_flow_rate_per_floor_area = 0.0 obj.heating_supply_air_flow_rate_per_floor_area = var_heating_supply_air_flow_rate_per_floor_area # real var_heating_fraction_of_autosized_heating_supply_air_flow_rate = 0.0 obj.heating_fraction_of_autosized_heating_supply_air_flow_rate = var_heating_fraction_of_autosized_heating_supply_air_flow_rate # real var_heating_fraction_of_autosized_cooling_supply_air_flow_rate = 0.0 obj.heating_fraction_of_autosized_cooling_supply_air_flow_rate = var_heating_fraction_of_autosized_cooling_supply_air_flow_rate # real var_heating_supply_air_flow_rate_per_unit_heating_capacity = 0.0 obj.heating_supply_air_flow_rate_per_unit_heating_capacity = var_heating_supply_air_flow_rate_per_unit_heating_capacity # alpha var_system_outdoor_air_method = "ZoneSum" obj.system_outdoor_air_method = var_system_outdoor_air_method # real var_zone_maximum_outdoor_air_fraction = 0.0001 obj.zone_maximum_outdoor_air_fraction = var_zone_maximum_outdoor_air_fraction # alpha var_cooling_design_capacity_method = "None" obj.cooling_design_capacity_method = var_cooling_design_capacity_method # real var_cooling_design_capacity = 0.0 obj.cooling_design_capacity = var_cooling_design_capacity # real var_cooling_design_capacity_per_floor_area = 0.0 obj.cooling_design_capacity_per_floor_area = var_cooling_design_capacity_per_floor_area # real var_fraction_of_autosized_cooling_design_capacity = 0.0 obj.fraction_of_autosized_cooling_design_capacity = var_fraction_of_autosized_cooling_design_capacity # alpha var_heating_design_capacity_method = "None" obj.heating_design_capacity_method = var_heating_design_capacity_method # real var_heating_design_capacity = 0.0 obj.heating_design_capacity = var_heating_design_capacity # real var_heating_design_capacity_per_floor_area = 0.0 obj.heating_design_capacity_per_floor_area = var_heating_design_capacity_per_floor_area # real var_fraction_of_autosized_heating_design_capacity = 0.0 obj.fraction_of_autosized_heating_design_capacity = var_fraction_of_autosized_heating_design_capacity # alpha var_central_cooling_capacity_control_method = "VAV" obj.central_cooling_capacity_control_method = var_central_cooling_capacity_control_method idf = IDF() idf.add(obj) idf.save(self.path, check=False) with open(self.path, mode='r') as f: for line in f: log.debug(line.strip()) idf2 = IDF(self.path) self.assertEqual(idf2.sizingsystems[0].airloop_name, var_airloop_name) self.assertEqual(idf2.sizingsystems[0].type_of_load_to_size_on, var_type_of_load_to_size_on) self.assertAlmostEqual(idf2.sizingsystems[0].design_outdoor_air_flow_rate, var_design_outdoor_air_flow_rate) self.assertAlmostEqual(idf2.sizingsystems[0].central_heating_maximum_system_air_flow_ratio, var_central_heating_maximum_system_air_flow_ratio) self.assertAlmostEqual(idf2.sizingsystems[0].preheat_design_temperature, var_preheat_design_temperature) self.assertAlmostEqual(idf2.sizingsystems[0].preheat_design_humidity_ratio, var_preheat_design_humidity_ratio) self.assertAlmostEqual(idf2.sizingsystems[0].precool_design_temperature, var_precool_design_temperature) self.assertAlmostEqual(idf2.sizingsystems[0].precool_design_humidity_ratio, var_precool_design_humidity_ratio) self.assertAlmostEqual(idf2.sizingsystems[0].central_cooling_design_supply_air_temperature, var_central_cooling_design_supply_air_temperature) self.assertAlmostEqual(idf2.sizingsystems[0].central_heating_design_supply_air_temperature, var_central_heating_design_supply_air_temperature) self.assertEqual(idf2.sizingsystems[0].type_of_zone_sum_to_use, var_type_of_zone_sum_to_use) self.assertEqual(idf2.sizingsystems[0].a_100_outdoor_air_in_cooling, var_a_100_outdoor_air_in_cooling) self.assertEqual(idf2.sizingsystems[0].a_100_outdoor_air_in_heating, var_a_100_outdoor_air_in_heating) self.assertAlmostEqual(idf2.sizingsystems[0].central_cooling_design_supply_air_humidity_ratio, var_central_cooling_design_supply_air_humidity_ratio) self.assertAlmostEqual(idf2.sizingsystems[0].central_heating_design_supply_air_humidity_ratio, var_central_heating_design_supply_air_humidity_ratio) self.assertEqual(idf2.sizingsystems[0].cooling_supply_air_flow_rate_method, var_cooling_supply_air_flow_rate_method) self.assertAlmostEqual(idf2.sizingsystems[0].cooling_supply_air_flow_rate, var_cooling_supply_air_flow_rate) self.assertAlmostEqual(idf2.sizingsystems[0].cooling_supply_air_flow_rate_per_floor_area, var_cooling_supply_air_flow_rate_per_floor_area) self.assertAlmostEqual(idf2.sizingsystems[0].cooling_fraction_of_autosized_cooling_supply_air_flow_rate, var_cooling_fraction_of_autosized_cooling_supply_air_flow_rate) self.assertAlmostEqual(idf2.sizingsystems[0].cooling_supply_air_flow_rate_per_unit_cooling_capacity, var_cooling_supply_air_flow_rate_per_unit_cooling_capacity) self.assertEqual(idf2.sizingsystems[0].heating_supply_air_flow_rate_method, var_heating_supply_air_flow_rate_method) self.assertAlmostEqual(idf2.sizingsystems[0].heating_supply_air_flow_rate, var_heating_supply_air_flow_rate) self.assertAlmostEqual(idf2.sizingsystems[0].heating_supply_air_flow_rate_per_floor_area, var_heating_supply_air_flow_rate_per_floor_area) self.assertAlmostEqual(idf2.sizingsystems[0].heating_fraction_of_autosized_heating_supply_air_flow_rate, var_heating_fraction_of_autosized_heating_supply_air_flow_rate) self.assertAlmostEqual(idf2.sizingsystems[0].heating_fraction_of_autosized_cooling_supply_air_flow_rate, var_heating_fraction_of_autosized_cooling_supply_air_flow_rate) self.assertAlmostEqual(idf2.sizingsystems[0].heating_supply_air_flow_rate_per_unit_heating_capacity, var_heating_supply_air_flow_rate_per_unit_heating_capacity) self.assertEqual(idf2.sizingsystems[0].system_outdoor_air_method, var_system_outdoor_air_method) self.assertAlmostEqual(idf2.sizingsystems[0].zone_maximum_outdoor_air_fraction, var_zone_maximum_outdoor_air_fraction) self.assertEqual(idf2.sizingsystems[0].cooling_design_capacity_method, var_cooling_design_capacity_method) self.assertAlmostEqual(idf2.sizingsystems[0].cooling_design_capacity, var_cooling_design_capacity) self.assertAlmostEqual(idf2.sizingsystems[0].cooling_design_capacity_per_floor_area, var_cooling_design_capacity_per_floor_area) self.assertAlmostEqual(idf2.sizingsystems[0].fraction_of_autosized_cooling_design_capacity, var_fraction_of_autosized_cooling_design_capacity) self.assertEqual(idf2.sizingsystems[0].heating_design_capacity_method, var_heating_design_capacity_method) self.assertAlmostEqual(idf2.sizingsystems[0].heating_design_capacity, var_heating_design_capacity) self.assertAlmostEqual(idf2.sizingsystems[0].heating_design_capacity_per_floor_area, var_heating_design_capacity_per_floor_area) self.assertAlmostEqual(idf2.sizingsystems[0].fraction_of_autosized_heating_design_capacity, var_fraction_of_autosized_heating_design_capacity) self.assertEqual(idf2.sizingsystems[0].central_cooling_capacity_control_method, var_central_cooling_capacity_control_method)

tests/test_sizingsystem.py

import os import tempfile import unittest import logging from pyidf import ValidationLevel import pyidf from pyidf.idf import IDF from pyidf.hvac_design_objects import SizingSystem log = logging.getLogger(__name__) class TestSizingSystem(unittest.TestCase): def setUp(self): self.fd, self.path = tempfile.mkstemp() def tearDown(self): os.remove(self.path) def test_create_sizingsystem(self): pyidf.validation_level = ValidationLevel.error obj = SizingSystem() # object-list var_airloop_name = "object-list|AirLoop Name" obj.airloop_name = var_airloop_name # alpha var_type_of_load_to_size_on = "Sensible" obj.type_of_load_to_size_on = var_type_of_load_to_size_on # real var_design_outdoor_air_flow_rate = 0.0 obj.design_outdoor_air_flow_rate = var_design_outdoor_air_flow_rate # real var_central_heating_maximum_system_air_flow_ratio = 0.5 obj.central_heating_maximum_system_air_flow_ratio = var_central_heating_maximum_system_air_flow_ratio # real var_preheat_design_temperature = 5.5 obj.preheat_design_temperature = var_preheat_design_temperature # real var_preheat_design_humidity_ratio = 6.6 obj.preheat_design_humidity_ratio = var_preheat_design_humidity_ratio # real var_precool_design_temperature = 7.7 obj.precool_design_temperature = var_precool_design_temperature # real var_precool_design_humidity_ratio = 8.8 obj.precool_design_humidity_ratio = var_precool_design_humidity_ratio # real var_central_cooling_design_supply_air_temperature = 9.9 obj.central_cooling_design_supply_air_temperature = var_central_cooling_design_supply_air_temperature # real var_central_heating_design_supply_air_temperature = 10.1 obj.central_heating_design_supply_air_temperature = var_central_heating_design_supply_air_temperature # alpha var_type_of_zone_sum_to_use = "Coincident" obj.type_of_zone_sum_to_use = var_type_of_zone_sum_to_use # alpha var_a_100_outdoor_air_in_cooling = "Yes" obj.a_100_outdoor_air_in_cooling = var_a_100_outdoor_air_in_cooling # alpha var_a_100_outdoor_air_in_heating = "Yes" obj.a_100_outdoor_air_in_heating = var_a_100_outdoor_air_in_heating # real var_central_cooling_design_supply_air_humidity_ratio = 14.14 obj.central_cooling_design_supply_air_humidity_ratio = var_central_cooling_design_supply_air_humidity_ratio # real var_central_heating_design_supply_air_humidity_ratio = 15.15 obj.central_heating_design_supply_air_humidity_ratio = var_central_heating_design_supply_air_humidity_ratio # alpha var_cooling_supply_air_flow_rate_method = "Flow/System" obj.cooling_supply_air_flow_rate_method = var_cooling_supply_air_flow_rate_method # real var_cooling_supply_air_flow_rate = 0.0 obj.cooling_supply_air_flow_rate = var_cooling_supply_air_flow_rate # real var_cooling_supply_air_flow_rate_per_floor_area = 0.0 obj.cooling_supply_air_flow_rate_per_floor_area = var_cooling_supply_air_flow_rate_per_floor_area # real var_cooling_fraction_of_autosized_cooling_supply_air_flow_rate = 0.0 obj.cooling_fraction_of_autosized_cooling_supply_air_flow_rate = var_cooling_fraction_of_autosized_cooling_supply_air_flow_rate # real var_cooling_supply_air_flow_rate_per_unit_cooling_capacity = 0.0 obj.cooling_supply_air_flow_rate_per_unit_cooling_capacity = var_cooling_supply_air_flow_rate_per_unit_cooling_capacity # alpha var_heating_supply_air_flow_rate_method = "Flow/System" obj.heating_supply_air_flow_rate_method = var_heating_supply_air_flow_rate_method # real var_heating_supply_air_flow_rate = 0.0 obj.heating_supply_air_flow_rate = var_heating_supply_air_flow_rate # real var_heating_supply_air_flow_rate_per_floor_area = 0.0 obj.heating_supply_air_flow_rate_per_floor_area = var_heating_supply_air_flow_rate_per_floor_area # real var_heating_fraction_of_autosized_heating_supply_air_flow_rate = 0.0 obj.heating_fraction_of_autosized_heating_supply_air_flow_rate = var_heating_fraction_of_autosized_heating_supply_air_flow_rate # real var_heating_fraction_of_autosized_cooling_supply_air_flow_rate = 0.0 obj.heating_fraction_of_autosized_cooling_supply_air_flow_rate = var_heating_fraction_of_autosized_cooling_supply_air_flow_rate # real var_heating_supply_air_flow_rate_per_unit_heating_capacity = 0.0 obj.heating_supply_air_flow_rate_per_unit_heating_capacity = var_heating_supply_air_flow_rate_per_unit_heating_capacity # alpha var_system_outdoor_air_method = "ZoneSum" obj.system_outdoor_air_method = var_system_outdoor_air_method # real var_zone_maximum_outdoor_air_fraction = 0.0001 obj.zone_maximum_outdoor_air_fraction = var_zone_maximum_outdoor_air_fraction # alpha var_cooling_design_capacity_method = "None" obj.cooling_design_capacity_method = var_cooling_design_capacity_method # real var_cooling_design_capacity = 0.0 obj.cooling_design_capacity = var_cooling_design_capacity # real var_cooling_design_capacity_per_floor_area = 0.0 obj.cooling_design_capacity_per_floor_area = var_cooling_design_capacity_per_floor_area # real var_fraction_of_autosized_cooling_design_capacity = 0.0 obj.fraction_of_autosized_cooling_design_capacity = var_fraction_of_autosized_cooling_design_capacity # alpha var_heating_design_capacity_method = "None" obj.heating_design_capacity_method = var_heating_design_capacity_method # real var_heating_design_capacity = 0.0 obj.heating_design_capacity = var_heating_design_capacity # real var_heating_design_capacity_per_floor_area = 0.0 obj.heating_design_capacity_per_floor_area = var_heating_design_capacity_per_floor_area # real var_fraction_of_autosized_heating_design_capacity = 0.0 obj.fraction_of_autosized_heating_design_capacity = var_fraction_of_autosized_heating_design_capacity # alpha var_central_cooling_capacity_control_method = "VAV" obj.central_cooling_capacity_control_method = var_central_cooling_capacity_control_method idf = IDF() idf.add(obj) idf.save(self.path, check=False) with open(self.path, mode='r') as f: for line in f: log.debug(line.strip()) idf2 = IDF(self.path) self.assertEqual(idf2.sizingsystems[0].airloop_name, var_airloop_name) self.assertEqual(idf2.sizingsystems[0].type_of_load_to_size_on, var_type_of_load_to_size_on) self.assertAlmostEqual(idf2.sizingsystems[0].design_outdoor_air_flow_rate, var_design_outdoor_air_flow_rate) self.assertAlmostEqual(idf2.sizingsystems[0].central_heating_maximum_system_air_flow_ratio, var_central_heating_maximum_system_air_flow_ratio) self.assertAlmostEqual(idf2.sizingsystems[0].preheat_design_temperature, var_preheat_design_temperature) self.assertAlmostEqual(idf2.sizingsystems[0].preheat_design_humidity_ratio, var_preheat_design_humidity_ratio) self.assertAlmostEqual(idf2.sizingsystems[0].precool_design_temperature, var_precool_design_temperature) self.assertAlmostEqual(idf2.sizingsystems[0].precool_design_humidity_ratio, var_precool_design_humidity_ratio) self.assertAlmostEqual(idf2.sizingsystems[0].central_cooling_design_supply_air_temperature, var_central_cooling_design_supply_air_temperature) self.assertAlmostEqual(idf2.sizingsystems[0].central_heating_design_supply_air_temperature, var_central_heating_design_supply_air_temperature) self.assertEqual(idf2.sizingsystems[0].type_of_zone_sum_to_use, var_type_of_zone_sum_to_use) self.assertEqual(idf2.sizingsystems[0].a_100_outdoor_air_in_cooling, var_a_100_outdoor_air_in_cooling) self.assertEqual(idf2.sizingsystems[0].a_100_outdoor_air_in_heating, var_a_100_outdoor_air_in_heating) self.assertAlmostEqual(idf2.sizingsystems[0].central_cooling_design_supply_air_humidity_ratio, var_central_cooling_design_supply_air_humidity_ratio) self.assertAlmostEqual(idf2.sizingsystems[0].central_heating_design_supply_air_humidity_ratio, var_central_heating_design_supply_air_humidity_ratio) self.assertEqual(idf2.sizingsystems[0].cooling_supply_air_flow_rate_method, var_cooling_supply_air_flow_rate_method) self.assertAlmostEqual(idf2.sizingsystems[0].cooling_supply_air_flow_rate, var_cooling_supply_air_flow_rate) self.assertAlmostEqual(idf2.sizingsystems[0].cooling_supply_air_flow_rate_per_floor_area, var_cooling_supply_air_flow_rate_per_floor_area) self.assertAlmostEqual(idf2.sizingsystems[0].cooling_fraction_of_autosized_cooling_supply_air_flow_rate, var_cooling_fraction_of_autosized_cooling_supply_air_flow_rate) self.assertAlmostEqual(idf2.sizingsystems[0].cooling_supply_air_flow_rate_per_unit_cooling_capacity, var_cooling_supply_air_flow_rate_per_unit_cooling_capacity) self.assertEqual(idf2.sizingsystems[0].heating_supply_air_flow_rate_method, var_heating_supply_air_flow_rate_method) self.assertAlmostEqual(idf2.sizingsystems[0].heating_supply_air_flow_rate, var_heating_supply_air_flow_rate) self.assertAlmostEqual(idf2.sizingsystems[0].heating_supply_air_flow_rate_per_floor_area, var_heating_supply_air_flow_rate_per_floor_area) self.assertAlmostEqual(idf2.sizingsystems[0].heating_fraction_of_autosized_heating_supply_air_flow_rate, var_heating_fraction_of_autosized_heating_supply_air_flow_rate) self.assertAlmostEqual(idf2.sizingsystems[0].heating_fraction_of_autosized_cooling_supply_air_flow_rate, var_heating_fraction_of_autosized_cooling_supply_air_flow_rate) self.assertAlmostEqual(idf2.sizingsystems[0].heating_supply_air_flow_rate_per_unit_heating_capacity, var_heating_supply_air_flow_rate_per_unit_heating_capacity) self.assertEqual(idf2.sizingsystems[0].system_outdoor_air_method, var_system_outdoor_air_method) self.assertAlmostEqual(idf2.sizingsystems[0].zone_maximum_outdoor_air_fraction, var_zone_maximum_outdoor_air_fraction) self.assertEqual(idf2.sizingsystems[0].cooling_design_capacity_method, var_cooling_design_capacity_method) self.assertAlmostEqual(idf2.sizingsystems[0].cooling_design_capacity, var_cooling_design_capacity) self.assertAlmostEqual(idf2.sizingsystems[0].cooling_design_capacity_per_floor_area, var_cooling_design_capacity_per_floor_area) self.assertAlmostEqual(idf2.sizingsystems[0].fraction_of_autosized_cooling_design_capacity, var_fraction_of_autosized_cooling_design_capacity) self.assertEqual(idf2.sizingsystems[0].heating_design_capacity_method, var_heating_design_capacity_method) self.assertAlmostEqual(idf2.sizingsystems[0].heating_design_capacity, var_heating_design_capacity) self.assertAlmostEqual(idf2.sizingsystems[0].heating_design_capacity_per_floor_area, var_heating_design_capacity_per_floor_area) self.assertAlmostEqual(idf2.sizingsystems[0].fraction_of_autosized_heating_design_capacity, var_fraction_of_autosized_heating_design_capacity) self.assertEqual(idf2.sizingsystems[0].central_cooling_capacity_control_method, var_central_cooling_capacity_control_method)

0.355663

0.141281

from typing import Optional from torch import nn from torch.nn import functional as F from ..ff import FF class SpeechLSTM(nn.Module): """A bidirectional LSTM encoder with subsampling for speech features. The number of LSTM layers is defined by the `layers` argument, i.e. `1_1_2_2_1_1` denotes 6 LSTM layers where the middle two applies a subsampling factor of 2 to their inputs. Subsampling in this context means that every N'th state will be passed to the next layer as input. Each LSTM layer is followed by a feed-forward projection layer whose non-linearity is given by the `activ` argument. Note: The input tensor should contain samples of equal lengths i.e. `bucket_by` in training configuration should be set to the acoustic features modality. Args: input_size: Input feature dimensionality. hidden_size: LSTM hidden state dimensionality. proj_size: Projection layer size. activ: Non-linearity to apply to intermediate projection layers. (Default: 'tanh') layers: A '_' separated list of integers that defines the subsampling factor for each LSTM. dropout: Use dropout (Default: 0.) Input: x: A `torch.Tensor` of shape `(n_timesteps, n_samples, input_size)` Output: hs: A `torch.Tensor` of shape `(n_timesteps, n_samples, hidden_size * 2)` that contains encoder hidden states for all timesteps. mask: `None` since this layer expects all equal frame inputs. """ def __init__(self, input_size: int, hidden_size: int, proj_size: int, layers: str, activ: Optional[str] = 'tanh', dropout: float = 0.0): super().__init__() self.input_size = input_size self.hidden_size = hidden_size self.proj_size = proj_size self.activ = activ self.layers = [int(i) for i in layers.split('_')] self.dropout = dropout self.n_layers = len(self.layers) # Doubles its size because of concatenation of forw-backw encs self.ctx_size = self.hidden_size * 2 # Fill 0-vector as <eos> to the end of the frames self.pad_tuple = (0, 0, 0, 0, 0, 1) # Projections and LSTMs self.ffs = nn.ModuleList() self.lstms = nn.ModuleList() if self.dropout > 0: self.do = nn.Dropout(self.dropout) for i, ss_factor in enumerate(self.layers): # Add LSTMs self.lstms.append(nn.LSTM( self.input_size if i == 0 else self.hidden_size, self.hidden_size, bidirectional=True)) # Add non-linear bottlenecks self.ffs.append(FF( self.ctx_size, self.proj_size, activ=self.activ)) def forward(self, x, **kwargs): # Generate a mask to detect padded sequences mask = x.ne(0).float().sum(2).ne(0).float() if mask.eq(0).nonzero().numel() > 0: raise RuntimeError("Non-homogeneous batch detected in SpeechLSTM layer.") # Pad with <eos> zero hs = F.pad(x, self.pad_tuple) for (ss_factor, f_lstm, f_ff) in zip(self.layers, self.lstms, self.ffs): if ss_factor > 1: # Skip states hs = f_ff(f_lstm(hs[::ss_factor])[0]) else: hs = f_ff(f_lstm(hs)[0]) if self.dropout > 0: hs = self.do(hs) # No mask is returned as batch should contain same-length sequences return hs, None

pysimt/layers/encoders/speech_lstm.py

from typing import Optional from torch import nn from torch.nn import functional as F from ..ff import FF class SpeechLSTM(nn.Module): """A bidirectional LSTM encoder with subsampling for speech features. The number of LSTM layers is defined by the `layers` argument, i.e. `1_1_2_2_1_1` denotes 6 LSTM layers where the middle two applies a subsampling factor of 2 to their inputs. Subsampling in this context means that every N'th state will be passed to the next layer as input. Each LSTM layer is followed by a feed-forward projection layer whose non-linearity is given by the `activ` argument. Note: The input tensor should contain samples of equal lengths i.e. `bucket_by` in training configuration should be set to the acoustic features modality. Args: input_size: Input feature dimensionality. hidden_size: LSTM hidden state dimensionality. proj_size: Projection layer size. activ: Non-linearity to apply to intermediate projection layers. (Default: 'tanh') layers: A '_' separated list of integers that defines the subsampling factor for each LSTM. dropout: Use dropout (Default: 0.) Input: x: A `torch.Tensor` of shape `(n_timesteps, n_samples, input_size)` Output: hs: A `torch.Tensor` of shape `(n_timesteps, n_samples, hidden_size * 2)` that contains encoder hidden states for all timesteps. mask: `None` since this layer expects all equal frame inputs. """ def __init__(self, input_size: int, hidden_size: int, proj_size: int, layers: str, activ: Optional[str] = 'tanh', dropout: float = 0.0): super().__init__() self.input_size = input_size self.hidden_size = hidden_size self.proj_size = proj_size self.activ = activ self.layers = [int(i) for i in layers.split('_')] self.dropout = dropout self.n_layers = len(self.layers) # Doubles its size because of concatenation of forw-backw encs self.ctx_size = self.hidden_size * 2 # Fill 0-vector as <eos> to the end of the frames self.pad_tuple = (0, 0, 0, 0, 0, 1) # Projections and LSTMs self.ffs = nn.ModuleList() self.lstms = nn.ModuleList() if self.dropout > 0: self.do = nn.Dropout(self.dropout) for i, ss_factor in enumerate(self.layers): # Add LSTMs self.lstms.append(nn.LSTM( self.input_size if i == 0 else self.hidden_size, self.hidden_size, bidirectional=True)) # Add non-linear bottlenecks self.ffs.append(FF( self.ctx_size, self.proj_size, activ=self.activ)) def forward(self, x, **kwargs): # Generate a mask to detect padded sequences mask = x.ne(0).float().sum(2).ne(0).float() if mask.eq(0).nonzero().numel() > 0: raise RuntimeError("Non-homogeneous batch detected in SpeechLSTM layer.") # Pad with <eos> zero hs = F.pad(x, self.pad_tuple) for (ss_factor, f_lstm, f_ff) in zip(self.layers, self.lstms, self.ffs): if ss_factor > 1: # Skip states hs = f_ff(f_lstm(hs[::ss_factor])[0]) else: hs = f_ff(f_lstm(hs)[0]) if self.dropout > 0: hs = self.do(hs) # No mask is returned as batch should contain same-length sequences return hs, None

0.962205

0.671773

from __future__ import (absolute_import, division, print_function, unicode_literals) from future.builtins import * import numpy as np from scipy import interpolate, linalg, signal class FirstOrder(signal.TransferFunction): """First order heat capacity differential equation model. The first oder heat capacity differential equation is C * dy/dt + K * y = u with C the heat capacity, K the thermal conductivity, y the temperature and u the heater power. This is a special case of a linear time invariant first order system a0 * dy/dt + a1 * y = b0 * du(t)/dt + b1 * u(t) The corresponding transferfunction is b0 * s^1 + b1 * s^0 G(s) = ------------------- a0 * s^1 + a1 * s^0 .. note:: We normalize the transfer function to `a0 = 1.` on instatiation. :param b: Numerator polynom. :param a: Denominator polynom. """ def __init__(self, b, a): # Normalize transfer function. b = np.array(b) / a[0] a = np.array(a) / a[0] super(FirstOrder, self).__init__(b, a) @classmethod def from_ck(cls, heat_capacity, thermal_conductivity): b = [0., 1. / heat_capacity] a = [1., thermal_conductivity / heat_capacity] return cls(b, a) @property def heat_capacity(self): return 1. / self.num[-1] @property def thermal_conductivity(self): return self.den[1] * self.heat_capacity @classmethod def fit(cls, t, y, u): """Fits a first order heat capacity model. :param t: A sequence of timestamps. :param y: A sequence of temperatures. :param u: A sequence of heater power values. """ yspline = interpolate.UnivariateSpline(t, y, s=0) uspline = interpolate.UnivariateSpline(t, u, s=0) ti = np.linspace(np.min(t), np.max(t), len(t)) yi = yspline(ti) dyi = yspline.derivative(n=1)(ti) ui = uspline(ti) result = linalg.lstsq(np.hstack((yi[:,None], ui[:, None])) , dyi)[0] b = np.r_[result[1]] a = np.r_[1., - result[0]] return cls(b, a)

heatcapacity/fit.py

from __future__ import (absolute_import, division, print_function, unicode_literals) from future.builtins import * import numpy as np from scipy import interpolate, linalg, signal class FirstOrder(signal.TransferFunction): """First order heat capacity differential equation model. The first oder heat capacity differential equation is C * dy/dt + K * y = u with C the heat capacity, K the thermal conductivity, y the temperature and u the heater power. This is a special case of a linear time invariant first order system a0 * dy/dt + a1 * y = b0 * du(t)/dt + b1 * u(t) The corresponding transferfunction is b0 * s^1 + b1 * s^0 G(s) = ------------------- a0 * s^1 + a1 * s^0 .. note:: We normalize the transfer function to `a0 = 1.` on instatiation. :param b: Numerator polynom. :param a: Denominator polynom. """ def __init__(self, b, a): # Normalize transfer function. b = np.array(b) / a[0] a = np.array(a) / a[0] super(FirstOrder, self).__init__(b, a) @classmethod def from_ck(cls, heat_capacity, thermal_conductivity): b = [0., 1. / heat_capacity] a = [1., thermal_conductivity / heat_capacity] return cls(b, a) @property def heat_capacity(self): return 1. / self.num[-1] @property def thermal_conductivity(self): return self.den[1] * self.heat_capacity @classmethod def fit(cls, t, y, u): """Fits a first order heat capacity model. :param t: A sequence of timestamps. :param y: A sequence of temperatures. :param u: A sequence of heater power values. """ yspline = interpolate.UnivariateSpline(t, y, s=0) uspline = interpolate.UnivariateSpline(t, u, s=0) ti = np.linspace(np.min(t), np.max(t), len(t)) yi = yspline(ti) dyi = yspline.derivative(n=1)(ti) ui = uspline(ti) result = linalg.lstsq(np.hstack((yi[:,None], ui[:, None])) , dyi)[0] b = np.r_[result[1]] a = np.r_[1., - result[0]] return cls(b, a)

0.928975

0.52476

from sage.structure.sage_object import SageObject from sage.rings.all import Integer, infinity, ZZ, QQ, CC from sage.modules.free_module import span from sage.modular.modform.constructor import Newform, CuspForms from sage.modular.arithgroup.congroup_gamma0 import is_Gamma0 from sage.misc.misc_c import prod class Lseries(SageObject): """ Base class for `L`-series attached to modular abelian varieties. This is a common base class for complex and `p`-adic `L`-series of modular abelian varieties. """ def __init__(self, abvar): """ Called when creating an L-series. INPUT: - ``abvar`` -- a modular abelian variety EXAMPLES:: sage: J0(11).lseries() Complex L-series attached to Abelian variety J0(11) of dimension 1 sage: J0(11).padic_lseries(7) 7-adic L-series attached to Abelian variety J0(11) of dimension 1 """ self.__abvar = abvar def abelian_variety(self): """ Return the abelian variety that this `L`-series is attached to. OUTPUT: a modular abelian variety EXAMPLES:: sage: J0(11).padic_lseries(7).abelian_variety() Abelian variety J0(11) of dimension 1 """ return self.__abvar class Lseries_complex(Lseries): """ A complex `L`-series attached to a modular abelian variety. EXAMPLES:: sage: A = J0(37) sage: A.lseries() Complex L-series attached to Abelian variety J0(37) of dimension 2 """ def __call__(self, s, prec=53): """ Evaluate this complex `L`-series at `s`. INPUT: - ``s`` -- complex number - ``prec`` -- integer (default: 53) the number of bits of precision used in computing the lseries of the newforms. OUTPUT: a complex number L(A, s). EXAMPLES:: sage: L = J0(23).lseries() sage: L(1) 0.248431866590600 sage: L(1, prec=100) 0.24843186659059968120725033931 sage: L = J0(389)[0].lseries() sage: L(1) # long time (2s) abstol 1e-10 -1.33139759782370e-19 sage: L(1, prec=100) # long time (2s) abstol 1e-20 6.0129758648142797032650287762e-39 sage: L.rational_part() 0 sage: L = J1(23)[0].lseries() sage: L(1) 0.248431866590600 sage: J = J0(11) * J1(11) sage: J.lseries()(1) 0.0644356903227915 sage: L = JH(17,[2]).lseries() sage: L(1) 0.386769938387780 """ abelian_variety = self.abelian_variety() # Check for easy dimension zero case if abelian_variety.dimension() == 0: return CC(1) try: factors = self.__factors[prec] return prod(L(s) for L in factors) except AttributeError: self.__factors = {} except KeyError: pass abelian_variety = self.abelian_variety() newforms = abelian_variety.newform_decomposition('a') factors = [newform.lseries(embedding=i, prec=prec) for newform in newforms for i in range(newform.base_ring().degree())] self.__factors[prec] = factors return prod(L(s) for L in factors) def __eq__(self, other): """ Compare this complex `L`-series to another one. INPUT: - ``other`` -- object OUTPUT: boolean EXAMPLES:: sage: L = J0(37)[0].lseries() sage: M = J0(37)[1].lseries() sage: L == M False sage: L == L True """ if not isinstance(other, Lseries_complex): return False return self.abelian_variety() == other.abelian_variety() def __ne__(self, other): """ Check whether ``self`` is not equal to ``other``. INPUT: - ``other`` -- object OUTPUT: boolean EXAMPLES:: sage: L = J0(37)[0].lseries() sage: M = J0(37)[1].lseries() sage: L != M True sage: L != L False """ return not (self == other) def _repr_(self): """ String representation of `L`-series. OUTPUT: a string EXAMPLES:: sage: L = J0(37).lseries() sage: L._repr_() 'Complex L-series attached to Abelian variety J0(37) of dimension 2' """ return "Complex L-series attached to %s" % self.abelian_variety() def vanishes_at_1(self): """ Return True if `L(1)=0` and return False otherwise. OUTPUT: a boolean EXAMPLES: Numerically, the `L`-series for `J_0(389)` appears to vanish at 1. This is confirmed by this algebraic computation:: sage: L = J0(389)[0].lseries(); L Complex L-series attached to Simple abelian subvariety 389a(1,389) of dimension 1 of J0(389) sage: L(1) # long time (2s) abstol 1e-10 -1.33139759782370e-19 sage: L.vanishes_at_1() True Numerically, one might guess that the `L`-series for `J_1(23)` and `J_1(31)` vanish at 1. This algebraic computation shows otherwise:: sage: L = J1(23).lseries(); L Complex L-series attached to Abelian variety J1(23) of dimension 12 sage: L(1) # long time (about 3 s) 0.000129519861426989 + 1.14001148377577e-19*I sage: L.vanishes_at_1() False sage: L(1, prec=100) # long time (about 3 s) 0.00012951986142702571478817757149 - 2.9734441752025676942763838067e-33*I sage: L = J1(31).lseries(); L Complex L-series attached to Abelian variety J1(31) of dimension 26 sage: abs(L(1) - 3.45014267547611e-7) < 1e-15 # long time (about 8 s) True sage: L.vanishes_at_1() # long time (about 6 s) False """ abelian_variety = self.abelian_variety() # Check for easy dimension zero case if abelian_variety.dimension() == 0: return False if not abelian_variety.is_simple(): from .constructor import AbelianVariety decomp = (AbelianVariety(f) for f in abelian_variety.newform_decomposition('a')) return any(S.lseries().vanishes_at_1() for S in decomp) modular_symbols = abelian_variety.modular_symbols() Phi = modular_symbols.rational_period_mapping() ambient_module = modular_symbols.ambient_module() e = ambient_module([0, infinity]) return Phi(e).is_zero() def rational_part(self): """ Return the rational part of this `L`-function at the central critical value 1. OUTPUT: a rational number EXAMPLES:: sage: A, B = J0(43).decomposition() sage: A.lseries().rational_part() 0 sage: B.lseries().rational_part() 2/7 """ abelian_variety = self.abelian_variety() modular_symbols = abelian_variety.modular_symbols() Phi = modular_symbols.rational_period_mapping() ambient_module = modular_symbols.ambient_module() if self.vanishes_at_1(): return QQ(0) else: s = ambient_module.sturm_bound() I = ambient_module.hecke_images(0, range(1, s+1)) PhiTe = span([Phi(ambient_module(I[n])) for n in range(I.nrows())], ZZ) ambient_plus = ambient_module.sign_submodule(1) ambient_plus_cusp = ambient_plus.cuspidal_submodule() PhiH1plus = span([Phi(x) for x in ambient_plus_cusp.integral_basis()], ZZ) return PhiTe.index_in(PhiH1plus) lratio = rational_part class Lseries_padic(Lseries): """ A `p`-adic `L`-series attached to a modular abelian variety. """ def __init__(self, abvar, p): """ Create a `p`-adic `L`-series. EXAMPLES:: sage: J0(37)[0].padic_lseries(389) 389-adic L-series attached to Simple abelian subvariety 37a(1,37) of dimension 1 of J0(37) """ Lseries.__init__(self, abvar) p = Integer(p) if not p.is_prime(): raise ValueError("p (=%s) must be prime"%p) self.__p = p def __eq__(self, other): """ Compare this `p`-adic `L`-series to another one. First the abelian varieties are compared; if they are the same, then the primes are compared. INPUT: other -- object OUTPUT: boolean EXAMPLES:: sage: L = J0(37)[0].padic_lseries(5) sage: M = J0(37)[1].padic_lseries(5) sage: K = J0(37)[0].padic_lseries(3) sage: L == K False sage: L == M False sage: L == L True """ if not isinstance(other, Lseries_padic): return False return (self.abelian_variety() == other.abelian_variety() and self.__p == other.__p) def __ne__(self, other): """ Check whether ``self`` is not equal to ``other``. INPUT: other -- object OUTPUT: boolean EXAMPLES:: sage: L = J0(37)[0].padic_lseries(5) sage: M = J0(37)[1].padic_lseries(5) sage: K = J0(37)[0].padic_lseries(3) sage: L != K True sage: L != M True sage: L != L False """ return not (self == other) def prime(self): """ Return the prime `p` of this `p`-adic `L`-series. EXAMPLES:: sage: J0(11).padic_lseries(7).prime() 7 """ return self.__p def power_series(self, n=2, prec=5): """ Return the `n`-th approximation to this `p`-adic `L`-series as a power series in `T`. Each coefficient is a `p`-adic number whose precision is provably correct. NOTE: This is not yet implemented. EXAMPLES:: sage: L = J0(37)[0].padic_lseries(5) sage: L.power_series() Traceback (most recent call last): ... NotImplementedError sage: L.power_series(3,7) Traceback (most recent call last): ... NotImplementedError """ raise NotImplementedError def _repr_(self): """ String representation of this `p`-adic `L`-series. EXAMPLES:: sage: L = J0(37)[0].padic_lseries(5) sage: L._repr_() '5-adic L-series attached to Simple abelian subvariety 37a(1,37) of dimension 1 of J0(37)' """ return "%s-adic L-series attached to %s" % (self.__p, self.abelian_variety())

src/sage/modular/abvar/lseries.py

from sage.structure.sage_object import SageObject from sage.rings.all import Integer, infinity, ZZ, QQ, CC from sage.modules.free_module import span from sage.modular.modform.constructor import Newform, CuspForms from sage.modular.arithgroup.congroup_gamma0 import is_Gamma0 from sage.misc.misc_c import prod class Lseries(SageObject): """ Base class for `L`-series attached to modular abelian varieties. This is a common base class for complex and `p`-adic `L`-series of modular abelian varieties. """ def __init__(self, abvar): """ Called when creating an L-series. INPUT: - ``abvar`` -- a modular abelian variety EXAMPLES:: sage: J0(11).lseries() Complex L-series attached to Abelian variety J0(11) of dimension 1 sage: J0(11).padic_lseries(7) 7-adic L-series attached to Abelian variety J0(11) of dimension 1 """ self.__abvar = abvar def abelian_variety(self): """ Return the abelian variety that this `L`-series is attached to. OUTPUT: a modular abelian variety EXAMPLES:: sage: J0(11).padic_lseries(7).abelian_variety() Abelian variety J0(11) of dimension 1 """ return self.__abvar class Lseries_complex(Lseries): """ A complex `L`-series attached to a modular abelian variety. EXAMPLES:: sage: A = J0(37) sage: A.lseries() Complex L-series attached to Abelian variety J0(37) of dimension 2 """ def __call__(self, s, prec=53): """ Evaluate this complex `L`-series at `s`. INPUT: - ``s`` -- complex number - ``prec`` -- integer (default: 53) the number of bits of precision used in computing the lseries of the newforms. OUTPUT: a complex number L(A, s). EXAMPLES:: sage: L = J0(23).lseries() sage: L(1) 0.248431866590600 sage: L(1, prec=100) 0.24843186659059968120725033931 sage: L = J0(389)[0].lseries() sage: L(1) # long time (2s) abstol 1e-10 -1.33139759782370e-19 sage: L(1, prec=100) # long time (2s) abstol 1e-20 6.0129758648142797032650287762e-39 sage: L.rational_part() 0 sage: L = J1(23)[0].lseries() sage: L(1) 0.248431866590600 sage: J = J0(11) * J1(11) sage: J.lseries()(1) 0.0644356903227915 sage: L = JH(17,[2]).lseries() sage: L(1) 0.386769938387780 """ abelian_variety = self.abelian_variety() # Check for easy dimension zero case if abelian_variety.dimension() == 0: return CC(1) try: factors = self.__factors[prec] return prod(L(s) for L in factors) except AttributeError: self.__factors = {} except KeyError: pass abelian_variety = self.abelian_variety() newforms = abelian_variety.newform_decomposition('a') factors = [newform.lseries(embedding=i, prec=prec) for newform in newforms for i in range(newform.base_ring().degree())] self.__factors[prec] = factors return prod(L(s) for L in factors) def __eq__(self, other): """ Compare this complex `L`-series to another one. INPUT: - ``other`` -- object OUTPUT: boolean EXAMPLES:: sage: L = J0(37)[0].lseries() sage: M = J0(37)[1].lseries() sage: L == M False sage: L == L True """ if not isinstance(other, Lseries_complex): return False return self.abelian_variety() == other.abelian_variety() def __ne__(self, other): """ Check whether ``self`` is not equal to ``other``. INPUT: - ``other`` -- object OUTPUT: boolean EXAMPLES:: sage: L = J0(37)[0].lseries() sage: M = J0(37)[1].lseries() sage: L != M True sage: L != L False """ return not (self == other) def _repr_(self): """ String representation of `L`-series. OUTPUT: a string EXAMPLES:: sage: L = J0(37).lseries() sage: L._repr_() 'Complex L-series attached to Abelian variety J0(37) of dimension 2' """ return "Complex L-series attached to %s" % self.abelian_variety() def vanishes_at_1(self): """ Return True if `L(1)=0` and return False otherwise. OUTPUT: a boolean EXAMPLES: Numerically, the `L`-series for `J_0(389)` appears to vanish at 1. This is confirmed by this algebraic computation:: sage: L = J0(389)[0].lseries(); L Complex L-series attached to Simple abelian subvariety 389a(1,389) of dimension 1 of J0(389) sage: L(1) # long time (2s) abstol 1e-10 -1.33139759782370e-19 sage: L.vanishes_at_1() True Numerically, one might guess that the `L`-series for `J_1(23)` and `J_1(31)` vanish at 1. This algebraic computation shows otherwise:: sage: L = J1(23).lseries(); L Complex L-series attached to Abelian variety J1(23) of dimension 12 sage: L(1) # long time (about 3 s) 0.000129519861426989 + 1.14001148377577e-19*I sage: L.vanishes_at_1() False sage: L(1, prec=100) # long time (about 3 s) 0.00012951986142702571478817757149 - 2.9734441752025676942763838067e-33*I sage: L = J1(31).lseries(); L Complex L-series attached to Abelian variety J1(31) of dimension 26 sage: abs(L(1) - 3.45014267547611e-7) < 1e-15 # long time (about 8 s) True sage: L.vanishes_at_1() # long time (about 6 s) False """ abelian_variety = self.abelian_variety() # Check for easy dimension zero case if abelian_variety.dimension() == 0: return False if not abelian_variety.is_simple(): from .constructor import AbelianVariety decomp = (AbelianVariety(f) for f in abelian_variety.newform_decomposition('a')) return any(S.lseries().vanishes_at_1() for S in decomp) modular_symbols = abelian_variety.modular_symbols() Phi = modular_symbols.rational_period_mapping() ambient_module = modular_symbols.ambient_module() e = ambient_module([0, infinity]) return Phi(e).is_zero() def rational_part(self): """ Return the rational part of this `L`-function at the central critical value 1. OUTPUT: a rational number EXAMPLES:: sage: A, B = J0(43).decomposition() sage: A.lseries().rational_part() 0 sage: B.lseries().rational_part() 2/7 """ abelian_variety = self.abelian_variety() modular_symbols = abelian_variety.modular_symbols() Phi = modular_symbols.rational_period_mapping() ambient_module = modular_symbols.ambient_module() if self.vanishes_at_1(): return QQ(0) else: s = ambient_module.sturm_bound() I = ambient_module.hecke_images(0, range(1, s+1)) PhiTe = span([Phi(ambient_module(I[n])) for n in range(I.nrows())], ZZ) ambient_plus = ambient_module.sign_submodule(1) ambient_plus_cusp = ambient_plus.cuspidal_submodule() PhiH1plus = span([Phi(x) for x in ambient_plus_cusp.integral_basis()], ZZ) return PhiTe.index_in(PhiH1plus) lratio = rational_part class Lseries_padic(Lseries): """ A `p`-adic `L`-series attached to a modular abelian variety. """ def __init__(self, abvar, p): """ Create a `p`-adic `L`-series. EXAMPLES:: sage: J0(37)[0].padic_lseries(389) 389-adic L-series attached to Simple abelian subvariety 37a(1,37) of dimension 1 of J0(37) """ Lseries.__init__(self, abvar) p = Integer(p) if not p.is_prime(): raise ValueError("p (=%s) must be prime"%p) self.__p = p def __eq__(self, other): """ Compare this `p`-adic `L`-series to another one. First the abelian varieties are compared; if they are the same, then the primes are compared. INPUT: other -- object OUTPUT: boolean EXAMPLES:: sage: L = J0(37)[0].padic_lseries(5) sage: M = J0(37)[1].padic_lseries(5) sage: K = J0(37)[0].padic_lseries(3) sage: L == K False sage: L == M False sage: L == L True """ if not isinstance(other, Lseries_padic): return False return (self.abelian_variety() == other.abelian_variety() and self.__p == other.__p) def __ne__(self, other): """ Check whether ``self`` is not equal to ``other``. INPUT: other -- object OUTPUT: boolean EXAMPLES:: sage: L = J0(37)[0].padic_lseries(5) sage: M = J0(37)[1].padic_lseries(5) sage: K = J0(37)[0].padic_lseries(3) sage: L != K True sage: L != M True sage: L != L False """ return not (self == other) def prime(self): """ Return the prime `p` of this `p`-adic `L`-series. EXAMPLES:: sage: J0(11).padic_lseries(7).prime() 7 """ return self.__p def power_series(self, n=2, prec=5): """ Return the `n`-th approximation to this `p`-adic `L`-series as a power series in `T`. Each coefficient is a `p`-adic number whose precision is provably correct. NOTE: This is not yet implemented. EXAMPLES:: sage: L = J0(37)[0].padic_lseries(5) sage: L.power_series() Traceback (most recent call last): ... NotImplementedError sage: L.power_series(3,7) Traceback (most recent call last): ... NotImplementedError """ raise NotImplementedError def _repr_(self): """ String representation of this `p`-adic `L`-series. EXAMPLES:: sage: L = J0(37)[0].padic_lseries(5) sage: L._repr_() '5-adic L-series attached to Simple abelian subvariety 37a(1,37) of dimension 1 of J0(37)' """ return "%s-adic L-series attached to %s" % (self.__p, self.abelian_variety())

0.875946

0.510558

import json import time, datetime import os import shutil class V2XDataCollector: def __init__(self, environment:str): self.environment = environment self.loggingDirectory = None self.initializationTimestamp = None self.hostBsmLogfile = None self.remoteBsmLogfile = None self.spatLogfile = None self.srmLogfile = None self.ssmLogfile = None self.msgCountsLogfile = None configFile = open("/nojournal/bin/mmitss-phase3-master-config.json", 'r') config = (json.load(configFile)) configFile.close() self.hostBsmDecoderPort = config["PortNumber"]["HostBsmDecoder"] if self.environment == "vehicle": self.baseName = "vehicle" else: self.baseName = config["IntersectionName"] self.path = "/nojournal/bin/v2x-data" if not os.path.exists(self.path + "/archive"): os.makedirs(self.path + "/archive") self.archive_leftover_directories() self.initialize_logfiles() def initialize_logfiles(self): self.initializationTimestamp = ('{:%m%d%Y_%H%M%S}'.format(datetime.datetime.now())) self.loggingDirectory = self.path + "/" + self.baseName + "_" + self.initializationTimestamp + "/" os.makedirs(self.loggingDirectory) self.initialize_msgCountsLogfile() self.initialize_bsmLogfile("remote") self.initialize_spatLogfile() self.initialize_srmLogfile() self.initialize_ssmLogfile() if self.environment == "vehicle": self.initialize_bsmLogfile("host") def initialize_msgCountsLogfile(self): msgCountsLogfileName = self.loggingDirectory + self.baseName + "_" + "msgCountsLog_" + self.initializationTimestamp + ".csv" self.msgCountsLogfile = open(msgCountsLogfileName, 'w', buffering=1) if self.environment == "roadside": self.msgCountsLogfile.write("log_timestamp_verbose,log_timestamp_posix,timestamp_verbose,timestamp_posix,interval_sec,msg_source,msg_type,msg_count,msg_served,msg_rejected\n") elif self.environment == "vehicle": self.msgCountsLogfile.write("log_timestamp_verbose,log_timestamp_posix,timestamp_verbose,timestamp_posix,interval_sec,msg_source,msg_type,msg_count\n") def initialize_bsmLogfile(self, origin): bsmLogfileName = self.loggingDirectory + self.baseName + "_" + origin + "BsmLog_" + self.initializationTimestamp + ".csv" if self.environment == "vehicle": csvHeader = ("log_timestamp_verbose,log_timestamp_posix,timestamp_verbose,timestamp_posix," + "temporaryId,secMark,latitude,longitude,elevation,speed,heading,type,length,width\n") elif self.environment == "roadside": csvHeader = ("log_timestamp_verbose,log_timestamp_posix,timestamp_verbose,timestamp_posix," + "temporaryId,secMark,latitude,longitude,elevation,speed,heading,type,length,width,onmap_status,position_on_map,current_approach,current_lane,current_signal_group,dist_to_stopbar\n") if origin == "host": self.hostBsmLogfile = open(bsmLogfileName, 'w') self.hostBsmLogfile.write(csvHeader) elif origin == "remote": self.remoteBsmLogfile = open(bsmLogfileName, 'w') self.remoteBsmLogfile.write(csvHeader) def initialize_spatLogfile(self): spatLogfileName = self.loggingDirectory + self.baseName + "_" + "spatLog_" + self.initializationTimestamp + ".csv" self.spatLogfile = open(spatLogfileName, 'w') csvHeader = ("log_timestamp_verbose,log_timestamp_posix,timestamp_verbose,timestamp_posix,regionalId,intersectionId,msgCount,moy,msom," + "v1_currState,v1_minEndTime,v1_maxEndTime,v1_elapsedTime," + "v2_currState,v2_minEndTime,v2_maxEndTime,v2_elapsedTime," + "v3_currState,v3_minEndTime,v3_maxEndTime,v3_elapsedTime," + "v4_currState,v4_minEndTime,v4_maxEndTime,v4_elapsedTime," + "v5_currState,v5_minEndTime,v5_maxEndTime,v5_elapsedTime," + "v6_currState,v6_minEndTime,v6_maxEndTime,v6_elapsedTime," + "v7_currState,v7_minEndTime,v7_maxEndTime,v7_elapsedTime," + "v8_currState,v8_minEndTime,v8_maxEndTime,v8_elapsedTime," + "p1_currState,p1_minEndTime,p1_maxEndTime,p1_elapsedTime," + "p2_currState,p2_minEndTime,p2_maxEndTime,p2_elapsedTime," + "p3_currState,p3_minEndTime,p3_maxEndTime,p3_elapsedTime," + "p4_currState,p4_minEndTime,p4_maxEndTime,p4_elapsedTime," + "p5_currState,p5_minEndTime,p5_maxEndTime,p5_elapsedTime," + "p6_currState,p6_minEndTime,p6_maxEndTime,p6_elapsedTime," + "p7_currState,p7_minEndTime,p7_maxEndTime,p7_elapsedTime," + "p8_currState,p8_minEndTime,p8_maxEndTime,p8_elapsedTime" + "\n") self.spatLogfile.write(csvHeader) def initialize_srmLogfile(self): srmLogfileName = self.loggingDirectory + self.baseName + "_" + "srmLog_" + self.initializationTimestamp + ".csv" self.srmLogfile = open(srmLogfileName, 'w', buffering=1) csvHeader = ("log_timestamp_verbose,log_timestamp_posix,timestamp_verbose" + "," + "timestamp_posix" + "," + "minuteOfYear" + "," + "msOfMinute" + "," + "msgCount" + "," + "regionalID" + "," + "intersectionID" + "," + "priorityRequestType" + "," + "basicVehicleRole" + "," + "laneID" + "," + "eTA_Minute" + "," + "eTA_Second" + "," + "eTA_Duration" + "," + "vehicleID" + "," + "latitude" + "," + "longitude" + "," + "elevation" + "," + "heading" + "," + "speed" + "," + "vehicleType" + "\n") self.srmLogfile.write(csvHeader) def initialize_ssmLogfile(self): ssmLogfileName = self.loggingDirectory + self.baseName + "_" + "ssmLog_" + self.initializationTimestamp + ".csv" self.ssmLogfile = open(ssmLogfileName, 'w', buffering=1) csvHeader = ("log_timestamp_verbose" + "," + "log_timestamp_posix" + "," + "timestamp_verbose" + "," + "timestamp_posix" + "," + "minuteOfYear" + "," + "msOfMinute" + "," + "sequenceNumber" + "," + "updateCount" + "," + "regionalID" + "," + "noOfRequest" + "," + "intersectionID" + "," + "r1_vehicleID,r1_msgCount,r1_basicVehicleRole,r1_inBoundLaneID,r1_ETA_Minute,r1_ETA_Second,r1_ETA_Duration,r1_priorityRequestStatus" + "," + "r2_vehicleID,r2_msgCount,r2_basicVehicleRole,r2_inBoundLaneID,r2_ETA_Minute,r2_ETA_Second,r2_ETA_Duration,r2_priorityRequestStatus" + "," + "r3_vehicleID,r3_msgCount,r3_basicVehicleRole,r3_inBoundLaneID,r3_ETA_Minute,r3_ETA_Second,r3_ETA_Duration,r3_priorityRequestStatus" + "," + "r4_vehicleID,r4_msgCount,r4_basicVehicleRole,r4_inBoundLaneID,r4_ETA_Minute,r4_ETA_Second,r4_ETA_Duration,r4_priorityRequestStatus" + "," + "r5_vehicleID,r5_msgCount,r5_basicVehicleRole,r5_inBoundLaneID,r5_ETA_Minute,r5_ETA_Second,r5_ETA_Duration,r5_priorityRequestStatus\n") self.ssmLogfile.write(csvHeader) def write_msgCount(self, msgCounts:json): csvRow = self.msgCounts_json_to_csv(msgCounts) self.msgCountsLogfile.write(csvRow) def write_bsm(self, bsmJson:json, senderPort:int): csvRow = self.bsm_json_to_csv(bsmJson) if ((self.environment == "vehicle") and (senderPort == self.hostBsmDecoderPort)): self.hostBsmLogfile.write(csvRow) else: self.remoteBsmLogfile.write(csvRow) def write_spat(self, spatJson:json): csvRow = self.spat_json_to_csv(spatJson) self.spatLogfile.write(csvRow) def write_srm(self, srmJson:json): csvRow = self.srm_json_to_csv(srmJson) self.srmLogfile.write(csvRow) def write_ssm(self, ssmJson:json): csvRow = self.ssm_json_to_csv(ssmJson) self.ssmLogfile.write(csvRow) def msgCounts_json_to_csv(self, jsonData:json): log_timestamp_posix = str(time.time()) log_timestamp_verbose = str(datetime.datetime.now()) timestamp_posix = str(jsonData["MsgInformation"]["Timestamp_posix"]) timestamp_verbose = str(jsonData["MsgInformation"]["Timestamp_verbose"]) timeInterval = str(jsonData["MsgInformation"]["TimeInterval"]) msgSource = str(jsonData["MsgInformation"]["MsgSource"]) msgType = str(jsonData["MsgInformation"]["MsgCountType"]) msgCount = str(jsonData["MsgInformation"]["MsgCount"]) csv = (log_timestamp_verbose + "," + log_timestamp_posix + "," + timestamp_verbose + "," + timestamp_posix + "," + timeInterval + "," + msgSource + "," + msgType + "," + msgCount) if self.environment == "roadside": msgServed = str(jsonData["MsgInformation"]["MsgServed"]) msgRejected = str(jsonData["MsgInformation"]["MsgRejected"]) csv = csv + "," + msgServed + "," + msgRejected csv = csv + "\n" return csv def bsm_json_to_csv(self, jsonData:json): log_timestamp_verbose = str(datetime.datetime.now()) log_timestamp_posix = str(time.time()) timestamp_verbose = str(jsonData["Timestamp_verbose"]) timestamp_posix = str(jsonData["Timestamp_posix"]) temporaryId = str(jsonData["BasicVehicle"]["temporaryID"]) secMark = str(jsonData["BasicVehicle"]["secMark_Second"]) latitude = str(jsonData["BasicVehicle"]["position"]["latitude_DecimalDegree"]) longitude = str(jsonData["BasicVehicle"]["position"]["longitude_DecimalDegree"]) elevation = str(jsonData["BasicVehicle"]["position"]["elevation_Meter"]) speed = str(jsonData["BasicVehicle"]["speed_MeterPerSecond"]) heading = str(jsonData["BasicVehicle"]["heading_Degree"]) vehType = str(jsonData["BasicVehicle"]["type"]) length = str(jsonData["BasicVehicle"]["size"]["length_cm"]) width = str(jsonData["BasicVehicle"]["size"]["width_cm"]) if self.environment == "roadside": onMap = str(jsonData["OnmapVehicle"]["onMap"]) approachId = str(jsonData["OnmapVehicle"]["approachId"]) laneId = str(jsonData["OnmapVehicle"]["laneId"]) signalGroup = str(jsonData["OnmapVehicle"]["signalGroup"]) distanceToStopbar = str(jsonData["OnmapVehicle"]["distanceToStopbar"]) locationOnMap = str(jsonData["OnmapVehicle"]["locationOnMap"]) csv = (log_timestamp_verbose + "," + log_timestamp_posix + "," + timestamp_verbose + "," + timestamp_posix + "," + temporaryId + "," + secMark + "," + latitude + "," + longitude + "," + elevation + "," + speed + "," + heading + "," + vehType + "," + length + "," + width + "," + onMap + "," + locationOnMap + "," + approachId + "," + laneId + "," + signalGroup + "," + distanceToStopbar + "\n") elif self.environment == "vehicle": csv = (log_timestamp_verbose + "," + log_timestamp_posix + "," + timestamp_verbose + "," + timestamp_posix + "," + temporaryId + "," + secMark + "," + latitude + "," + longitude + "," + elevation + "," + speed + "," + heading + "," + vehType + "," + length + "," + width + "\n") return csv def spat_json_to_csv(self, jsonData:json): log_timestamp_verbose = str(datetime.datetime.now()) log_timestamp_posix = str(time.time()) timestamp_verbose = str(jsonData["Timestamp_verbose"]) timestamp_posix = str(jsonData["Timestamp_posix"]) regionalId = str(jsonData["Spat"]["IntersectionState"]["regionalID"]) intersectionId = str(jsonData["Spat"]["IntersectionState"]["intersectionID"]) msgCnt = str(jsonData["Spat"]["msgCnt"]) moy = str(jsonData["Spat"]["minuteOfYear"]) msom = str(jsonData["Spat"]["msOfMinute"]) v1_currState = str(jsonData["Spat"]["phaseState"][0]["currState"]) v1_minEndTime = str(jsonData["Spat"]["phaseState"][0]["minEndTime"]) v1_maxEndTime = str(jsonData["Spat"]["phaseState"][0]["maxEndTime"]) v1_elapsedTime = str(jsonData["Spat"]["phaseState"][0]["elapsedTime"]) v2_currState = str(jsonData["Spat"]["phaseState"][1]["currState"]) v2_minEndTime = str(jsonData["Spat"]["phaseState"][1]["minEndTime"]) v2_maxEndTime = str(jsonData["Spat"]["phaseState"][1]["maxEndTime"]) v2_elapsedTime = str(jsonData["Spat"]["phaseState"][1]["elapsedTime"]) v3_currState = str(jsonData["Spat"]["phaseState"][2]["currState"]) v3_minEndTime = str(jsonData["Spat"]["phaseState"][2]["minEndTime"]) v3_maxEndTime = str(jsonData["Spat"]["phaseState"][2]["maxEndTime"]) v3_elapsedTime = str(jsonData["Spat"]["phaseState"][2]["elapsedTime"]) v4_currState = str(jsonData["Spat"]["phaseState"][3]["currState"]) v4_minEndTime = str(jsonData["Spat"]["phaseState"][3]["minEndTime"]) v4_maxEndTime = str(jsonData["Spat"]["phaseState"][3]["maxEndTime"]) v4_elapsedTime = str(jsonData["Spat"]["phaseState"][3]["elapsedTime"]) v5_currState = str(jsonData["Spat"]["phaseState"][4]["currState"]) v5_minEndTime = str(jsonData["Spat"]["phaseState"][4]["minEndTime"]) v5_maxEndTime = str(jsonData["Spat"]["phaseState"][4]["maxEndTime"]) v5_elapsedTime = str(jsonData["Spat"]["phaseState"][4]["elapsedTime"]) v6_currState = str(jsonData["Spat"]["phaseState"][5]["currState"]) v6_minEndTime = str(jsonData["Spat"]["phaseState"][5]["minEndTime"]) v6_maxEndTime = str(jsonData["Spat"]["phaseState"][5]["maxEndTime"]) v6_elapsedTime = str(jsonData["Spat"]["phaseState"][5]["elapsedTime"]) v7_currState = str(jsonData["Spat"]["phaseState"][6]["currState"]) v7_minEndTime = str(jsonData["Spat"]["phaseState"][6]["minEndTime"]) v7_maxEndTime = str(jsonData["Spat"]["phaseState"][6]["maxEndTime"]) v7_elapsedTime = str(jsonData["Spat"]["phaseState"][6]["elapsedTime"]) v8_currState = str(jsonData["Spat"]["phaseState"][7]["currState"]) v8_minEndTime = str(jsonData["Spat"]["phaseState"][7]["minEndTime"]) v8_maxEndTime = str(jsonData["Spat"]["phaseState"][7]["maxEndTime"]) v8_elapsedTime = str(jsonData["Spat"]["phaseState"][7]["elapsedTime"]) p1_currState = str(jsonData["Spat"]["pedPhaseState"][0]["currState"]) p1_minEndTime = str(jsonData["Spat"]["pedPhaseState"][0]["minEndTime"]) p1_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][0]["maxEndTime"]) p1_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][0]["elapsedTime"]) p2_currState = str(jsonData["Spat"]["pedPhaseState"][1]["currState"]) p2_minEndTime = str(jsonData["Spat"]["pedPhaseState"][1]["minEndTime"]) p2_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][1]["maxEndTime"]) p2_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][1]["elapsedTime"]) p3_currState = str(jsonData["Spat"]["pedPhaseState"][2]["currState"]) p3_minEndTime = str(jsonData["Spat"]["pedPhaseState"][2]["minEndTime"]) p3_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][2]["maxEndTime"]) p3_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][2]["elapsedTime"]) p4_currState = str(jsonData["Spat"]["pedPhaseState"][3]["currState"]) p4_minEndTime = str(jsonData["Spat"]["pedPhaseState"][3]["minEndTime"]) p4_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][3]["maxEndTime"]) p4_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][3]["elapsedTime"]) p5_currState = str(jsonData["Spat"]["pedPhaseState"][4]["currState"]) p5_minEndTime = str(jsonData["Spat"]["pedPhaseState"][4]["minEndTime"]) p5_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][4]["maxEndTime"]) p5_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][4]["elapsedTime"]) p6_currState = str(jsonData["Spat"]["pedPhaseState"][5]["currState"]) p6_minEndTime = str(jsonData["Spat"]["pedPhaseState"][5]["minEndTime"]) p6_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][5]["maxEndTime"]) p6_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][5]["elapsedTime"]) p7_currState = str(jsonData["Spat"]["pedPhaseState"][6]["currState"]) p7_minEndTime = str(jsonData["Spat"]["pedPhaseState"][6]["minEndTime"]) p7_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][6]["maxEndTime"]) p7_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][6]["elapsedTime"]) p8_currState = str(jsonData["Spat"]["pedPhaseState"][7]["currState"]) p8_minEndTime = str(jsonData["Spat"]["pedPhaseState"][7]["minEndTime"]) p8_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][7]["maxEndTime"]) p8_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][7]["elapsedTime"]) csv = (log_timestamp_verbose + "," + log_timestamp_posix + "," + timestamp_verbose + "," + timestamp_posix + "," + regionalId + "," + intersectionId + "," + msgCnt + "," + moy + "," + msom + "," + v1_currState + "," + v1_minEndTime + "," + v1_maxEndTime + "," + v1_elapsedTime + "," + v2_currState + "," + v2_minEndTime + "," + v2_maxEndTime + "," + v2_elapsedTime + "," + v3_currState + "," + v3_minEndTime + "," + v3_maxEndTime + "," + v3_elapsedTime + "," + v4_currState + "," + v4_minEndTime + "," + v4_maxEndTime + "," + v4_elapsedTime + "," + v5_currState + "," + v5_minEndTime + "," + v5_maxEndTime + "," + v5_elapsedTime + "," + v6_currState + "," + v6_minEndTime + "," + v6_maxEndTime + "," + v6_elapsedTime + "," + v7_currState + "," + v7_minEndTime + "," + v7_maxEndTime + "," + v7_elapsedTime + "," + v8_currState + "," + v8_minEndTime + "," + v8_maxEndTime + "," + v8_elapsedTime + "," + p1_currState + "," + p1_minEndTime + "," + p1_maxEndTime + "," + p1_elapsedTime + "," + p2_currState + "," + p2_minEndTime + "," + p2_maxEndTime + "," + p2_elapsedTime + "," + p3_currState + "," + p3_minEndTime + "," + p3_maxEndTime + "," + p3_elapsedTime + "," + p4_currState + "," + p4_minEndTime + "," + p4_maxEndTime + "," + p4_elapsedTime + "," + p5_currState + "," + p5_minEndTime + "," + p5_maxEndTime + "," + p5_elapsedTime + "," + p6_currState + "," + p6_minEndTime + "," + p6_maxEndTime + "," + p6_elapsedTime + "," + p7_currState + "," + p7_minEndTime + "," + p7_maxEndTime + "," + p7_elapsedTime + "," + p8_currState + "," + p8_minEndTime + "," + p8_maxEndTime + "," + p8_elapsedTime + "\n") return csv def srm_json_to_csv(self, jsonData:json): log_timestamp_verbose = str(datetime.datetime.now()) log_timestamp_posix = str(time.time()) timestamp_verbose = str(jsonData["Timestamp_verbose"]) timestamp_posix = str(jsonData["Timestamp_posix"]) minuteOfYear = str(jsonData["SignalRequest"]["minuteOfYear"]) msOfMinute = str(jsonData["SignalRequest"]["msOfMinute"]) msgCount = str(jsonData["SignalRequest"]["msgCount"]) regionalID = str(jsonData["SignalRequest"]["regionalID"]) intersectionID = str(jsonData["SignalRequest"]["intersectionID"]) priorityRequestType = str(jsonData["SignalRequest"]["priorityRequestType"]) basicVehicleRole = str(jsonData["SignalRequest"]["basicVehicleRole"]) laneID = str(jsonData["SignalRequest"]["inBoundLane"]["LaneID"]) eTA_Minute = str(jsonData["SignalRequest"]["expectedTimeOfArrival"]["ETA_Minute"]) eTA_Second = str(jsonData["SignalRequest"]["expectedTimeOfArrival"]["ETA_Second"]) eTA_Duration = str(jsonData["SignalRequest"]["expectedTimeOfArrival"]["ETA_Duration"]) vehicleID = str(jsonData["SignalRequest"]["vehicleID"]) latitude = str(jsonData["SignalRequest"]["position"]["latitude_DecimalDegree"]) longitude = str(jsonData["SignalRequest"]["position"]["longitude_DecimalDegree"]) elevation = str(jsonData["SignalRequest"]["position"]["elevation_Meter"]) heading = str(jsonData["SignalRequest"]["heading_Degree"]) speed = str(jsonData["SignalRequest"]["speed_MeterPerSecond"]) vehicleType = str(jsonData["SignalRequest"]["vehicleType"]) csv = (log_timestamp_verbose + "," + log_timestamp_posix + "," + timestamp_verbose + "," + timestamp_posix + "," + minuteOfYear + "," + msOfMinute + "," + msgCount + "," + regionalID + "," + intersectionID + "," + priorityRequestType + "," + basicVehicleRole + "," + laneID + "," + eTA_Minute + "," + eTA_Second + "," + eTA_Duration + "," + vehicleID + "," + latitude + "," + longitude + "," + elevation + "," + heading + "," + speed + "," + vehicleType + "\n") return csv def ssm_json_to_csv(self, jsonData:json): log_timestamp_verbose = str(datetime.datetime.now()) log_timestamp_posix = str(time.time()) timestamp_verbose = str(jsonData["Timestamp_verbose"]) timestamp_posix = str(jsonData["Timestamp_posix"]) noOfRequest = (jsonData["noOfRequest"]) minuteOfYear = str(jsonData["SignalStatus"]["minuteOfYear"]) msOfMinute = str(jsonData["SignalStatus"]["msOfMinute"]) sequenceNumber = str(jsonData["SignalStatus"]["sequenceNumber"]) updateCount = str(jsonData["SignalStatus"]["updateCount"]) regionalID = str(jsonData["SignalStatus"]["regionalID"]) intersectionID = str(jsonData["SignalStatus"]["intersectionID"]) static_csv = (log_timestamp_verbose + "," + log_timestamp_posix + "," + timestamp_verbose + "," + timestamp_posix + "," + minuteOfYear + "," + msOfMinute + "," + sequenceNumber + "," + updateCount + "," + regionalID + "," + str(noOfRequest) + "," + intersectionID) dynamic_csv = "" for request in range(0,noOfRequest): vehicleID = str(jsonData["SignalStatus"]["requestorInfo"][request]["vehicleID"]) msgCount = str(jsonData["SignalStatus"]["requestorInfo"][request]["msgCount"]) basicVehicleRole = str(jsonData["SignalStatus"]["requestorInfo"][request]["basicVehicleRole"]) inBoundLaneID = str(jsonData["SignalStatus"]["requestorInfo"][request]["inBoundLaneID"]) ETA_Minute = str(jsonData["SignalStatus"]["requestorInfo"][request]["ETA_Minute"]) ETA_Second = str(jsonData["SignalStatus"]["requestorInfo"][request]["ETA_Second"]) ETA_Duration = str(jsonData["SignalStatus"]["requestorInfo"][request]["ETA_Duration"]) priorityRequestStatus = str(jsonData["SignalStatus"]["requestorInfo"][request]["priorityRequestStatus"]) request_csv = ("," + vehicleID + "," + msgCount + "," + basicVehicleRole + "," + inBoundLaneID + "," + ETA_Minute + "," + ETA_Second + "," + ETA_Duration + "," + priorityRequestStatus ) dynamic_csv = dynamic_csv + request_csv csv = static_csv + dynamic_csv + "\n" return csv def decode_and_store_data(self, data:bytes, senderPort:int): try: receivedMsg = json.loads(data.decode()) if receivedMsg["MsgType"] == "BSM": self.write_bsm(receivedMsg,senderPort) elif receivedMsg["MsgType"] == "SPaT": self.write_spat(receivedMsg) elif receivedMsg["MsgType"] == "SRM": self.write_srm(receivedMsg) elif receivedMsg["MsgType"] == "SSM": self.write_ssm(receivedMsg) elif receivedMsg["MsgType"] == "MsgCount": self.write_msgCount(receivedMsg) except: print("Failed decoding of received message at: " + str(time.time())) def close_logfiles(self): if not self.msgCountsLogfile.closed: self.msgCountsLogfile.close() if not self.remoteBsmLogfile.closed: self.remoteBsmLogfile.close() if not self.spatLogfile.closed: self.spatLogfile.close() if not self.srmLogfile.closed: self.srmLogfile.close() if not self.ssmLogfile.closed: self.ssmLogfile.close() if self.environment == "vehicle": if not self.hostBsmLogfile.closed: self.hostBsmLogfile.close() self.archive_current_directory() def archive_current_directory(self): shutil.move(self.loggingDirectory, (self.path + "/archive/")) def archive_leftover_directories(self): directories = list(os.walk(self.path))[0][1] directories.remove("archive") if len(directories) > 0: for directory in directories: shutil.move((self.path + "/" + directory), (self.path + "/archive/")) directories = list(os.walk(self.path))[0][1] directories.remove("archive") if __name__ == "__main__": pass

src/common/v2x-data-collector/V2XDataCollector.py

import json import time, datetime import os import shutil class V2XDataCollector: def __init__(self, environment:str): self.environment = environment self.loggingDirectory = None self.initializationTimestamp = None self.hostBsmLogfile = None self.remoteBsmLogfile = None self.spatLogfile = None self.srmLogfile = None self.ssmLogfile = None self.msgCountsLogfile = None configFile = open("/nojournal/bin/mmitss-phase3-master-config.json", 'r') config = (json.load(configFile)) configFile.close() self.hostBsmDecoderPort = config["PortNumber"]["HostBsmDecoder"] if self.environment == "vehicle": self.baseName = "vehicle" else: self.baseName = config["IntersectionName"] self.path = "/nojournal/bin/v2x-data" if not os.path.exists(self.path + "/archive"): os.makedirs(self.path + "/archive") self.archive_leftover_directories() self.initialize_logfiles() def initialize_logfiles(self): self.initializationTimestamp = ('{:%m%d%Y_%H%M%S}'.format(datetime.datetime.now())) self.loggingDirectory = self.path + "/" + self.baseName + "_" + self.initializationTimestamp + "/" os.makedirs(self.loggingDirectory) self.initialize_msgCountsLogfile() self.initialize_bsmLogfile("remote") self.initialize_spatLogfile() self.initialize_srmLogfile() self.initialize_ssmLogfile() if self.environment == "vehicle": self.initialize_bsmLogfile("host") def initialize_msgCountsLogfile(self): msgCountsLogfileName = self.loggingDirectory + self.baseName + "_" + "msgCountsLog_" + self.initializationTimestamp + ".csv" self.msgCountsLogfile = open(msgCountsLogfileName, 'w', buffering=1) if self.environment == "roadside": self.msgCountsLogfile.write("log_timestamp_verbose,log_timestamp_posix,timestamp_verbose,timestamp_posix,interval_sec,msg_source,msg_type,msg_count,msg_served,msg_rejected\n") elif self.environment == "vehicle": self.msgCountsLogfile.write("log_timestamp_verbose,log_timestamp_posix,timestamp_verbose,timestamp_posix,interval_sec,msg_source,msg_type,msg_count\n") def initialize_bsmLogfile(self, origin): bsmLogfileName = self.loggingDirectory + self.baseName + "_" + origin + "BsmLog_" + self.initializationTimestamp + ".csv" if self.environment == "vehicle": csvHeader = ("log_timestamp_verbose,log_timestamp_posix,timestamp_verbose,timestamp_posix," + "temporaryId,secMark,latitude,longitude,elevation,speed,heading,type,length,width\n") elif self.environment == "roadside": csvHeader = ("log_timestamp_verbose,log_timestamp_posix,timestamp_verbose,timestamp_posix," + "temporaryId,secMark,latitude,longitude,elevation,speed,heading,type,length,width,onmap_status,position_on_map,current_approach,current_lane,current_signal_group,dist_to_stopbar\n") if origin == "host": self.hostBsmLogfile = open(bsmLogfileName, 'w') self.hostBsmLogfile.write(csvHeader) elif origin == "remote": self.remoteBsmLogfile = open(bsmLogfileName, 'w') self.remoteBsmLogfile.write(csvHeader) def initialize_spatLogfile(self): spatLogfileName = self.loggingDirectory + self.baseName + "_" + "spatLog_" + self.initializationTimestamp + ".csv" self.spatLogfile = open(spatLogfileName, 'w') csvHeader = ("log_timestamp_verbose,log_timestamp_posix,timestamp_verbose,timestamp_posix,regionalId,intersectionId,msgCount,moy,msom," + "v1_currState,v1_minEndTime,v1_maxEndTime,v1_elapsedTime," + "v2_currState,v2_minEndTime,v2_maxEndTime,v2_elapsedTime," + "v3_currState,v3_minEndTime,v3_maxEndTime,v3_elapsedTime," + "v4_currState,v4_minEndTime,v4_maxEndTime,v4_elapsedTime," + "v5_currState,v5_minEndTime,v5_maxEndTime,v5_elapsedTime," + "v6_currState,v6_minEndTime,v6_maxEndTime,v6_elapsedTime," + "v7_currState,v7_minEndTime,v7_maxEndTime,v7_elapsedTime," + "v8_currState,v8_minEndTime,v8_maxEndTime,v8_elapsedTime," + "p1_currState,p1_minEndTime,p1_maxEndTime,p1_elapsedTime," + "p2_currState,p2_minEndTime,p2_maxEndTime,p2_elapsedTime," + "p3_currState,p3_minEndTime,p3_maxEndTime,p3_elapsedTime," + "p4_currState,p4_minEndTime,p4_maxEndTime,p4_elapsedTime," + "p5_currState,p5_minEndTime,p5_maxEndTime,p5_elapsedTime," + "p6_currState,p6_minEndTime,p6_maxEndTime,p6_elapsedTime," + "p7_currState,p7_minEndTime,p7_maxEndTime,p7_elapsedTime," + "p8_currState,p8_minEndTime,p8_maxEndTime,p8_elapsedTime" + "\n") self.spatLogfile.write(csvHeader) def initialize_srmLogfile(self): srmLogfileName = self.loggingDirectory + self.baseName + "_" + "srmLog_" + self.initializationTimestamp + ".csv" self.srmLogfile = open(srmLogfileName, 'w', buffering=1) csvHeader = ("log_timestamp_verbose,log_timestamp_posix,timestamp_verbose" + "," + "timestamp_posix" + "," + "minuteOfYear" + "," + "msOfMinute" + "," + "msgCount" + "," + "regionalID" + "," + "intersectionID" + "," + "priorityRequestType" + "," + "basicVehicleRole" + "," + "laneID" + "," + "eTA_Minute" + "," + "eTA_Second" + "," + "eTA_Duration" + "," + "vehicleID" + "," + "latitude" + "," + "longitude" + "," + "elevation" + "," + "heading" + "," + "speed" + "," + "vehicleType" + "\n") self.srmLogfile.write(csvHeader) def initialize_ssmLogfile(self): ssmLogfileName = self.loggingDirectory + self.baseName + "_" + "ssmLog_" + self.initializationTimestamp + ".csv" self.ssmLogfile = open(ssmLogfileName, 'w', buffering=1) csvHeader = ("log_timestamp_verbose" + "," + "log_timestamp_posix" + "," + "timestamp_verbose" + "," + "timestamp_posix" + "," + "minuteOfYear" + "," + "msOfMinute" + "," + "sequenceNumber" + "," + "updateCount" + "," + "regionalID" + "," + "noOfRequest" + "," + "intersectionID" + "," + "r1_vehicleID,r1_msgCount,r1_basicVehicleRole,r1_inBoundLaneID,r1_ETA_Minute,r1_ETA_Second,r1_ETA_Duration,r1_priorityRequestStatus" + "," + "r2_vehicleID,r2_msgCount,r2_basicVehicleRole,r2_inBoundLaneID,r2_ETA_Minute,r2_ETA_Second,r2_ETA_Duration,r2_priorityRequestStatus" + "," + "r3_vehicleID,r3_msgCount,r3_basicVehicleRole,r3_inBoundLaneID,r3_ETA_Minute,r3_ETA_Second,r3_ETA_Duration,r3_priorityRequestStatus" + "," + "r4_vehicleID,r4_msgCount,r4_basicVehicleRole,r4_inBoundLaneID,r4_ETA_Minute,r4_ETA_Second,r4_ETA_Duration,r4_priorityRequestStatus" + "," + "r5_vehicleID,r5_msgCount,r5_basicVehicleRole,r5_inBoundLaneID,r5_ETA_Minute,r5_ETA_Second,r5_ETA_Duration,r5_priorityRequestStatus\n") self.ssmLogfile.write(csvHeader) def write_msgCount(self, msgCounts:json): csvRow = self.msgCounts_json_to_csv(msgCounts) self.msgCountsLogfile.write(csvRow) def write_bsm(self, bsmJson:json, senderPort:int): csvRow = self.bsm_json_to_csv(bsmJson) if ((self.environment == "vehicle") and (senderPort == self.hostBsmDecoderPort)): self.hostBsmLogfile.write(csvRow) else: self.remoteBsmLogfile.write(csvRow) def write_spat(self, spatJson:json): csvRow = self.spat_json_to_csv(spatJson) self.spatLogfile.write(csvRow) def write_srm(self, srmJson:json): csvRow = self.srm_json_to_csv(srmJson) self.srmLogfile.write(csvRow) def write_ssm(self, ssmJson:json): csvRow = self.ssm_json_to_csv(ssmJson) self.ssmLogfile.write(csvRow) def msgCounts_json_to_csv(self, jsonData:json): log_timestamp_posix = str(time.time()) log_timestamp_verbose = str(datetime.datetime.now()) timestamp_posix = str(jsonData["MsgInformation"]["Timestamp_posix"]) timestamp_verbose = str(jsonData["MsgInformation"]["Timestamp_verbose"]) timeInterval = str(jsonData["MsgInformation"]["TimeInterval"]) msgSource = str(jsonData["MsgInformation"]["MsgSource"]) msgType = str(jsonData["MsgInformation"]["MsgCountType"]) msgCount = str(jsonData["MsgInformation"]["MsgCount"]) csv = (log_timestamp_verbose + "," + log_timestamp_posix + "," + timestamp_verbose + "," + timestamp_posix + "," + timeInterval + "," + msgSource + "," + msgType + "," + msgCount) if self.environment == "roadside": msgServed = str(jsonData["MsgInformation"]["MsgServed"]) msgRejected = str(jsonData["MsgInformation"]["MsgRejected"]) csv = csv + "," + msgServed + "," + msgRejected csv = csv + "\n" return csv def bsm_json_to_csv(self, jsonData:json): log_timestamp_verbose = str(datetime.datetime.now()) log_timestamp_posix = str(time.time()) timestamp_verbose = str(jsonData["Timestamp_verbose"]) timestamp_posix = str(jsonData["Timestamp_posix"]) temporaryId = str(jsonData["BasicVehicle"]["temporaryID"]) secMark = str(jsonData["BasicVehicle"]["secMark_Second"]) latitude = str(jsonData["BasicVehicle"]["position"]["latitude_DecimalDegree"]) longitude = str(jsonData["BasicVehicle"]["position"]["longitude_DecimalDegree"]) elevation = str(jsonData["BasicVehicle"]["position"]["elevation_Meter"]) speed = str(jsonData["BasicVehicle"]["speed_MeterPerSecond"]) heading = str(jsonData["BasicVehicle"]["heading_Degree"]) vehType = str(jsonData["BasicVehicle"]["type"]) length = str(jsonData["BasicVehicle"]["size"]["length_cm"]) width = str(jsonData["BasicVehicle"]["size"]["width_cm"]) if self.environment == "roadside": onMap = str(jsonData["OnmapVehicle"]["onMap"]) approachId = str(jsonData["OnmapVehicle"]["approachId"]) laneId = str(jsonData["OnmapVehicle"]["laneId"]) signalGroup = str(jsonData["OnmapVehicle"]["signalGroup"]) distanceToStopbar = str(jsonData["OnmapVehicle"]["distanceToStopbar"]) locationOnMap = str(jsonData["OnmapVehicle"]["locationOnMap"]) csv = (log_timestamp_verbose + "," + log_timestamp_posix + "," + timestamp_verbose + "," + timestamp_posix + "," + temporaryId + "," + secMark + "," + latitude + "," + longitude + "," + elevation + "," + speed + "," + heading + "," + vehType + "," + length + "," + width + "," + onMap + "," + locationOnMap + "," + approachId + "," + laneId + "," + signalGroup + "," + distanceToStopbar + "\n") elif self.environment == "vehicle": csv = (log_timestamp_verbose + "," + log_timestamp_posix + "," + timestamp_verbose + "," + timestamp_posix + "," + temporaryId + "," + secMark + "," + latitude + "," + longitude + "," + elevation + "," + speed + "," + heading + "," + vehType + "," + length + "," + width + "\n") return csv def spat_json_to_csv(self, jsonData:json): log_timestamp_verbose = str(datetime.datetime.now()) log_timestamp_posix = str(time.time()) timestamp_verbose = str(jsonData["Timestamp_verbose"]) timestamp_posix = str(jsonData["Timestamp_posix"]) regionalId = str(jsonData["Spat"]["IntersectionState"]["regionalID"]) intersectionId = str(jsonData["Spat"]["IntersectionState"]["intersectionID"]) msgCnt = str(jsonData["Spat"]["msgCnt"]) moy = str(jsonData["Spat"]["minuteOfYear"]) msom = str(jsonData["Spat"]["msOfMinute"]) v1_currState = str(jsonData["Spat"]["phaseState"][0]["currState"]) v1_minEndTime = str(jsonData["Spat"]["phaseState"][0]["minEndTime"]) v1_maxEndTime = str(jsonData["Spat"]["phaseState"][0]["maxEndTime"]) v1_elapsedTime = str(jsonData["Spat"]["phaseState"][0]["elapsedTime"]) v2_currState = str(jsonData["Spat"]["phaseState"][1]["currState"]) v2_minEndTime = str(jsonData["Spat"]["phaseState"][1]["minEndTime"]) v2_maxEndTime = str(jsonData["Spat"]["phaseState"][1]["maxEndTime"]) v2_elapsedTime = str(jsonData["Spat"]["phaseState"][1]["elapsedTime"]) v3_currState = str(jsonData["Spat"]["phaseState"][2]["currState"]) v3_minEndTime = str(jsonData["Spat"]["phaseState"][2]["minEndTime"]) v3_maxEndTime = str(jsonData["Spat"]["phaseState"][2]["maxEndTime"]) v3_elapsedTime = str(jsonData["Spat"]["phaseState"][2]["elapsedTime"]) v4_currState = str(jsonData["Spat"]["phaseState"][3]["currState"]) v4_minEndTime = str(jsonData["Spat"]["phaseState"][3]["minEndTime"]) v4_maxEndTime = str(jsonData["Spat"]["phaseState"][3]["maxEndTime"]) v4_elapsedTime = str(jsonData["Spat"]["phaseState"][3]["elapsedTime"]) v5_currState = str(jsonData["Spat"]["phaseState"][4]["currState"]) v5_minEndTime = str(jsonData["Spat"]["phaseState"][4]["minEndTime"]) v5_maxEndTime = str(jsonData["Spat"]["phaseState"][4]["maxEndTime"]) v5_elapsedTime = str(jsonData["Spat"]["phaseState"][4]["elapsedTime"]) v6_currState = str(jsonData["Spat"]["phaseState"][5]["currState"]) v6_minEndTime = str(jsonData["Spat"]["phaseState"][5]["minEndTime"]) v6_maxEndTime = str(jsonData["Spat"]["phaseState"][5]["maxEndTime"]) v6_elapsedTime = str(jsonData["Spat"]["phaseState"][5]["elapsedTime"]) v7_currState = str(jsonData["Spat"]["phaseState"][6]["currState"]) v7_minEndTime = str(jsonData["Spat"]["phaseState"][6]["minEndTime"]) v7_maxEndTime = str(jsonData["Spat"]["phaseState"][6]["maxEndTime"]) v7_elapsedTime = str(jsonData["Spat"]["phaseState"][6]["elapsedTime"]) v8_currState = str(jsonData["Spat"]["phaseState"][7]["currState"]) v8_minEndTime = str(jsonData["Spat"]["phaseState"][7]["minEndTime"]) v8_maxEndTime = str(jsonData["Spat"]["phaseState"][7]["maxEndTime"]) v8_elapsedTime = str(jsonData["Spat"]["phaseState"][7]["elapsedTime"]) p1_currState = str(jsonData["Spat"]["pedPhaseState"][0]["currState"]) p1_minEndTime = str(jsonData["Spat"]["pedPhaseState"][0]["minEndTime"]) p1_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][0]["maxEndTime"]) p1_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][0]["elapsedTime"]) p2_currState = str(jsonData["Spat"]["pedPhaseState"][1]["currState"]) p2_minEndTime = str(jsonData["Spat"]["pedPhaseState"][1]["minEndTime"]) p2_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][1]["maxEndTime"]) p2_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][1]["elapsedTime"]) p3_currState = str(jsonData["Spat"]["pedPhaseState"][2]["currState"]) p3_minEndTime = str(jsonData["Spat"]["pedPhaseState"][2]["minEndTime"]) p3_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][2]["maxEndTime"]) p3_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][2]["elapsedTime"]) p4_currState = str(jsonData["Spat"]["pedPhaseState"][3]["currState"]) p4_minEndTime = str(jsonData["Spat"]["pedPhaseState"][3]["minEndTime"]) p4_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][3]["maxEndTime"]) p4_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][3]["elapsedTime"]) p5_currState = str(jsonData["Spat"]["pedPhaseState"][4]["currState"]) p5_minEndTime = str(jsonData["Spat"]["pedPhaseState"][4]["minEndTime"]) p5_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][4]["maxEndTime"]) p5_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][4]["elapsedTime"]) p6_currState = str(jsonData["Spat"]["pedPhaseState"][5]["currState"]) p6_minEndTime = str(jsonData["Spat"]["pedPhaseState"][5]["minEndTime"]) p6_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][5]["maxEndTime"]) p6_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][5]["elapsedTime"]) p7_currState = str(jsonData["Spat"]["pedPhaseState"][6]["currState"]) p7_minEndTime = str(jsonData["Spat"]["pedPhaseState"][6]["minEndTime"]) p7_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][6]["maxEndTime"]) p7_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][6]["elapsedTime"]) p8_currState = str(jsonData["Spat"]["pedPhaseState"][7]["currState"]) p8_minEndTime = str(jsonData["Spat"]["pedPhaseState"][7]["minEndTime"]) p8_maxEndTime = str(jsonData["Spat"]["pedPhaseState"][7]["maxEndTime"]) p8_elapsedTime = str(jsonData["Spat"]["pedPhaseState"][7]["elapsedTime"]) csv = (log_timestamp_verbose + "," + log_timestamp_posix + "," + timestamp_verbose + "," + timestamp_posix + "," + regionalId + "," + intersectionId + "," + msgCnt + "," + moy + "," + msom + "," + v1_currState + "," + v1_minEndTime + "," + v1_maxEndTime + "," + v1_elapsedTime + "," + v2_currState + "," + v2_minEndTime + "," + v2_maxEndTime + "," + v2_elapsedTime + "," + v3_currState + "," + v3_minEndTime + "," + v3_maxEndTime + "," + v3_elapsedTime + "," + v4_currState + "," + v4_minEndTime + "," + v4_maxEndTime + "," + v4_elapsedTime + "," + v5_currState + "," + v5_minEndTime + "," + v5_maxEndTime + "," + v5_elapsedTime + "," + v6_currState + "," + v6_minEndTime + "," + v6_maxEndTime + "," + v6_elapsedTime + "," + v7_currState + "," + v7_minEndTime + "," + v7_maxEndTime + "," + v7_elapsedTime + "," + v8_currState + "," + v8_minEndTime + "," + v8_maxEndTime + "," + v8_elapsedTime + "," + p1_currState + "," + p1_minEndTime + "," + p1_maxEndTime + "," + p1_elapsedTime + "," + p2_currState + "," + p2_minEndTime + "," + p2_maxEndTime + "," + p2_elapsedTime + "," + p3_currState + "," + p3_minEndTime + "," + p3_maxEndTime + "," + p3_elapsedTime + "," + p4_currState + "," + p4_minEndTime + "," + p4_maxEndTime + "," + p4_elapsedTime + "," + p5_currState + "," + p5_minEndTime + "," + p5_maxEndTime + "," + p5_elapsedTime + "," + p6_currState + "," + p6_minEndTime + "," + p6_maxEndTime + "," + p6_elapsedTime + "," + p7_currState + "," + p7_minEndTime + "," + p7_maxEndTime + "," + p7_elapsedTime + "," + p8_currState + "," + p8_minEndTime + "," + p8_maxEndTime + "," + p8_elapsedTime + "\n") return csv def srm_json_to_csv(self, jsonData:json): log_timestamp_verbose = str(datetime.datetime.now()) log_timestamp_posix = str(time.time()) timestamp_verbose = str(jsonData["Timestamp_verbose"]) timestamp_posix = str(jsonData["Timestamp_posix"]) minuteOfYear = str(jsonData["SignalRequest"]["minuteOfYear"]) msOfMinute = str(jsonData["SignalRequest"]["msOfMinute"]) msgCount = str(jsonData["SignalRequest"]["msgCount"]) regionalID = str(jsonData["SignalRequest"]["regionalID"]) intersectionID = str(jsonData["SignalRequest"]["intersectionID"]) priorityRequestType = str(jsonData["SignalRequest"]["priorityRequestType"]) basicVehicleRole = str(jsonData["SignalRequest"]["basicVehicleRole"]) laneID = str(jsonData["SignalRequest"]["inBoundLane"]["LaneID"]) eTA_Minute = str(jsonData["SignalRequest"]["expectedTimeOfArrival"]["ETA_Minute"]) eTA_Second = str(jsonData["SignalRequest"]["expectedTimeOfArrival"]["ETA_Second"]) eTA_Duration = str(jsonData["SignalRequest"]["expectedTimeOfArrival"]["ETA_Duration"]) vehicleID = str(jsonData["SignalRequest"]["vehicleID"]) latitude = str(jsonData["SignalRequest"]["position"]["latitude_DecimalDegree"]) longitude = str(jsonData["SignalRequest"]["position"]["longitude_DecimalDegree"]) elevation = str(jsonData["SignalRequest"]["position"]["elevation_Meter"]) heading = str(jsonData["SignalRequest"]["heading_Degree"]) speed = str(jsonData["SignalRequest"]["speed_MeterPerSecond"]) vehicleType = str(jsonData["SignalRequest"]["vehicleType"]) csv = (log_timestamp_verbose + "," + log_timestamp_posix + "," + timestamp_verbose + "," + timestamp_posix + "," + minuteOfYear + "," + msOfMinute + "," + msgCount + "," + regionalID + "," + intersectionID + "," + priorityRequestType + "," + basicVehicleRole + "," + laneID + "," + eTA_Minute + "," + eTA_Second + "," + eTA_Duration + "," + vehicleID + "," + latitude + "," + longitude + "," + elevation + "," + heading + "," + speed + "," + vehicleType + "\n") return csv def ssm_json_to_csv(self, jsonData:json): log_timestamp_verbose = str(datetime.datetime.now()) log_timestamp_posix = str(time.time()) timestamp_verbose = str(jsonData["Timestamp_verbose"]) timestamp_posix = str(jsonData["Timestamp_posix"]) noOfRequest = (jsonData["noOfRequest"]) minuteOfYear = str(jsonData["SignalStatus"]["minuteOfYear"]) msOfMinute = str(jsonData["SignalStatus"]["msOfMinute"]) sequenceNumber = str(jsonData["SignalStatus"]["sequenceNumber"]) updateCount = str(jsonData["SignalStatus"]["updateCount"]) regionalID = str(jsonData["SignalStatus"]["regionalID"]) intersectionID = str(jsonData["SignalStatus"]["intersectionID"]) static_csv = (log_timestamp_verbose + "," + log_timestamp_posix + "," + timestamp_verbose + "," + timestamp_posix + "," + minuteOfYear + "," + msOfMinute + "," + sequenceNumber + "," + updateCount + "," + regionalID + "," + str(noOfRequest) + "," + intersectionID) dynamic_csv = "" for request in range(0,noOfRequest): vehicleID = str(jsonData["SignalStatus"]["requestorInfo"][request]["vehicleID"]) msgCount = str(jsonData["SignalStatus"]["requestorInfo"][request]["msgCount"]) basicVehicleRole = str(jsonData["SignalStatus"]["requestorInfo"][request]["basicVehicleRole"]) inBoundLaneID = str(jsonData["SignalStatus"]["requestorInfo"][request]["inBoundLaneID"]) ETA_Minute = str(jsonData["SignalStatus"]["requestorInfo"][request]["ETA_Minute"]) ETA_Second = str(jsonData["SignalStatus"]["requestorInfo"][request]["ETA_Second"]) ETA_Duration = str(jsonData["SignalStatus"]["requestorInfo"][request]["ETA_Duration"]) priorityRequestStatus = str(jsonData["SignalStatus"]["requestorInfo"][request]["priorityRequestStatus"]) request_csv = ("," + vehicleID + "," + msgCount + "," + basicVehicleRole + "," + inBoundLaneID + "," + ETA_Minute + "," + ETA_Second + "," + ETA_Duration + "," + priorityRequestStatus ) dynamic_csv = dynamic_csv + request_csv csv = static_csv + dynamic_csv + "\n" return csv def decode_and_store_data(self, data:bytes, senderPort:int): try: receivedMsg = json.loads(data.decode()) if receivedMsg["MsgType"] == "BSM": self.write_bsm(receivedMsg,senderPort) elif receivedMsg["MsgType"] == "SPaT": self.write_spat(receivedMsg) elif receivedMsg["MsgType"] == "SRM": self.write_srm(receivedMsg) elif receivedMsg["MsgType"] == "SSM": self.write_ssm(receivedMsg) elif receivedMsg["MsgType"] == "MsgCount": self.write_msgCount(receivedMsg) except: print("Failed decoding of received message at: " + str(time.time())) def close_logfiles(self): if not self.msgCountsLogfile.closed: self.msgCountsLogfile.close() if not self.remoteBsmLogfile.closed: self.remoteBsmLogfile.close() if not self.spatLogfile.closed: self.spatLogfile.close() if not self.srmLogfile.closed: self.srmLogfile.close() if not self.ssmLogfile.closed: self.ssmLogfile.close() if self.environment == "vehicle": if not self.hostBsmLogfile.closed: self.hostBsmLogfile.close() self.archive_current_directory() def archive_current_directory(self): shutil.move(self.loggingDirectory, (self.path + "/archive/")) def archive_leftover_directories(self): directories = list(os.walk(self.path))[0][1] directories.remove("archive") if len(directories) > 0: for directory in directories: shutil.move((self.path + "/" + directory), (self.path + "/archive/")) directories = list(os.walk(self.path))[0][1] directories.remove("archive") if __name__ == "__main__": pass

0.116337

0.107204

SC ={ 'DRV_ERROR_CODES': 20001, 'DRV_SUCCESS': 20002, 'DRV_VXDNOTINSTALLED' : 20003, 'DRV_ERROR_SCAN' : 20004, 'DRV_ERROR_CHECK_SUM' : 20005, 'DRV_ERROR_FILELOAD' : 20006, 'DRV_UNKNOWN_FUNCTION' : 20007, 'DRV_ERROR_VXD_INIT' : 20008, 'DRV_ERROR_ADDRESS' : 20009, 'DRV_ERROR_PAGELOCK' : 20010, 'DRV_ERROR_PAGEUNLOCK' : 20011, 'DRV_ERROR_BOARDTEST' : 20012, 'DRV_ERROR_ACK' : 20013, 'DRV_ERROR_UP_FIFO' : 20014, 'DRV_ERROR_PATTERN' : 20015, 'DRV_ACQUISITION_ERRORS' : 20017, 'DRV_ACQ_BUFFER' : 20018, 'DRV_ACQ_DOWNFIFO_FULL' : 20019, 'DRV_PROC_UNKONWN_INSTRUCTION' : 20020, 'DRV_ILLEGAL_OP_CODE' : 20021, 'DRV_KINETIC_TIME_NOT_MET' : 20022, 'DRV_ACCUM_TIME_NOT_MET' : 20023, 'DRV_NO_NEW_DATA' : 20024, 'DRV_PCI_DMA_FAIL' : 20025, 'DRV_SPOOLERROR' : 20026, 'DRV_SPOOLSETUPERROR' : 20027, 'DRV_FILESIZELIMITERROR' : 20028, 'DRV_ERROR_FILESAVE' : 20029, 'DRV_TEMPERATURE_CODES' : 20033, 'DRV_TEMPERATURE_OFF' : 20034, 'DRV_TEMPERATURE_NOT_STABILIZED' : 20035, 'DRV_TEMPERATURE_STABILIZED' : 20036, 'DRV_TEMPERATURE_NOT_REACHED' : 20037, 'DRV_TEMPERATURE_OUT_RANGE' : 20038, 'DRV_TEMPERATURE_NOT_SUPPORTED' : 20039, 'DRV_TEMPERATURE_DRIFT' : 20040, 'DRV_TEMP_CODES' : 20033, 'DRV_TEMP_OFF' : 20034, 'DRV_TEMP_NOT_STABILIZED' : 20035, 'DRV_TEMP_STABILIZED' : 20036, 'DRV_TEMP_NOT_REACHED' : 20037, 'DRV_TEMP_OUT_RANGE' : 20038, 'DRV_TEMP_NOT_SUPPORTED' : 20039, 'DRV_TEMP_DRIFT' : 20040, 'DRV_GENERAL_ERRORS' : 20049, 'DRV_INVALID_AUX' : 20050, 'DRV_COF_NOTLOADED' : 20051, 'DRV_FPGAPROG' : 20052, 'DRV_FLEXERROR' : 20053, 'DRV_GPIBERROR' : 20054, 'DRV_EEPROMVERSIONERROR' : 20055, 'DRV_DATATYPE' : 20064, 'DRV_DRIVER_ERRORS' : 20065, 'DRV_P1INVALID' : 20066, 'DRV_P2INVALID' : 20067, 'DRV_P3INVALID' : 20068, 'DRV_P4INVALID' : 20069, 'DRV_INIERROR' : 20070, 'DRV_COFERROR' : 20071, 'DRV_ACQUIRING' : 20072, 'DRV_IDLE' : 20073, 'DRV_TEMPCYCLE' : 20074, 'DRV_NOT_INITIALIZED' : 20075, 'DRV_P5INVALID' : 20076, 'DRV_P6INVALID' : 20077, 'DRV_INVALID_MODE' : 20078, 'DRV_INVALID_FILTER' : 20079, 'DRV_I2CERRORS' : 20080, 'DRV_I2CDEVNOTFOUND' : 20081, 'DRV_I2CTIMEOUT' : 20082, 'DRV_P7INVALID' : 20083, 'DRV_P8INVALID' : 20084, 'DRV_P9INVALID' : 20085, 'DRV_P10INVALID' : 20086, 'DRV_P11INVALID' : 20087, 'DRV_USBERROR' : 20089, 'DRV_IOCERROR' : 20090, 'DRV_VRMVERSIONERROR' : 20091, 'DRV_GATESTEPERROR' : 20092, 'DRV_USB_INTERRUPT_ENDPOINT_ERROR' : 20093, 'DRV_RANDOM_TRACK_ERROR' : 20094, 'DRV_INVALID_TRIGGER_MODE' : 20095, 'DRV_LOAD_FIRMWARE_ERROR' : 20096, 'DRV_DIVIDE_BY_ZERO_ERROR' : 20097, 'DRV_INVALID_RINGEXPOSURES' : 20098, 'DRV_BINNING_ERROR' : 20099, 'DRV_INVALID_AMPLIFIER' : 20100, 'DRV_INVALID_COUNTCONVERT_MODE' : 20101, 'DRV_ERROR_NOCAMERA' : 20990, 'DRV_NOT_SUPPORTED' : 20991, 'DRV_NOT_AVAILABLE' : 20992, 'DRV_ERROR_MAP' : 20115, 'DRV_ERROR_UNMAP' : 20116, 'DRV_ERROR_MDL' : 20117, 'DRV_ERROR_UNMDL' : 20118, 'DRV_ERROR_BUFFSIZE' : 20119, 'DRV_ERROR_NOHANDLE' : 20121, 'DRV_GATING_NOT_AVAILABLE' : 20130, 'DRV_FPGA_VOLTAGE_ERROR' : 20131, 'DRV_OW_CMD_FAIL' : 20150, 'DRV_OWMEMORY_BAD_ADDR' : 20151, 'DRV_OWCMD_NOT_AVAILABLE' : 20152, 'DRV_OW_NO_SLAVES' : 20153, 'DRV_OW_NOT_INITIALIZED' : 20154, 'DRV_OW_ERROR_SLAVE_NUM' : 20155, 'DRV_MSTIMINGS_ERROR' : 20156, 'DRV_OA_NULL_ERROR' : 20173, 'DRV_OA_PARSE_DTD_ERROR' : 20174, 'DRV_OA_DTD_VALIDATE_ERROR' : 20175, 'DRV_OA_FILE_ACCESS_ERROR' : 20176, 'DRV_OA_FILE_DOES_NOT_EXIST' : 20177, 'DRV_OA_XML_INVALID_OR_NOT_FOUND_ERROR' : 20178, 'DRV_OA_PRESET_FILE_NOT_LOADED' : 20179, 'DRV_OA_USER_FILE_NOT_LOADED' : 20180, 'DRV_OA_PRESET_AND_USER_FILE_NOT_LOADED' : 20181, 'DRV_OA_INVALID_FILE' : 20182, 'DRV_OA_FILE_HAS_BEEN_MODIFIED' : 20183, 'DRV_OA_BUFFER_FULL' : 20184, 'DRV_OA_INVALID_STRING_LENGTH' : 20185, 'DRV_OA_INVALID_CHARS_IN_NAME' : 20186, 'DRV_OA_INVALID_NAMING' : 20187, 'DRV_OA_GET_CAMERA_ERROR' : 20188, 'DRV_OA_MODE_ALREADY_EXISTS' : 20189, 'DRV_OA_STRINGS_NOT_EQUAL' : 20190, 'DRV_OA_NO_USER_DATA' : 20191, 'DRV_OA_VALUE_NOT_SUPPORTED' : 20192, 'DRV_OA_MODE_DOES_NOT_EXIST' : 20193, 'DRV_OA_CAMERA_NOT_SUPPORTED' : 20194, 'DRV_OA_FAILED_TO_GET_MODE' : 20195, 'DRV_PROCESSING_FAILED' : 20211} # Flipped _SC = dict((val, key) for key, val in SC.items())

labscript_devices/AndorSolis/andor_sdk/status_codes.py

SC ={ 'DRV_ERROR_CODES': 20001, 'DRV_SUCCESS': 20002, 'DRV_VXDNOTINSTALLED' : 20003, 'DRV_ERROR_SCAN' : 20004, 'DRV_ERROR_CHECK_SUM' : 20005, 'DRV_ERROR_FILELOAD' : 20006, 'DRV_UNKNOWN_FUNCTION' : 20007, 'DRV_ERROR_VXD_INIT' : 20008, 'DRV_ERROR_ADDRESS' : 20009, 'DRV_ERROR_PAGELOCK' : 20010, 'DRV_ERROR_PAGEUNLOCK' : 20011, 'DRV_ERROR_BOARDTEST' : 20012, 'DRV_ERROR_ACK' : 20013, 'DRV_ERROR_UP_FIFO' : 20014, 'DRV_ERROR_PATTERN' : 20015, 'DRV_ACQUISITION_ERRORS' : 20017, 'DRV_ACQ_BUFFER' : 20018, 'DRV_ACQ_DOWNFIFO_FULL' : 20019, 'DRV_PROC_UNKONWN_INSTRUCTION' : 20020, 'DRV_ILLEGAL_OP_CODE' : 20021, 'DRV_KINETIC_TIME_NOT_MET' : 20022, 'DRV_ACCUM_TIME_NOT_MET' : 20023, 'DRV_NO_NEW_DATA' : 20024, 'DRV_PCI_DMA_FAIL' : 20025, 'DRV_SPOOLERROR' : 20026, 'DRV_SPOOLSETUPERROR' : 20027, 'DRV_FILESIZELIMITERROR' : 20028, 'DRV_ERROR_FILESAVE' : 20029, 'DRV_TEMPERATURE_CODES' : 20033, 'DRV_TEMPERATURE_OFF' : 20034, 'DRV_TEMPERATURE_NOT_STABILIZED' : 20035, 'DRV_TEMPERATURE_STABILIZED' : 20036, 'DRV_TEMPERATURE_NOT_REACHED' : 20037, 'DRV_TEMPERATURE_OUT_RANGE' : 20038, 'DRV_TEMPERATURE_NOT_SUPPORTED' : 20039, 'DRV_TEMPERATURE_DRIFT' : 20040, 'DRV_TEMP_CODES' : 20033, 'DRV_TEMP_OFF' : 20034, 'DRV_TEMP_NOT_STABILIZED' : 20035, 'DRV_TEMP_STABILIZED' : 20036, 'DRV_TEMP_NOT_REACHED' : 20037, 'DRV_TEMP_OUT_RANGE' : 20038, 'DRV_TEMP_NOT_SUPPORTED' : 20039, 'DRV_TEMP_DRIFT' : 20040, 'DRV_GENERAL_ERRORS' : 20049, 'DRV_INVALID_AUX' : 20050, 'DRV_COF_NOTLOADED' : 20051, 'DRV_FPGAPROG' : 20052, 'DRV_FLEXERROR' : 20053, 'DRV_GPIBERROR' : 20054, 'DRV_EEPROMVERSIONERROR' : 20055, 'DRV_DATATYPE' : 20064, 'DRV_DRIVER_ERRORS' : 20065, 'DRV_P1INVALID' : 20066, 'DRV_P2INVALID' : 20067, 'DRV_P3INVALID' : 20068, 'DRV_P4INVALID' : 20069, 'DRV_INIERROR' : 20070, 'DRV_COFERROR' : 20071, 'DRV_ACQUIRING' : 20072, 'DRV_IDLE' : 20073, 'DRV_TEMPCYCLE' : 20074, 'DRV_NOT_INITIALIZED' : 20075, 'DRV_P5INVALID' : 20076, 'DRV_P6INVALID' : 20077, 'DRV_INVALID_MODE' : 20078, 'DRV_INVALID_FILTER' : 20079, 'DRV_I2CERRORS' : 20080, 'DRV_I2CDEVNOTFOUND' : 20081, 'DRV_I2CTIMEOUT' : 20082, 'DRV_P7INVALID' : 20083, 'DRV_P8INVALID' : 20084, 'DRV_P9INVALID' : 20085, 'DRV_P10INVALID' : 20086, 'DRV_P11INVALID' : 20087, 'DRV_USBERROR' : 20089, 'DRV_IOCERROR' : 20090, 'DRV_VRMVERSIONERROR' : 20091, 'DRV_GATESTEPERROR' : 20092, 'DRV_USB_INTERRUPT_ENDPOINT_ERROR' : 20093, 'DRV_RANDOM_TRACK_ERROR' : 20094, 'DRV_INVALID_TRIGGER_MODE' : 20095, 'DRV_LOAD_FIRMWARE_ERROR' : 20096, 'DRV_DIVIDE_BY_ZERO_ERROR' : 20097, 'DRV_INVALID_RINGEXPOSURES' : 20098, 'DRV_BINNING_ERROR' : 20099, 'DRV_INVALID_AMPLIFIER' : 20100, 'DRV_INVALID_COUNTCONVERT_MODE' : 20101, 'DRV_ERROR_NOCAMERA' : 20990, 'DRV_NOT_SUPPORTED' : 20991, 'DRV_NOT_AVAILABLE' : 20992, 'DRV_ERROR_MAP' : 20115, 'DRV_ERROR_UNMAP' : 20116, 'DRV_ERROR_MDL' : 20117, 'DRV_ERROR_UNMDL' : 20118, 'DRV_ERROR_BUFFSIZE' : 20119, 'DRV_ERROR_NOHANDLE' : 20121, 'DRV_GATING_NOT_AVAILABLE' : 20130, 'DRV_FPGA_VOLTAGE_ERROR' : 20131, 'DRV_OW_CMD_FAIL' : 20150, 'DRV_OWMEMORY_BAD_ADDR' : 20151, 'DRV_OWCMD_NOT_AVAILABLE' : 20152, 'DRV_OW_NO_SLAVES' : 20153, 'DRV_OW_NOT_INITIALIZED' : 20154, 'DRV_OW_ERROR_SLAVE_NUM' : 20155, 'DRV_MSTIMINGS_ERROR' : 20156, 'DRV_OA_NULL_ERROR' : 20173, 'DRV_OA_PARSE_DTD_ERROR' : 20174, 'DRV_OA_DTD_VALIDATE_ERROR' : 20175, 'DRV_OA_FILE_ACCESS_ERROR' : 20176, 'DRV_OA_FILE_DOES_NOT_EXIST' : 20177, 'DRV_OA_XML_INVALID_OR_NOT_FOUND_ERROR' : 20178, 'DRV_OA_PRESET_FILE_NOT_LOADED' : 20179, 'DRV_OA_USER_FILE_NOT_LOADED' : 20180, 'DRV_OA_PRESET_AND_USER_FILE_NOT_LOADED' : 20181, 'DRV_OA_INVALID_FILE' : 20182, 'DRV_OA_FILE_HAS_BEEN_MODIFIED' : 20183, 'DRV_OA_BUFFER_FULL' : 20184, 'DRV_OA_INVALID_STRING_LENGTH' : 20185, 'DRV_OA_INVALID_CHARS_IN_NAME' : 20186, 'DRV_OA_INVALID_NAMING' : 20187, 'DRV_OA_GET_CAMERA_ERROR' : 20188, 'DRV_OA_MODE_ALREADY_EXISTS' : 20189, 'DRV_OA_STRINGS_NOT_EQUAL' : 20190, 'DRV_OA_NO_USER_DATA' : 20191, 'DRV_OA_VALUE_NOT_SUPPORTED' : 20192, 'DRV_OA_MODE_DOES_NOT_EXIST' : 20193, 'DRV_OA_CAMERA_NOT_SUPPORTED' : 20194, 'DRV_OA_FAILED_TO_GET_MODE' : 20195, 'DRV_PROCESSING_FAILED' : 20211} # Flipped _SC = dict((val, key) for key, val in SC.items())

0.159021

0.058319

import serial import struct import time import sys from numpy import * class sutterMP285 : 'Class which allows interaction with the Sutter Manipulator 285' def __init__(self): self.verbose = 1. # level of messages self.timeOut = 30 # timeout in sec # initialize serial connection to controller try: self.ser = serial.Serial(port='COM1',baudrate=9600,bytesize=serial.EIGHTBITS,parity=serial.PARITY_NONE,stopbits=serial.STOPBITS_ONE,timeout=self.timeOut) self.connected = 1 if self.verbose: print self.ser except serial.SerialException: print 'No connection to Sutter MP-285 could be established!' sys.exit(1) # set move velocity to 200 self.setVelocity(200,10) self.updatePanel() # update controller panel (stepM,currentV,vScaleF)= self.getStatus() if currentV == 200: print 'sutterMP285 ready' else: print 'sutterMP285: WARNING Sutter did not respond at startup.' # destructor def __del__(self): self.ser.close() if self.verbose : print 'Connection to Sutter MP-285 closed' def getPosition(self): # send commend to get position self.ser.write('c\r') # read position from controller xyzb = self.ser.read(13) # convert bytes into 'signed long' numbers xyz_um = array(struct.unpack('lll', xyzb[:12]))/self.stepMult if self.verbose: print 'sutterMP285 : Stage position ' print 'X: %g um \n Y: %g um\n Z: %g um' % (xyz_um[0],xyz_um[1],xyz_um[2]) return xyz_um # Moves the three axes to specified location. def gotoPosition(self,pos): if len(pos) != 3: print 'Length of position argument has to be three' sys.exit(1) xyzb = struct.pack('lll',int(pos[0]*self.stepMult),int(pos[1]*self.stepMult),int(pos[2]*self.stepMult)) # convert integer values into bytes startt = time.time() # start timer self.ser.write('m'+xyzb+'\r') # send position to controller; add the "m" and the CR to create the move command cr = [] cr = self.ser.read(1) # read carriage return and ignore endt = time.time() # stop timer if len(cr)== 0: print 'Sutter did not finish moving before timeout (%d sec).' % self.timeOut else: print 'sutterMP285: Sutter move completed in (%.2f sec)' % (endt-startt) # this function changes the velocity of the sutter motions def setVelocity(self,Vel,vScalF=10): # Change velocity command 'V'xxCR where xx= unsigned short (16bit) int velocity # set by bits 14 to 0, and bit 15 indicates ustep resolution 0=10, 1=50 uSteps/step # V is ascii 86 # convert velocity into unsigned short - 2-byte - integeter velb = struct.pack('H',int(Vel)) # change last bit of 2nd byte to 1 for ustep resolution = 50 if vScalF == 50: velb2 = double(struct.unpack('B',velb[1])) + 128 velb = velb[0] + struct.pack('B',velb2) self.ser.write('V'+velb+'\r') self.ser.read(1) # Update Panel # causes the Sutter to display the XYZ info on the front panel def updatePanel(self): self.ser.write('n\r') #Sutter replies with a CR self.ser.read(1) # read and ignore the carriage return ## Set Origin # sets the origin of the coordinate system to the current position def setOrigin(self): self.ser.write('o\r') # Sutter replies with a CR self.ser.read(1) # read and ignor the carrage return # Reset controller def sendReset(self): self.ser.write('r\r') # Sutter does not reply # Queries the status of the controller. def getStatus(self): if self.verbose : print 'sutterMP285: get status info' self.ser.write('s\r') # send status command rrr = self.ser.read(32) # read return of 32 bytes without carriage return self.ser.read(1) # read and ignore the carriage return rrr statusbytes = struct.unpack(32*'B',rrr) print statusbytes # the value of STEP_MUL ("Multiplier yields msteps/nm") is at bytes 25 & 26 self.stepMult = double(statusbytes[25])*256 + double(statusbytes[24]) # the value of "XSPEED" and scale factor is at bytes 29 & 30 if statusbytes[29] > 127: self.vScaleFactor = 50 else: self.vScaleFactor = 10 #print double(127 & statusbytes[29])*256 #print double(statusbytes[28]), statusbytes[28] #print double(statusbytes[29]), statusbytes[29] self.currentVelocity = double(127 & statusbytes[29])*256+double(statusbytes[28]) #vScaleFactor = struct.unpack('lll', rrr[30:31]) if self.verbose: print 'step_mul (usteps/um): %g' % self.stepMult print 'xspeed" [velocity] (usteps/sec): %g' % self.currentVelocity print 'velocity scale factor (usteps/step): %g' % self.vScaleFactor # return (self.stepMult,self.currentVelocity,self.vScaleFactor)

sutterMP285.py

import serial import struct import time import sys from numpy import * class sutterMP285 : 'Class which allows interaction with the Sutter Manipulator 285' def __init__(self): self.verbose = 1. # level of messages self.timeOut = 30 # timeout in sec # initialize serial connection to controller try: self.ser = serial.Serial(port='COM1',baudrate=9600,bytesize=serial.EIGHTBITS,parity=serial.PARITY_NONE,stopbits=serial.STOPBITS_ONE,timeout=self.timeOut) self.connected = 1 if self.verbose: print self.ser except serial.SerialException: print 'No connection to Sutter MP-285 could be established!' sys.exit(1) # set move velocity to 200 self.setVelocity(200,10) self.updatePanel() # update controller panel (stepM,currentV,vScaleF)= self.getStatus() if currentV == 200: print 'sutterMP285 ready' else: print 'sutterMP285: WARNING Sutter did not respond at startup.' # destructor def __del__(self): self.ser.close() if self.verbose : print 'Connection to Sutter MP-285 closed' def getPosition(self): # send commend to get position self.ser.write('c\r') # read position from controller xyzb = self.ser.read(13) # convert bytes into 'signed long' numbers xyz_um = array(struct.unpack('lll', xyzb[:12]))/self.stepMult if self.verbose: print 'sutterMP285 : Stage position ' print 'X: %g um \n Y: %g um\n Z: %g um' % (xyz_um[0],xyz_um[1],xyz_um[2]) return xyz_um # Moves the three axes to specified location. def gotoPosition(self,pos): if len(pos) != 3: print 'Length of position argument has to be three' sys.exit(1) xyzb = struct.pack('lll',int(pos[0]*self.stepMult),int(pos[1]*self.stepMult),int(pos[2]*self.stepMult)) # convert integer values into bytes startt = time.time() # start timer self.ser.write('m'+xyzb+'\r') # send position to controller; add the "m" and the CR to create the move command cr = [] cr = self.ser.read(1) # read carriage return and ignore endt = time.time() # stop timer if len(cr)== 0: print 'Sutter did not finish moving before timeout (%d sec).' % self.timeOut else: print 'sutterMP285: Sutter move completed in (%.2f sec)' % (endt-startt) # this function changes the velocity of the sutter motions def setVelocity(self,Vel,vScalF=10): # Change velocity command 'V'xxCR where xx= unsigned short (16bit) int velocity # set by bits 14 to 0, and bit 15 indicates ustep resolution 0=10, 1=50 uSteps/step # V is ascii 86 # convert velocity into unsigned short - 2-byte - integeter velb = struct.pack('H',int(Vel)) # change last bit of 2nd byte to 1 for ustep resolution = 50 if vScalF == 50: velb2 = double(struct.unpack('B',velb[1])) + 128 velb = velb[0] + struct.pack('B',velb2) self.ser.write('V'+velb+'\r') self.ser.read(1) # Update Panel # causes the Sutter to display the XYZ info on the front panel def updatePanel(self): self.ser.write('n\r') #Sutter replies with a CR self.ser.read(1) # read and ignore the carriage return ## Set Origin # sets the origin of the coordinate system to the current position def setOrigin(self): self.ser.write('o\r') # Sutter replies with a CR self.ser.read(1) # read and ignor the carrage return # Reset controller def sendReset(self): self.ser.write('r\r') # Sutter does not reply # Queries the status of the controller. def getStatus(self): if self.verbose : print 'sutterMP285: get status info' self.ser.write('s\r') # send status command rrr = self.ser.read(32) # read return of 32 bytes without carriage return self.ser.read(1) # read and ignore the carriage return rrr statusbytes = struct.unpack(32*'B',rrr) print statusbytes # the value of STEP_MUL ("Multiplier yields msteps/nm") is at bytes 25 & 26 self.stepMult = double(statusbytes[25])*256 + double(statusbytes[24]) # the value of "XSPEED" and scale factor is at bytes 29 & 30 if statusbytes[29] > 127: self.vScaleFactor = 50 else: self.vScaleFactor = 10 #print double(127 & statusbytes[29])*256 #print double(statusbytes[28]), statusbytes[28] #print double(statusbytes[29]), statusbytes[29] self.currentVelocity = double(127 & statusbytes[29])*256+double(statusbytes[28]) #vScaleFactor = struct.unpack('lll', rrr[30:31]) if self.verbose: print 'step_mul (usteps/um): %g' % self.stepMult print 'xspeed" [velocity] (usteps/sec): %g' % self.currentVelocity print 'velocity scale factor (usteps/step): %g' % self.vScaleFactor # return (self.stepMult,self.currentVelocity,self.vScaleFactor)

0.182244

0.151749

import sys import copy import tempfile import pytest import torch import numpy as np import nibabel as nib import torchio as tio from ..utils import TorchioTestCase class TestImage(TorchioTestCase): """Tests for `Image`.""" def test_image_not_found(self): with self.assertRaises(FileNotFoundError): tio.ScalarImage('nopath') @pytest.mark.skipif(sys.platform == 'win32', reason='Path not valid') def test_wrong_path_value(self): with self.assertRaises(RuntimeError): tio.ScalarImage('~&./@#"!?X7=+') def test_wrong_path_type(self): with self.assertRaises(TypeError): tio.ScalarImage(5) def test_wrong_affine(self): with self.assertRaises(TypeError): tio.ScalarImage(5, affine=1) def test_tensor_flip(self): sample_input = torch.ones((4, 30, 30, 30)) tio.RandomFlip()(sample_input) def test_tensor_affine(self): sample_input = torch.ones((4, 10, 10, 10)) tio.RandomAffine()(sample_input) def test_wrong_scalar_image_type(self): data = torch.ones((1, 10, 10, 10)) with self.assertRaises(ValueError): tio.ScalarImage(tensor=data, type=tio.LABEL) def test_wrong_label_map_type(self): data = torch.ones((1, 10, 10, 10)) with self.assertRaises(ValueError): tio.LabelMap(tensor=data, type=tio.INTENSITY) def test_no_input(self): with self.assertRaises(ValueError): tio.ScalarImage() def test_bad_key(self): with self.assertRaises(ValueError): tio.ScalarImage(path='', data=5) def test_repr(self): subject = tio.Subject( t1=tio.ScalarImage(self.get_image_path('repr_test')), ) assert 'memory' not in repr(subject['t1']) subject.load() assert 'memory' in repr(subject['t1']) def test_data_tensor(self): subject = copy.deepcopy(self.sample_subject) subject.load() self.assertIs(subject.t1.data, subject.t1.tensor) def test_bad_affine(self): with self.assertRaises(ValueError): tio.ScalarImage(tensor=torch.rand(1, 2, 3, 4), affine=np.eye(3)) def test_nans_tensor(self): tensor = np.random.rand(1, 2, 3, 4) tensor[0, 0, 0, 0] = np.nan with self.assertWarns(RuntimeWarning): image = tio.ScalarImage(tensor=tensor, check_nans=True) image.set_check_nans(False) def test_get_center(self): tensor = torch.rand(1, 3, 3, 3) image = tio.ScalarImage(tensor=tensor) ras = image.get_center() lps = image.get_center(lps=True) self.assertEqual(ras, (1, 1, 1)) self.assertEqual(lps, (-1, -1, 1)) def test_with_list_of_missing_files(self): with self.assertRaises(FileNotFoundError): tio.ScalarImage(path=['nopath', 'error']) def test_with_a_list_of_paths(self): shape = (5, 5, 5) path1 = self.get_image_path('path1', shape=shape) path2 = self.get_image_path('path2', shape=shape) image = tio.ScalarImage(path=[path1, path2]) self.assertEqual(image.shape, (2, 5, 5, 5)) self.assertEqual(image[tio.STEM], ['path1', 'path2']) def test_with_a_list_of_images_with_different_shapes(self): path1 = self.get_image_path('path1', shape=(5, 5, 5)) path2 = self.get_image_path('path2', shape=(7, 5, 5)) image = tio.ScalarImage(path=[path1, path2]) with self.assertRaises(RuntimeError): image.load() def test_with_a_list_of_images_with_different_affines(self): path1 = self.get_image_path('path1', spacing=(1, 1, 1)) path2 = self.get_image_path('path2', spacing=(1, 2, 1)) image = tio.ScalarImage(path=[path1, path2]) with self.assertWarns(RuntimeWarning): image.load() def test_with_a_list_of_2d_paths(self): shape = (5, 6) path1 = self.get_image_path('path1', shape=shape, suffix='.nii') path2 = self.get_image_path('path2', shape=shape, suffix='.img') path3 = self.get_image_path('path3', shape=shape, suffix='.hdr') image = tio.ScalarImage(path=[path1, path2, path3]) self.assertEqual(image.shape, (3, 5, 6, 1)) self.assertEqual(image[tio.STEM], ['path1', 'path2', 'path3']) def test_axis_name_2d(self): path = self.get_image_path('im2d', shape=(5, 6)) image = tio.ScalarImage(path) height_idx = image.axis_name_to_index('t') width_idx = image.axis_name_to_index('l') self.assertEqual(image.height, image.shape[height_idx]) self.assertEqual(image.width, image.shape[width_idx]) def test_plot(self): image = self.sample_subject.t1 image.plot(show=False, output_path=self.dir / 'image.png') def test_data_type_uint16_array(self): tensor = np.random.rand(1, 3, 3, 3).astype(np.uint16) image = tio.ScalarImage(tensor=tensor) self.assertEqual(image.data.dtype, torch.int32) def test_data_type_uint32_array(self): tensor = np.random.rand(1, 3, 3, 3).astype(np.uint32) image = tio.ScalarImage(tensor=tensor) self.assertEqual(image.data.dtype, torch.int64) def test_save_image_with_data_type_boolean(self): tensor = np.random.rand(1, 3, 3, 3).astype(bool) image = tio.ScalarImage(tensor=tensor) image.save(self.dir / 'image.nii') def test_load_uint(self): affine = np.eye(4) for dtype in np.uint16, np.uint32: data = np.ones((3, 3, 3), dtype=dtype) img = nib.Nifti1Image(data, affine) with tempfile.NamedTemporaryFile(suffix='.nii', delete=False) as f: nib.save(img, f.name) tio.ScalarImage(f.name).load() def test_pil_3d(self): with self.assertRaises(RuntimeError): tio.ScalarImage(tensor=torch.rand(1, 2, 3, 4)).as_pil() def test_pil_1(self): tio.ScalarImage(tensor=torch.rand(1, 2, 3, 1)).as_pil() def test_pil_2(self): with self.assertRaises(RuntimeError): tio.ScalarImage(tensor=torch.rand(2, 2, 3, 1)).as_pil() def test_pil_3(self): tio.ScalarImage(tensor=torch.rand(3, 2, 3, 1)).as_pil() def test_set_data(self): with self.assertWarns(DeprecationWarning): im = self.sample_subject.t1 im.data = im.data def test_no_type(self): with self.assertWarns(UserWarning): tio.Image(tensor=torch.rand(1, 2, 3, 4)) def test_custom_reader(self): path = self.dir / 'im.npy' def numpy_reader(path): return np.load(path), np.eye(4) def assert_shape(shape_in, shape_out): np.save(path, np.random.rand(*shape_in)) image = tio.ScalarImage(path, reader=numpy_reader) assert image.shape == shape_out assert_shape((5, 5), (1, 5, 5, 1)) assert_shape((5, 5, 3), (3, 5, 5, 1)) assert_shape((3, 5, 5), (3, 5, 5, 1)) assert_shape((5, 5, 5), (1, 5, 5, 5)) assert_shape((1, 5, 5, 5), (1, 5, 5, 5)) assert_shape((4, 5, 5, 5), (4, 5, 5, 5)) def test_fast_gif(self): with self.assertWarns(UserWarning): with tempfile.NamedTemporaryFile(suffix='.gif', delete=False) as f: self.sample_subject.t1.to_gif(0, 0.0001, f.name) def test_gif_rgb(self): with tempfile.NamedTemporaryFile(suffix='.gif', delete=False) as f: tio.ScalarImage(tensor=torch.rand(3, 4, 5, 6)).to_gif(0, 1, f.name)

tests/data/test_image.py

import sys import copy import tempfile import pytest import torch import numpy as np import nibabel as nib import torchio as tio from ..utils import TorchioTestCase class TestImage(TorchioTestCase): """Tests for `Image`.""" def test_image_not_found(self): with self.assertRaises(FileNotFoundError): tio.ScalarImage('nopath') @pytest.mark.skipif(sys.platform == 'win32', reason='Path not valid') def test_wrong_path_value(self): with self.assertRaises(RuntimeError): tio.ScalarImage('~&./@#"!?X7=+') def test_wrong_path_type(self): with self.assertRaises(TypeError): tio.ScalarImage(5) def test_wrong_affine(self): with self.assertRaises(TypeError): tio.ScalarImage(5, affine=1) def test_tensor_flip(self): sample_input = torch.ones((4, 30, 30, 30)) tio.RandomFlip()(sample_input) def test_tensor_affine(self): sample_input = torch.ones((4, 10, 10, 10)) tio.RandomAffine()(sample_input) def test_wrong_scalar_image_type(self): data = torch.ones((1, 10, 10, 10)) with self.assertRaises(ValueError): tio.ScalarImage(tensor=data, type=tio.LABEL) def test_wrong_label_map_type(self): data = torch.ones((1, 10, 10, 10)) with self.assertRaises(ValueError): tio.LabelMap(tensor=data, type=tio.INTENSITY) def test_no_input(self): with self.assertRaises(ValueError): tio.ScalarImage() def test_bad_key(self): with self.assertRaises(ValueError): tio.ScalarImage(path='', data=5) def test_repr(self): subject = tio.Subject( t1=tio.ScalarImage(self.get_image_path('repr_test')), ) assert 'memory' not in repr(subject['t1']) subject.load() assert 'memory' in repr(subject['t1']) def test_data_tensor(self): subject = copy.deepcopy(self.sample_subject) subject.load() self.assertIs(subject.t1.data, subject.t1.tensor) def test_bad_affine(self): with self.assertRaises(ValueError): tio.ScalarImage(tensor=torch.rand(1, 2, 3, 4), affine=np.eye(3)) def test_nans_tensor(self): tensor = np.random.rand(1, 2, 3, 4) tensor[0, 0, 0, 0] = np.nan with self.assertWarns(RuntimeWarning): image = tio.ScalarImage(tensor=tensor, check_nans=True) image.set_check_nans(False) def test_get_center(self): tensor = torch.rand(1, 3, 3, 3) image = tio.ScalarImage(tensor=tensor) ras = image.get_center() lps = image.get_center(lps=True) self.assertEqual(ras, (1, 1, 1)) self.assertEqual(lps, (-1, -1, 1)) def test_with_list_of_missing_files(self): with self.assertRaises(FileNotFoundError): tio.ScalarImage(path=['nopath', 'error']) def test_with_a_list_of_paths(self): shape = (5, 5, 5) path1 = self.get_image_path('path1', shape=shape) path2 = self.get_image_path('path2', shape=shape) image = tio.ScalarImage(path=[path1, path2]) self.assertEqual(image.shape, (2, 5, 5, 5)) self.assertEqual(image[tio.STEM], ['path1', 'path2']) def test_with_a_list_of_images_with_different_shapes(self): path1 = self.get_image_path('path1', shape=(5, 5, 5)) path2 = self.get_image_path('path2', shape=(7, 5, 5)) image = tio.ScalarImage(path=[path1, path2]) with self.assertRaises(RuntimeError): image.load() def test_with_a_list_of_images_with_different_affines(self): path1 = self.get_image_path('path1', spacing=(1, 1, 1)) path2 = self.get_image_path('path2', spacing=(1, 2, 1)) image = tio.ScalarImage(path=[path1, path2]) with self.assertWarns(RuntimeWarning): image.load() def test_with_a_list_of_2d_paths(self): shape = (5, 6) path1 = self.get_image_path('path1', shape=shape, suffix='.nii') path2 = self.get_image_path('path2', shape=shape, suffix='.img') path3 = self.get_image_path('path3', shape=shape, suffix='.hdr') image = tio.ScalarImage(path=[path1, path2, path3]) self.assertEqual(image.shape, (3, 5, 6, 1)) self.assertEqual(image[tio.STEM], ['path1', 'path2', 'path3']) def test_axis_name_2d(self): path = self.get_image_path('im2d', shape=(5, 6)) image = tio.ScalarImage(path) height_idx = image.axis_name_to_index('t') width_idx = image.axis_name_to_index('l') self.assertEqual(image.height, image.shape[height_idx]) self.assertEqual(image.width, image.shape[width_idx]) def test_plot(self): image = self.sample_subject.t1 image.plot(show=False, output_path=self.dir / 'image.png') def test_data_type_uint16_array(self): tensor = np.random.rand(1, 3, 3, 3).astype(np.uint16) image = tio.ScalarImage(tensor=tensor) self.assertEqual(image.data.dtype, torch.int32) def test_data_type_uint32_array(self): tensor = np.random.rand(1, 3, 3, 3).astype(np.uint32) image = tio.ScalarImage(tensor=tensor) self.assertEqual(image.data.dtype, torch.int64) def test_save_image_with_data_type_boolean(self): tensor = np.random.rand(1, 3, 3, 3).astype(bool) image = tio.ScalarImage(tensor=tensor) image.save(self.dir / 'image.nii') def test_load_uint(self): affine = np.eye(4) for dtype in np.uint16, np.uint32: data = np.ones((3, 3, 3), dtype=dtype) img = nib.Nifti1Image(data, affine) with tempfile.NamedTemporaryFile(suffix='.nii', delete=False) as f: nib.save(img, f.name) tio.ScalarImage(f.name).load() def test_pil_3d(self): with self.assertRaises(RuntimeError): tio.ScalarImage(tensor=torch.rand(1, 2, 3, 4)).as_pil() def test_pil_1(self): tio.ScalarImage(tensor=torch.rand(1, 2, 3, 1)).as_pil() def test_pil_2(self): with self.assertRaises(RuntimeError): tio.ScalarImage(tensor=torch.rand(2, 2, 3, 1)).as_pil() def test_pil_3(self): tio.ScalarImage(tensor=torch.rand(3, 2, 3, 1)).as_pil() def test_set_data(self): with self.assertWarns(DeprecationWarning): im = self.sample_subject.t1 im.data = im.data def test_no_type(self): with self.assertWarns(UserWarning): tio.Image(tensor=torch.rand(1, 2, 3, 4)) def test_custom_reader(self): path = self.dir / 'im.npy' def numpy_reader(path): return np.load(path), np.eye(4) def assert_shape(shape_in, shape_out): np.save(path, np.random.rand(*shape_in)) image = tio.ScalarImage(path, reader=numpy_reader) assert image.shape == shape_out assert_shape((5, 5), (1, 5, 5, 1)) assert_shape((5, 5, 3), (3, 5, 5, 1)) assert_shape((3, 5, 5), (3, 5, 5, 1)) assert_shape((5, 5, 5), (1, 5, 5, 5)) assert_shape((1, 5, 5, 5), (1, 5, 5, 5)) assert_shape((4, 5, 5, 5), (4, 5, 5, 5)) def test_fast_gif(self): with self.assertWarns(UserWarning): with tempfile.NamedTemporaryFile(suffix='.gif', delete=False) as f: self.sample_subject.t1.to_gif(0, 0.0001, f.name) def test_gif_rgb(self): with tempfile.NamedTemporaryFile(suffix='.gif', delete=False) as f: tio.ScalarImage(tensor=torch.rand(3, 4, 5, 6)).to_gif(0, 1, f.name)

0.615203

0.583619

from __future__ import absolute_import, division, print_function import logging from collections import defaultdict, namedtuple import pytest import torch import pyro import pyro.distributions as dist import pyro.optim as optim from pyro.contrib.gp.kernels import Cosine, Matern32, RBF, WhiteNoise from pyro.contrib.gp.likelihoods import Gaussian from pyro.contrib.gp.models import (GPLVM, GPRegression, SparseGPRegression, VariationalGP, VariationalSparseGP) from pyro.infer import SVI, Trace_ELBO from pyro.infer.mcmc.hmc import HMC from pyro.infer.mcmc.mcmc import MCMC from pyro.params import param_with_module_name from tests.common import assert_equal logging.basicConfig(format='%(levelname)s %(message)s') logger = logging.getLogger('pyro') logger.setLevel(logging.INFO) T = namedtuple("TestGPModel", ["model_class", "X", "y", "kernel", "likelihood"]) X = torch.tensor([[1., 5., 3.], [4., 3., 7.]]) y1D = torch.tensor([2., 1.]) y2D = torch.tensor([[1., 2.], [3., 3.], [1., 4.], [-1., 1.]]) kernel = RBF(input_dim=3, variance=torch.tensor(3.), lengthscale=torch.tensor(2.)) noise = torch.tensor(1e-6) likelihood = Gaussian(noise) TEST_CASES = [ T( GPRegression, X, y1D, kernel, noise ), T( GPRegression, X, y2D, kernel, noise ), T( SparseGPRegression, X, y1D, kernel, noise ), T( SparseGPRegression, X, y2D, kernel, noise ), T( VariationalGP, X, y1D, kernel, likelihood ), T( VariationalGP, X, y2D, kernel, likelihood ), T( VariationalSparseGP, X, y1D, kernel, likelihood ), T( VariationalSparseGP, X, y2D, kernel, likelihood ), ] TEST_IDS = [t[0].__name__ + "_y{}D".format(str(t[2].dim())) for t in TEST_CASES] @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) def test_model(model_class, X, y, kernel, likelihood): if model_class is SparseGPRegression or model_class is VariationalSparseGP: gp = model_class(X, None, kernel, X, likelihood) else: gp = model_class(X, None, kernel, likelihood) loc, var = gp.model() if model_class is VariationalGP or model_class is VariationalSparseGP: assert_equal(loc.norm().item(), 0) assert_equal(var, torch.ones(var.shape[-1]).expand(var.shape)) else: assert_equal(loc.norm().item(), 0) assert_equal(var, kernel(X).diag()) @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) def test_forward(model_class, X, y, kernel, likelihood): if model_class is SparseGPRegression or model_class is VariationalSparseGP: gp = model_class(X, y, kernel, X, likelihood) else: gp = model_class(X, y, kernel, likelihood) # test shape Xnew = torch.tensor([[2.0, 3.0, 1.0]]) loc0, cov0 = gp(Xnew, full_cov=True) loc1, var1 = gp(Xnew, full_cov=False) assert loc0.dim() == y.dim() assert loc0.shape[-1] == Xnew.shape[0] # test latent shape assert loc0.shape[:-1] == y.shape[:-1] assert cov0.shape[:-2] == y.shape[:-1] assert cov0.shape[-1] == cov0.shape[-2] assert cov0.shape[-1] == Xnew.shape[0] assert_equal(loc0, loc1) n = Xnew.shape[0] cov0_diag = torch.stack([mat.diag() for mat in cov0.view(-1, n, n)]).reshape(var1.shape) assert_equal(cov0_diag, var1) # test trivial forward: Xnew = X loc, cov = gp(X, full_cov=True) if model_class is VariationalGP or model_class is VariationalSparseGP: assert_equal(loc.norm().item(), 0) assert_equal(cov, torch.eye(cov.shape[-1]).expand(cov.shape)) else: assert_equal(loc, y) assert_equal(cov.norm().item(), 0) # test same input forward: Xnew[0,:] = Xnew[1,:] = ... Xnew = torch.tensor([[2.0, 3.0, 1.0]]).expand(10, 3) loc, cov = gp(Xnew, full_cov=True) loc_diff = loc - loc[..., :1].expand(y.shape[:-1] + (10,)) assert_equal(loc_diff.norm().item(), 0) cov_diff = cov - cov[..., :1, :1].expand(y.shape[:-1] + (10, 10)) assert_equal(cov_diff.norm().item(), 0) # test noise kernel forward: kernel = WhiteNoise gp.kernel = WhiteNoise(input_dim=3, variance=torch.tensor(10.)) loc, cov = gp(X, full_cov=True) assert_equal(loc.norm().item(), 0) assert_equal(cov, torch.eye(cov.shape[-1]).expand(cov.shape) * 10) @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) def test_forward_with_empty_latent_shape(model_class, X, y, kernel, likelihood): # regression models don't use latent_shape, no need for test if model_class is GPRegression or model_class is SparseGPRegression: return elif model_class is VariationalGP: gp = model_class(X, y, kernel, likelihood, latent_shape=torch.Size([])) else: # model_class is VariationalSparseGP gp = model_class(X, y, kernel, X, likelihood, latent_shape=torch.Size([])) # test shape Xnew = torch.tensor([[2.0, 3.0, 1.0]]) loc0, cov0 = gp(Xnew, full_cov=True) loc1, var1 = gp(Xnew, full_cov=False) assert loc0.shape[-1] == Xnew.shape[0] assert cov0.shape[-1] == cov0.shape[-2] assert cov0.shape[-1] == Xnew.shape[0] # test latent shape assert loc0.shape[:-1] == torch.Size([]) assert cov0.shape[:-2] == torch.Size([]) assert_equal(loc0, loc1) assert_equal(cov0.diag(), var1) @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) @pytest.mark.init(rng_seed=0) def test_inference(model_class, X, y, kernel, likelihood): # skip variational GP models because variance/lengthscale highly # depend on variational parameters if model_class is VariationalGP or model_class is VariationalSparseGP: return elif model_class is GPRegression: gp = model_class(X, y, RBF(input_dim=3), likelihood) else: # model_class is SparseGPRegression gp = model_class(X, y, RBF(input_dim=3), X, likelihood) # fix inducing points because variance/lengthscale highly depend on it gp.fix_param("Xu") generator = dist.MultivariateNormal(torch.zeros(X.shape[0]), kernel(X)) target_y = generator(sample_shape=torch.Size([1000])).detach() gp.set_data(X, target_y) gp.optimize(optim.Adam({"lr": 0.01}), num_steps=1000) y_cov = gp.kernel(X) target_y_cov = kernel(X) assert_equal(y_cov, target_y_cov, prec=0.1) @pytest.mark.init(rng_seed=0) def test_inference_sgpr(): N = 1000 X = dist.Uniform(torch.zeros(N), torch.ones(N)*5).sample() y = 0.5 * torch.sin(3*X) + dist.Normal(torch.zeros(N), torch.ones(N)*0.5).sample() kernel = RBF(input_dim=1) Xu = torch.arange(0, 5.5, 0.5) sgpr = SparseGPRegression(X, y, kernel, Xu) sgpr.optimize(optim.Adam({"lr": 0.01}), num_steps=1000) Xnew = torch.arange(0, 5.05, 0.05) loc, var = sgpr(Xnew, full_cov=False) target = 0.5 * torch.sin(3*Xnew) assert_equal((loc - target).abs().mean().item(), 0, prec=0.07) @pytest.mark.init(rng_seed=0) def test_inference_vsgp(): N = 1000 X = dist.Uniform(torch.zeros(N), torch.ones(N)*5).sample() y = 0.5 * torch.sin(3*X) + dist.Normal(torch.zeros(N), torch.ones(N)*0.5).sample() kernel = RBF(input_dim=1) Xu = torch.arange(0, 5.5, 0.5) vsgp = VariationalSparseGP(X, y, kernel, Xu, Gaussian()) vsgp.optimize(optim.Adam({"lr": 0.03}), num_steps=1000) Xnew = torch.arange(0, 5.05, 0.05) loc, var = vsgp(Xnew, full_cov=False) target = 0.5 * torch.sin(3*Xnew) assert_equal((loc - target).abs().mean().item(), 0, prec=0.06) @pytest.mark.init(rng_seed=0) def test_inference_whiten_vsgp(): N = 1000 X = dist.Uniform(torch.zeros(N), torch.ones(N)*5).sample() y = 0.5 * torch.sin(3*X) + dist.Normal(torch.zeros(N), torch.ones(N)*0.5).sample() kernel = RBF(input_dim=1) Xu = torch.arange(0, 5.5, 0.5) vsgp = VariationalSparseGP(X, y, kernel, Xu, Gaussian(), whiten=True) vsgp.optimize(optim.Adam({"lr": 0.01}), num_steps=1000) Xnew = torch.arange(0, 5.05, 0.05) loc, var = vsgp(Xnew, full_cov=False) target = 0.5 * torch.sin(3*Xnew) assert_equal((loc - target).abs().mean().item(), 0, prec=0.07) @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) def test_inference_with_empty_latent_shape(model_class, X, y, kernel, likelihood): # regression models don't use latent_shape (default=torch.Size([])) if model_class is GPRegression or model_class is SparseGPRegression: return elif model_class is VariationalGP: gp = model_class(X, y, kernel, likelihood, latent_shape=torch.Size([])) else: # model_class is SparseVariationalGP gp = model_class(X, y, kernel, X, likelihood, latent_shape=torch.Size([])) gp.optimize(num_steps=1) @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) def test_inference_with_whiten(model_class, X, y, kernel, likelihood): # regression models don't use whiten if model_class is GPRegression or model_class is SparseGPRegression: return elif model_class is VariationalGP: gp = model_class(X, y, kernel, likelihood, whiten=True) else: # model_class is SparseVariationalGP gp = model_class(X, y, kernel, X, likelihood, whiten=True) gp.optimize(num_steps=1) @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) def test_hmc(model_class, X, y, kernel, likelihood): if model_class is SparseGPRegression or model_class is VariationalSparseGP: gp = model_class(X, y, kernel, X, likelihood) else: gp = model_class(X, y, kernel, likelihood) kernel.set_prior("variance", dist.Uniform(torch.tensor(0.5), torch.tensor(1.5))) kernel.set_prior("lengthscale", dist.Uniform(torch.tensor(1.0), torch.tensor(3.0))) hmc_kernel = HMC(gp.model, step_size=1) mcmc_run = MCMC(hmc_kernel, num_samples=10) post_trace = defaultdict(list) for trace, _ in mcmc_run._traces(): variance_name = param_with_module_name(kernel.name, "variance") post_trace["variance"].append(trace.nodes[variance_name]["value"]) lengthscale_name = param_with_module_name(kernel.name, "lengthscale") post_trace["lengthscale"].append(trace.nodes[lengthscale_name]["value"]) if model_class is VariationalGP: f_name = param_with_module_name(gp.name, "f") post_trace["f"].append(trace.nodes[f_name]["value"]) if model_class is VariationalSparseGP: u_name = param_with_module_name(gp.name, "u") post_trace["u"].append(trace.nodes[u_name]["value"]) for param in post_trace: param_mean = torch.mean(torch.stack(post_trace[param]), 0) logger.info("Posterior mean - {}".format(param)) logger.info(param_mean) def test_inference_deepGP(): gp1 = GPRegression(X, None, kernel, name="GPR1") Z, _ = gp1.model() gp2 = VariationalSparseGP(Z, y2D, Matern32(input_dim=3), Z.clone(), likelihood, name="GPR2") def model(): Z, _ = gp1.model() gp2.set_data(Z, y2D) gp2.model() def guide(): gp1.guide() gp2.guide() svi = SVI(model, guide, optim.Adam({}), Trace_ELBO()) svi.step() @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) def test_gplvm(model_class, X, y, kernel, likelihood): if model_class is SparseGPRegression or model_class is VariationalSparseGP: gp = model_class(X, y, kernel, X, likelihood) else: gp = model_class(X, y, kernel, likelihood) gplvm = GPLVM(gp) # test inference gplvm.optimize(num_steps=1) # test forward gplvm(Xnew=X) def _pre_test_mean_function(): def f(x): return 2 * x + 3 + 5 * torch.sin(7 * x) X = torch.arange(100) y = f(X) Xnew = torch.arange(100, 150) ynew = f(Xnew) kernel = Cosine(input_dim=1) def trend(x): a = pyro.param("a", torch.tensor(0.)) b = pyro.param("b", torch.tensor(1.)) return a * x + b return X, y, Xnew, ynew, kernel, trend def _mape(y_true, y_pred): return ((y_pred - y_true) / y_true).abs().mean() def _post_test_mean_function(model, Xnew, y_true): assert_equal(pyro.param("a").item(), 2, prec=0.02) assert_equal(pyro.param("b").item(), 3, prec=0.02) y_pred, _ = model(Xnew) assert_equal(_mape(y_true, y_pred).item(), 0, prec=0.02) def test_mean_function_GPR(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() model = GPRegression(X, y, kernel, mean_function=mean_fn) model.optimize(optim.Adam({"lr": 0.01})) _post_test_mean_function(model, Xnew, ynew) def test_mean_function_SGPR(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() Xu = X[::20].clone() model = SparseGPRegression(X, y, kernel, Xu, mean_function=mean_fn) model.optimize(optim.Adam({"lr": 0.01})) _post_test_mean_function(model, Xnew, ynew) def test_mean_function_SGPR_DTC(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() Xu = X[::20].clone() model = SparseGPRegression(X, y, kernel, Xu, mean_function=mean_fn, approx="DTC") model.optimize(optim.Adam({"lr": 0.01})) _post_test_mean_function(model, Xnew, ynew) def test_mean_function_SGPR_FITC(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() Xu = X[::20].clone() model = SparseGPRegression(X, y, kernel, Xu, mean_function=mean_fn, approx="FITC") model.optimize(optim.Adam({"lr": 0.01})) _post_test_mean_function(model, Xnew, ynew) def test_mean_function_VGP(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() likelihood = Gaussian() model = VariationalGP(X, y, kernel, likelihood, mean_function=mean_fn) model.optimize(optim.Adam({"lr": 0.01})) _post_test_mean_function(model, Xnew, ynew) def test_mean_function_VGP_whiten(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() likelihood = Gaussian() model = VariationalGP(X, y, kernel, likelihood, mean_function=mean_fn, whiten=True) model.optimize(optim.Adam({"lr": 0.1})) _post_test_mean_function(model, Xnew, ynew) def test_mean_function_VSGP(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() Xu = X[::20].clone() likelihood = Gaussian() model = VariationalSparseGP(X, y, kernel, Xu, likelihood, mean_function=mean_fn) model.optimize(optim.Adam({"lr": 0.02})) _post_test_mean_function(model, Xnew, ynew) def test_mean_function_VSGP_whiten(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() Xu = X[::20].clone() likelihood = Gaussian() model = VariationalSparseGP(X, y, kernel, Xu, likelihood, mean_function=mean_fn, whiten=True) model.optimize(optim.Adam({"lr": 0.1})) _post_test_mean_function(model, Xnew, ynew)

tests/contrib/gp/test_models.py

from __future__ import absolute_import, division, print_function import logging from collections import defaultdict, namedtuple import pytest import torch import pyro import pyro.distributions as dist import pyro.optim as optim from pyro.contrib.gp.kernels import Cosine, Matern32, RBF, WhiteNoise from pyro.contrib.gp.likelihoods import Gaussian from pyro.contrib.gp.models import (GPLVM, GPRegression, SparseGPRegression, VariationalGP, VariationalSparseGP) from pyro.infer import SVI, Trace_ELBO from pyro.infer.mcmc.hmc import HMC from pyro.infer.mcmc.mcmc import MCMC from pyro.params import param_with_module_name from tests.common import assert_equal logging.basicConfig(format='%(levelname)s %(message)s') logger = logging.getLogger('pyro') logger.setLevel(logging.INFO) T = namedtuple("TestGPModel", ["model_class", "X", "y", "kernel", "likelihood"]) X = torch.tensor([[1., 5., 3.], [4., 3., 7.]]) y1D = torch.tensor([2., 1.]) y2D = torch.tensor([[1., 2.], [3., 3.], [1., 4.], [-1., 1.]]) kernel = RBF(input_dim=3, variance=torch.tensor(3.), lengthscale=torch.tensor(2.)) noise = torch.tensor(1e-6) likelihood = Gaussian(noise) TEST_CASES = [ T( GPRegression, X, y1D, kernel, noise ), T( GPRegression, X, y2D, kernel, noise ), T( SparseGPRegression, X, y1D, kernel, noise ), T( SparseGPRegression, X, y2D, kernel, noise ), T( VariationalGP, X, y1D, kernel, likelihood ), T( VariationalGP, X, y2D, kernel, likelihood ), T( VariationalSparseGP, X, y1D, kernel, likelihood ), T( VariationalSparseGP, X, y2D, kernel, likelihood ), ] TEST_IDS = [t[0].__name__ + "_y{}D".format(str(t[2].dim())) for t in TEST_CASES] @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) def test_model(model_class, X, y, kernel, likelihood): if model_class is SparseGPRegression or model_class is VariationalSparseGP: gp = model_class(X, None, kernel, X, likelihood) else: gp = model_class(X, None, kernel, likelihood) loc, var = gp.model() if model_class is VariationalGP or model_class is VariationalSparseGP: assert_equal(loc.norm().item(), 0) assert_equal(var, torch.ones(var.shape[-1]).expand(var.shape)) else: assert_equal(loc.norm().item(), 0) assert_equal(var, kernel(X).diag()) @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) def test_forward(model_class, X, y, kernel, likelihood): if model_class is SparseGPRegression or model_class is VariationalSparseGP: gp = model_class(X, y, kernel, X, likelihood) else: gp = model_class(X, y, kernel, likelihood) # test shape Xnew = torch.tensor([[2.0, 3.0, 1.0]]) loc0, cov0 = gp(Xnew, full_cov=True) loc1, var1 = gp(Xnew, full_cov=False) assert loc0.dim() == y.dim() assert loc0.shape[-1] == Xnew.shape[0] # test latent shape assert loc0.shape[:-1] == y.shape[:-1] assert cov0.shape[:-2] == y.shape[:-1] assert cov0.shape[-1] == cov0.shape[-2] assert cov0.shape[-1] == Xnew.shape[0] assert_equal(loc0, loc1) n = Xnew.shape[0] cov0_diag = torch.stack([mat.diag() for mat in cov0.view(-1, n, n)]).reshape(var1.shape) assert_equal(cov0_diag, var1) # test trivial forward: Xnew = X loc, cov = gp(X, full_cov=True) if model_class is VariationalGP or model_class is VariationalSparseGP: assert_equal(loc.norm().item(), 0) assert_equal(cov, torch.eye(cov.shape[-1]).expand(cov.shape)) else: assert_equal(loc, y) assert_equal(cov.norm().item(), 0) # test same input forward: Xnew[0,:] = Xnew[1,:] = ... Xnew = torch.tensor([[2.0, 3.0, 1.0]]).expand(10, 3) loc, cov = gp(Xnew, full_cov=True) loc_diff = loc - loc[..., :1].expand(y.shape[:-1] + (10,)) assert_equal(loc_diff.norm().item(), 0) cov_diff = cov - cov[..., :1, :1].expand(y.shape[:-1] + (10, 10)) assert_equal(cov_diff.norm().item(), 0) # test noise kernel forward: kernel = WhiteNoise gp.kernel = WhiteNoise(input_dim=3, variance=torch.tensor(10.)) loc, cov = gp(X, full_cov=True) assert_equal(loc.norm().item(), 0) assert_equal(cov, torch.eye(cov.shape[-1]).expand(cov.shape) * 10) @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) def test_forward_with_empty_latent_shape(model_class, X, y, kernel, likelihood): # regression models don't use latent_shape, no need for test if model_class is GPRegression or model_class is SparseGPRegression: return elif model_class is VariationalGP: gp = model_class(X, y, kernel, likelihood, latent_shape=torch.Size([])) else: # model_class is VariationalSparseGP gp = model_class(X, y, kernel, X, likelihood, latent_shape=torch.Size([])) # test shape Xnew = torch.tensor([[2.0, 3.0, 1.0]]) loc0, cov0 = gp(Xnew, full_cov=True) loc1, var1 = gp(Xnew, full_cov=False) assert loc0.shape[-1] == Xnew.shape[0] assert cov0.shape[-1] == cov0.shape[-2] assert cov0.shape[-1] == Xnew.shape[0] # test latent shape assert loc0.shape[:-1] == torch.Size([]) assert cov0.shape[:-2] == torch.Size([]) assert_equal(loc0, loc1) assert_equal(cov0.diag(), var1) @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) @pytest.mark.init(rng_seed=0) def test_inference(model_class, X, y, kernel, likelihood): # skip variational GP models because variance/lengthscale highly # depend on variational parameters if model_class is VariationalGP or model_class is VariationalSparseGP: return elif model_class is GPRegression: gp = model_class(X, y, RBF(input_dim=3), likelihood) else: # model_class is SparseGPRegression gp = model_class(X, y, RBF(input_dim=3), X, likelihood) # fix inducing points because variance/lengthscale highly depend on it gp.fix_param("Xu") generator = dist.MultivariateNormal(torch.zeros(X.shape[0]), kernel(X)) target_y = generator(sample_shape=torch.Size([1000])).detach() gp.set_data(X, target_y) gp.optimize(optim.Adam({"lr": 0.01}), num_steps=1000) y_cov = gp.kernel(X) target_y_cov = kernel(X) assert_equal(y_cov, target_y_cov, prec=0.1) @pytest.mark.init(rng_seed=0) def test_inference_sgpr(): N = 1000 X = dist.Uniform(torch.zeros(N), torch.ones(N)*5).sample() y = 0.5 * torch.sin(3*X) + dist.Normal(torch.zeros(N), torch.ones(N)*0.5).sample() kernel = RBF(input_dim=1) Xu = torch.arange(0, 5.5, 0.5) sgpr = SparseGPRegression(X, y, kernel, Xu) sgpr.optimize(optim.Adam({"lr": 0.01}), num_steps=1000) Xnew = torch.arange(0, 5.05, 0.05) loc, var = sgpr(Xnew, full_cov=False) target = 0.5 * torch.sin(3*Xnew) assert_equal((loc - target).abs().mean().item(), 0, prec=0.07) @pytest.mark.init(rng_seed=0) def test_inference_vsgp(): N = 1000 X = dist.Uniform(torch.zeros(N), torch.ones(N)*5).sample() y = 0.5 * torch.sin(3*X) + dist.Normal(torch.zeros(N), torch.ones(N)*0.5).sample() kernel = RBF(input_dim=1) Xu = torch.arange(0, 5.5, 0.5) vsgp = VariationalSparseGP(X, y, kernel, Xu, Gaussian()) vsgp.optimize(optim.Adam({"lr": 0.03}), num_steps=1000) Xnew = torch.arange(0, 5.05, 0.05) loc, var = vsgp(Xnew, full_cov=False) target = 0.5 * torch.sin(3*Xnew) assert_equal((loc - target).abs().mean().item(), 0, prec=0.06) @pytest.mark.init(rng_seed=0) def test_inference_whiten_vsgp(): N = 1000 X = dist.Uniform(torch.zeros(N), torch.ones(N)*5).sample() y = 0.5 * torch.sin(3*X) + dist.Normal(torch.zeros(N), torch.ones(N)*0.5).sample() kernel = RBF(input_dim=1) Xu = torch.arange(0, 5.5, 0.5) vsgp = VariationalSparseGP(X, y, kernel, Xu, Gaussian(), whiten=True) vsgp.optimize(optim.Adam({"lr": 0.01}), num_steps=1000) Xnew = torch.arange(0, 5.05, 0.05) loc, var = vsgp(Xnew, full_cov=False) target = 0.5 * torch.sin(3*Xnew) assert_equal((loc - target).abs().mean().item(), 0, prec=0.07) @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) def test_inference_with_empty_latent_shape(model_class, X, y, kernel, likelihood): # regression models don't use latent_shape (default=torch.Size([])) if model_class is GPRegression or model_class is SparseGPRegression: return elif model_class is VariationalGP: gp = model_class(X, y, kernel, likelihood, latent_shape=torch.Size([])) else: # model_class is SparseVariationalGP gp = model_class(X, y, kernel, X, likelihood, latent_shape=torch.Size([])) gp.optimize(num_steps=1) @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) def test_inference_with_whiten(model_class, X, y, kernel, likelihood): # regression models don't use whiten if model_class is GPRegression or model_class is SparseGPRegression: return elif model_class is VariationalGP: gp = model_class(X, y, kernel, likelihood, whiten=True) else: # model_class is SparseVariationalGP gp = model_class(X, y, kernel, X, likelihood, whiten=True) gp.optimize(num_steps=1) @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) def test_hmc(model_class, X, y, kernel, likelihood): if model_class is SparseGPRegression or model_class is VariationalSparseGP: gp = model_class(X, y, kernel, X, likelihood) else: gp = model_class(X, y, kernel, likelihood) kernel.set_prior("variance", dist.Uniform(torch.tensor(0.5), torch.tensor(1.5))) kernel.set_prior("lengthscale", dist.Uniform(torch.tensor(1.0), torch.tensor(3.0))) hmc_kernel = HMC(gp.model, step_size=1) mcmc_run = MCMC(hmc_kernel, num_samples=10) post_trace = defaultdict(list) for trace, _ in mcmc_run._traces(): variance_name = param_with_module_name(kernel.name, "variance") post_trace["variance"].append(trace.nodes[variance_name]["value"]) lengthscale_name = param_with_module_name(kernel.name, "lengthscale") post_trace["lengthscale"].append(trace.nodes[lengthscale_name]["value"]) if model_class is VariationalGP: f_name = param_with_module_name(gp.name, "f") post_trace["f"].append(trace.nodes[f_name]["value"]) if model_class is VariationalSparseGP: u_name = param_with_module_name(gp.name, "u") post_trace["u"].append(trace.nodes[u_name]["value"]) for param in post_trace: param_mean = torch.mean(torch.stack(post_trace[param]), 0) logger.info("Posterior mean - {}".format(param)) logger.info(param_mean) def test_inference_deepGP(): gp1 = GPRegression(X, None, kernel, name="GPR1") Z, _ = gp1.model() gp2 = VariationalSparseGP(Z, y2D, Matern32(input_dim=3), Z.clone(), likelihood, name="GPR2") def model(): Z, _ = gp1.model() gp2.set_data(Z, y2D) gp2.model() def guide(): gp1.guide() gp2.guide() svi = SVI(model, guide, optim.Adam({}), Trace_ELBO()) svi.step() @pytest.mark.parametrize("model_class, X, y, kernel, likelihood", TEST_CASES, ids=TEST_IDS) def test_gplvm(model_class, X, y, kernel, likelihood): if model_class is SparseGPRegression or model_class is VariationalSparseGP: gp = model_class(X, y, kernel, X, likelihood) else: gp = model_class(X, y, kernel, likelihood) gplvm = GPLVM(gp) # test inference gplvm.optimize(num_steps=1) # test forward gplvm(Xnew=X) def _pre_test_mean_function(): def f(x): return 2 * x + 3 + 5 * torch.sin(7 * x) X = torch.arange(100) y = f(X) Xnew = torch.arange(100, 150) ynew = f(Xnew) kernel = Cosine(input_dim=1) def trend(x): a = pyro.param("a", torch.tensor(0.)) b = pyro.param("b", torch.tensor(1.)) return a * x + b return X, y, Xnew, ynew, kernel, trend def _mape(y_true, y_pred): return ((y_pred - y_true) / y_true).abs().mean() def _post_test_mean_function(model, Xnew, y_true): assert_equal(pyro.param("a").item(), 2, prec=0.02) assert_equal(pyro.param("b").item(), 3, prec=0.02) y_pred, _ = model(Xnew) assert_equal(_mape(y_true, y_pred).item(), 0, prec=0.02) def test_mean_function_GPR(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() model = GPRegression(X, y, kernel, mean_function=mean_fn) model.optimize(optim.Adam({"lr": 0.01})) _post_test_mean_function(model, Xnew, ynew) def test_mean_function_SGPR(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() Xu = X[::20].clone() model = SparseGPRegression(X, y, kernel, Xu, mean_function=mean_fn) model.optimize(optim.Adam({"lr": 0.01})) _post_test_mean_function(model, Xnew, ynew) def test_mean_function_SGPR_DTC(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() Xu = X[::20].clone() model = SparseGPRegression(X, y, kernel, Xu, mean_function=mean_fn, approx="DTC") model.optimize(optim.Adam({"lr": 0.01})) _post_test_mean_function(model, Xnew, ynew) def test_mean_function_SGPR_FITC(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() Xu = X[::20].clone() model = SparseGPRegression(X, y, kernel, Xu, mean_function=mean_fn, approx="FITC") model.optimize(optim.Adam({"lr": 0.01})) _post_test_mean_function(model, Xnew, ynew) def test_mean_function_VGP(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() likelihood = Gaussian() model = VariationalGP(X, y, kernel, likelihood, mean_function=mean_fn) model.optimize(optim.Adam({"lr": 0.01})) _post_test_mean_function(model, Xnew, ynew) def test_mean_function_VGP_whiten(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() likelihood = Gaussian() model = VariationalGP(X, y, kernel, likelihood, mean_function=mean_fn, whiten=True) model.optimize(optim.Adam({"lr": 0.1})) _post_test_mean_function(model, Xnew, ynew) def test_mean_function_VSGP(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() Xu = X[::20].clone() likelihood = Gaussian() model = VariationalSparseGP(X, y, kernel, Xu, likelihood, mean_function=mean_fn) model.optimize(optim.Adam({"lr": 0.02})) _post_test_mean_function(model, Xnew, ynew) def test_mean_function_VSGP_whiten(): X, y, Xnew, ynew, kernel, mean_fn = _pre_test_mean_function() Xu = X[::20].clone() likelihood = Gaussian() model = VariationalSparseGP(X, y, kernel, Xu, likelihood, mean_function=mean_fn, whiten=True) model.optimize(optim.Adam({"lr": 0.1})) _post_test_mean_function(model, Xnew, ynew)

0.836921

0.504516

from numpy import random import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation def adjust_spines(ax, spines): """ removing the spines from a matplotlib graphics. taken from matplotlib gallery anonymous author. parameters ---------- ax: a matplolib axes object handler of the object to work with spines: list of char location of the spines """ for loc, spine in ax.spines.items(): if loc in spines: pass # print 'skipped' # spine.set_position(('outward',10)) # outward by 10 points # spine.set_smart_bounds(true) else: spine.set_color('none') # don't draw spine # turn off ticks where there is no spine if 'left' in spines: ax.yaxis.set_ticks_position('left') else: # no yaxis ticks ax.yaxis.set_ticks([]) if 'bottom' in spines: ax.xaxis.set_ticks_position('bottom') else: # no xaxis ticks ax.xaxis.set_ticks([]) rmax = 0.2 colored = False if colored: fig = plt.figure(figsize=(15,5)) ax = plt.axes(xlim=(0-rmax,1+rmax), ylim=(0-rmax,1+rmax)) adjust_spines(ax, ['']) #Remove the axis else: fig = plt.figure(figsize=(5, 5)) ax = plt.axes(xlim=(0-rmax,1+rmax), ylim=(0-rmax,3+rmax)) adjust_spines(ax, ['']) #Remove the axis tmax= 300 #If colored is true need to be possible to divide by 3 ndrops = 10 drop_life = 50 rmax = 0.4 # Need to define the position of all drops # Their opacity at a given time and the related diameter def init(): # Generate the random position of the drops fall = np.zeros((ndrops, 4, tmax)) for i in range(ndrops): fall_t = np.random.randint(tmax) coor = np.random.rand(2) k = 0 radius = np.linspace(0.05, rmax, drop_life) opacity = np.linspace(1, 0, drop_life) for j in range(fall_t, min(fall_t + drop_life, tmax)): fall[i,0,j] = opacity[k] fall[i,1,j] = radius[k] fall[i,2,j] = coor[0] fall[i,3,j] = coor[1] k +=1 return fall fall = init() def ink_rain(t): """Generate a drawing of diluted inkdrops""" plt.cla() adjust_spines(ax, ['']) #Remove the axis ax.set_xlim(0-rmax, 1+rmax) ax.set_ylim(0-rmax, 1+rmax) for i in range(ndrops): if fall[i,0,t] > 0.1: circle = plt.Circle((fall[i, 2, t], fall[i, 3, t]), radius=fall[i, 1,t], fc="black", alpha=fall[i, 0,t]) ax.add_patch(circle) return ax if __name__ == "__main__": anim = FuncAnimation(fig, ink_rain, init_func=init, frames=tmax, interval=20) anim.save('Rainy_sunday.avi') #anim.save('spreading_ink.avi') plt.show()

inkdrops.py

from numpy import random import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation def adjust_spines(ax, spines): """ removing the spines from a matplotlib graphics. taken from matplotlib gallery anonymous author. parameters ---------- ax: a matplolib axes object handler of the object to work with spines: list of char location of the spines """ for loc, spine in ax.spines.items(): if loc in spines: pass # print 'skipped' # spine.set_position(('outward',10)) # outward by 10 points # spine.set_smart_bounds(true) else: spine.set_color('none') # don't draw spine # turn off ticks where there is no spine if 'left' in spines: ax.yaxis.set_ticks_position('left') else: # no yaxis ticks ax.yaxis.set_ticks([]) if 'bottom' in spines: ax.xaxis.set_ticks_position('bottom') else: # no xaxis ticks ax.xaxis.set_ticks([]) rmax = 0.2 colored = False if colored: fig = plt.figure(figsize=(15,5)) ax = plt.axes(xlim=(0-rmax,1+rmax), ylim=(0-rmax,1+rmax)) adjust_spines(ax, ['']) #Remove the axis else: fig = plt.figure(figsize=(5, 5)) ax = plt.axes(xlim=(0-rmax,1+rmax), ylim=(0-rmax,3+rmax)) adjust_spines(ax, ['']) #Remove the axis tmax= 300 #If colored is true need to be possible to divide by 3 ndrops = 10 drop_life = 50 rmax = 0.4 # Need to define the position of all drops # Their opacity at a given time and the related diameter def init(): # Generate the random position of the drops fall = np.zeros((ndrops, 4, tmax)) for i in range(ndrops): fall_t = np.random.randint(tmax) coor = np.random.rand(2) k = 0 radius = np.linspace(0.05, rmax, drop_life) opacity = np.linspace(1, 0, drop_life) for j in range(fall_t, min(fall_t + drop_life, tmax)): fall[i,0,j] = opacity[k] fall[i,1,j] = radius[k] fall[i,2,j] = coor[0] fall[i,3,j] = coor[1] k +=1 return fall fall = init() def ink_rain(t): """Generate a drawing of diluted inkdrops""" plt.cla() adjust_spines(ax, ['']) #Remove the axis ax.set_xlim(0-rmax, 1+rmax) ax.set_ylim(0-rmax, 1+rmax) for i in range(ndrops): if fall[i,0,t] > 0.1: circle = plt.Circle((fall[i, 2, t], fall[i, 3, t]), radius=fall[i, 1,t], fc="black", alpha=fall[i, 0,t]) ax.add_patch(circle) return ax if __name__ == "__main__": anim = FuncAnimation(fig, ink_rain, init_func=init, frames=tmax, interval=20) anim.save('Rainy_sunday.avi') #anim.save('spreading_ink.avi') plt.show()

0.388618

0.59408

from vmaf.config import VmafConfig dataset_name = 'test_image' yuv_fmt = 'yuv444p' ref_videos = [ {'content_id': 0, 'content_name': '100007', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '100007.yuv'), 'width': 481}, {'content_id': 1, 'content_name': '100039', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '100039.yuv'), 'width': 481}, {'content_id': 2, 'content_name': '100075', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '100075.yuv'), 'width': 481}, {'content_id': 4, 'content_name': '100098', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '100098.yuv'), 'width': 481}, {'content_id': 5, 'content_name': '100099', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '100099.yuv'), 'width': 481}, {'content_id': 6, 'content_name': '10081', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '10081.yuv'), 'width': 481}, {'content_id': 7, 'content_name': '101027', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '101027.yuv'), 'width': 481}, {'content_id': 12, 'content_name': '102062', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '102062.yuv'), 'width': 481}, {'content_id': 13, 'content_name': '1012576_0m14s_15', 'height': 486, 'path': VmafConfig.test_resource_path('test_image_yuv', '1012576_0m14s_15.yuv'), 'width': 720}, ] dis_videos = [ {'asset_id': 0, 'content_id': 0, 'path': VmafConfig.test_resource_path('test_image_yuv', '100007.yuv'), 'groundtruth': 0}, {'asset_id': 1, 'content_id': 1, 'path': VmafConfig.test_resource_path('test_image_yuv', '100039.yuv'), 'groundtruth': 0}, {'asset_id': 2, 'content_id': 2, 'path': VmafConfig.test_resource_path('test_image_yuv', '100075.yuv'), 'groundtruth': 0}, {'asset_id': 4, 'content_id': 4, 'path': VmafConfig.test_resource_path('test_image_yuv', '100098.yuv'), 'groundtruth': 0}, {'asset_id': 5, 'content_id': 5, 'path': VmafConfig.test_resource_path('test_image_yuv', '100099.yuv'), 'groundtruth': 0}, {'asset_id': 6, 'content_id': 6, 'path': VmafConfig.test_resource_path('test_image_yuv', '10081.yuv'), 'groundtruth': 0}, {'asset_id': 7, 'content_id': 7, 'path': VmafConfig.test_resource_path('test_image_yuv', '101027.yuv'), 'groundtruth': 0}, {'asset_id': 12, 'content_id': 12, 'path': VmafConfig.test_resource_path('test_image_yuv', '102062.yuv'), 'groundtruth': 0}, {'asset_id': 13, 'content_id': 13, 'path': VmafConfig.test_resource_path('test_image_yuv', '1012576_0m14s_15.yuv'), 'groundtruth': 0}, {'asset_id': 50, 'content_id': 0, 'path': VmafConfig.test_resource_path('test_image_yuv', '100007_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 51, 'content_id': 1, 'path': VmafConfig.test_resource_path('test_image_yuv', '100039_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 52, 'content_id': 2, 'path': VmafConfig.test_resource_path('test_image_yuv', '100075_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 54, 'content_id': 4, 'path': VmafConfig.test_resource_path('test_image_yuv', '100098_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 55, 'content_id': 5, 'path': VmafConfig.test_resource_path('test_image_yuv', '100099_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 56, 'content_id': 6, 'path': VmafConfig.test_resource_path('test_image_yuv', '10081_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 57, 'content_id': 7, 'path': VmafConfig.test_resource_path('test_image_yuv', '101027_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 62, 'content_id': 12, 'path': VmafConfig.test_resource_path('test_image_yuv', '102062_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 63, 'content_id': 13, 'path': VmafConfig.test_resource_path('test_image_yuv', '1012576_0m14s_15_noisy.yuv'), 'groundtruth': 1} ]

python/test/resource/test_image_dataset_noisy.py

from vmaf.config import VmafConfig dataset_name = 'test_image' yuv_fmt = 'yuv444p' ref_videos = [ {'content_id': 0, 'content_name': '100007', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '100007.yuv'), 'width': 481}, {'content_id': 1, 'content_name': '100039', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '100039.yuv'), 'width': 481}, {'content_id': 2, 'content_name': '100075', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '100075.yuv'), 'width': 481}, {'content_id': 4, 'content_name': '100098', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '100098.yuv'), 'width': 481}, {'content_id': 5, 'content_name': '100099', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '100099.yuv'), 'width': 481}, {'content_id': 6, 'content_name': '10081', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '10081.yuv'), 'width': 481}, {'content_id': 7, 'content_name': '101027', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '101027.yuv'), 'width': 481}, {'content_id': 12, 'content_name': '102062', 'height': 321, 'path': VmafConfig.test_resource_path('test_image_yuv', '102062.yuv'), 'width': 481}, {'content_id': 13, 'content_name': '1012576_0m14s_15', 'height': 486, 'path': VmafConfig.test_resource_path('test_image_yuv', '1012576_0m14s_15.yuv'), 'width': 720}, ] dis_videos = [ {'asset_id': 0, 'content_id': 0, 'path': VmafConfig.test_resource_path('test_image_yuv', '100007.yuv'), 'groundtruth': 0}, {'asset_id': 1, 'content_id': 1, 'path': VmafConfig.test_resource_path('test_image_yuv', '100039.yuv'), 'groundtruth': 0}, {'asset_id': 2, 'content_id': 2, 'path': VmafConfig.test_resource_path('test_image_yuv', '100075.yuv'), 'groundtruth': 0}, {'asset_id': 4, 'content_id': 4, 'path': VmafConfig.test_resource_path('test_image_yuv', '100098.yuv'), 'groundtruth': 0}, {'asset_id': 5, 'content_id': 5, 'path': VmafConfig.test_resource_path('test_image_yuv', '100099.yuv'), 'groundtruth': 0}, {'asset_id': 6, 'content_id': 6, 'path': VmafConfig.test_resource_path('test_image_yuv', '10081.yuv'), 'groundtruth': 0}, {'asset_id': 7, 'content_id': 7, 'path': VmafConfig.test_resource_path('test_image_yuv', '101027.yuv'), 'groundtruth': 0}, {'asset_id': 12, 'content_id': 12, 'path': VmafConfig.test_resource_path('test_image_yuv', '102062.yuv'), 'groundtruth': 0}, {'asset_id': 13, 'content_id': 13, 'path': VmafConfig.test_resource_path('test_image_yuv', '1012576_0m14s_15.yuv'), 'groundtruth': 0}, {'asset_id': 50, 'content_id': 0, 'path': VmafConfig.test_resource_path('test_image_yuv', '100007_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 51, 'content_id': 1, 'path': VmafConfig.test_resource_path('test_image_yuv', '100039_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 52, 'content_id': 2, 'path': VmafConfig.test_resource_path('test_image_yuv', '100075_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 54, 'content_id': 4, 'path': VmafConfig.test_resource_path('test_image_yuv', '100098_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 55, 'content_id': 5, 'path': VmafConfig.test_resource_path('test_image_yuv', '100099_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 56, 'content_id': 6, 'path': VmafConfig.test_resource_path('test_image_yuv', '10081_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 57, 'content_id': 7, 'path': VmafConfig.test_resource_path('test_image_yuv', '101027_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 62, 'content_id': 12, 'path': VmafConfig.test_resource_path('test_image_yuv', '102062_noisy.yuv'), 'groundtruth': 1}, {'asset_id': 63, 'content_id': 13, 'path': VmafConfig.test_resource_path('test_image_yuv', '1012576_0m14s_15_noisy.yuv'), 'groundtruth': 1} ]

0.433742

0.159479

"""MySQL prerequisite.""" import logging import os from spet.lib.utilities import download from spet.lib.utilities import execute from spet.lib.utilities import extract from spet.lib.utilities import prettify class MySQL: """MySQL prerequisite. Args: mysql_ver (str): Version number for MySQL glibc_ver (str): Version number for MySQL glibc. root_dir (str): The main directory for SPET. Attributes: mysql_ver (str): Version number for MySQL. mysql_ver (str): Version number for MySQL glibc. src_dir (str): The source directory for installing packages. mysql_dir (str): The source directory for MySQL. """ def __init__(self, mysql_ver, glibc_ver, root_dir): self.mysql_ver = mysql_ver self.glibc_ver = glibc_ver self.src_dir = root_dir + "/src" self.mysql_dir = self.src_dir + "/mysql" def download(self): """Download MySQL. Returns: Boolean: True if download was successful otherwise False. """ minor_ver = ".".join(self.mysql_ver.split(".")[:2]) archive = "mysql-{}-linux-glibc{}-x86_64.tar.gz".format( self.mysql_ver, self.glibc_ver) url = "https://dev.mysql.com/get/Downloads/MySQL-{}/{}".format( minor_ver, archive) archive_path = "{}/{}".format(self.src_dir, archive) if os.path.isfile(archive_path): return True logging.info("Downloading MySQL.") download.file(url, archive_path) if os.path.isfile(archive_path): return True return False def extract(self): """Extract MySQL. Returns: Boolean: True if extraction was successful otherwise False. """ dir_path = "{}/mysql-{}-linux-glibc{}-x86_64".format( self.src_dir, self.mysql_ver, self.glibc_ver) file_path = dir_path + ".tar.gz" if os.path.isdir(self.mysql_dir): return True if not os.path.isfile(file_path): prettify.error_message( 'Cannot extract MySQL because "{}" could not be found.'.format( file_path)) return False logging.info("Extracting MySQL.") extract.tar(file_path, self.src_dir) os.rename(dir_path, self.mysql_dir) if os.path.isdir(self.mysql_dir): return True return False def setup(self): """Extract MySQL. Returns: Boolean: True if extraction was successful otherwise False. """ files_dir = self.mysql_dir + "/mysql-files" if not os.path.isdir(self.mysql_dir): prettify.error_message( 'Cannot setup MySQL because "{}" could not be found.'.format( self.mysql_dir)) return False if os.path.isdir(files_dir) and os.listdir(files_dir): return True os.makedirs(files_dir, exist_ok=True) os.chmod(files_dir, 0o750) execute.output( "./bin/mysqld --initialize-insecure --user=root --basedir={} " "--datadir={}".format(self.mysql_dir, files_dir), working_dir=self.mysql_dir, ) execute.output( "./bin/mysql_ssl_rsa_setup --user=root --basedir={} --datadir={}". format(self.mysql_dir, files_dir), working_dir=self.mysql_dir, ) return True

spet/lib/prerequisites/mysql.py

"""MySQL prerequisite.""" import logging import os from spet.lib.utilities import download from spet.lib.utilities import execute from spet.lib.utilities import extract from spet.lib.utilities import prettify class MySQL: """MySQL prerequisite. Args: mysql_ver (str): Version number for MySQL glibc_ver (str): Version number for MySQL glibc. root_dir (str): The main directory for SPET. Attributes: mysql_ver (str): Version number for MySQL. mysql_ver (str): Version number for MySQL glibc. src_dir (str): The source directory for installing packages. mysql_dir (str): The source directory for MySQL. """ def __init__(self, mysql_ver, glibc_ver, root_dir): self.mysql_ver = mysql_ver self.glibc_ver = glibc_ver self.src_dir = root_dir + "/src" self.mysql_dir = self.src_dir + "/mysql" def download(self): """Download MySQL. Returns: Boolean: True if download was successful otherwise False. """ minor_ver = ".".join(self.mysql_ver.split(".")[:2]) archive = "mysql-{}-linux-glibc{}-x86_64.tar.gz".format( self.mysql_ver, self.glibc_ver) url = "https://dev.mysql.com/get/Downloads/MySQL-{}/{}".format( minor_ver, archive) archive_path = "{}/{}".format(self.src_dir, archive) if os.path.isfile(archive_path): return True logging.info("Downloading MySQL.") download.file(url, archive_path) if os.path.isfile(archive_path): return True return False def extract(self): """Extract MySQL. Returns: Boolean: True if extraction was successful otherwise False. """ dir_path = "{}/mysql-{}-linux-glibc{}-x86_64".format( self.src_dir, self.mysql_ver, self.glibc_ver) file_path = dir_path + ".tar.gz" if os.path.isdir(self.mysql_dir): return True if not os.path.isfile(file_path): prettify.error_message( 'Cannot extract MySQL because "{}" could not be found.'.format( file_path)) return False logging.info("Extracting MySQL.") extract.tar(file_path, self.src_dir) os.rename(dir_path, self.mysql_dir) if os.path.isdir(self.mysql_dir): return True return False def setup(self): """Extract MySQL. Returns: Boolean: True if extraction was successful otherwise False. """ files_dir = self.mysql_dir + "/mysql-files" if not os.path.isdir(self.mysql_dir): prettify.error_message( 'Cannot setup MySQL because "{}" could not be found.'.format( self.mysql_dir)) return False if os.path.isdir(files_dir) and os.listdir(files_dir): return True os.makedirs(files_dir, exist_ok=True) os.chmod(files_dir, 0o750) execute.output( "./bin/mysqld --initialize-insecure --user=root --basedir={} " "--datadir={}".format(self.mysql_dir, files_dir), working_dir=self.mysql_dir, ) execute.output( "./bin/mysql_ssl_rsa_setup --user=root --basedir={} --datadir={}". format(self.mysql_dir, files_dir), working_dir=self.mysql_dir, ) return True

0.753829

0.098209

from django.db import migrations, models import django.db.models.deletion import martor.models class Migration(migrations.Migration): initial = True dependencies = [ ('tag', '0001_initial'), ] operations = [ migrations.CreateModel( name='Comment', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('published_at', models.DateTimeField(auto_now_add=True)), ('comment', models.TextField(max_length=1000)), ('lft', models.PositiveIntegerField(editable=False)), ('rght', models.PositiveIntegerField(editable=False)), ('tree_id', models.PositiveIntegerField(db_index=True, editable=False)), ('level', models.PositiveIntegerField(editable=False)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Post', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=100)), ('slug', models.SlugField(max_length=100, unique=True)), ('description', models.CharField(max_length=300)), ('post', martor.models.MartorField()), ('published_at', models.DateTimeField(auto_now_add=True)), ('updated_at', models.DateTimeField(auto_now=True)), ('published', models.PositiveSmallIntegerField(choices=[(1, 'Published'), (0, 'Draft')])), ], ), migrations.CreateModel( name='PostSeries', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('order', models.PositiveSmallIntegerField()), ('post', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='blog.post')), ], options={ 'ordering': ('order',), }, ), migrations.CreateModel( name='Series', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=100)), ('slug', models.SlugField(max_length=100, unique=True)), ('posts', models.ManyToManyField(related_name='series_posts', through='blog.PostSeries', to='blog.Post')), ('tags', models.ManyToManyField(related_name='series_tags', to='tag.Tag')), ], ), migrations.AddField( model_name='postseries', name='series', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='blog.series'), ), ]

django/codentino/apps/blog/migrations/0001_initial.py

from django.db import migrations, models import django.db.models.deletion import martor.models class Migration(migrations.Migration): initial = True dependencies = [ ('tag', '0001_initial'), ] operations = [ migrations.CreateModel( name='Comment', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('published_at', models.DateTimeField(auto_now_add=True)), ('comment', models.TextField(max_length=1000)), ('lft', models.PositiveIntegerField(editable=False)), ('rght', models.PositiveIntegerField(editable=False)), ('tree_id', models.PositiveIntegerField(db_index=True, editable=False)), ('level', models.PositiveIntegerField(editable=False)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Post', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=100)), ('slug', models.SlugField(max_length=100, unique=True)), ('description', models.CharField(max_length=300)), ('post', martor.models.MartorField()), ('published_at', models.DateTimeField(auto_now_add=True)), ('updated_at', models.DateTimeField(auto_now=True)), ('published', models.PositiveSmallIntegerField(choices=[(1, 'Published'), (0, 'Draft')])), ], ), migrations.CreateModel( name='PostSeries', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('order', models.PositiveSmallIntegerField()), ('post', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='blog.post')), ], options={ 'ordering': ('order',), }, ), migrations.CreateModel( name='Series', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=100)), ('slug', models.SlugField(max_length=100, unique=True)), ('posts', models.ManyToManyField(related_name='series_posts', through='blog.PostSeries', to='blog.Post')), ('tags', models.ManyToManyField(related_name='series_tags', to='tag.Tag')), ], ), migrations.AddField( model_name='postseries', name='series', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='blog.series'), ), ]

0.60743

0.191365

import numpy from io_funcs.binary_io import BinaryIOCollection import logging class MinMaxNormalisation(object): def __init__(self, feature_dimension, min_value = 0.01, max_value = 0.99, min_vector = 0.0, max_vector = 0.0, exclude_columns=[]): # this is the wrong name for this logger because we can also normalise labels here too logger = logging.getLogger("acoustic_norm") self.target_min_value = min_value self.target_max_value = max_value self.feature_dimension = feature_dimension self.min_vector = min_vector self.max_vector = max_vector self.exclude_columns = exclude_columns if type(min_vector) != float: try: assert( len(self.min_vector) == self.feature_dimension) except AssertionError: logger.critical('inconsistent feature_dimension (%d) and length of min_vector (%d)' % (self.feature_dimension,len(self.min_vector))) raise if type(max_vector) != float: try: assert( len(self.max_vector) == self.feature_dimension) except AssertionError: logger.critical('inconsistent feature_dimension (%d) and length of max_vector (%d)' % (self.feature_dimension,len(self.max_vector))) raise logger.debug('MinMaxNormalisation created for feature dimension of %d' % self.feature_dimension) def load_min_max_values(self, label_norm_file): logger = logging.getLogger("acoustic_norm") io_funcs = BinaryIOCollection() min_max_vector, frame_number = io_funcs.load_binary_file_frame(label_norm_file, 1) min_max_vector = numpy.reshape(min_max_vector, (-1, )) self.min_vector = min_max_vector[0:frame_number//2] self.max_vector = min_max_vector[frame_number//2:] logger.info('Loaded min max values from the trained data for feature dimension of %d' % self.feature_dimension) def find_min_max_values(self, in_file_list): logger = logging.getLogger("acoustic_norm") file_number = len(in_file_list) min_value_matrix = numpy.zeros((file_number, self.feature_dimension)) max_value_matrix = numpy.zeros((file_number, self.feature_dimension)) io_funcs = BinaryIOCollection() for i in range(file_number): features = io_funcs.load_binary_file(in_file_list[i], self.feature_dimension) temp_min = numpy.amin(features, axis = 0) temp_max = numpy.amax(features, axis = 0) min_value_matrix[i, ] = temp_min; max_value_matrix[i, ] = temp_max; self.min_vector = numpy.amin(min_value_matrix, axis = 0) self.max_vector = numpy.amax(max_value_matrix, axis = 0) self.min_vector = numpy.reshape(self.min_vector, (1, self.feature_dimension)) self.max_vector = numpy.reshape(self.max_vector, (1, self.feature_dimension)) # po=numpy.get_printoptions() # numpy.set_printoptions(precision=2, threshold=20, linewidth=1000, edgeitems=4) logger.info('across %d files found min/max values of length %d:' % (file_number,self.feature_dimension) ) logger.info(' min: %s' % self.min_vector) logger.info(' max: %s' % self.max_vector) # restore the print options # numpy.set_printoptions(po) def normalise_data(self, in_file_list, out_file_list): file_number = len(in_file_list) fea_max_min_diff = self.max_vector - self.min_vector diff_value = self.target_max_value - self.target_min_value fea_max_min_diff = numpy.reshape(fea_max_min_diff, (1, self.feature_dimension)) target_max_min_diff = numpy.zeros((1, self.feature_dimension)) target_max_min_diff.fill(diff_value) target_max_min_diff[fea_max_min_diff <= 0.0] = 1.0 fea_max_min_diff[fea_max_min_diff <= 0.0] = 1.0 io_funcs = BinaryIOCollection() for i in range(file_number): features = io_funcs.load_binary_file(in_file_list[i], self.feature_dimension) frame_number = features.size // self.feature_dimension fea_min_matrix = numpy.tile(self.min_vector, (frame_number, 1)) target_min_matrix = numpy.tile(self.target_min_value, (frame_number, self.feature_dimension)) fea_diff_matrix = numpy.tile(fea_max_min_diff, (frame_number, 1)) diff_norm_matrix = numpy.tile(target_max_min_diff, (frame_number, 1)) / fea_diff_matrix norm_features = diff_norm_matrix * (features - fea_min_matrix) + target_min_matrix ## If we are to keep some columns unnormalised, use advanced indexing to ## reinstate original values: m,n = numpy.shape(features) for col in self.exclude_columns: norm_features[list(range(m)),[col]*m] = features[list(range(m)),[col]*m] io_funcs.array_to_binary_file(norm_features, out_file_list[i]) # norm_features = numpy.array(norm_features, 'float32') # fid = open(out_file_list[i], 'wb') # norm_features.tofile(fid) # fid.close() def denormalise_data(self, in_file_list, out_file_list): logger = logging.getLogger("acoustic_norm") file_number = len(in_file_list) logger.info('MinMaxNormalisation.denormalise_data for %d files' % file_number) # print self.max_vector, self.min_vector fea_max_min_diff = self.max_vector - self.min_vector diff_value = self.target_max_value - self.target_min_value # logger.debug('reshaping fea_max_min_diff from shape %s to (1,%d)' % (fea_max_min_diff.shape, self.feature_dimension) ) fea_max_min_diff = numpy.reshape(fea_max_min_diff, (1, self.feature_dimension)) target_max_min_diff = numpy.zeros((1, self.feature_dimension)) target_max_min_diff.fill(diff_value) target_max_min_diff[fea_max_min_diff <= 0.0] = 1.0 fea_max_min_diff[fea_max_min_diff <= 0.0] = 1.0 io_funcs = BinaryIOCollection() for i in range(file_number): features = io_funcs.load_binary_file(in_file_list[i], self.feature_dimension) frame_number = features.size // self.feature_dimension fea_min_matrix = numpy.tile(self.min_vector, (frame_number, 1)) target_min_matrix = numpy.tile(self.target_min_value, (frame_number, self.feature_dimension)) fea_diff_matrix = numpy.tile(fea_max_min_diff, (frame_number, 1)) diff_norm_matrix = fea_diff_matrix / numpy.tile(target_max_min_diff, (frame_number, 1)) norm_features = diff_norm_matrix * (features - target_min_matrix) + fea_min_matrix io_funcs.array_to_binary_file(norm_features, out_file_list[i]) def normal_standardization(self, in_file_list, out_file_list): mean_vector = self.compute_mean(in_file_list) std_vector = self.compute_std(in_file_list, mean_vector) io_funcs = BinaryIOCollection() file_number = len(in_file_list) for i in range(file_number): features = io_funcs.load_binary_file(in_file_list[i], self.feature_dimension) current_frame_number = features.size // self.feature_dimension mean_matrix = numpy.tile(mean_vector, (current_frame_number, 1)) std_matrix = numpy.tile(std_vector, (current_frame_number, 1)) norm_features = (features - mean_matrix) / std_matrix io_funcs.array_to_binary_file(norm_features, out_file_list[i]) def compute_mean(self, file_list): logger = logging.getLogger("acoustic_norm") mean_vector = numpy.zeros((1, self.feature_dimension)) all_frame_number = 0 io_funcs = BinaryIOCollection() for file_name in file_list: features = io_funcs.load_binary_file(file_name, self.feature_dimension) current_frame_number = features.size // self.feature_dimension mean_vector += numpy.reshape(numpy.sum(features, axis=0), (1, self.feature_dimension)) all_frame_number += current_frame_number mean_vector /= float(all_frame_number) # po=numpy.get_printoptions() # numpy.set_printoptions(precision=2, threshold=20, linewidth=1000, edgeitems=4) logger.info('computed mean vector of length %d :' % mean_vector.shape[1] ) logger.info(' mean: %s' % mean_vector) # restore the print options # numpy.set_printoptions(po) return mean_vector def compute_std(self, file_list, mean_vector): logger = logging.getLogger("acoustic_norm") std_vector = numpy.zeros((1, self.feature_dimension)) all_frame_number = 0 io_funcs = BinaryIOCollection() for file_name in file_list: features = io_funcs.load_binary_file(file_name, self.feature_dimension) current_frame_number = features.size // self.feature_dimension mean_matrix = numpy.tile(mean_vector, (current_frame_number, 1)) std_vector += numpy.reshape(numpy.sum((features - mean_matrix) ** 2, axis=0), (1, self.feature_dimension)) all_frame_number += current_frame_number std_vector /= float(all_frame_number) std_vector = std_vector ** 0.5 # po=numpy.get_printoptions() # numpy.set_printoptions(precision=2, threshold=20, linewidth=1000, edgeitems=4) logger.info('computed std vector of length %d' % std_vector.shape[1] ) logger.info(' std: %s' % std_vector) # restore the print options # numpy.set_printoptions(po) return std_vector if __name__ == '__main__': in_file_list = ['/group/project/dnn_tts/data/nick/sp/nick/herald_001.sp'] out_file_list = ['/group/project/dnn_tts/herald_001.sp'] out_file_list1 = ['/group/project/dnn_tts/herald_001.test.sp'] feature_dimension = 1025 normaliser = MinMaxNormalisation(feature_dimension, min_value = 0.01, max_value = 0.99) normaliser.find_min_max_values(in_file_list) tmp_min_vector = normaliser.min_vector tmp_max_vector = normaliser.max_vector normaliser.normalise_data(in_file_list, out_file_list) denormaliser = MinMaxNormalisation(feature_dimension, min_value = 0.01, max_value = 0.99, min_vector = tmp_min_vector, max_vector = tmp_max_vector) denormaliser.denormalise_data(out_file_list, out_file_list1)

src/frontend/min_max_norm.py

import numpy from io_funcs.binary_io import BinaryIOCollection import logging class MinMaxNormalisation(object): def __init__(self, feature_dimension, min_value = 0.01, max_value = 0.99, min_vector = 0.0, max_vector = 0.0, exclude_columns=[]): # this is the wrong name for this logger because we can also normalise labels here too logger = logging.getLogger("acoustic_norm") self.target_min_value = min_value self.target_max_value = max_value self.feature_dimension = feature_dimension self.min_vector = min_vector self.max_vector = max_vector self.exclude_columns = exclude_columns if type(min_vector) != float: try: assert( len(self.min_vector) == self.feature_dimension) except AssertionError: logger.critical('inconsistent feature_dimension (%d) and length of min_vector (%d)' % (self.feature_dimension,len(self.min_vector))) raise if type(max_vector) != float: try: assert( len(self.max_vector) == self.feature_dimension) except AssertionError: logger.critical('inconsistent feature_dimension (%d) and length of max_vector (%d)' % (self.feature_dimension,len(self.max_vector))) raise logger.debug('MinMaxNormalisation created for feature dimension of %d' % self.feature_dimension) def load_min_max_values(self, label_norm_file): logger = logging.getLogger("acoustic_norm") io_funcs = BinaryIOCollection() min_max_vector, frame_number = io_funcs.load_binary_file_frame(label_norm_file, 1) min_max_vector = numpy.reshape(min_max_vector, (-1, )) self.min_vector = min_max_vector[0:frame_number//2] self.max_vector = min_max_vector[frame_number//2:] logger.info('Loaded min max values from the trained data for feature dimension of %d' % self.feature_dimension) def find_min_max_values(self, in_file_list): logger = logging.getLogger("acoustic_norm") file_number = len(in_file_list) min_value_matrix = numpy.zeros((file_number, self.feature_dimension)) max_value_matrix = numpy.zeros((file_number, self.feature_dimension)) io_funcs = BinaryIOCollection() for i in range(file_number): features = io_funcs.load_binary_file(in_file_list[i], self.feature_dimension) temp_min = numpy.amin(features, axis = 0) temp_max = numpy.amax(features, axis = 0) min_value_matrix[i, ] = temp_min; max_value_matrix[i, ] = temp_max; self.min_vector = numpy.amin(min_value_matrix, axis = 0) self.max_vector = numpy.amax(max_value_matrix, axis = 0) self.min_vector = numpy.reshape(self.min_vector, (1, self.feature_dimension)) self.max_vector = numpy.reshape(self.max_vector, (1, self.feature_dimension)) # po=numpy.get_printoptions() # numpy.set_printoptions(precision=2, threshold=20, linewidth=1000, edgeitems=4) logger.info('across %d files found min/max values of length %d:' % (file_number,self.feature_dimension) ) logger.info(' min: %s' % self.min_vector) logger.info(' max: %s' % self.max_vector) # restore the print options # numpy.set_printoptions(po) def normalise_data(self, in_file_list, out_file_list): file_number = len(in_file_list) fea_max_min_diff = self.max_vector - self.min_vector diff_value = self.target_max_value - self.target_min_value fea_max_min_diff = numpy.reshape(fea_max_min_diff, (1, self.feature_dimension)) target_max_min_diff = numpy.zeros((1, self.feature_dimension)) target_max_min_diff.fill(diff_value) target_max_min_diff[fea_max_min_diff <= 0.0] = 1.0 fea_max_min_diff[fea_max_min_diff <= 0.0] = 1.0 io_funcs = BinaryIOCollection() for i in range(file_number): features = io_funcs.load_binary_file(in_file_list[i], self.feature_dimension) frame_number = features.size // self.feature_dimension fea_min_matrix = numpy.tile(self.min_vector, (frame_number, 1)) target_min_matrix = numpy.tile(self.target_min_value, (frame_number, self.feature_dimension)) fea_diff_matrix = numpy.tile(fea_max_min_diff, (frame_number, 1)) diff_norm_matrix = numpy.tile(target_max_min_diff, (frame_number, 1)) / fea_diff_matrix norm_features = diff_norm_matrix * (features - fea_min_matrix) + target_min_matrix ## If we are to keep some columns unnormalised, use advanced indexing to ## reinstate original values: m,n = numpy.shape(features) for col in self.exclude_columns: norm_features[list(range(m)),[col]*m] = features[list(range(m)),[col]*m] io_funcs.array_to_binary_file(norm_features, out_file_list[i]) # norm_features = numpy.array(norm_features, 'float32') # fid = open(out_file_list[i], 'wb') # norm_features.tofile(fid) # fid.close() def denormalise_data(self, in_file_list, out_file_list): logger = logging.getLogger("acoustic_norm") file_number = len(in_file_list) logger.info('MinMaxNormalisation.denormalise_data for %d files' % file_number) # print self.max_vector, self.min_vector fea_max_min_diff = self.max_vector - self.min_vector diff_value = self.target_max_value - self.target_min_value # logger.debug('reshaping fea_max_min_diff from shape %s to (1,%d)' % (fea_max_min_diff.shape, self.feature_dimension) ) fea_max_min_diff = numpy.reshape(fea_max_min_diff, (1, self.feature_dimension)) target_max_min_diff = numpy.zeros((1, self.feature_dimension)) target_max_min_diff.fill(diff_value) target_max_min_diff[fea_max_min_diff <= 0.0] = 1.0 fea_max_min_diff[fea_max_min_diff <= 0.0] = 1.0 io_funcs = BinaryIOCollection() for i in range(file_number): features = io_funcs.load_binary_file(in_file_list[i], self.feature_dimension) frame_number = features.size // self.feature_dimension fea_min_matrix = numpy.tile(self.min_vector, (frame_number, 1)) target_min_matrix = numpy.tile(self.target_min_value, (frame_number, self.feature_dimension)) fea_diff_matrix = numpy.tile(fea_max_min_diff, (frame_number, 1)) diff_norm_matrix = fea_diff_matrix / numpy.tile(target_max_min_diff, (frame_number, 1)) norm_features = diff_norm_matrix * (features - target_min_matrix) + fea_min_matrix io_funcs.array_to_binary_file(norm_features, out_file_list[i]) def normal_standardization(self, in_file_list, out_file_list): mean_vector = self.compute_mean(in_file_list) std_vector = self.compute_std(in_file_list, mean_vector) io_funcs = BinaryIOCollection() file_number = len(in_file_list) for i in range(file_number): features = io_funcs.load_binary_file(in_file_list[i], self.feature_dimension) current_frame_number = features.size // self.feature_dimension mean_matrix = numpy.tile(mean_vector, (current_frame_number, 1)) std_matrix = numpy.tile(std_vector, (current_frame_number, 1)) norm_features = (features - mean_matrix) / std_matrix io_funcs.array_to_binary_file(norm_features, out_file_list[i]) def compute_mean(self, file_list): logger = logging.getLogger("acoustic_norm") mean_vector = numpy.zeros((1, self.feature_dimension)) all_frame_number = 0 io_funcs = BinaryIOCollection() for file_name in file_list: features = io_funcs.load_binary_file(file_name, self.feature_dimension) current_frame_number = features.size // self.feature_dimension mean_vector += numpy.reshape(numpy.sum(features, axis=0), (1, self.feature_dimension)) all_frame_number += current_frame_number mean_vector /= float(all_frame_number) # po=numpy.get_printoptions() # numpy.set_printoptions(precision=2, threshold=20, linewidth=1000, edgeitems=4) logger.info('computed mean vector of length %d :' % mean_vector.shape[1] ) logger.info(' mean: %s' % mean_vector) # restore the print options # numpy.set_printoptions(po) return mean_vector def compute_std(self, file_list, mean_vector): logger = logging.getLogger("acoustic_norm") std_vector = numpy.zeros((1, self.feature_dimension)) all_frame_number = 0 io_funcs = BinaryIOCollection() for file_name in file_list: features = io_funcs.load_binary_file(file_name, self.feature_dimension) current_frame_number = features.size // self.feature_dimension mean_matrix = numpy.tile(mean_vector, (current_frame_number, 1)) std_vector += numpy.reshape(numpy.sum((features - mean_matrix) ** 2, axis=0), (1, self.feature_dimension)) all_frame_number += current_frame_number std_vector /= float(all_frame_number) std_vector = std_vector ** 0.5 # po=numpy.get_printoptions() # numpy.set_printoptions(precision=2, threshold=20, linewidth=1000, edgeitems=4) logger.info('computed std vector of length %d' % std_vector.shape[1] ) logger.info(' std: %s' % std_vector) # restore the print options # numpy.set_printoptions(po) return std_vector if __name__ == '__main__': in_file_list = ['/group/project/dnn_tts/data/nick/sp/nick/herald_001.sp'] out_file_list = ['/group/project/dnn_tts/herald_001.sp'] out_file_list1 = ['/group/project/dnn_tts/herald_001.test.sp'] feature_dimension = 1025 normaliser = MinMaxNormalisation(feature_dimension, min_value = 0.01, max_value = 0.99) normaliser.find_min_max_values(in_file_list) tmp_min_vector = normaliser.min_vector tmp_max_vector = normaliser.max_vector normaliser.normalise_data(in_file_list, out_file_list) denormaliser = MinMaxNormalisation(feature_dimension, min_value = 0.01, max_value = 0.99, min_vector = tmp_min_vector, max_vector = tmp_max_vector) denormaliser.denormalise_data(out_file_list, out_file_list1)

0.454956

0.386127

from functools import partial import glob import os import shutil import threading import unittest from absl.testing import absltest import jax import jax.numpy as jnp import jax.profiler from jax.config import config import jax.test_util as jtu try: import portpicker except ImportError: portpicker = None try: from tensorflow.python.profiler import profiler_client from tensorflow.python.profiler import profiler_v2 as tf_profiler except ImportError: profiler_client = None tf_profiler = None config.parse_flags_with_absl() class ProfilerTest(unittest.TestCase): # These tests simply test that the profiler API does not crash; they do not # check functional correctness. def setUp(self): super().setUp() self.worker_start = threading.Event() self.profile_done = False @unittest.skipIf(not portpicker, "Test requires portpicker") def testStartServer(self): port = portpicker.pick_unused_port() jax.profiler.start_server(port=port) del port def testTraceContext(self): x = 3 with jax.profiler.TraceContext("mycontext"): x = x + 2 def testTraceFunction(self): @jax.profiler.trace_function def f(x): return x + 2 self.assertEqual(f(7), 9) @partial(jax.profiler.trace_function, name="aname") def g(x): return x + 2 self.assertEqual(g(7), 9) @partial(jax.profiler.trace_function, name="aname", akwarg="hello") def h(x): return x + 2 self.assertEqual(h(7), 9) def testDeviceMemoryProfile(self): x = jnp.ones((20,)) + 7. self.assertIsInstance(jax.profiler.device_memory_profile(), bytes) del x def _check_xspace_pb_exist(self, logdir): path = os.path.join(logdir, 'plugins', 'profile', '*', '*.xplane.pb') self.assertEqual(1, len(glob.glob(path)), 'Expected one path match: ' + path) @unittest.skipIf(not (portpicker and profiler_client and tf_profiler), "Test requires tensorflow.profiler and portpicker") def testSingleWorkerSamplingMode(self, delay_ms=None): def on_worker(port, worker_start): # Must keep return value `server` around. server = jax.profiler.start_server(port) # noqa: F841 worker_start.set() x = jnp.ones((1000, 1000)) while True: with jax.profiler.TraceContext("atracecontext"): jnp.dot(x, x.T).block_until_ready() if self.profile_done: break def on_profile(port, logdir, worker_start): worker_start.wait() options = tf_profiler.ProfilerOptions( host_tracer_level=2, python_tracer_level=2, device_tracer_level=1, delay_ms=delay_ms, ) # Request for 1000 milliseconds of profile. duration_ms = 1000 profiler_client.trace('localhost:{}'.format(port), logdir, duration_ms, '', 1000, options) self.profile_done = True logdir = absltest.get_default_test_tmpdir() # Remove any existing log files. shutil.rmtree(logdir, ignore_errors=True) port = portpicker.pick_unused_port() thread_profiler = threading.Thread( target=on_profile, args=(port, logdir, self.worker_start)) thread_worker = threading.Thread( target=on_worker, args=(port, self.worker_start)) thread_worker.start() thread_profiler.start() thread_profiler.join() thread_worker.join(120) self._check_xspace_pb_exist(logdir) if __name__ == "__main__": absltest.main(testLoader=jtu.JaxTestLoader())

tests/profiler_test.py

from functools import partial import glob import os import shutil import threading import unittest from absl.testing import absltest import jax import jax.numpy as jnp import jax.profiler from jax.config import config import jax.test_util as jtu try: import portpicker except ImportError: portpicker = None try: from tensorflow.python.profiler import profiler_client from tensorflow.python.profiler import profiler_v2 as tf_profiler except ImportError: profiler_client = None tf_profiler = None config.parse_flags_with_absl() class ProfilerTest(unittest.TestCase): # These tests simply test that the profiler API does not crash; they do not # check functional correctness. def setUp(self): super().setUp() self.worker_start = threading.Event() self.profile_done = False @unittest.skipIf(not portpicker, "Test requires portpicker") def testStartServer(self): port = portpicker.pick_unused_port() jax.profiler.start_server(port=port) del port def testTraceContext(self): x = 3 with jax.profiler.TraceContext("mycontext"): x = x + 2 def testTraceFunction(self): @jax.profiler.trace_function def f(x): return x + 2 self.assertEqual(f(7), 9) @partial(jax.profiler.trace_function, name="aname") def g(x): return x + 2 self.assertEqual(g(7), 9) @partial(jax.profiler.trace_function, name="aname", akwarg="hello") def h(x): return x + 2 self.assertEqual(h(7), 9) def testDeviceMemoryProfile(self): x = jnp.ones((20,)) + 7. self.assertIsInstance(jax.profiler.device_memory_profile(), bytes) del x def _check_xspace_pb_exist(self, logdir): path = os.path.join(logdir, 'plugins', 'profile', '*', '*.xplane.pb') self.assertEqual(1, len(glob.glob(path)), 'Expected one path match: ' + path) @unittest.skipIf(not (portpicker and profiler_client and tf_profiler), "Test requires tensorflow.profiler and portpicker") def testSingleWorkerSamplingMode(self, delay_ms=None): def on_worker(port, worker_start): # Must keep return value `server` around. server = jax.profiler.start_server(port) # noqa: F841 worker_start.set() x = jnp.ones((1000, 1000)) while True: with jax.profiler.TraceContext("atracecontext"): jnp.dot(x, x.T).block_until_ready() if self.profile_done: break def on_profile(port, logdir, worker_start): worker_start.wait() options = tf_profiler.ProfilerOptions( host_tracer_level=2, python_tracer_level=2, device_tracer_level=1, delay_ms=delay_ms, ) # Request for 1000 milliseconds of profile. duration_ms = 1000 profiler_client.trace('localhost:{}'.format(port), logdir, duration_ms, '', 1000, options) self.profile_done = True logdir = absltest.get_default_test_tmpdir() # Remove any existing log files. shutil.rmtree(logdir, ignore_errors=True) port = portpicker.pick_unused_port() thread_profiler = threading.Thread( target=on_profile, args=(port, logdir, self.worker_start)) thread_worker = threading.Thread( target=on_worker, args=(port, self.worker_start)) thread_worker.start() thread_profiler.start() thread_profiler.join() thread_worker.join(120) self._check_xspace_pb_exist(logdir) if __name__ == "__main__": absltest.main(testLoader=jtu.JaxTestLoader())

0.498535

0.193776

from __future__ import absolute_import, print_function, unicode_literals import re import debug # pyflakes:ignore from ietf.submit.parsers.base import FileParser class PlainParser(FileParser): ext = 'txt' mimetypes = ['text/plain', ] def __init__(self, fd): super(PlainParser, self).__init__(fd) # If some error is found after this method invocation # no other file parsing is recommended def critical_parse(self): super(PlainParser, self).critical_parse() self.check_file_charset() self.parse_name() return self.parsed_info def check_file_charset(self): charset = self.parsed_info.charset if not charset in ['us-ascii', 'utf-8',]: self.parsed_info.add_error('A plain text ASCII document is required. ' 'Found an unexpected encoding: "%s". ' 'You probably have one or more non-ascii characters in your file.' % charset ) if self.fd.charset and charset != self.fd.charset: self.parsed_info.add_error("Unexpected charset mismatch: upload: %s, libmagic: %s" % (self.fd.charset, charset)) def parse_name(self): self.fd.file.seek(0) draftre = re.compile(r'(draft-\S+)') revisionre = re.compile(r'.*-(\d+)$') limit = 80 if self.parsed_info.charset in ['us-ascii', 'utf-8']: while limit: limit -= 1 line = self.fd.readline().decode(self.parsed_info.charset) match = draftre.search(line) if not match: continue name = match.group(1) name = re.sub(r'^[^\w]+', '', name) name = re.sub(r'[^\w]+$', '', name) name = re.sub(r'\.txt$', '', name) extra_chars = re.sub(r'[0-9a-z\-]', '', name) if extra_chars: if len(extra_chars) == 1: self.parsed_info.add_error(('The document name on the first page, "%s", contains a disallowed character with byte code: %s ' % (name.decode('utf-8','replace'), ord(extra_chars[0]))) + '(see https://www.ietf.org/id-info/guidelines.html#naming for details).') else: self.parsed_info.add_error(('The document name on the first page, "%s", contains disallowed characters with byte codes: %s ' % (name.decode('utf-8','replace'), (', '.join([ str(ord(c)) for c in extra_chars] )))) + '(see https://www.ietf.org/id-info/guidelines.html#naming for details).') match_revision = revisionre.match(name) if match_revision: self.parsed_info.metadata.rev = match_revision.group(1) else: self.parsed_info.add_error('The name found on the first page of the document does not contain a revision: "%s"' % (name.decode('utf-8','replace'),)) name = re.sub(r'-\d+$', '', name) self.parsed_info.metadata.name = name return self.parsed_info.add_error('The first page of the document does not contain a legitimate name that starts with draft-*')

ietf/submit/parsers/plain_parser.py

from __future__ import absolute_import, print_function, unicode_literals import re import debug # pyflakes:ignore from ietf.submit.parsers.base import FileParser class PlainParser(FileParser): ext = 'txt' mimetypes = ['text/plain', ] def __init__(self, fd): super(PlainParser, self).__init__(fd) # If some error is found after this method invocation # no other file parsing is recommended def critical_parse(self): super(PlainParser, self).critical_parse() self.check_file_charset() self.parse_name() return self.parsed_info def check_file_charset(self): charset = self.parsed_info.charset if not charset in ['us-ascii', 'utf-8',]: self.parsed_info.add_error('A plain text ASCII document is required. ' 'Found an unexpected encoding: "%s". ' 'You probably have one or more non-ascii characters in your file.' % charset ) if self.fd.charset and charset != self.fd.charset: self.parsed_info.add_error("Unexpected charset mismatch: upload: %s, libmagic: %s" % (self.fd.charset, charset)) def parse_name(self): self.fd.file.seek(0) draftre = re.compile(r'(draft-\S+)') revisionre = re.compile(r'.*-(\d+)$') limit = 80 if self.parsed_info.charset in ['us-ascii', 'utf-8']: while limit: limit -= 1 line = self.fd.readline().decode(self.parsed_info.charset) match = draftre.search(line) if not match: continue name = match.group(1) name = re.sub(r'^[^\w]+', '', name) name = re.sub(r'[^\w]+$', '', name) name = re.sub(r'\.txt$', '', name) extra_chars = re.sub(r'[0-9a-z\-]', '', name) if extra_chars: if len(extra_chars) == 1: self.parsed_info.add_error(('The document name on the first page, "%s", contains a disallowed character with byte code: %s ' % (name.decode('utf-8','replace'), ord(extra_chars[0]))) + '(see https://www.ietf.org/id-info/guidelines.html#naming for details).') else: self.parsed_info.add_error(('The document name on the first page, "%s", contains disallowed characters with byte codes: %s ' % (name.decode('utf-8','replace'), (', '.join([ str(ord(c)) for c in extra_chars] )))) + '(see https://www.ietf.org/id-info/guidelines.html#naming for details).') match_revision = revisionre.match(name) if match_revision: self.parsed_info.metadata.rev = match_revision.group(1) else: self.parsed_info.add_error('The name found on the first page of the document does not contain a revision: "%s"' % (name.decode('utf-8','replace'),)) name = re.sub(r'-\d+$', '', name) self.parsed_info.metadata.name = name return self.parsed_info.add_error('The first page of the document does not contain a legitimate name that starts with draft-*')

0.4917

0.100481

import six as _six from flytekit.common import sdk_bases as _sdk_bases from flytekit.common.exceptions import user as _user_exceptions from flytekit.models.core import identifier as _core_identifier class Identifier(_core_identifier.Identifier, metaclass=_sdk_bases.ExtendedSdkType): _STRING_TO_TYPE_MAP = { "lp": _core_identifier.ResourceType.LAUNCH_PLAN, "wf": _core_identifier.ResourceType.WORKFLOW, "tsk": _core_identifier.ResourceType.TASK, } _TYPE_TO_STRING_MAP = {v: k for k, v in _six.iteritems(_STRING_TO_TYPE_MAP)} @classmethod def promote_from_model(cls, base_model): """ :param flytekit.models.core.identifier.Identifier base_model: :rtype: Identifier """ return cls( base_model.resource_type, base_model.project, base_model.domain, base_model.name, base_model.version, ) @classmethod def from_python_std(cls, string): """ Parses a string in the correct format into an identifier :param Text string: :rtype: Identifier """ segments = string.split(":") if len(segments) != 5: raise _user_exceptions.FlyteValueException( "The provided string was not in a parseable format. The string for an identifier must be in the format" " entity_type:project:domain:name:version. Received: {}".format(string) ) resource_type, project, domain, name, version = segments if resource_type not in cls._STRING_TO_TYPE_MAP: raise _user_exceptions.FlyteValueException( "The provided string could not be parsed. The first element of an identifier must be one of: {}. " "Received: {}".format(list(cls._STRING_TO_TYPE_MAP.keys()), resource_type) ) resource_type = cls._STRING_TO_TYPE_MAP[resource_type] return cls(resource_type, project, domain, name, version) def __str__(self): return "{}:{}:{}:{}:{}".format( type(self)._TYPE_TO_STRING_MAP.get(self.resource_type, "<unknown>"), self.project, self.domain, self.name, self.version, ) class WorkflowExecutionIdentifier(_core_identifier.WorkflowExecutionIdentifier, metaclass=_sdk_bases.ExtendedSdkType): @classmethod def promote_from_model(cls, base_model): """ :param flytekit.models.core.identifier.WorkflowExecutionIdentifier base_model: :rtype: WorkflowExecutionIdentifier """ return cls( base_model.project, base_model.domain, base_model.name, ) @classmethod def from_python_std(cls, string): """ Parses a string in the correct format into an identifier :param Text string: :rtype: WorkflowExecutionIdentifier """ segments = string.split(":") if len(segments) != 4: raise _user_exceptions.FlyteValueException( string, "The provided string was not in a parseable format. The string for an identifier must be in the format" " ex:project:domain:name.", ) resource_type, project, domain, name = segments if resource_type != "ex": raise _user_exceptions.FlyteValueException( resource_type, "The provided string could not be parsed. The first element of an execution identifier must be 'ex'.", ) return cls( project, domain, name, ) def __str__(self): return "ex:{}:{}:{}".format(self.project, self.domain, self.name) class TaskExecutionIdentifier(_core_identifier.TaskExecutionIdentifier, metaclass=_sdk_bases.ExtendedSdkType): @classmethod def promote_from_model(cls, base_model): """ :param flytekit.models.core.identifier.TaskExecutionIdentifier base_model: :rtype: TaskExecutionIdentifier """ return cls( task_id=base_model.task_id, node_execution_id=base_model.node_execution_id, retry_attempt=base_model.retry_attempt, ) @classmethod def from_python_std(cls, string): """ Parses a string in the correct format into an identifier :param Text string: :rtype: TaskExecutionIdentifier """ segments = string.split(":") if len(segments) != 10: raise _user_exceptions.FlyteValueException( string, "The provided string was not in a parseable format. The string for an identifier must be in the format" " te:exec_project:exec_domain:exec_name:node_id:task_project:task_domain:task_name:task_version:retry.", ) resource_type, ep, ed, en, node_id, tp, td, tn, tv, retry = segments if resource_type != "te": raise _user_exceptions.FlyteValueException( resource_type, "The provided string could not be parsed. The first element of an execution identifier must be 'ex'.", ) return cls( task_id=Identifier(_core_identifier.ResourceType.TASK, tp, td, tn, tv), node_execution_id=_core_identifier.NodeExecutionIdentifier( node_id=node_id, execution_id=_core_identifier.WorkflowExecutionIdentifier(ep, ed, en), ), retry_attempt=int(retry), ) def __str__(self): return "te:{ep}:{ed}:{en}:{node_id}:{tp}:{td}:{tn}:{tv}:{retry}".format( ep=self.node_execution_id.execution_id.project, ed=self.node_execution_id.execution_id.domain, en=self.node_execution_id.execution_id.name, node_id=self.node_execution_id.node_id, tp=self.task_id.project, td=self.task_id.domain, tn=self.task_id.name, tv=self.task_id.version, retry=self.retry_attempt, )

flytekit/common/core/identifier.py

import six as _six from flytekit.common import sdk_bases as _sdk_bases from flytekit.common.exceptions import user as _user_exceptions from flytekit.models.core import identifier as _core_identifier class Identifier(_core_identifier.Identifier, metaclass=_sdk_bases.ExtendedSdkType): _STRING_TO_TYPE_MAP = { "lp": _core_identifier.ResourceType.LAUNCH_PLAN, "wf": _core_identifier.ResourceType.WORKFLOW, "tsk": _core_identifier.ResourceType.TASK, } _TYPE_TO_STRING_MAP = {v: k for k, v in _six.iteritems(_STRING_TO_TYPE_MAP)} @classmethod def promote_from_model(cls, base_model): """ :param flytekit.models.core.identifier.Identifier base_model: :rtype: Identifier """ return cls( base_model.resource_type, base_model.project, base_model.domain, base_model.name, base_model.version, ) @classmethod def from_python_std(cls, string): """ Parses a string in the correct format into an identifier :param Text string: :rtype: Identifier """ segments = string.split(":") if len(segments) != 5: raise _user_exceptions.FlyteValueException( "The provided string was not in a parseable format. The string for an identifier must be in the format" " entity_type:project:domain:name:version. Received: {}".format(string) ) resource_type, project, domain, name, version = segments if resource_type not in cls._STRING_TO_TYPE_MAP: raise _user_exceptions.FlyteValueException( "The provided string could not be parsed. The first element of an identifier must be one of: {}. " "Received: {}".format(list(cls._STRING_TO_TYPE_MAP.keys()), resource_type) ) resource_type = cls._STRING_TO_TYPE_MAP[resource_type] return cls(resource_type, project, domain, name, version) def __str__(self): return "{}:{}:{}:{}:{}".format( type(self)._TYPE_TO_STRING_MAP.get(self.resource_type, "<unknown>"), self.project, self.domain, self.name, self.version, ) class WorkflowExecutionIdentifier(_core_identifier.WorkflowExecutionIdentifier, metaclass=_sdk_bases.ExtendedSdkType): @classmethod def promote_from_model(cls, base_model): """ :param flytekit.models.core.identifier.WorkflowExecutionIdentifier base_model: :rtype: WorkflowExecutionIdentifier """ return cls( base_model.project, base_model.domain, base_model.name, ) @classmethod def from_python_std(cls, string): """ Parses a string in the correct format into an identifier :param Text string: :rtype: WorkflowExecutionIdentifier """ segments = string.split(":") if len(segments) != 4: raise _user_exceptions.FlyteValueException( string, "The provided string was not in a parseable format. The string for an identifier must be in the format" " ex:project:domain:name.", ) resource_type, project, domain, name = segments if resource_type != "ex": raise _user_exceptions.FlyteValueException( resource_type, "The provided string could not be parsed. The first element of an execution identifier must be 'ex'.", ) return cls( project, domain, name, ) def __str__(self): return "ex:{}:{}:{}".format(self.project, self.domain, self.name) class TaskExecutionIdentifier(_core_identifier.TaskExecutionIdentifier, metaclass=_sdk_bases.ExtendedSdkType): @classmethod def promote_from_model(cls, base_model): """ :param flytekit.models.core.identifier.TaskExecutionIdentifier base_model: :rtype: TaskExecutionIdentifier """ return cls( task_id=base_model.task_id, node_execution_id=base_model.node_execution_id, retry_attempt=base_model.retry_attempt, ) @classmethod def from_python_std(cls, string): """ Parses a string in the correct format into an identifier :param Text string: :rtype: TaskExecutionIdentifier """ segments = string.split(":") if len(segments) != 10: raise _user_exceptions.FlyteValueException( string, "The provided string was not in a parseable format. The string for an identifier must be in the format" " te:exec_project:exec_domain:exec_name:node_id:task_project:task_domain:task_name:task_version:retry.", ) resource_type, ep, ed, en, node_id, tp, td, tn, tv, retry = segments if resource_type != "te": raise _user_exceptions.FlyteValueException( resource_type, "The provided string could not be parsed. The first element of an execution identifier must be 'ex'.", ) return cls( task_id=Identifier(_core_identifier.ResourceType.TASK, tp, td, tn, tv), node_execution_id=_core_identifier.NodeExecutionIdentifier( node_id=node_id, execution_id=_core_identifier.WorkflowExecutionIdentifier(ep, ed, en), ), retry_attempt=int(retry), ) def __str__(self): return "te:{ep}:{ed}:{en}:{node_id}:{tp}:{td}:{tn}:{tv}:{retry}".format( ep=self.node_execution_id.execution_id.project, ed=self.node_execution_id.execution_id.domain, en=self.node_execution_id.execution_id.name, node_id=self.node_execution_id.node_id, tp=self.task_id.project, td=self.task_id.domain, tn=self.task_id.name, tv=self.task_id.version, retry=self.retry_attempt, )

0.677794

0.108378

MV_OK = 0x00000000 # < 成功，无错误 | en:Successed, no error MV_E_HANDLE = 0x80000000 # < 错误或无效的句柄 | en:Error or invalid handle MV_E_SUPPORT = 0x80000001 # < 不支持的功能 | en:Not supported function MV_E_BUFOVER = 0x80000002 # < 缓存已满 | en:Cache is full MV_E_CALLORDER = 0x80000003 # < 函数调用顺序错误 | en:Function calling order error MV_E_PARAMETER = 0x80000004 # < 错误的参数 | en:Incorrect parameter MV_E_RESOURCE = 0x80000006 # < 资源申请失败 | en:Applying resource failed MV_E_NODATA = 0x80000007 # < 无数据 | en:No data MV_E_PRECONDITION = 0x80000008 # < 前置条件有误，或运行环境已发生变化 | en:Precondition error, or running environment changed MV_E_VERSION = 0x80000009 # < 版本不匹配 | en:Version mismatches MV_E_NOENOUGH_BUF = 0x8000000A # < 传入的内存空间不足 | en:Insufficient memory MV_E_ABNORMAL_IMAGE = 0x8000000B # < 异常图像，可能是丢包导致图像不完整 | en:Abnormal image, maybe incomplete image because of lost packet MV_E_LOAD_LIBRARY = 0x8000000C # < 动态导入DLL失败 | en:Load library failed MV_E_UNKNOW = 0x800000FF # < 未知的错误 | en:Unknown error MV_E_GC_GENERIC = 0x80000100 # < 通用错误 | en:General error MV_E_GC_ARGUMENT = 0x80000101 # < 参数非法 | en:Illegal parameters MV_E_GC_RANGE = 0x80000102 # < 值超出范围 | en:The value is out of range MV_E_GC_PROPERTY = 0x80000103 # < 属性 | en:Property MV_E_GC_RUNTIME = 0x80000104 # < 运行环境有问题 | en:Running environment error MV_E_GC_LOGICAL = 0x80000105 # < 逻辑错误 | en:Logical error MV_E_GC_ACCESS = 0x80000106 # < 节点访问条件有误 | en:Node accessing condition error MV_E_GC_TIMEOUT = 0x80000107 # < 超时 | en:Timeout MV_E_GC_DYNAMICCAST = 0x80000108 # < 转换异常 | en:Transformation exception MV_E_GC_UNKNOW = 0x800001FF # < GenICam未知错误 | en:GenICam unknown error MV_E_NOT_IMPLEMENTED = 0x80000200 # < 命令不被设备支持 | en:The command is not supported by device MV_E_INVALID_ADDRESS = 0x80000201 # < 访问的目标地址不存在 | en:The target address being accessed does not exist MV_E_WRITE_PROTECT = 0x80000202 # < 目标地址不可写 | en:The target address is not writable MV_E_ACCESS_DENIED = 0x80000203 # < 设备无访问权限 | en:No permission MV_E_BUSY = 0x80000204 # < 设备忙，或网络断开 | en:Device is busy, or network disconnected MV_E_PACKET = 0x80000205 # < 网络包数据错误 | en:Network data packet error MV_E_NETER = 0x80000206 # < 网络相关错误 | en:Network error MV_E_IP_CONFLICT = 0x80000221 # < 设备IP冲突 | en:Device IP conflict MV_E_USB_READ = 0x80000300 # < 读usb出错 | en:Reading USB error MV_E_USB_WRITE = 0x80000301 # < 写usb出错 | en:Writing USB error MV_E_USB_DEVICE = 0x80000302 # < 设备异常 | en:Device exception MV_E_USB_GENICAM = 0x80000303 # < GenICam相关错误 | en:GenICam error MV_E_USB_BANDWIDTH = 0x80000304 # < 带宽不足该错误码新增 | en:Insufficient bandwidth, this error code is newly added MV_E_USB_DRIVER = 0x80000305 # < 驱动不匹配或者未装驱动 | en:Driver mismatch or unmounted drive MV_E_USB_UNKNOW = 0x800003FF # < USB未知的错误 | en:USB unknown error MV_E_UPG_FILE_MISMATCH = 0x80000400 # < 升级固件不匹配 | en:Firmware mismatches MV_E_UPG_LANGUSGE_MISMATCH = 0x80000401 # < 升级固件语言不匹配 | en:Firmware language mismatches MV_E_UPG_CONFLICT = 0x80000402 # < 升级冲突（设备已经在升级了再次请求升级即返回此错误） | en:Upgrading conflicted (repeated upgrading requests during device upgrade) MV_E_UPG_INNER_ERR = 0x80000403 # < 升级时相机内部出现错误 | en:Camera internal error during upgrade MV_E_UPG_UNKNOW = 0x800004FF # < 升级时未知错误 | en:Unknown error during upgrade

hik-driver/MVS/Samples/Python/MvImport/MvErrorDefine_const.py

MV_OK = 0x00000000 # < 成功，无错误 | en:Successed, no error MV_E_HANDLE = 0x80000000 # < 错误或无效的句柄 | en:Error or invalid handle MV_E_SUPPORT = 0x80000001 # < 不支持的功能 | en:Not supported function MV_E_BUFOVER = 0x80000002 # < 缓存已满 | en:Cache is full MV_E_CALLORDER = 0x80000003 # < 函数调用顺序错误 | en:Function calling order error MV_E_PARAMETER = 0x80000004 # < 错误的参数 | en:Incorrect parameter MV_E_RESOURCE = 0x80000006 # < 资源申请失败 | en:Applying resource failed MV_E_NODATA = 0x80000007 # < 无数据 | en:No data MV_E_PRECONDITION = 0x80000008 # < 前置条件有误，或运行环境已发生变化 | en:Precondition error, or running environment changed MV_E_VERSION = 0x80000009 # < 版本不匹配 | en:Version mismatches MV_E_NOENOUGH_BUF = 0x8000000A # < 传入的内存空间不足 | en:Insufficient memory MV_E_ABNORMAL_IMAGE = 0x8000000B # < 异常图像，可能是丢包导致图像不完整 | en:Abnormal image, maybe incomplete image because of lost packet MV_E_LOAD_LIBRARY = 0x8000000C # < 动态导入DLL失败 | en:Load library failed MV_E_UNKNOW = 0x800000FF # < 未知的错误 | en:Unknown error MV_E_GC_GENERIC = 0x80000100 # < 通用错误 | en:General error MV_E_GC_ARGUMENT = 0x80000101 # < 参数非法 | en:Illegal parameters MV_E_GC_RANGE = 0x80000102 # < 值超出范围 | en:The value is out of range MV_E_GC_PROPERTY = 0x80000103 # < 属性 | en:Property MV_E_GC_RUNTIME = 0x80000104 # < 运行环境有问题 | en:Running environment error MV_E_GC_LOGICAL = 0x80000105 # < 逻辑错误 | en:Logical error MV_E_GC_ACCESS = 0x80000106 # < 节点访问条件有误 | en:Node accessing condition error MV_E_GC_TIMEOUT = 0x80000107 # < 超时 | en:Timeout MV_E_GC_DYNAMICCAST = 0x80000108 # < 转换异常 | en:Transformation exception MV_E_GC_UNKNOW = 0x800001FF # < GenICam未知错误 | en:GenICam unknown error MV_E_NOT_IMPLEMENTED = 0x80000200 # < 命令不被设备支持 | en:The command is not supported by device MV_E_INVALID_ADDRESS = 0x80000201 # < 访问的目标地址不存在 | en:The target address being accessed does not exist MV_E_WRITE_PROTECT = 0x80000202 # < 目标地址不可写 | en:The target address is not writable MV_E_ACCESS_DENIED = 0x80000203 # < 设备无访问权限 | en:No permission MV_E_BUSY = 0x80000204 # < 设备忙，或网络断开 | en:Device is busy, or network disconnected MV_E_PACKET = 0x80000205 # < 网络包数据错误 | en:Network data packet error MV_E_NETER = 0x80000206 # < 网络相关错误 | en:Network error MV_E_IP_CONFLICT = 0x80000221 # < 设备IP冲突 | en:Device IP conflict MV_E_USB_READ = 0x80000300 # < 读usb出错 | en:Reading USB error MV_E_USB_WRITE = 0x80000301 # < 写usb出错 | en:Writing USB error MV_E_USB_DEVICE = 0x80000302 # < 设备异常 | en:Device exception MV_E_USB_GENICAM = 0x80000303 # < GenICam相关错误 | en:GenICam error MV_E_USB_BANDWIDTH = 0x80000304 # < 带宽不足该错误码新增 | en:Insufficient bandwidth, this error code is newly added MV_E_USB_DRIVER = 0x80000305 # < 驱动不匹配或者未装驱动 | en:Driver mismatch or unmounted drive MV_E_USB_UNKNOW = 0x800003FF # < USB未知的错误 | en:USB unknown error MV_E_UPG_FILE_MISMATCH = 0x80000400 # < 升级固件不匹配 | en:Firmware mismatches MV_E_UPG_LANGUSGE_MISMATCH = 0x80000401 # < 升级固件语言不匹配 | en:Firmware language mismatches MV_E_UPG_CONFLICT = 0x80000402 # < 升级冲突（设备已经在升级了再次请求升级即返回此错误） | en:Upgrading conflicted (repeated upgrading requests during device upgrade) MV_E_UPG_INNER_ERR = 0x80000403 # < 升级时相机内部出现错误 | en:Camera internal error during upgrade MV_E_UPG_UNKNOW = 0x800004FF # < 升级时未知错误 | en:Unknown error during upgrade

0.300746

0.07843

import os import re from time import time from pygaggle.rerank.base import Query, Text from pygaggle.rerank.transformer import MonoT5 def main(): DATA_DIR = "/home/l224016/projects/ia376_projeto_final/data/robust04/" RUNS_DIR = "/home/l224016/projects/ia376_projeto_final/data/robust04/runs" SEGMENTS_DIR = "/home/l224016/projects/ia376_projeto_final/data/robust04/segment_texts/" QUERY_DOC_DIR = "/home/l224016/projects/ia376_projeto_final/data/robust04/query_doc_ids/" OUTPUT_DIR = "/home/l224016/projects/ia376_projeto_final/data/robust04/monot5/" list_of_segment_texts = [ 'segment_texts_1000hits.txt', 'segment_texts_2000hits.txt', 'segment_texts_3000hits.txt', 'segment_texts_4000hits.txt', 'segment_texts_5000hits.txt', 'segment_texts_6000hits.txt', 'segment_texts_7000hits.txt', 'segment_texts_8000hits.txt', 'segment_texts_9000hits.txt', 'segment_texts_10000hits.txt' ] list_of_query_doc_ids = [ 'segment_query_doc_ids_1000hits.tsv', 'segment_query_doc_ids_2000hits.tsv', 'segment_query_doc_ids_3000hits.tsv', 'segment_query_doc_ids_4000hits.tsv', 'segment_query_doc_ids_5000hits.tsv', 'segment_query_doc_ids_6000hits.tsv', 'segment_query_doc_ids_7000hits.tsv', 'segment_query_doc_ids_8000hits.tsv', 'segment_query_doc_ids_9000hits.tsv', 'segment_query_doc_ids_10000hits.tsv' ] # Model reranker = MonoT5() segment_texts = os.path.join(SEGMENTS_DIR, 'segment_texts_5000hits_001.txt') query_doc_ids = os.path.join(QUERY_DOC_DIR, 'segment_query_doc_ids_5000hits_001.tsv') monot5_results = os.path.join(OUTPUT_DIR, 'monot5_results_5000hits_001.txt') with open(segment_texts) as seg_file, open(query_doc_ids) as qdoc_file: passages = [] qdoc_first_line = qdoc_file.readline() qdoc_first_line = qdoc_first_line.replace("\n", "").split('\t') query_id_old = qdoc_first_line[0] doc_id = qdoc_first_line[1] seg_first_line = seg_file.readline() result = re.search(r'Query\:(.*?)Document\:', seg_first_line) query_text = result.group(1).strip() result = re.search(r'Document\:(.*?)Relevant\:', seg_first_line) segment_text = result.group(1).strip() passages.append([doc_id, segment_text]) for seg_line, qdoc_line in zip(seg_file, qdoc_file): qdoc_line = qdoc_line.replace("\n", "").split('\t') query_id = qdoc_line[0] doc_id = qdoc_line[1] if query_id == query_id_old: result = re.search(r'Document\:(.*?)Relevant\:', seg_line) segment_text = result.group(1).strip() passages.append([doc_id, segment_text]) else: # Reranker using pygaggle query = Query(query_text) texts = [ Text(p[1], {'docid': p[0]}, 0) for p in passages] start = time() ranked_results = reranker.rerank(query, texts) end = time() time_elapsed = end - start print("Time Elapsed: {:.1f}".format(time_elapsed)) # Get scores from the reranker final_t5_scores = {} for result in ranked_results: if result.metadata["docid"] not in final_t5_scores: final_t5_scores[result.metadata["docid"]] = result.score else: if final_t5_scores[result.metadata["docid"]] < result.score: final_t5_scores[result.metadata["docid"]] = result.score # Writes a run file in the TREC format for rank, (docid, score) in enumerate(final_t5_scores.items()): with open(monot5_results, mode='a') as writer: writer.write(f'{query_id_old} Q0 {docid} {rank + 1} {1 / (rank + 1)} T5\n') # Restart variables for a new query passages = [] query_id_old = query_id result = re.search(r'Query\:(.*?)Document\:', seg_line) query_text = result.group(1).strip() result = re.search(r'Document\:(.*?)Relevant\:', seg_line) segment_text = result.group(1).strip() passages.append([doc_id, segment_text]) # Reranker using pygaggle query = Query(query_text) texts = [ Text(p[1], {'docid': p[0]}, 0) for p in passages] start = time() ranked_results = reranker.rerank(query, texts) end = time() time_elapsed = end - start print("Time Elapsed: {:.1f}".format(time_elapsed)) # Get scores from the reranker final_t5_scores = {} for result in ranked_results: if result.metadata["docid"] not in final_t5_scores: final_t5_scores[result.metadata["docid"]] = result.score else: if final_t5_scores[result.metadata["docid"]] < result.score: final_t5_scores[result.metadata["docid"]] = result.score # Writes a run file in the TREC format for rank, (docid, score) in enumerate(final_t5_scores.items()): with open(monot5_results, mode='a') as writer: writer.write(f'{query_id_old} Q0 {docid} {rank + 1} {1 / (rank + 1)} T5\n') if __name__ == "__main__": main()

codigos/t5_reranker_robust04.py

import os import re from time import time from pygaggle.rerank.base import Query, Text from pygaggle.rerank.transformer import MonoT5 def main(): DATA_DIR = "/home/l224016/projects/ia376_projeto_final/data/robust04/" RUNS_DIR = "/home/l224016/projects/ia376_projeto_final/data/robust04/runs" SEGMENTS_DIR = "/home/l224016/projects/ia376_projeto_final/data/robust04/segment_texts/" QUERY_DOC_DIR = "/home/l224016/projects/ia376_projeto_final/data/robust04/query_doc_ids/" OUTPUT_DIR = "/home/l224016/projects/ia376_projeto_final/data/robust04/monot5/" list_of_segment_texts = [ 'segment_texts_1000hits.txt', 'segment_texts_2000hits.txt', 'segment_texts_3000hits.txt', 'segment_texts_4000hits.txt', 'segment_texts_5000hits.txt', 'segment_texts_6000hits.txt', 'segment_texts_7000hits.txt', 'segment_texts_8000hits.txt', 'segment_texts_9000hits.txt', 'segment_texts_10000hits.txt' ] list_of_query_doc_ids = [ 'segment_query_doc_ids_1000hits.tsv', 'segment_query_doc_ids_2000hits.tsv', 'segment_query_doc_ids_3000hits.tsv', 'segment_query_doc_ids_4000hits.tsv', 'segment_query_doc_ids_5000hits.tsv', 'segment_query_doc_ids_6000hits.tsv', 'segment_query_doc_ids_7000hits.tsv', 'segment_query_doc_ids_8000hits.tsv', 'segment_query_doc_ids_9000hits.tsv', 'segment_query_doc_ids_10000hits.tsv' ] # Model reranker = MonoT5() segment_texts = os.path.join(SEGMENTS_DIR, 'segment_texts_5000hits_001.txt') query_doc_ids = os.path.join(QUERY_DOC_DIR, 'segment_query_doc_ids_5000hits_001.tsv') monot5_results = os.path.join(OUTPUT_DIR, 'monot5_results_5000hits_001.txt') with open(segment_texts) as seg_file, open(query_doc_ids) as qdoc_file: passages = [] qdoc_first_line = qdoc_file.readline() qdoc_first_line = qdoc_first_line.replace("\n", "").split('\t') query_id_old = qdoc_first_line[0] doc_id = qdoc_first_line[1] seg_first_line = seg_file.readline() result = re.search(r'Query\:(.*?)Document\:', seg_first_line) query_text = result.group(1).strip() result = re.search(r'Document\:(.*?)Relevant\:', seg_first_line) segment_text = result.group(1).strip() passages.append([doc_id, segment_text]) for seg_line, qdoc_line in zip(seg_file, qdoc_file): qdoc_line = qdoc_line.replace("\n", "").split('\t') query_id = qdoc_line[0] doc_id = qdoc_line[1] if query_id == query_id_old: result = re.search(r'Document\:(.*?)Relevant\:', seg_line) segment_text = result.group(1).strip() passages.append([doc_id, segment_text]) else: # Reranker using pygaggle query = Query(query_text) texts = [ Text(p[1], {'docid': p[0]}, 0) for p in passages] start = time() ranked_results = reranker.rerank(query, texts) end = time() time_elapsed = end - start print("Time Elapsed: {:.1f}".format(time_elapsed)) # Get scores from the reranker final_t5_scores = {} for result in ranked_results: if result.metadata["docid"] not in final_t5_scores: final_t5_scores[result.metadata["docid"]] = result.score else: if final_t5_scores[result.metadata["docid"]] < result.score: final_t5_scores[result.metadata["docid"]] = result.score # Writes a run file in the TREC format for rank, (docid, score) in enumerate(final_t5_scores.items()): with open(monot5_results, mode='a') as writer: writer.write(f'{query_id_old} Q0 {docid} {rank + 1} {1 / (rank + 1)} T5\n') # Restart variables for a new query passages = [] query_id_old = query_id result = re.search(r'Query\:(.*?)Document\:', seg_line) query_text = result.group(1).strip() result = re.search(r'Document\:(.*?)Relevant\:', seg_line) segment_text = result.group(1).strip() passages.append([doc_id, segment_text]) # Reranker using pygaggle query = Query(query_text) texts = [ Text(p[1], {'docid': p[0]}, 0) for p in passages] start = time() ranked_results = reranker.rerank(query, texts) end = time() time_elapsed = end - start print("Time Elapsed: {:.1f}".format(time_elapsed)) # Get scores from the reranker final_t5_scores = {} for result in ranked_results: if result.metadata["docid"] not in final_t5_scores: final_t5_scores[result.metadata["docid"]] = result.score else: if final_t5_scores[result.metadata["docid"]] < result.score: final_t5_scores[result.metadata["docid"]] = result.score # Writes a run file in the TREC format for rank, (docid, score) in enumerate(final_t5_scores.items()): with open(monot5_results, mode='a') as writer: writer.write(f'{query_id_old} Q0 {docid} {rank + 1} {1 / (rank + 1)} T5\n') if __name__ == "__main__": main()

0.331552

0.138958

from PySide2.QtWidgets import QMainWindow, QWidget, QVBoxLayout, QFileDialog from PySide2.QtCore import Qt # parent UI from ui_node_manager_mainwindow import Ui_MainWindow # custom content from Node import Node from Node_ListWidget import Node_ListWidget from NodeContentWidget import NodeContentWidget from SaveDialog import SaveDialog import json import os class MainWindow(QMainWindow): nodes = [] node_list_widgets = [] def __init__(self): super(MainWindow, self).__init__() self.ui = Ui_MainWindow() self.ui.setupUi(self) self.ui.nodes_list_widget.setFixedWidth(200) self.setWindowTitle('Node Manager') self.load_stylesheet('dark') self.ui.add_new_node_pushButton.clicked.connect(self.add_new_node_pushButton_clicked) self.ui.import_nodes_pushButton.clicked.connect(self.import_button_clicked) self.ui.save_pushButton.clicked.connect(self.save_button_clicked) def add_new_node_pushButton_clicked(self): node_content_widget = NodeContentWidget() new_node = Node(node_content_widget) node_content_widget.node = new_node #load_node(new_node) new_node.title_changed.connect(self.update_nodes_list_names) # this will update the list view self.nodes.append(new_node) self.rebuild_nodes_list() self.set_current_node(self.nodes[-1]) def rebuild_nodes_list(self): if self.ui.nodes_scrollArea.widget().layout().count() != 0: # clear nodes layout for i in reversed(range(self.ui.nodes_scrollArea.widget().layout().count())): # 'The new widget is deleted when its parent is deleted' - Docs self.ui.nodes_scrollArea.widget().layout().itemAt(i).widget().setParent(None) scroll_area_content_widget = QWidget() # create new widget and layout for the scroll area nodes_layout = QVBoxLayout() nodes_layout.setAlignment(Qt.AlignTop) for n in self.nodes: # create a new node list widget for every node node_widget = Node_ListWidget(n) node_widget.double_clicked.connect(self.node_widget_double_clicked) nodes_layout.addWidget(node_widget) self.node_list_widgets.append(node_widget) scroll_area_content_widget.setLayout(nodes_layout) self.ui.nodes_scrollArea.setWidget(scroll_area_content_widget) def update_nodes_list_names(self): for node_list_widget in self.node_list_widgets: node_list_widget.update_display_title() def set_current_node(self, node: Node): # clear node_content_placeholder_widget layout = self.ui.node_content_placeholder_widget.layout() for i in reversed(range(layout.count())): item = layout.itemAt(i) item.widget().setParent(self) # removing the widget from the layout without deleting the widget layout.addWidget(node.content_widget) node.content_widget.show() def node_widget_double_clicked(self, node): self.set_current_node(node) def load_stylesheet(self, ss): ss_content = '' try: f = open('stuff/stylesheets/'+ss+'.txt') ss_content = f.read() f.close() finally: self.setStyleSheet(ss_content) def import_nodes(self, j_nodes, dir): print('importing nodes') o_nodes = json.loads(j_nodes) nodes_list = o_nodes['nodes'] for n in nodes_list: print('parsing node', n['title']) new_node = Node() new_node.title = n['title'] new_node.description = n['description'] new_node.type = n['type'] new_node.module_name = n['module name'] new_node.class_name = n['class name'] new_node.design_style = n['design style'] new_node.color = n['color'] new_node.has_main_widget = n['has main widget'] if new_node.has_main_widget: new_node.widget_position = n['widget position'] new_node.custom_input_widgets = n['custom input widgets'] new_node.inputs = n['inputs'] new_node.outputs = n['outputs'] # load custom files module_name_separator = '___' # main code node_path = dir+'/'+new_node.module_name print(new_node.module_name) f = open(node_path+'/'+new_node.module_name+module_name_separator+'METACODE.py') new_node.meta_code = f.read() f.close() node_widgets_path = node_path + '/widgets' # main widget code if new_node.has_main_widget: f = open(node_widgets_path+'/'+new_node.module_name+module_name_separator+'main_widget'+module_name_separator+'METACODE.py') new_node.main_widget_content = f.read() f.close() # custom input widgets for ciw in new_node.custom_input_widgets: f = open(node_widgets_path+'/'+new_node.module_name+module_name_separator+ciw+module_name_separator+'METACODE.py') new_node.custom_input_widget_contents.append(f.read()) f.close() new_node_content_widget = NodeContentWidget() new_node.title_changed.connect(self.update_nodes_list_names) # this will update the list view new_node_content_widget.load_node(new_node) new_node.content_widget = new_node_content_widget self.nodes.append(new_node) print('finished parsing') print(self.nodes) self.rebuild_nodes_list() def import_button_clicked(self): file_path = QFileDialog.getOpenFileName(self, 'select the json file you want to import', '../packages')[0] f_content = '' try: f = open(file_path) f_content = f.read() f.close() except Exception as e: return self.import_nodes(f_content, os.path.dirname(file_path) + '/nodes') def save_button_clicked(self): # the dialog does the whole saving process save_dialog = SaveDialog(self, self.nodes) save_dialog.exec_()

pyScript_NodeManager/MainWindow.py

from PySide2.QtWidgets import QMainWindow, QWidget, QVBoxLayout, QFileDialog from PySide2.QtCore import Qt # parent UI from ui_node_manager_mainwindow import Ui_MainWindow # custom content from Node import Node from Node_ListWidget import Node_ListWidget from NodeContentWidget import NodeContentWidget from SaveDialog import SaveDialog import json import os class MainWindow(QMainWindow): nodes = [] node_list_widgets = [] def __init__(self): super(MainWindow, self).__init__() self.ui = Ui_MainWindow() self.ui.setupUi(self) self.ui.nodes_list_widget.setFixedWidth(200) self.setWindowTitle('Node Manager') self.load_stylesheet('dark') self.ui.add_new_node_pushButton.clicked.connect(self.add_new_node_pushButton_clicked) self.ui.import_nodes_pushButton.clicked.connect(self.import_button_clicked) self.ui.save_pushButton.clicked.connect(self.save_button_clicked) def add_new_node_pushButton_clicked(self): node_content_widget = NodeContentWidget() new_node = Node(node_content_widget) node_content_widget.node = new_node #load_node(new_node) new_node.title_changed.connect(self.update_nodes_list_names) # this will update the list view self.nodes.append(new_node) self.rebuild_nodes_list() self.set_current_node(self.nodes[-1]) def rebuild_nodes_list(self): if self.ui.nodes_scrollArea.widget().layout().count() != 0: # clear nodes layout for i in reversed(range(self.ui.nodes_scrollArea.widget().layout().count())): # 'The new widget is deleted when its parent is deleted' - Docs self.ui.nodes_scrollArea.widget().layout().itemAt(i).widget().setParent(None) scroll_area_content_widget = QWidget() # create new widget and layout for the scroll area nodes_layout = QVBoxLayout() nodes_layout.setAlignment(Qt.AlignTop) for n in self.nodes: # create a new node list widget for every node node_widget = Node_ListWidget(n) node_widget.double_clicked.connect(self.node_widget_double_clicked) nodes_layout.addWidget(node_widget) self.node_list_widgets.append(node_widget) scroll_area_content_widget.setLayout(nodes_layout) self.ui.nodes_scrollArea.setWidget(scroll_area_content_widget) def update_nodes_list_names(self): for node_list_widget in self.node_list_widgets: node_list_widget.update_display_title() def set_current_node(self, node: Node): # clear node_content_placeholder_widget layout = self.ui.node_content_placeholder_widget.layout() for i in reversed(range(layout.count())): item = layout.itemAt(i) item.widget().setParent(self) # removing the widget from the layout without deleting the widget layout.addWidget(node.content_widget) node.content_widget.show() def node_widget_double_clicked(self, node): self.set_current_node(node) def load_stylesheet(self, ss): ss_content = '' try: f = open('stuff/stylesheets/'+ss+'.txt') ss_content = f.read() f.close() finally: self.setStyleSheet(ss_content) def import_nodes(self, j_nodes, dir): print('importing nodes') o_nodes = json.loads(j_nodes) nodes_list = o_nodes['nodes'] for n in nodes_list: print('parsing node', n['title']) new_node = Node() new_node.title = n['title'] new_node.description = n['description'] new_node.type = n['type'] new_node.module_name = n['module name'] new_node.class_name = n['class name'] new_node.design_style = n['design style'] new_node.color = n['color'] new_node.has_main_widget = n['has main widget'] if new_node.has_main_widget: new_node.widget_position = n['widget position'] new_node.custom_input_widgets = n['custom input widgets'] new_node.inputs = n['inputs'] new_node.outputs = n['outputs'] # load custom files module_name_separator = '___' # main code node_path = dir+'/'+new_node.module_name print(new_node.module_name) f = open(node_path+'/'+new_node.module_name+module_name_separator+'METACODE.py') new_node.meta_code = f.read() f.close() node_widgets_path = node_path + '/widgets' # main widget code if new_node.has_main_widget: f = open(node_widgets_path+'/'+new_node.module_name+module_name_separator+'main_widget'+module_name_separator+'METACODE.py') new_node.main_widget_content = f.read() f.close() # custom input widgets for ciw in new_node.custom_input_widgets: f = open(node_widgets_path+'/'+new_node.module_name+module_name_separator+ciw+module_name_separator+'METACODE.py') new_node.custom_input_widget_contents.append(f.read()) f.close() new_node_content_widget = NodeContentWidget() new_node.title_changed.connect(self.update_nodes_list_names) # this will update the list view new_node_content_widget.load_node(new_node) new_node.content_widget = new_node_content_widget self.nodes.append(new_node) print('finished parsing') print(self.nodes) self.rebuild_nodes_list() def import_button_clicked(self): file_path = QFileDialog.getOpenFileName(self, 'select the json file you want to import', '../packages')[0] f_content = '' try: f = open(file_path) f_content = f.read() f.close() except Exception as e: return self.import_nodes(f_content, os.path.dirname(file_path) + '/nodes') def save_button_clicked(self): # the dialog does the whole saving process save_dialog = SaveDialog(self, self.nodes) save_dialog.exec_()

0.211906

0.053874

from scipy.interpolate import InterpolatedUnivariateSpline as _spline import numpy as np import scipy.integrate as intg class NaNException(Exception): pass def hmf_integral_gtm(M, dndm, mass_density=False): """ Cumulatively integrate dn/dm. Parameters ---------- M : array_like Array of masses. dndm : array_like Array of dn/dm (corresponding to M) mass_density : bool, `False` Whether to calculate mass density (or number density). Returns ------- ngtm : array_like Cumulative integral of dndm. Examples -------- Using a simple power-law mass function: >>> import numpy as np >>> m = np.logspace(10,18,500) >>> dndm = m**-2 >>> ngtm = hmf_integral_gtm(m,dndm) >>> np.allclose(ngtm,1/m) #1/m is the analytic integral to infinity. True The function always integrates to m=1e18, and extrapolates with a spline if data not provided: >>> m = np.logspace(10,12,500) >>> dndm = m**-2 >>> ngtm = hmf_integral_gtm(m,dndm) >>> np.allclose(ngtm,1/m) #1/m is the analytic integral to infinity. True """ # Eliminate NaN's m = M[np.logical_not(np.isnan(dndm))] dndm = dndm[np.logical_not(np.isnan(dndm))] dndlnm = m * dndm if len(m) < 4: raise NaNException("There are too few real numbers in dndm: len(dndm) = %s, #NaN's = %s" % (len(M), len(M) - len(dndm))) # Calculate the mass function (and its integral) from the highest M up to 10**18 if m[-1] < m[0] * 10 ** 18 / m[3]: m_upper = np.arange(np.log(m[-1]), np.log(10 ** 18), np.log(m[1]) - np.log(m[0])) mf_func = _spline(np.log(m), np.log(dndlnm), k=1) mf = mf_func(m_upper) if not mass_density: int_upper = intg.simps(np.exp(mf), dx=m_upper[2] - m_upper[1], even='first') else: int_upper = intg.simps(np.exp(m_upper + mf), dx=m_upper[2] - m_upper[1], even='first') else: int_upper = 0 # Calculate the cumulative integral (backwards) of [m*]dndlnm if not mass_density: ngtm = np.concatenate((intg.cumtrapz(dndlnm[::-1], dx=np.log(m[1]) - np.log(m[0]))[::-1], np.zeros(1))) else: ngtm = np.concatenate((intg.cumtrapz(m[::-1] * dndlnm[::-1], dx=np.log(m[1]) - np.log(m[0]))[::-1], np.zeros(1))) return (ngtm + int_upper)

hmf/integrate_hmf.py

from scipy.interpolate import InterpolatedUnivariateSpline as _spline import numpy as np import scipy.integrate as intg class NaNException(Exception): pass def hmf_integral_gtm(M, dndm, mass_density=False): """ Cumulatively integrate dn/dm. Parameters ---------- M : array_like Array of masses. dndm : array_like Array of dn/dm (corresponding to M) mass_density : bool, `False` Whether to calculate mass density (or number density). Returns ------- ngtm : array_like Cumulative integral of dndm. Examples -------- Using a simple power-law mass function: >>> import numpy as np >>> m = np.logspace(10,18,500) >>> dndm = m**-2 >>> ngtm = hmf_integral_gtm(m,dndm) >>> np.allclose(ngtm,1/m) #1/m is the analytic integral to infinity. True The function always integrates to m=1e18, and extrapolates with a spline if data not provided: >>> m = np.logspace(10,12,500) >>> dndm = m**-2 >>> ngtm = hmf_integral_gtm(m,dndm) >>> np.allclose(ngtm,1/m) #1/m is the analytic integral to infinity. True """ # Eliminate NaN's m = M[np.logical_not(np.isnan(dndm))] dndm = dndm[np.logical_not(np.isnan(dndm))] dndlnm = m * dndm if len(m) < 4: raise NaNException("There are too few real numbers in dndm: len(dndm) = %s, #NaN's = %s" % (len(M), len(M) - len(dndm))) # Calculate the mass function (and its integral) from the highest M up to 10**18 if m[-1] < m[0] * 10 ** 18 / m[3]: m_upper = np.arange(np.log(m[-1]), np.log(10 ** 18), np.log(m[1]) - np.log(m[0])) mf_func = _spline(np.log(m), np.log(dndlnm), k=1) mf = mf_func(m_upper) if not mass_density: int_upper = intg.simps(np.exp(mf), dx=m_upper[2] - m_upper[1], even='first') else: int_upper = intg.simps(np.exp(m_upper + mf), dx=m_upper[2] - m_upper[1], even='first') else: int_upper = 0 # Calculate the cumulative integral (backwards) of [m*]dndlnm if not mass_density: ngtm = np.concatenate((intg.cumtrapz(dndlnm[::-1], dx=np.log(m[1]) - np.log(m[0]))[::-1], np.zeros(1))) else: ngtm = np.concatenate((intg.cumtrapz(m[::-1] * dndlnm[::-1], dx=np.log(m[1]) - np.log(m[0]))[::-1], np.zeros(1))) return (ngtm + int_upper)

0.890491

0.524273

import numpy as np from optuna import Trial from sklearn.decomposition import PCA import mutation_prediction.data as data import mutation_prediction.embeddings.aaindex as aaindex from mutation_prediction.data import Dataset from mutation_prediction.embeddings import Embedding, EmbeddingMatrix class AcidsOneHot(EmbeddingMatrix): def __init__(self): super(AcidsOneHot, self).__init__(np.eye(data.num_acids())) class ZScales(EmbeddingMatrix): def __init__(self): super(ZScales, self).__init__( np.asarray( [ [0.24, -2.32, 0.60, -0.14, 1.30], [0.84, -1.67, 3.75, 0.18, -2.65], [3.98, 0.93, 1.93, -2.46, 0.75], [3.11, 0.26, -0.11, -3.04, -0.25], [-4.22, 1.94, 1.06, 0.54, -0.62], [2.05, -4.06, 0.36, -0.82, -0.38], [2.47, 1.95, 0.26, 3.90, 0.09], [-3.89, -1.73, -1.71, -0.84, 0.26], [2.29, 0.89, -2.49, 1.49, 0.31], [-4.28, -1.30, -1.49, -0.72, 0.84], [-2.85, -0.22, 0.47, 1.94, -0.98], [3.05, 1.62, 1.04, -1.15, 1.61], [1.66, 0.27, 1.84, 0.70, 2.00], [1.75, 0.50, -1.44, -1.34, 0.66], [3.52, 2.50, -3.50, 1.99, -0.17], [2.39, -1.07, 1.15, -1.39, 0.67], [0.75, -2.18, -1.12, -1.46, -0.40], [-2.59, -2.64, -1.54, -0.85, -0.02], [-4.36, 3.94, 0.59, 3.44, -1.59], [-2.54, 2.44, 0.43, 0.04, -1.47], ] ) ) class VHSE(EmbeddingMatrix): def __init__(self): super(VHSE, self).__init__( np.asarray( [ [0.15, -1.11, -1.35, -0.92, 0.02, -0.91, 0.36, -0.48], [0.18, -1.67, -0.46, -0.21, 0.0, 1.2, -1.61, -0.19], [-1.15, 0.67, -0.41, -0.01, -2.68, 1.31, 0.03, 0.56], [-1.18, 0.4, 0.1, 0.36, -2.16, -0.17, 0.91, 0.02], [1.52, 0.61, 0.96, -0.16, 0.25, 0.28, -1.33, -0.2], [-0.2, -1.53, -2.63, 2.28, -0.53, -1.18, 2.01, -1.34], [-0.43, -0.25, 0.37, 0.19, 0.51, 1.28, 0.93, 0.65], [1.27, -0.14, 0.3, -1.8, 0.3, -1.61, -0.16, -0.13], [-1.17, 0.7, 0.7, 0.8, 1.64, 0.67, 1.63, 0.13], [1.36, 0.07, 0.26, -0.8, 0.22, -1.37, 0.08, -0.62], [1.01, -0.53, 0.43, 0.0, 0.23, 0.1, -0.86, -0.68], [-0.99, 0.0, -0.37, 0.69, -0.55, 0.85, 0.73, -0.8], [0.22, -0.17, -0.5, 0.05, -0.01, -1.34, -0.19, 3.56], [-0.96, 0.12, 0.18, 0.16, 0.09, 0.42, -0.2, -0.41], [-1.47, 1.45, 1.24, 1.27, 1.55, 1.47, 1.3, 0.83], [-0.67, -0.86, -1.07, -0.41, -0.32, 0.27, -0.64, 0.11], [-0.34, -0.51, -0.55, -1.06, -0.06, -0.01, -0.79, 0.39], [0.76, -0.92, -0.17, -1.91, 0.22, -1.4, -0.24, -0.03], [1.5, 2.06, 1.79, 0.75, 0.75, -0.13, -1.01, -0.85], [0.61, 1.6, 1.17, 0.73, 0.53, 0.25, -0.96, -0.52], ] ) ) class PcScores(EmbeddingMatrix): matrix = None def __init__(self): if PcScores.matrix is None: matrix = aaindex.read_aaindex1() matrix = (matrix - matrix.mean(axis=0)) / matrix.std(axis=0) pca = PCA(n_components=11) PcScores.matrix = pca.fit_transform(matrix) super(PcScores, self).__init__(PcScores.matrix) class SScales(EmbeddingMatrix): matrix = None def __init__(self): if SScales.matrix is None: matrix = aaindex.read_aaindex1( keys=[ "BIOV880101", "BLAM930101", "<KEY>", "TSAJ990101", "NAKH920106", "NAKH920107", "NAKH920108", "CEDJ970104", "LIFS790101", "MIYS990104", "ARGP820101", "DAWD720101", "FAUJ880109", ] ) SScales.matrix = (matrix - matrix.mean(axis=0)) / matrix.std(axis=0) super(SScales, self).__init__(SScales.matrix) class AaIndex(EmbeddingMatrix): matrix = None def __init__(self, keys=None): if AaIndex.matrix is None: matrix = aaindex.read_aaindex1(keys=keys) AaIndex.matrix = (matrix - matrix.mean(axis=0)) / matrix.std(axis=0) super(AaIndex, self).__init__(AaIndex.matrix) class ProtVec(Embedding): matrix = None def embed_update(self, dataset: Dataset, trial: Trial = None) -> np.ndarray: return self.embed(dataset) def embed(self, dataset: Dataset) -> np.ndarray: if ProtVec.matrix is None: ProtVec.matrix = _read_protvec() embedded = np.zeros((len(dataset), 100)) sequence_length = len(dataset.get_sequence()) num_mutations = dataset.get_num_mutations() positions = dataset.get_positions() for i, sequence in enumerate(dataset.get_sequences()): three_grams = set() for p in positions[i, : num_mutations[i]]: p = int(p) if 2 <= p: three_grams.add((p - 2, p - 1, p)) if 1 <= p < sequence_length - 1: three_grams.add((p - 1, p, p + 1)) if p < sequence_length - 2: three_grams.add((p, p + 1, p + 2)) vectors = [ ProtVec.matrix[sequence[g[0]], sequence[g[1]], sequence[g[2]]] for g in three_grams ] if len(vectors) > 0: embedded[i] = np.asarray(vectors).mean(axis=0) return embedded def get_matrix(self) -> np.ndarray: return self.matrix def _read_protvec(): with open("data/embeddings/protVec_100d_3grams.csv") as fd: matrix = np.zeros((data.num_acids(), data.num_acids(), data.num_acids(), 100)) for row in fd: row = row.replace('"', "") parts = row.split("\t") try: i = data.acid_to_index(parts[0][0]) j = data.acid_to_index(parts[0][1]) k = data.acid_to_index(parts[0][2]) except KeyError: continue vector = np.asarray([float(v) for v in parts[1:]]) matrix[i, j, k] = vector return matrix

mutation_prediction/embeddings/acids.py

import numpy as np from optuna import Trial from sklearn.decomposition import PCA import mutation_prediction.data as data import mutation_prediction.embeddings.aaindex as aaindex from mutation_prediction.data import Dataset from mutation_prediction.embeddings import Embedding, EmbeddingMatrix class AcidsOneHot(EmbeddingMatrix): def __init__(self): super(AcidsOneHot, self).__init__(np.eye(data.num_acids())) class ZScales(EmbeddingMatrix): def __init__(self): super(ZScales, self).__init__( np.asarray( [ [0.24, -2.32, 0.60, -0.14, 1.30], [0.84, -1.67, 3.75, 0.18, -2.65], [3.98, 0.93, 1.93, -2.46, 0.75], [3.11, 0.26, -0.11, -3.04, -0.25], [-4.22, 1.94, 1.06, 0.54, -0.62], [2.05, -4.06, 0.36, -0.82, -0.38], [2.47, 1.95, 0.26, 3.90, 0.09], [-3.89, -1.73, -1.71, -0.84, 0.26], [2.29, 0.89, -2.49, 1.49, 0.31], [-4.28, -1.30, -1.49, -0.72, 0.84], [-2.85, -0.22, 0.47, 1.94, -0.98], [3.05, 1.62, 1.04, -1.15, 1.61], [1.66, 0.27, 1.84, 0.70, 2.00], [1.75, 0.50, -1.44, -1.34, 0.66], [3.52, 2.50, -3.50, 1.99, -0.17], [2.39, -1.07, 1.15, -1.39, 0.67], [0.75, -2.18, -1.12, -1.46, -0.40], [-2.59, -2.64, -1.54, -0.85, -0.02], [-4.36, 3.94, 0.59, 3.44, -1.59], [-2.54, 2.44, 0.43, 0.04, -1.47], ] ) ) class VHSE(EmbeddingMatrix): def __init__(self): super(VHSE, self).__init__( np.asarray( [ [0.15, -1.11, -1.35, -0.92, 0.02, -0.91, 0.36, -0.48], [0.18, -1.67, -0.46, -0.21, 0.0, 1.2, -1.61, -0.19], [-1.15, 0.67, -0.41, -0.01, -2.68, 1.31, 0.03, 0.56], [-1.18, 0.4, 0.1, 0.36, -2.16, -0.17, 0.91, 0.02], [1.52, 0.61, 0.96, -0.16, 0.25, 0.28, -1.33, -0.2], [-0.2, -1.53, -2.63, 2.28, -0.53, -1.18, 2.01, -1.34], [-0.43, -0.25, 0.37, 0.19, 0.51, 1.28, 0.93, 0.65], [1.27, -0.14, 0.3, -1.8, 0.3, -1.61, -0.16, -0.13], [-1.17, 0.7, 0.7, 0.8, 1.64, 0.67, 1.63, 0.13], [1.36, 0.07, 0.26, -0.8, 0.22, -1.37, 0.08, -0.62], [1.01, -0.53, 0.43, 0.0, 0.23, 0.1, -0.86, -0.68], [-0.99, 0.0, -0.37, 0.69, -0.55, 0.85, 0.73, -0.8], [0.22, -0.17, -0.5, 0.05, -0.01, -1.34, -0.19, 3.56], [-0.96, 0.12, 0.18, 0.16, 0.09, 0.42, -0.2, -0.41], [-1.47, 1.45, 1.24, 1.27, 1.55, 1.47, 1.3, 0.83], [-0.67, -0.86, -1.07, -0.41, -0.32, 0.27, -0.64, 0.11], [-0.34, -0.51, -0.55, -1.06, -0.06, -0.01, -0.79, 0.39], [0.76, -0.92, -0.17, -1.91, 0.22, -1.4, -0.24, -0.03], [1.5, 2.06, 1.79, 0.75, 0.75, -0.13, -1.01, -0.85], [0.61, 1.6, 1.17, 0.73, 0.53, 0.25, -0.96, -0.52], ] ) ) class PcScores(EmbeddingMatrix): matrix = None def __init__(self): if PcScores.matrix is None: matrix = aaindex.read_aaindex1() matrix = (matrix - matrix.mean(axis=0)) / matrix.std(axis=0) pca = PCA(n_components=11) PcScores.matrix = pca.fit_transform(matrix) super(PcScores, self).__init__(PcScores.matrix) class SScales(EmbeddingMatrix): matrix = None def __init__(self): if SScales.matrix is None: matrix = aaindex.read_aaindex1( keys=[ "BIOV880101", "BLAM930101", "<KEY>", "TSAJ990101", "NAKH920106", "NAKH920107", "NAKH920108", "CEDJ970104", "LIFS790101", "MIYS990104", "ARGP820101", "DAWD720101", "FAUJ880109", ] ) SScales.matrix = (matrix - matrix.mean(axis=0)) / matrix.std(axis=0) super(SScales, self).__init__(SScales.matrix) class AaIndex(EmbeddingMatrix): matrix = None def __init__(self, keys=None): if AaIndex.matrix is None: matrix = aaindex.read_aaindex1(keys=keys) AaIndex.matrix = (matrix - matrix.mean(axis=0)) / matrix.std(axis=0) super(AaIndex, self).__init__(AaIndex.matrix) class ProtVec(Embedding): matrix = None def embed_update(self, dataset: Dataset, trial: Trial = None) -> np.ndarray: return self.embed(dataset) def embed(self, dataset: Dataset) -> np.ndarray: if ProtVec.matrix is None: ProtVec.matrix = _read_protvec() embedded = np.zeros((len(dataset), 100)) sequence_length = len(dataset.get_sequence()) num_mutations = dataset.get_num_mutations() positions = dataset.get_positions() for i, sequence in enumerate(dataset.get_sequences()): three_grams = set() for p in positions[i, : num_mutations[i]]: p = int(p) if 2 <= p: three_grams.add((p - 2, p - 1, p)) if 1 <= p < sequence_length - 1: three_grams.add((p - 1, p, p + 1)) if p < sequence_length - 2: three_grams.add((p, p + 1, p + 2)) vectors = [ ProtVec.matrix[sequence[g[0]], sequence[g[1]], sequence[g[2]]] for g in three_grams ] if len(vectors) > 0: embedded[i] = np.asarray(vectors).mean(axis=0) return embedded def get_matrix(self) -> np.ndarray: return self.matrix def _read_protvec(): with open("data/embeddings/protVec_100d_3grams.csv") as fd: matrix = np.zeros((data.num_acids(), data.num_acids(), data.num_acids(), 100)) for row in fd: row = row.replace('"', "") parts = row.split("\t") try: i = data.acid_to_index(parts[0][0]) j = data.acid_to_index(parts[0][1]) k = data.acid_to_index(parts[0][2]) except KeyError: continue vector = np.asarray([float(v) for v in parts[1:]]) matrix[i, j, k] = vector return matrix

0.691602

0.439807

from typing import Optional import torch import torch.nn as nn from torch import Tensor, LongTensor from torch_geometric.typing import OptTensor from tsl.nn.functional import reverse_tensor from tsl.nn.layers.graph_convs.grin_cell import GRIL from tsl.utils.parser_utils import str_to_bool from ...base.embedding import StaticGraphEmbedding class GRINModel(nn.Module): r""" Graph Recurrent Imputation Network with DCRNN cells. From Cini et al., ”Filling the G ap s: Multivariate Time Series Imputation by Graph Neural Networks” ICLR 2022 Args: input_size (int): Size of the input. hidden_size (int): Number of units in the DCRNN hidden layer. ff_size (int): Number of units in the nonlinear readout. embedding_size (int, optional): Number of features in the optional node embeddings. exog_size (int): Number of channels for the exogenous variables. n_layers (int): Number DCRNN cells. n_nodes (int, optional): Number of nodes in the input graph. kernel_size (int): Order of the spatial diffusion process. layer_norm (bool, optional): Whther to use layer normalization dropout (float, optional): Dropout probability in the DCRNN cells. ff_dropout (float, optional): Dropout probability in the readout. merge_mode (str, optional): Strategy used to merge representations coming from the two branches of the bidirectional model. (default: `mlp`) """ def __init__(self, input_size: int, hidden_size: int, ff_size: int, embedding_size: Optional[int] = None, exog_size: Optional[int] = None, n_layers: int = 1, n_nodes: Optional[int] = None, kernel_size: int = 2, decoder_order: int = 1, layer_norm: bool = False, dropout: float = 0., ff_dropout: float = 0., merge_mode: str = 'mlp'): super(GRINModel, self).__init__() self.fwd_gril = GRIL(input_size=input_size, hidden_size=hidden_size, exog_size=exog_size, n_layers=n_layers, dropout=dropout, kernel_size=kernel_size, decoder_order=decoder_order, n_nodes=n_nodes, layer_norm=layer_norm) self.bwd_gril = GRIL(input_size=input_size, hidden_size=hidden_size, exog_size=exog_size, n_layers=n_layers, dropout=dropout, kernel_size=kernel_size, decoder_order=decoder_order, n_nodes=n_nodes, layer_norm=layer_norm) if embedding_size is not None: assert n_nodes is not None self.emb = StaticGraphEmbedding(n_nodes, embedding_size) else: self.register_parameter('emb', None) self.merge_mode = merge_mode if merge_mode == 'mlp': in_channels = 4 * hidden_size + input_size + embedding_size self.out = nn.Sequential(nn.Linear(in_channels, ff_size), nn.ReLU(), nn.Dropout(ff_dropout), nn.Linear(ff_size, input_size)) elif merge_mode in ['mean', 'sum', 'min', 'max']: self.out = getattr(torch, merge_mode) else: raise ValueError("Merge option %s not allowed." % merge_mode) def forward(self, x: Tensor, edge_index: LongTensor, edge_weight: OptTensor = None, mask: OptTensor = None, u: OptTensor = None): # x: [batch, steps, nodes, channels] fwd_out, fwd_pred, fwd_repr, _ = self.fwd_gril(x, edge_index, edge_weight, mask=mask, u=u) # Backward rev_x = reverse_tensor(x, dim=1) rev_mask = reverse_tensor(mask, dim=1) if mask is not None else None rev_u = reverse_tensor(u, dim=1) if u is not None else None *bwd, _ = self.bwd_gril(rev_x, edge_index, edge_weight, mask=rev_mask, u=rev_u) bwd_out, bwd_pred, bwd_repr = [reverse_tensor(res, 1) for res in bwd] if self.merge_mode == 'mlp': inputs = [fwd_repr, bwd_repr, mask] if self.emb is not None: b, s, *_ = fwd_repr.size() # fwd_h: [b s n c] inputs += [self.emb(expand=(b, s, -1, -1))] imputation = torch.cat(inputs, dim=-1) imputation = self.out(imputation) else: imputation = torch.stack([fwd_out, bwd_out], dim=-1) imputation = self.out(imputation, dim=-1) return imputation, (fwd_out, bwd_out, fwd_pred, bwd_pred) @staticmethod def add_model_specific_args(parser): parser.add_argument('--hidden-size', type=int) parser.add_argument('--ff-size', type=int) parser.add_argument('--embedding-size', type=int, default=None) parser.add_argument('--n-layers', type=int, default=1) parser.add_argument('--n-nodes', type=int, default=None) parser.add_argument('--kernel-size', type=int, default=2) parser.add_argument('--decoder-order', type=int, default=1) parser.add_argument('--layer-norm', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--dropout', type=float, default=0.) parser.add_argument('--ff-dropout', type=float, default=0.) parser.add_argument('--merge-mode', type=str, default='mlp', choices=['mlp', 'mean', 'sum', 'min', 'max']) return parser

tsl/nn/models/imputation/grin_model.py

from typing import Optional import torch import torch.nn as nn from torch import Tensor, LongTensor from torch_geometric.typing import OptTensor from tsl.nn.functional import reverse_tensor from tsl.nn.layers.graph_convs.grin_cell import GRIL from tsl.utils.parser_utils import str_to_bool from ...base.embedding import StaticGraphEmbedding class GRINModel(nn.Module): r""" Graph Recurrent Imputation Network with DCRNN cells. From Cini et al., ”Filling the G ap s: Multivariate Time Series Imputation by Graph Neural Networks” ICLR 2022 Args: input_size (int): Size of the input. hidden_size (int): Number of units in the DCRNN hidden layer. ff_size (int): Number of units in the nonlinear readout. embedding_size (int, optional): Number of features in the optional node embeddings. exog_size (int): Number of channels for the exogenous variables. n_layers (int): Number DCRNN cells. n_nodes (int, optional): Number of nodes in the input graph. kernel_size (int): Order of the spatial diffusion process. layer_norm (bool, optional): Whther to use layer normalization dropout (float, optional): Dropout probability in the DCRNN cells. ff_dropout (float, optional): Dropout probability in the readout. merge_mode (str, optional): Strategy used to merge representations coming from the two branches of the bidirectional model. (default: `mlp`) """ def __init__(self, input_size: int, hidden_size: int, ff_size: int, embedding_size: Optional[int] = None, exog_size: Optional[int] = None, n_layers: int = 1, n_nodes: Optional[int] = None, kernel_size: int = 2, decoder_order: int = 1, layer_norm: bool = False, dropout: float = 0., ff_dropout: float = 0., merge_mode: str = 'mlp'): super(GRINModel, self).__init__() self.fwd_gril = GRIL(input_size=input_size, hidden_size=hidden_size, exog_size=exog_size, n_layers=n_layers, dropout=dropout, kernel_size=kernel_size, decoder_order=decoder_order, n_nodes=n_nodes, layer_norm=layer_norm) self.bwd_gril = GRIL(input_size=input_size, hidden_size=hidden_size, exog_size=exog_size, n_layers=n_layers, dropout=dropout, kernel_size=kernel_size, decoder_order=decoder_order, n_nodes=n_nodes, layer_norm=layer_norm) if embedding_size is not None: assert n_nodes is not None self.emb = StaticGraphEmbedding(n_nodes, embedding_size) else: self.register_parameter('emb', None) self.merge_mode = merge_mode if merge_mode == 'mlp': in_channels = 4 * hidden_size + input_size + embedding_size self.out = nn.Sequential(nn.Linear(in_channels, ff_size), nn.ReLU(), nn.Dropout(ff_dropout), nn.Linear(ff_size, input_size)) elif merge_mode in ['mean', 'sum', 'min', 'max']: self.out = getattr(torch, merge_mode) else: raise ValueError("Merge option %s not allowed." % merge_mode) def forward(self, x: Tensor, edge_index: LongTensor, edge_weight: OptTensor = None, mask: OptTensor = None, u: OptTensor = None): # x: [batch, steps, nodes, channels] fwd_out, fwd_pred, fwd_repr, _ = self.fwd_gril(x, edge_index, edge_weight, mask=mask, u=u) # Backward rev_x = reverse_tensor(x, dim=1) rev_mask = reverse_tensor(mask, dim=1) if mask is not None else None rev_u = reverse_tensor(u, dim=1) if u is not None else None *bwd, _ = self.bwd_gril(rev_x, edge_index, edge_weight, mask=rev_mask, u=rev_u) bwd_out, bwd_pred, bwd_repr = [reverse_tensor(res, 1) for res in bwd] if self.merge_mode == 'mlp': inputs = [fwd_repr, bwd_repr, mask] if self.emb is not None: b, s, *_ = fwd_repr.size() # fwd_h: [b s n c] inputs += [self.emb(expand=(b, s, -1, -1))] imputation = torch.cat(inputs, dim=-1) imputation = self.out(imputation) else: imputation = torch.stack([fwd_out, bwd_out], dim=-1) imputation = self.out(imputation, dim=-1) return imputation, (fwd_out, bwd_out, fwd_pred, bwd_pred) @staticmethod def add_model_specific_args(parser): parser.add_argument('--hidden-size', type=int) parser.add_argument('--ff-size', type=int) parser.add_argument('--embedding-size', type=int, default=None) parser.add_argument('--n-layers', type=int, default=1) parser.add_argument('--n-nodes', type=int, default=None) parser.add_argument('--kernel-size', type=int, default=2) parser.add_argument('--decoder-order', type=int, default=1) parser.add_argument('--layer-norm', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--dropout', type=float, default=0.) parser.add_argument('--ff-dropout', type=float, default=0.) parser.add_argument('--merge-mode', type=str, default='mlp', choices=['mlp', 'mean', 'sum', 'min', 'max']) return parser

0.963057

0.582135

import datetime, copy, os import matchesDealEngine class CBaseStrategy(object): def __init__(self, stockCode): super(CBaseStrategy, self).__init__() self.stockCode = stockCode self.customInit() self.initCashe() #最新数据 self.currentData = {} #连接池，用于发送信号 self.requesHandlerObjList = [] #数据缓存 #当前时间，最近的一个行情数据时间 self.currentMDDateTime = datetime.datetime(1990,1,1,0,0,0) self.MDList = [] #行情数据 self.TDList = [] #逐笔成交数据 self.ODList = [] #成交队列数据 #交易数据 self.dealEngine = matchesDealEngine.CMatchesDealEngine(stockCode, self) #------------------------------ #listener 调用接口 #------------------------------ #获得连接对象 def getRequesHandlerObjList(self, requesHandlerObjList): self.requesHandlerObjList = requesHandlerObjList def dataListener(self, dataType, data): if dataType == 1: #逐笔成交数据 self.dealEngine.onRtnTradeSettlement(data) self.onRtnTradeSettlement(data) self.saveTradeSettlement(data) elif dataType == 2: #报单队列 self.onRtnOrderQueue(data) self.saveOrderQuene(data) else: if data["dateTime"] > self.currentMDDateTime: self.dealEngine.onRtnMarketData(data) self.onRtnMarketData(data) self.currentMDDateTime = copy.copy(data["dateTime"]) self.saveMarketData(data) #自动保存缓存触发 if (datetime.datetime.now() - self.preSaveCacheTime)> datetime.timedelta(minutes = 1): self.autosaveCache() #self.saveCacheAdd(MDList = self.MDList, TDList = self.TDList, ODList = self.ODList) #------------------------------ #cache 相关函数 #------------------------------ def initCashe(self): self.cacheFilePath = "cache/%s%s.cache" %(self.stockCode, self.name) self.preSaveCacheTime = datetime.datetime.now() self.loadCache() #读取缓存 def loadCache(self): if not os.path.isfile(self.cacheFilePath): self.cacheFile = open(self.cacheFilePath, "w") self.cacheFile.close execfile(self.cacheFilePath) #保存缓存 def saveCache(self, **objDict): self.cacheFile = open(self.cacheFilePath, "w") content = "" for key, value in objDict.items(): content += "self.%s = %s\n" %(key, str(value)) self.cacheFile.write(content) self.cacheFile.close() self.preSaveCacheTime = datetime.datetime.now() #保存缓存 def saveCacheAdd(self, **objDict): self.cacheFile = open(self.cacheFilePath, "a") content = "" for key, value in objDict.items(): content += "self.%s = %s\n" %(key, str(value)) self.cacheFile.write(content) self.cacheFile.close() self.preSaveCacheTime = datetime.datetime.now() #------------------------------ #数据保存相关函数 #------------------------------ def saveMarketData(self, data): self.MDList.append(copy.copy(data)) if len(self.MDList) > 300: del self.MDList[0] def saveTradeSettlement(self, data): self.TDList.append(copy.copy(data)) if len(self.TDList) > 300: del self.TDList[0] def saveOrderQuene(self, data): self.ODList.append(copy.copy(data)) if len(self.ODList) > 300: del self.ODList[0] #------------------------------ #交易方法 #------------------------------ def buy(self, mode, price, vol, dateTime): return self.dealEngine.buy(mode, price, vol, dateTime) def sell(self, mode, price, vol, dateTime): return self.dealEngine.sell(mode, price, vol, dateTime) #报单成交明细 def dealed(self, dealObj, avePrice, vol, dateTime): pass #报单失败 def quotedFailed(self, failedCode): #失败原因 pass #------------------------------ #继承重载函数 #------------------------------ #自定义初始化函数 def customInit(self): self.name = "baseStrategy" #行情数据触发函数 def onRtnMarketData(self, data): pass #逐笔成交触发函数 def onRtnTradeSettlement(self, data): pass #买一队列触发函数 def onRtnOrderQueue(self, data): pass #当日结束 def dayEnd(self): pass #数据结束 def dataEnd(self): pass #自动保存缓存触发函数 def autosaveCache(self): #self.saveCache(data = self.data) pass

baseStrategy.py

import datetime, copy, os import matchesDealEngine class CBaseStrategy(object): def __init__(self, stockCode): super(CBaseStrategy, self).__init__() self.stockCode = stockCode self.customInit() self.initCashe() #最新数据 self.currentData = {} #连接池，用于发送信号 self.requesHandlerObjList = [] #数据缓存 #当前时间，最近的一个行情数据时间 self.currentMDDateTime = datetime.datetime(1990,1,1,0,0,0) self.MDList = [] #行情数据 self.TDList = [] #逐笔成交数据 self.ODList = [] #成交队列数据 #交易数据 self.dealEngine = matchesDealEngine.CMatchesDealEngine(stockCode, self) #------------------------------ #listener 调用接口 #------------------------------ #获得连接对象 def getRequesHandlerObjList(self, requesHandlerObjList): self.requesHandlerObjList = requesHandlerObjList def dataListener(self, dataType, data): if dataType == 1: #逐笔成交数据 self.dealEngine.onRtnTradeSettlement(data) self.onRtnTradeSettlement(data) self.saveTradeSettlement(data) elif dataType == 2: #报单队列 self.onRtnOrderQueue(data) self.saveOrderQuene(data) else: if data["dateTime"] > self.currentMDDateTime: self.dealEngine.onRtnMarketData(data) self.onRtnMarketData(data) self.currentMDDateTime = copy.copy(data["dateTime"]) self.saveMarketData(data) #自动保存缓存触发 if (datetime.datetime.now() - self.preSaveCacheTime)> datetime.timedelta(minutes = 1): self.autosaveCache() #self.saveCacheAdd(MDList = self.MDList, TDList = self.TDList, ODList = self.ODList) #------------------------------ #cache 相关函数 #------------------------------ def initCashe(self): self.cacheFilePath = "cache/%s%s.cache" %(self.stockCode, self.name) self.preSaveCacheTime = datetime.datetime.now() self.loadCache() #读取缓存 def loadCache(self): if not os.path.isfile(self.cacheFilePath): self.cacheFile = open(self.cacheFilePath, "w") self.cacheFile.close execfile(self.cacheFilePath) #保存缓存 def saveCache(self, **objDict): self.cacheFile = open(self.cacheFilePath, "w") content = "" for key, value in objDict.items(): content += "self.%s = %s\n" %(key, str(value)) self.cacheFile.write(content) self.cacheFile.close() self.preSaveCacheTime = datetime.datetime.now() #保存缓存 def saveCacheAdd(self, **objDict): self.cacheFile = open(self.cacheFilePath, "a") content = "" for key, value in objDict.items(): content += "self.%s = %s\n" %(key, str(value)) self.cacheFile.write(content) self.cacheFile.close() self.preSaveCacheTime = datetime.datetime.now() #------------------------------ #数据保存相关函数 #------------------------------ def saveMarketData(self, data): self.MDList.append(copy.copy(data)) if len(self.MDList) > 300: del self.MDList[0] def saveTradeSettlement(self, data): self.TDList.append(copy.copy(data)) if len(self.TDList) > 300: del self.TDList[0] def saveOrderQuene(self, data): self.ODList.append(copy.copy(data)) if len(self.ODList) > 300: del self.ODList[0] #------------------------------ #交易方法 #------------------------------ def buy(self, mode, price, vol, dateTime): return self.dealEngine.buy(mode, price, vol, dateTime) def sell(self, mode, price, vol, dateTime): return self.dealEngine.sell(mode, price, vol, dateTime) #报单成交明细 def dealed(self, dealObj, avePrice, vol, dateTime): pass #报单失败 def quotedFailed(self, failedCode): #失败原因 pass #------------------------------ #继承重载函数 #------------------------------ #自定义初始化函数 def customInit(self): self.name = "baseStrategy" #行情数据触发函数 def onRtnMarketData(self, data): pass #逐笔成交触发函数 def onRtnTradeSettlement(self, data): pass #买一队列触发函数 def onRtnOrderQueue(self, data): pass #当日结束 def dayEnd(self): pass #数据结束 def dataEnd(self): pass #自动保存缓存触发函数 def autosaveCache(self): #self.saveCache(data = self.data) pass

0.081695

0.124665

from bresenham import bresenham from scipy.spatial import Voronoi import numpy as np from queue import PriorityQueue import networkx as nx def closest_node(graph, current_position): ''' Compute the closest node in the graph to the current position ''' closest_node = None dist = 100000 xy_position = (current_position[0], current_position[1]) for p in graph.nodes: d = heuristic(xy_position, p) if d < dist: closest_node = p dist = d return closest_node def create_grid_and_edges(data, drone_altitude, safety_distance): ''' Create a grid representation of a 2D configuration space and a Voronoi Graph ''' # minimum and maximum north coordinates north_min = np.floor(np.min(data[:, 0] - data[:, 3])) north_max = np.ceil(np.max(data[:, 0] + data[:, 3])) # minimum and maximum east coordinates east_min = np.floor(np.min(data[:, 1] - data[:, 4])) east_max = np.ceil(np.max(data[:, 1] + data[:, 4])) # given the minimum and maximum coordinates we can # calculate the size of the grid. north_size = int(np.ceil(north_max - north_min)) east_size = int(np.ceil(east_max - east_min)) # Initialize an empty grid grid = np.zeros((north_size, east_size)) # Initialize an empty list for Voronoi points points = [] # Populate the grid with obstacles for i in range(data.shape[0]): north, east, alt, d_north, d_east, d_alt = data[i, :] if alt + d_alt + safety_distance > drone_altitude: obstacle = [ int(np.clip(north - d_north - safety_distance - north_min, 0, north_size - 1)), int(np.clip(north + d_north + safety_distance - north_min, 0, north_size - 1)), int(np.clip(east - d_east - safety_distance - east_min, 0, east_size - 1)), int(np.clip(east + d_east + safety_distance - east_min, 0, east_size - 1)), ] grid[obstacle[0]:obstacle[1] + 1, obstacle[2]:obstacle[3] + 1] = 1 # add center of obstacles to points list points.append([north - north_min, east - east_min]) # create a voronoi graph based on location of obstacle centres graph = Voronoi(points) # check each edge from graph.ridge_vertices for collision edges = [] for v in graph.ridge_vertices: p1 = graph.vertices[v[0]] p2 = graph.vertices[v[1]] cells = list(bresenham(int(p1[0]), int(p1[1]), int(p2[0]), int(p2[1]))) hit = False for c in cells: # First check if we're off the map if np.amin(c) < 0 or c[0] >= grid.shape[0] or c[1] >= grid.shape[1]: hit = True break # Next check if we're in collision if grid[c[0], c[1]] == 1: hit = True break # If the edge does not hit on obstacle # add it to the list if not hit: # array to tuple for future graph creation step) p1 = (p1[0], p1[1]) p2 = (p2[0], p2[1]) edges.append((p1, p2)) return grid, edges, int(north_min), int(east_min) def create_graph(edges): graph = nx.Graph() for elem in edges: p1 = elem[0] p2 = elem[1] dist = heuristic(p1, p2) graph.add_edge(p1, p2, weight=dist) return graph def heuristic(n1, n2): return(np.linalg.norm(np.array(n2) - np.array(n1))) def a_star_graph(graph, start, goal, h): ''' A* working with NetworkX graphs ''' path = [] path_cost = 0 queue = PriorityQueue() queue.put((0, start)) visited = set(start) branch = {} found = False while not queue.empty(): item = queue.get() current_node = item[1] if current_node == start: current_cost = 0.0 else: current_cost = branch[current_node][0] if current_node == goal: print('Found a path.') found = True break else: for next_node in graph[current_node]: cost = graph.edges[current_node, next_node]['weight'] branch_cost = current_cost + cost queue_cost = branch_cost + h(next_node, goal) if next_node not in visited: visited.add(next_node) branch[next_node] = (branch_cost, current_node) queue.put((queue_cost, next_node)) if found: # retrace steps n = goal path_cost = branch[n][0] path.append(goal) while branch[n][1] != start: path.append(branch[n][1]) n = branch[n][1] path.append(branch[n][1]) else: print('**********************') print('Failed to find a path!') print('**********************') return path[::-1], path_cost

lib/voronoi_utils.py

from bresenham import bresenham from scipy.spatial import Voronoi import numpy as np from queue import PriorityQueue import networkx as nx def closest_node(graph, current_position): ''' Compute the closest node in the graph to the current position ''' closest_node = None dist = 100000 xy_position = (current_position[0], current_position[1]) for p in graph.nodes: d = heuristic(xy_position, p) if d < dist: closest_node = p dist = d return closest_node def create_grid_and_edges(data, drone_altitude, safety_distance): ''' Create a grid representation of a 2D configuration space and a Voronoi Graph ''' # minimum and maximum north coordinates north_min = np.floor(np.min(data[:, 0] - data[:, 3])) north_max = np.ceil(np.max(data[:, 0] + data[:, 3])) # minimum and maximum east coordinates east_min = np.floor(np.min(data[:, 1] - data[:, 4])) east_max = np.ceil(np.max(data[:, 1] + data[:, 4])) # given the minimum and maximum coordinates we can # calculate the size of the grid. north_size = int(np.ceil(north_max - north_min)) east_size = int(np.ceil(east_max - east_min)) # Initialize an empty grid grid = np.zeros((north_size, east_size)) # Initialize an empty list for Voronoi points points = [] # Populate the grid with obstacles for i in range(data.shape[0]): north, east, alt, d_north, d_east, d_alt = data[i, :] if alt + d_alt + safety_distance > drone_altitude: obstacle = [ int(np.clip(north - d_north - safety_distance - north_min, 0, north_size - 1)), int(np.clip(north + d_north + safety_distance - north_min, 0, north_size - 1)), int(np.clip(east - d_east - safety_distance - east_min, 0, east_size - 1)), int(np.clip(east + d_east + safety_distance - east_min, 0, east_size - 1)), ] grid[obstacle[0]:obstacle[1] + 1, obstacle[2]:obstacle[3] + 1] = 1 # add center of obstacles to points list points.append([north - north_min, east - east_min]) # create a voronoi graph based on location of obstacle centres graph = Voronoi(points) # check each edge from graph.ridge_vertices for collision edges = [] for v in graph.ridge_vertices: p1 = graph.vertices[v[0]] p2 = graph.vertices[v[1]] cells = list(bresenham(int(p1[0]), int(p1[1]), int(p2[0]), int(p2[1]))) hit = False for c in cells: # First check if we're off the map if np.amin(c) < 0 or c[0] >= grid.shape[0] or c[1] >= grid.shape[1]: hit = True break # Next check if we're in collision if grid[c[0], c[1]] == 1: hit = True break # If the edge does not hit on obstacle # add it to the list if not hit: # array to tuple for future graph creation step) p1 = (p1[0], p1[1]) p2 = (p2[0], p2[1]) edges.append((p1, p2)) return grid, edges, int(north_min), int(east_min) def create_graph(edges): graph = nx.Graph() for elem in edges: p1 = elem[0] p2 = elem[1] dist = heuristic(p1, p2) graph.add_edge(p1, p2, weight=dist) return graph def heuristic(n1, n2): return(np.linalg.norm(np.array(n2) - np.array(n1))) def a_star_graph(graph, start, goal, h): ''' A* working with NetworkX graphs ''' path = [] path_cost = 0 queue = PriorityQueue() queue.put((0, start)) visited = set(start) branch = {} found = False while not queue.empty(): item = queue.get() current_node = item[1] if current_node == start: current_cost = 0.0 else: current_cost = branch[current_node][0] if current_node == goal: print('Found a path.') found = True break else: for next_node in graph[current_node]: cost = graph.edges[current_node, next_node]['weight'] branch_cost = current_cost + cost queue_cost = branch_cost + h(next_node, goal) if next_node not in visited: visited.add(next_node) branch[next_node] = (branch_cost, current_node) queue.put((queue_cost, next_node)) if found: # retrace steps n = goal path_cost = branch[n][0] path.append(goal) while branch[n][1] != start: path.append(branch[n][1]) n = branch[n][1] path.append(branch[n][1]) else: print('**********************') print('Failed to find a path!') print('**********************') return path[::-1], path_cost

0.729905

0.574275

from cStringIO import StringIO from zope.interface import implements from zope.interface.verify import verifyObject from twisted.trial import unittest from twisted.internet import reactor from twisted.internet.address import IPv4Address from twisted.internet.defer import Deferred from twisted.web import server, resource, util from twisted.internet import defer, interfaces, error, task from twisted.web import iweb, http, http_headers from twisted.python import log class DummyRequest: """ Represents a dummy or fake request. @ivar _finishedDeferreds: C{None} or a C{list} of L{Deferreds} which will be called back with C{None} when C{finish} is called or which will be errbacked if C{processingFailed} is called. @type headers: C{dict} @ivar headers: A mapping of header name to header value for all request headers. @type outgoingHeaders: C{dict} @ivar outgoingHeaders: A mapping of header name to header value for all response headers. @type responseCode: C{int} @ivar responseCode: The response code which was passed to C{setResponseCode}. @type written: C{list} of C{str} @ivar written: The bytes which have been written to the request. """ uri = 'http://dummy/' method = 'GET' client = None def registerProducer(self, prod,s): self.go = 1 while self.go: prod.resumeProducing() def unregisterProducer(self): self.go = 0 def __init__(self, postpath, session=None): self.sitepath = [] self.written = [] self.finished = 0 self.postpath = postpath self.prepath = [] self.session = None self.protoSession = session or server.Session(0, self) self.args = {} self.outgoingHeaders = {} self.responseHeaders = http_headers.Headers() self.responseCode = None self.headers = {} self._finishedDeferreds = [] def getHeader(self, name): """ Retrieve the value of a request header. @type name: C{str} @param name: The name of the request header for which to retrieve the value. Header names are compared case-insensitively. @rtype: C{str} or L{NoneType} @return: The value of the specified request header. """ return self.headers.get(name.lower(), None) def setHeader(self, name, value): """TODO: make this assert on write() if the header is content-length """ self.outgoingHeaders[name.lower()] = value def getSession(self): if self.session: return self.session assert not self.written, "Session cannot be requested after data has been written." self.session = self.protoSession return self.session def write(self, data): self.written.append(data) def notifyFinish(self): """ Return a L{Deferred} which is called back with C{None} when the request is finished. This will probably only work if you haven't called C{finish} yet. """ finished = Deferred() self._finishedDeferreds.append(finished) return finished def finish(self): """ Record that the request is finished and callback and L{Deferred}s waiting for notification of this. """ self.finished = self.finished + 1 if self._finishedDeferreds is not None: observers = self._finishedDeferreds self._finishedDeferreds = None for obs in observers: obs.callback(None) def processingFailed(self, reason): """ Errback and L{Deferreds} waiting for finish notification. """ if self._finishedDeferreds is not None: observers = self._finishedDeferreds self._finishedDeferreds = None for obs in observers: obs.errback(reason) def addArg(self, name, value): self.args[name] = [value] def setResponseCode(self, code, message=None): """ Set the HTTP status response code, but takes care that this is called before any data is written. """ assert not self.written, "Response code cannot be set after data has been written: %s." % "@@@@".join(self.written) self.responseCode = code self.responseMessage = message def setLastModified(self, when): assert not self.written, "Last-Modified cannot be set after data has been written: %s." % "@@@@".join(self.written) def setETag(self, tag): assert not self.written, "ETag cannot be set after data has been written: %s." % "@@@@".join(self.written) def getClientIP(self): """ Return the IPv4 address of the client which made this request, if there is one, otherwise C{None}. """ if isinstance(self.client, IPv4Address): return self.client.host return None class ResourceTestCase(unittest.TestCase): def testListEntities(self): r = resource.Resource() self.failUnlessEqual([], r.listEntities()) class SimpleResource(resource.Resource): def render(self, request): if http.CACHED in (request.setLastModified(10), request.setETag('MatchingTag')): return '' else: return "correct" class DummyChannel: class TCP: port = 80 def __init__(self): self.written = StringIO() self.producers = [] def getPeer(self): return IPv4Address("TCP", '192.168.1.1', 12344) def write(self, bytes): assert isinstance(bytes, str) self.written.write(bytes) def writeSequence(self, iovec): map(self.write, iovec) def getHost(self): return IPv4Address("TCP", '10.0.0.1', self.port) def registerProducer(self, producer, streaming): self.producers.append((producer, streaming)) class SSL(TCP): implements(interfaces.ISSLTransport) site = server.Site(resource.Resource()) def __init__(self): self.transport = self.TCP() def requestDone(self, request): pass class SiteTest(unittest.TestCase): def test_simplestSite(self): """ L{Site.getResourceFor} returns the C{""} child of the root resource it is constructed with when processing a request for I{/}. """ sres1 = SimpleResource() sres2 = SimpleResource() sres1.putChild("",sres2) site = server.Site(sres1) self.assertIdentical( site.getResourceFor(DummyRequest([''])), sres2, "Got the wrong resource.") class SessionTest(unittest.TestCase): """ Tests for L{server.Session}. """ def setUp(self): """ Create a site with one active session using a deterministic, easily controlled clock. """ self.clock = task.Clock() self.uid = 'unique' self.site = server.Site(resource.Resource()) self.session = server.Session(self.site, self.uid, self.clock) self.site.sessions[self.uid] = self.session def test_defaultReactor(self): """ If not value is passed to L{server.Session.__init__}, the global reactor is used. """ session = server.Session(server.Site(resource.Resource()), '123') self.assertIdentical(session._reactor, reactor) def test_startCheckingExpiration(self): """ L{server.Session.startCheckingExpiration} causes the session to expire after L{server.Session.sessionTimeout} seconds without activity. """ self.session.startCheckingExpiration() # Advance to almost the timeout - nothing should happen. self.clock.advance(self.session.sessionTimeout - 1) self.assertIn(self.uid, self.site.sessions) # Advance to the timeout, the session should expire. self.clock.advance(1) self.assertNotIn(self.uid, self.site.sessions) # There should be no calls left over, either. self.assertFalse(self.clock.calls) def test_expire(self): """ L{server.Session.expire} expires the session. """ self.session.expire() # It should be gone from the session dictionary. self.assertNotIn(self.uid, self.site.sessions) # And there should be no pending delayed calls. self.assertFalse(self.clock.calls) def test_expireWhileChecking(self): """ L{server.Session.expire} expires the session even if the timeout call isn't due yet. """ self.session.startCheckingExpiration() self.test_expire() def test_notifyOnExpire(self): """ A function registered with L{server.Session.notifyOnExpire} is called when the session expires. """ callbackRan = [False] def expired(): callbackRan[0] = True self.session.notifyOnExpire(expired) self.session.expire() self.assertTrue(callbackRan[0]) def test_touch(self): """ L{server.Session.touch} updates L{server.Session.lastModified} and delays session timeout. """ # Make sure it works before startCheckingExpiration self.clock.advance(3) self.session.touch() self.assertEqual(self.session.lastModified, 3) # And after startCheckingExpiration self.session.startCheckingExpiration() self.clock.advance(self.session.sessionTimeout - 1) self.session.touch() self.clock.advance(self.session.sessionTimeout - 1) self.assertIn(self.uid, self.site.sessions) # It should have advanced it by just sessionTimeout, no more. self.clock.advance(1) self.assertNotIn(self.uid, self.site.sessions) def test_startCheckingExpirationParameterDeprecated(self): """ L{server.Session.startCheckingExpiration} emits a deprecation warning if it is invoked with a parameter. """ self.session.startCheckingExpiration(123) warnings = self.flushWarnings([ self.test_startCheckingExpirationParameterDeprecated]) self.assertEqual(len(warnings), 1) self.assertEqual(warnings[0]['category'], DeprecationWarning) self.assertEqual( warnings[0]['message'], "The lifetime parameter to startCheckingExpiration is deprecated " "since Twisted 9.0. See Session.sessionTimeout instead.") def test_checkExpiredDeprecated(self): """ L{server.Session.checkExpired} is deprecated. """ self.session.checkExpired() warnings = self.flushWarnings([self.test_checkExpiredDeprecated]) self.assertEqual(warnings[0]['category'], DeprecationWarning) self.assertEqual( warnings[0]['message'], "Session.checkExpired is deprecated since Twisted 9.0; sessions " "check themselves now, you don't need to.") self.assertEqual(len(warnings), 1) # Conditional requests: # If-None-Match, If-Modified-Since # make conditional request: # normal response if condition succeeds # if condition fails: # response code # no body def httpBody(whole): return whole.split('\r\n\r\n', 1)[1] def httpHeader(whole, key): key = key.lower() headers = whole.split('\r\n\r\n', 1)[0] for header in headers.split('\r\n'): if header.lower().startswith(key): return header.split(':', 1)[1].strip() return None def httpCode(whole): l1 = whole.split('\r\n', 1)[0] return int(l1.split()[1]) class ConditionalTest(unittest.TestCase): """ web.server's handling of conditional requests for cache validation. """ # XXX: test web.distrib. def setUp(self): self.resrc = SimpleResource() self.resrc.putChild('', self.resrc) self.site = server.Site(self.resrc) self.site = server.Site(self.resrc) self.site.logFile = log.logfile # HELLLLLLLLLLP! This harness is Very Ugly. self.channel = self.site.buildProtocol(None) self.transport = http.StringTransport() self.transport.close = lambda *a, **kw: None self.transport.disconnecting = lambda *a, **kw: 0 self.transport.getPeer = lambda *a, **kw: "peer" self.transport.getHost = lambda *a, **kw: "host" self.channel.makeConnection(self.transport) for l in ["GET / HTTP/1.1", "Accept: text/html"]: self.channel.lineReceived(l) def tearDown(self): self.channel.connectionLost(None) def _modifiedTest(self, modifiedSince): """ Given the value C{modifiedSince} for the I{If-Modified-Since} header, verify that a response with a 200 code and the resource as the body is returned. """ self.channel.lineReceived("If-Modified-Since: " + modifiedSince) self.channel.lineReceived('') result = self.transport.getvalue() self.failUnlessEqual(httpCode(result), http.OK) self.failUnlessEqual(httpBody(result), "correct") def test_modified(self): """ If a request is made with an I{If-Modified-Since} header value with a timestamp indicating a time before the last modification of the requested resource, a 200 response is returned along with a response body containing the resource. """ self._modifiedTest(http.datetimeToString(1)) def test_unmodified(self): """ If a request is made with an I{If-Modified-Since} header value with a timestamp indicating a time after the last modification of the request resource, a 304 response is returned along with an empty response body. """ self.channel.lineReceived("If-Modified-Since: %s" % http.datetimeToString(100)) self.channel.lineReceived('') result = self.transport.getvalue() self.failUnlessEqual(httpCode(result), http.NOT_MODIFIED) self.failUnlessEqual(httpBody(result), "") def test_invalidTimestamp(self): """ If a request is made with an I{If-Modified-Since} header value which cannot be parsed, the header is treated as not having been present and a normal 200 response is returned with a response body containing the resource. """ self._modifiedTest("like, maybe a week ago, I guess?") def test_invalidTimestampYear(self): """ If a request is made with an I{If-Modified-Since} header value which contains a string in the year position which is not an integer, the header is treated as not having been present and a normal 200 response is returned with a response body containing the resource. """ self._modifiedTest("Thu, 01 Jan blah 00:00:10 GMT") def test_invalidTimestampTooLongAgo(self): """ If a request is made with an I{If-Modified-Since} header value which contains a year before the epoch, the header is treated as not having been present and a normal 200 response is returned with a response body containing the resource. """ self._modifiedTest("Thu, 01 Jan 1899 00:00:10 GMT") def test_invalidTimestampMonth(self): """ If a request is made with an I{If-Modified-Since} header value which contains a string in the month position which is not a recognized month abbreviation, the header is treated as not having been present and a normal 200 response is returned with a response body containing the resource. """ self._modifiedTest("Thu, 01 Blah 1970 00:00:10 GMT") def test_etagMatchedNot(self): """If-None-Match ETag cache validator (positive)""" self.channel.lineReceived("If-None-Match: unmatchedTag") self.channel.lineReceived('') result = self.transport.getvalue() self.failUnlessEqual(httpCode(result), http.OK) self.failUnlessEqual(httpBody(result), "correct") def test_etagMatched(self): """If-None-Match ETag cache validator (negative)""" self.channel.lineReceived("If-None-Match: MatchingTag") self.channel.lineReceived('') result = self.transport.getvalue() self.failUnlessEqual(httpHeader(result, "ETag"), "MatchingTag") self.failUnlessEqual(httpCode(result), http.NOT_MODIFIED) self.failUnlessEqual(httpBody(result), "") from twisted.web import google class GoogleTestCase(unittest.TestCase): def testCheckGoogle(self): raise unittest.SkipTest("no violation of google ToS") d = google.checkGoogle('site:www.twistedmatrix.com twisted') d.addCallback(self.assertEquals, 'http://twistedmatrix.com/') return d class RequestTests(unittest.TestCase): """ Tests for the HTTP request class, L{server.Request}. """ def test_interface(self): """ L{server.Request} instances provide L{iweb.IRequest}. """ self.assertTrue( verifyObject(iweb.IRequest, server.Request(DummyChannel(), True))) def testChildLink(self): request = server.Request(DummyChannel(), 1) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') self.assertEqual(request.childLink('baz'), 'bar/baz') request = server.Request(DummyChannel(), 1) request.gotLength(0) request.requestReceived('GET', '/foo/bar/', 'HTTP/1.0') self.assertEqual(request.childLink('baz'), 'baz') def testPrePathURLSimple(self): request = server.Request(DummyChannel(), 1) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') request.setHost('example.com', 80) self.assertEqual(request.prePathURL(), 'http://example.com/foo/bar') def testPrePathURLNonDefault(self): d = DummyChannel() d.transport.port = 81 request = server.Request(d, 1) request.setHost('example.com', 81) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') self.assertEqual(request.prePathURL(), 'http://example.com:81/foo/bar') def testPrePathURLSSLPort(self): d = DummyChannel() d.transport.port = 443 request = server.Request(d, 1) request.setHost('example.com', 443) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') self.assertEqual(request.prePathURL(), 'http://example.com:443/foo/bar') def testPrePathURLSSLPortAndSSL(self): d = DummyChannel() d.transport = DummyChannel.SSL() d.transport.port = 443 request = server.Request(d, 1) request.setHost('example.com', 443) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') self.assertEqual(request.prePathURL(), 'https://example.com/foo/bar') def testPrePathURLHTTPPortAndSSL(self): d = DummyChannel() d.transport = DummyChannel.SSL() d.transport.port = 80 request = server.Request(d, 1) request.setHost('example.com', 80) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') self.assertEqual(request.prePathURL(), 'https://example.com:80/foo/bar') def testPrePathURLSSLNonDefault(self): d = DummyChannel() d.transport = DummyChannel.SSL() d.transport.port = 81 request = server.Request(d, 1) request.setHost('example.com', 81) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') self.assertEqual(request.prePathURL(), 'https://example.com:81/foo/bar') def testPrePathURLSetSSLHost(self): d = DummyChannel() d.transport.port = 81 request = server.Request(d, 1) request.setHost('foo.com', 81, 1) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') self.assertEqual(request.prePathURL(), 'https://foo.com:81/foo/bar') def test_prePathURLQuoting(self): """ L{Request.prePathURL} quotes special characters in the URL segments to preserve the original meaning. """ d = DummyChannel() request = server.Request(d, 1) request.setHost('example.com', 80) request.gotLength(0) request.requestReceived('GET', '/foo%2Fbar', 'HTTP/1.0') self.assertEqual(request.prePathURL(), 'http://example.com/foo%2Fbar') class RootResource(resource.Resource): isLeaf=0 def getChildWithDefault(self, name, request): request.rememberRootURL() return resource.Resource.getChildWithDefault(self, name, request) def render(self, request): return '' class RememberURLTest(unittest.TestCase): def createServer(self, r): chan = DummyChannel() chan.site = server.Site(r) return chan def testSimple(self): r = resource.Resource() r.isLeaf=0 rr = RootResource() r.putChild('foo', rr) rr.putChild('', rr) rr.putChild('bar', resource.Resource()) chan = self.createServer(r) for url in ['/foo/', '/foo/bar', '/foo/bar/baz', '/foo/bar/']: request = server.Request(chan, 1) request.setHost('example.com', 81) request.gotLength(0) request.requestReceived('GET', url, 'HTTP/1.0') self.assertEqual(request.getRootURL(), "http://example.com/foo") def testRoot(self): rr = RootResource() rr.putChild('', rr) rr.putChild('bar', resource.Resource()) chan = self.createServer(rr) for url in ['/', '/bar', '/bar/baz', '/bar/']: request = server.Request(chan, 1) request.setHost('example.com', 81) request.gotLength(0) request.requestReceived('GET', url, 'HTTP/1.0') self.assertEqual(request.getRootURL(), "http://example.com/") class NewRenderResource(resource.Resource): def render_GET(self, request): return "hi hi" def render_HEH(self, request): return "ho ho" class NewRenderTestCase(unittest.TestCase): def _getReq(self): d = DummyChannel() d.site.resource.putChild('newrender', NewRenderResource()) d.transport.port = 81 request = server.Request(d, 1) request.setHost('example.com', 81) request.gotLength(0) return request def testGoodMethods(self): req = self._getReq() req.requestReceived('GET', '/newrender', 'HTTP/1.0') self.assertEquals(req.transport.getvalue().splitlines()[-1], 'hi hi') req = self._getReq() req.requestReceived('HEH', '/newrender', 'HTTP/1.0') self.assertEquals(req.transport.getvalue().splitlines()[-1], 'ho ho') def testBadMethods(self): req = self._getReq() req.requestReceived('CONNECT', '/newrender', 'HTTP/1.0') self.assertEquals(req.code, 501) req = self._getReq() req.requestReceived('hlalauguG', '/newrender', 'HTTP/1.0') self.assertEquals(req.code, 501) def testImplicitHead(self): req = self._getReq() req.requestReceived('HEAD', '/newrender', 'HTTP/1.0') self.assertEquals(req.code, 200) self.assertEquals(-1, req.transport.getvalue().find('hi hi')) class SDResource(resource.Resource): def __init__(self,default): self.default=default def getChildWithDefault(self,name,request): d=defer.succeed(self.default) return util.DeferredResource(d).getChildWithDefault(name, request) class SDTest(unittest.TestCase): def testDeferredResource(self): r = resource.Resource() r.isLeaf = 1 s = SDResource(r) d = DummyRequest(['foo', 'bar', 'baz']) resource.getChildForRequest(s, d) self.assertEqual(d.postpath, ['bar', 'baz']) class DummyRequestForLogTest(DummyRequest): uri = '/dummy' # parent class uri has "http://", which doesn't really happen code = 123 clientproto = 'HTTP/1.0' sentLength = None client = IPv4Address('TCP', '1.2.3.4', 12345) class TestLogEscaping(unittest.TestCase): def setUp(self): self.site = http.HTTPFactory() self.site.logFile = StringIO() self.request = DummyRequestForLogTest(self.site, False) def testSimple(self): http._logDateTime = "[%02d/%3s/%4d:%02d:%02d:%02d +0000]" % ( 25, 'Oct', 2004, 12, 31, 59) self.site.log(self.request) self.site.logFile.seek(0) self.assertEqual( self.site.logFile.read(), '1.2.3.4 - - [25/Oct/2004:12:31:59 +0000] "GET /dummy HTTP/1.0" 123 - "-" "-"\n') def testMethodQuote(self): http._logDateTime = "[%02d/%3s/%4d:%02d:%02d:%02d +0000]" % ( 25, 'Oct', 2004, 12, 31, 59) self.request.method = 'G"T' self.site.log(self.request) self.site.logFile.seek(0) self.assertEqual( self.site.logFile.read(), '1.2.3.4 - - [25/Oct/2004:12:31:59 +0000] "G\\"T /dummy HTTP/1.0" 123 - "-" "-"\n') def testRequestQuote(self): http._logDateTime = "[%02d/%3s/%4d:%02d:%02d:%02d +0000]" % ( 25, 'Oct', 2004, 12, 31, 59) self.request.uri='/dummy"withquote' self.site.log(self.request) self.site.logFile.seek(0) self.assertEqual( self.site.logFile.read(), '1.2.3.4 - - [25/Oct/2004:12:31:59 +0000] "GET /dummy\\"withquote HTTP/1.0" 123 - "-" "-"\n') def testProtoQuote(self): http._logDateTime = "[%02d/%3s/%4d:%02d:%02d:%02d +0000]" % ( 25, 'Oct', 2004, 12, 31, 59) self.request.clientproto='HT"P/1.0' self.site.log(self.request) self.site.logFile.seek(0) self.assertEqual( self.site.logFile.read(), '1.2.3.4 - - [25/Oct/2004:12:31:59 +0000] "GET /dummy HT\\"P/1.0" 123 - "-" "-"\n') def testRefererQuote(self): http._logDateTime = "[%02d/%3s/%4d:%02d:%02d:%02d +0000]" % ( 25, 'Oct', 2004, 12, 31, 59) self.request.headers['referer'] = 'http://malicious" ".website.invalid' self.site.log(self.request) self.site.logFile.seek(0) self.assertEqual( self.site.logFile.read(), '1.2.3.4 - - [25/Oct/2004:12:31:59 +0000] "GET /dummy HTTP/1.0" 123 - "http://malicious\\" \\".website.invalid" "-"\n') def testUserAgentQuote(self): http._logDateTime = "[%02d/%3s/%4d:%02d:%02d:%02d +0000]" % ( 25, 'Oct', 2004, 12, 31, 59) self.request.headers['user-agent'] = 'Malicious Web" Evil' self.site.log(self.request) self.site.logFile.seek(0) self.assertEqual( self.site.logFile.read(), '1.2.3.4 - - [25/Oct/2004:12:31:59 +0000] "GET /dummy HTTP/1.0" 123 - "-" "Malicious Web\\" Evil"\n')

twisted/web/test/test_web.py

from cStringIO import StringIO from zope.interface import implements from zope.interface.verify import verifyObject from twisted.trial import unittest from twisted.internet import reactor from twisted.internet.address import IPv4Address from twisted.internet.defer import Deferred from twisted.web import server, resource, util from twisted.internet import defer, interfaces, error, task from twisted.web import iweb, http, http_headers from twisted.python import log class DummyRequest: """ Represents a dummy or fake request. @ivar _finishedDeferreds: C{None} or a C{list} of L{Deferreds} which will be called back with C{None} when C{finish} is called or which will be errbacked if C{processingFailed} is called. @type headers: C{dict} @ivar headers: A mapping of header name to header value for all request headers. @type outgoingHeaders: C{dict} @ivar outgoingHeaders: A mapping of header name to header value for all response headers. @type responseCode: C{int} @ivar responseCode: The response code which was passed to C{setResponseCode}. @type written: C{list} of C{str} @ivar written: The bytes which have been written to the request. """ uri = 'http://dummy/' method = 'GET' client = None def registerProducer(self, prod,s): self.go = 1 while self.go: prod.resumeProducing() def unregisterProducer(self): self.go = 0 def __init__(self, postpath, session=None): self.sitepath = [] self.written = [] self.finished = 0 self.postpath = postpath self.prepath = [] self.session = None self.protoSession = session or server.Session(0, self) self.args = {} self.outgoingHeaders = {} self.responseHeaders = http_headers.Headers() self.responseCode = None self.headers = {} self._finishedDeferreds = [] def getHeader(self, name): """ Retrieve the value of a request header. @type name: C{str} @param name: The name of the request header for which to retrieve the value. Header names are compared case-insensitively. @rtype: C{str} or L{NoneType} @return: The value of the specified request header. """ return self.headers.get(name.lower(), None) def setHeader(self, name, value): """TODO: make this assert on write() if the header is content-length """ self.outgoingHeaders[name.lower()] = value def getSession(self): if self.session: return self.session assert not self.written, "Session cannot be requested after data has been written." self.session = self.protoSession return self.session def write(self, data): self.written.append(data) def notifyFinish(self): """ Return a L{Deferred} which is called back with C{None} when the request is finished. This will probably only work if you haven't called C{finish} yet. """ finished = Deferred() self._finishedDeferreds.append(finished) return finished def finish(self): """ Record that the request is finished and callback and L{Deferred}s waiting for notification of this. """ self.finished = self.finished + 1 if self._finishedDeferreds is not None: observers = self._finishedDeferreds self._finishedDeferreds = None for obs in observers: obs.callback(None) def processingFailed(self, reason): """ Errback and L{Deferreds} waiting for finish notification. """ if self._finishedDeferreds is not None: observers = self._finishedDeferreds self._finishedDeferreds = None for obs in observers: obs.errback(reason) def addArg(self, name, value): self.args[name] = [value] def setResponseCode(self, code, message=None): """ Set the HTTP status response code, but takes care that this is called before any data is written. """ assert not self.written, "Response code cannot be set after data has been written: %s." % "@@@@".join(self.written) self.responseCode = code self.responseMessage = message def setLastModified(self, when): assert not self.written, "Last-Modified cannot be set after data has been written: %s." % "@@@@".join(self.written) def setETag(self, tag): assert not self.written, "ETag cannot be set after data has been written: %s." % "@@@@".join(self.written) def getClientIP(self): """ Return the IPv4 address of the client which made this request, if there is one, otherwise C{None}. """ if isinstance(self.client, IPv4Address): return self.client.host return None class ResourceTestCase(unittest.TestCase): def testListEntities(self): r = resource.Resource() self.failUnlessEqual([], r.listEntities()) class SimpleResource(resource.Resource): def render(self, request): if http.CACHED in (request.setLastModified(10), request.setETag('MatchingTag')): return '' else: return "correct" class DummyChannel: class TCP: port = 80 def __init__(self): self.written = StringIO() self.producers = [] def getPeer(self): return IPv4Address("TCP", '192.168.1.1', 12344) def write(self, bytes): assert isinstance(bytes, str) self.written.write(bytes) def writeSequence(self, iovec): map(self.write, iovec) def getHost(self): return IPv4Address("TCP", '10.0.0.1', self.port) def registerProducer(self, producer, streaming): self.producers.append((producer, streaming)) class SSL(TCP): implements(interfaces.ISSLTransport) site = server.Site(resource.Resource()) def __init__(self): self.transport = self.TCP() def requestDone(self, request): pass class SiteTest(unittest.TestCase): def test_simplestSite(self): """ L{Site.getResourceFor} returns the C{""} child of the root resource it is constructed with when processing a request for I{/}. """ sres1 = SimpleResource() sres2 = SimpleResource() sres1.putChild("",sres2) site = server.Site(sres1) self.assertIdentical( site.getResourceFor(DummyRequest([''])), sres2, "Got the wrong resource.") class SessionTest(unittest.TestCase): """ Tests for L{server.Session}. """ def setUp(self): """ Create a site with one active session using a deterministic, easily controlled clock. """ self.clock = task.Clock() self.uid = 'unique' self.site = server.Site(resource.Resource()) self.session = server.Session(self.site, self.uid, self.clock) self.site.sessions[self.uid] = self.session def test_defaultReactor(self): """ If not value is passed to L{server.Session.__init__}, the global reactor is used. """ session = server.Session(server.Site(resource.Resource()), '123') self.assertIdentical(session._reactor, reactor) def test_startCheckingExpiration(self): """ L{server.Session.startCheckingExpiration} causes the session to expire after L{server.Session.sessionTimeout} seconds without activity. """ self.session.startCheckingExpiration() # Advance to almost the timeout - nothing should happen. self.clock.advance(self.session.sessionTimeout - 1) self.assertIn(self.uid, self.site.sessions) # Advance to the timeout, the session should expire. self.clock.advance(1) self.assertNotIn(self.uid, self.site.sessions) # There should be no calls left over, either. self.assertFalse(self.clock.calls) def test_expire(self): """ L{server.Session.expire} expires the session. """ self.session.expire() # It should be gone from the session dictionary. self.assertNotIn(self.uid, self.site.sessions) # And there should be no pending delayed calls. self.assertFalse(self.clock.calls) def test_expireWhileChecking(self): """ L{server.Session.expire} expires the session even if the timeout call isn't due yet. """ self.session.startCheckingExpiration() self.test_expire() def test_notifyOnExpire(self): """ A function registered with L{server.Session.notifyOnExpire} is called when the session expires. """ callbackRan = [False] def expired(): callbackRan[0] = True self.session.notifyOnExpire(expired) self.session.expire() self.assertTrue(callbackRan[0]) def test_touch(self): """ L{server.Session.touch} updates L{server.Session.lastModified} and delays session timeout. """ # Make sure it works before startCheckingExpiration self.clock.advance(3) self.session.touch() self.assertEqual(self.session.lastModified, 3) # And after startCheckingExpiration self.session.startCheckingExpiration() self.clock.advance(self.session.sessionTimeout - 1) self.session.touch() self.clock.advance(self.session.sessionTimeout - 1) self.assertIn(self.uid, self.site.sessions) # It should have advanced it by just sessionTimeout, no more. self.clock.advance(1) self.assertNotIn(self.uid, self.site.sessions) def test_startCheckingExpirationParameterDeprecated(self): """ L{server.Session.startCheckingExpiration} emits a deprecation warning if it is invoked with a parameter. """ self.session.startCheckingExpiration(123) warnings = self.flushWarnings([ self.test_startCheckingExpirationParameterDeprecated]) self.assertEqual(len(warnings), 1) self.assertEqual(warnings[0]['category'], DeprecationWarning) self.assertEqual( warnings[0]['message'], "The lifetime parameter to startCheckingExpiration is deprecated " "since Twisted 9.0. See Session.sessionTimeout instead.") def test_checkExpiredDeprecated(self): """ L{server.Session.checkExpired} is deprecated. """ self.session.checkExpired() warnings = self.flushWarnings([self.test_checkExpiredDeprecated]) self.assertEqual(warnings[0]['category'], DeprecationWarning) self.assertEqual( warnings[0]['message'], "Session.checkExpired is deprecated since Twisted 9.0; sessions " "check themselves now, you don't need to.") self.assertEqual(len(warnings), 1) # Conditional requests: # If-None-Match, If-Modified-Since # make conditional request: # normal response if condition succeeds # if condition fails: # response code # no body def httpBody(whole): return whole.split('\r\n\r\n', 1)[1] def httpHeader(whole, key): key = key.lower() headers = whole.split('\r\n\r\n', 1)[0] for header in headers.split('\r\n'): if header.lower().startswith(key): return header.split(':', 1)[1].strip() return None def httpCode(whole): l1 = whole.split('\r\n', 1)[0] return int(l1.split()[1]) class ConditionalTest(unittest.TestCase): """ web.server's handling of conditional requests for cache validation. """ # XXX: test web.distrib. def setUp(self): self.resrc = SimpleResource() self.resrc.putChild('', self.resrc) self.site = server.Site(self.resrc) self.site = server.Site(self.resrc) self.site.logFile = log.logfile # HELLLLLLLLLLP! This harness is Very Ugly. self.channel = self.site.buildProtocol(None) self.transport = http.StringTransport() self.transport.close = lambda *a, **kw: None self.transport.disconnecting = lambda *a, **kw: 0 self.transport.getPeer = lambda *a, **kw: "peer" self.transport.getHost = lambda *a, **kw: "host" self.channel.makeConnection(self.transport) for l in ["GET / HTTP/1.1", "Accept: text/html"]: self.channel.lineReceived(l) def tearDown(self): self.channel.connectionLost(None) def _modifiedTest(self, modifiedSince): """ Given the value C{modifiedSince} for the I{If-Modified-Since} header, verify that a response with a 200 code and the resource as the body is returned. """ self.channel.lineReceived("If-Modified-Since: " + modifiedSince) self.channel.lineReceived('') result = self.transport.getvalue() self.failUnlessEqual(httpCode(result), http.OK) self.failUnlessEqual(httpBody(result), "correct") def test_modified(self): """ If a request is made with an I{If-Modified-Since} header value with a timestamp indicating a time before the last modification of the requested resource, a 200 response is returned along with a response body containing the resource. """ self._modifiedTest(http.datetimeToString(1)) def test_unmodified(self): """ If a request is made with an I{If-Modified-Since} header value with a timestamp indicating a time after the last modification of the request resource, a 304 response is returned along with an empty response body. """ self.channel.lineReceived("If-Modified-Since: %s" % http.datetimeToString(100)) self.channel.lineReceived('') result = self.transport.getvalue() self.failUnlessEqual(httpCode(result), http.NOT_MODIFIED) self.failUnlessEqual(httpBody(result), "") def test_invalidTimestamp(self): """ If a request is made with an I{If-Modified-Since} header value which cannot be parsed, the header is treated as not having been present and a normal 200 response is returned with a response body containing the resource. """ self._modifiedTest("like, maybe a week ago, I guess?") def test_invalidTimestampYear(self): """ If a request is made with an I{If-Modified-Since} header value which contains a string in the year position which is not an integer, the header is treated as not having been present and a normal 200 response is returned with a response body containing the resource. """ self._modifiedTest("Thu, 01 Jan blah 00:00:10 GMT") def test_invalidTimestampTooLongAgo(self): """ If a request is made with an I{If-Modified-Since} header value which contains a year before the epoch, the header is treated as not having been present and a normal 200 response is returned with a response body containing the resource. """ self._modifiedTest("Thu, 01 Jan 1899 00:00:10 GMT") def test_invalidTimestampMonth(self): """ If a request is made with an I{If-Modified-Since} header value which contains a string in the month position which is not a recognized month abbreviation, the header is treated as not having been present and a normal 200 response is returned with a response body containing the resource. """ self._modifiedTest("Thu, 01 Blah 1970 00:00:10 GMT") def test_etagMatchedNot(self): """If-None-Match ETag cache validator (positive)""" self.channel.lineReceived("If-None-Match: unmatchedTag") self.channel.lineReceived('') result = self.transport.getvalue() self.failUnlessEqual(httpCode(result), http.OK) self.failUnlessEqual(httpBody(result), "correct") def test_etagMatched(self): """If-None-Match ETag cache validator (negative)""" self.channel.lineReceived("If-None-Match: MatchingTag") self.channel.lineReceived('') result = self.transport.getvalue() self.failUnlessEqual(httpHeader(result, "ETag"), "MatchingTag") self.failUnlessEqual(httpCode(result), http.NOT_MODIFIED) self.failUnlessEqual(httpBody(result), "") from twisted.web import google class GoogleTestCase(unittest.TestCase): def testCheckGoogle(self): raise unittest.SkipTest("no violation of google ToS") d = google.checkGoogle('site:www.twistedmatrix.com twisted') d.addCallback(self.assertEquals, 'http://twistedmatrix.com/') return d class RequestTests(unittest.TestCase): """ Tests for the HTTP request class, L{server.Request}. """ def test_interface(self): """ L{server.Request} instances provide L{iweb.IRequest}. """ self.assertTrue( verifyObject(iweb.IRequest, server.Request(DummyChannel(), True))) def testChildLink(self): request = server.Request(DummyChannel(), 1) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') self.assertEqual(request.childLink('baz'), 'bar/baz') request = server.Request(DummyChannel(), 1) request.gotLength(0) request.requestReceived('GET', '/foo/bar/', 'HTTP/1.0') self.assertEqual(request.childLink('baz'), 'baz') def testPrePathURLSimple(self): request = server.Request(DummyChannel(), 1) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') request.setHost('example.com', 80) self.assertEqual(request.prePathURL(), 'http://example.com/foo/bar') def testPrePathURLNonDefault(self): d = DummyChannel() d.transport.port = 81 request = server.Request(d, 1) request.setHost('example.com', 81) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') self.assertEqual(request.prePathURL(), 'http://example.com:81/foo/bar') def testPrePathURLSSLPort(self): d = DummyChannel() d.transport.port = 443 request = server.Request(d, 1) request.setHost('example.com', 443) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') self.assertEqual(request.prePathURL(), 'http://example.com:443/foo/bar') def testPrePathURLSSLPortAndSSL(self): d = DummyChannel() d.transport = DummyChannel.SSL() d.transport.port = 443 request = server.Request(d, 1) request.setHost('example.com', 443) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') self.assertEqual(request.prePathURL(), 'https://example.com/foo/bar') def testPrePathURLHTTPPortAndSSL(self): d = DummyChannel() d.transport = DummyChannel.SSL() d.transport.port = 80 request = server.Request(d, 1) request.setHost('example.com', 80) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') self.assertEqual(request.prePathURL(), 'https://example.com:80/foo/bar') def testPrePathURLSSLNonDefault(self): d = DummyChannel() d.transport = DummyChannel.SSL() d.transport.port = 81 request = server.Request(d, 1) request.setHost('example.com', 81) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') self.assertEqual(request.prePathURL(), 'https://example.com:81/foo/bar') def testPrePathURLSetSSLHost(self): d = DummyChannel() d.transport.port = 81 request = server.Request(d, 1) request.setHost('foo.com', 81, 1) request.gotLength(0) request.requestReceived('GET', '/foo/bar', 'HTTP/1.0') self.assertEqual(request.prePathURL(), 'https://foo.com:81/foo/bar') def test_prePathURLQuoting(self): """ L{Request.prePathURL} quotes special characters in the URL segments to preserve the original meaning. """ d = DummyChannel() request = server.Request(d, 1) request.setHost('example.com', 80) request.gotLength(0) request.requestReceived('GET', '/foo%2Fbar', 'HTTP/1.0') self.assertEqual(request.prePathURL(), 'http://example.com/foo%2Fbar') class RootResource(resource.Resource): isLeaf=0 def getChildWithDefault(self, name, request): request.rememberRootURL() return resource.Resource.getChildWithDefault(self, name, request) def render(self, request): return '' class RememberURLTest(unittest.TestCase): def createServer(self, r): chan = DummyChannel() chan.site = server.Site(r) return chan def testSimple(self): r = resource.Resource() r.isLeaf=0 rr = RootResource() r.putChild('foo', rr) rr.putChild('', rr) rr.putChild('bar', resource.Resource()) chan = self.createServer(r) for url in ['/foo/', '/foo/bar', '/foo/bar/baz', '/foo/bar/']: request = server.Request(chan, 1) request.setHost('example.com', 81) request.gotLength(0) request.requestReceived('GET', url, 'HTTP/1.0') self.assertEqual(request.getRootURL(), "http://example.com/foo") def testRoot(self): rr = RootResource() rr.putChild('', rr) rr.putChild('bar', resource.Resource()) chan = self.createServer(rr) for url in ['/', '/bar', '/bar/baz', '/bar/']: request = server.Request(chan, 1) request.setHost('example.com', 81) request.gotLength(0) request.requestReceived('GET', url, 'HTTP/1.0') self.assertEqual(request.getRootURL(), "http://example.com/") class NewRenderResource(resource.Resource): def render_GET(self, request): return "hi hi" def render_HEH(self, request): return "ho ho" class NewRenderTestCase(unittest.TestCase): def _getReq(self): d = DummyChannel() d.site.resource.putChild('newrender', NewRenderResource()) d.transport.port = 81 request = server.Request(d, 1) request.setHost('example.com', 81) request.gotLength(0) return request def testGoodMethods(self): req = self._getReq() req.requestReceived('GET', '/newrender', 'HTTP/1.0') self.assertEquals(req.transport.getvalue().splitlines()[-1], 'hi hi') req = self._getReq() req.requestReceived('HEH', '/newrender', 'HTTP/1.0') self.assertEquals(req.transport.getvalue().splitlines()[-1], 'ho ho') def testBadMethods(self): req = self._getReq() req.requestReceived('CONNECT', '/newrender', 'HTTP/1.0') self.assertEquals(req.code, 501) req = self._getReq() req.requestReceived('hlalauguG', '/newrender', 'HTTP/1.0') self.assertEquals(req.code, 501) def testImplicitHead(self): req = self._getReq() req.requestReceived('HEAD', '/newrender', 'HTTP/1.0') self.assertEquals(req.code, 200) self.assertEquals(-1, req.transport.getvalue().find('hi hi')) class SDResource(resource.Resource): def __init__(self,default): self.default=default def getChildWithDefault(self,name,request): d=defer.succeed(self.default) return util.DeferredResource(d).getChildWithDefault(name, request) class SDTest(unittest.TestCase): def testDeferredResource(self): r = resource.Resource() r.isLeaf = 1 s = SDResource(r) d = DummyRequest(['foo', 'bar', 'baz']) resource.getChildForRequest(s, d) self.assertEqual(d.postpath, ['bar', 'baz']) class DummyRequestForLogTest(DummyRequest): uri = '/dummy' # parent class uri has "http://", which doesn't really happen code = 123 clientproto = 'HTTP/1.0' sentLength = None client = IPv4Address('TCP', '1.2.3.4', 12345) class TestLogEscaping(unittest.TestCase): def setUp(self): self.site = http.HTTPFactory() self.site.logFile = StringIO() self.request = DummyRequestForLogTest(self.site, False) def testSimple(self): http._logDateTime = "[%02d/%3s/%4d:%02d:%02d:%02d +0000]" % ( 25, 'Oct', 2004, 12, 31, 59) self.site.log(self.request) self.site.logFile.seek(0) self.assertEqual( self.site.logFile.read(), '1.2.3.4 - - [25/Oct/2004:12:31:59 +0000] "GET /dummy HTTP/1.0" 123 - "-" "-"\n') def testMethodQuote(self): http._logDateTime = "[%02d/%3s/%4d:%02d:%02d:%02d +0000]" % ( 25, 'Oct', 2004, 12, 31, 59) self.request.method = 'G"T' self.site.log(self.request) self.site.logFile.seek(0) self.assertEqual( self.site.logFile.read(), '1.2.3.4 - - [25/Oct/2004:12:31:59 +0000] "G\\"T /dummy HTTP/1.0" 123 - "-" "-"\n') def testRequestQuote(self): http._logDateTime = "[%02d/%3s/%4d:%02d:%02d:%02d +0000]" % ( 25, 'Oct', 2004, 12, 31, 59) self.request.uri='/dummy"withquote' self.site.log(self.request) self.site.logFile.seek(0) self.assertEqual( self.site.logFile.read(), '1.2.3.4 - - [25/Oct/2004:12:31:59 +0000] "GET /dummy\\"withquote HTTP/1.0" 123 - "-" "-"\n') def testProtoQuote(self): http._logDateTime = "[%02d/%3s/%4d:%02d:%02d:%02d +0000]" % ( 25, 'Oct', 2004, 12, 31, 59) self.request.clientproto='HT"P/1.0' self.site.log(self.request) self.site.logFile.seek(0) self.assertEqual( self.site.logFile.read(), '1.2.3.4 - - [25/Oct/2004:12:31:59 +0000] "GET /dummy HT\\"P/1.0" 123 - "-" "-"\n') def testRefererQuote(self): http._logDateTime = "[%02d/%3s/%4d:%02d:%02d:%02d +0000]" % ( 25, 'Oct', 2004, 12, 31, 59) self.request.headers['referer'] = 'http://malicious" ".website.invalid' self.site.log(self.request) self.site.logFile.seek(0) self.assertEqual( self.site.logFile.read(), '1.2.3.4 - - [25/Oct/2004:12:31:59 +0000] "GET /dummy HTTP/1.0" 123 - "http://malicious\\" \\".website.invalid" "-"\n') def testUserAgentQuote(self): http._logDateTime = "[%02d/%3s/%4d:%02d:%02d:%02d +0000]" % ( 25, 'Oct', 2004, 12, 31, 59) self.request.headers['user-agent'] = 'Malicious Web" Evil' self.site.log(self.request) self.site.logFile.seek(0) self.assertEqual( self.site.logFile.read(), '1.2.3.4 - - [25/Oct/2004:12:31:59 +0000] "GET /dummy HTTP/1.0" 123 - "-" "Malicious Web\\" Evil"\n')

0.437583

0.188884

import logging from threading import Thread, Lock from impacket.structure import Structure lock = Lock() class RemComMessage(Structure): structure = ( ('Command','4096s=""'), ('WorkingDir','260s=""'), ('Priority','<L=0x20'), ('ProcessID','<L=0x01'), ('Machine','260s=""'), ('NoWait','<L=0'), ) class RemComResponse(Structure): structure = ( ('ErrorCode','<L=0'), ('ReturnCode','<L=0'), ) RemComSTDOUT = "RemCom_stdout" RemComSTDIN = "RemCom_stdin" RemComSTDERR = "RemCom_stderr" class CommanderPipes(Thread): def __init__(self, connection, pipe, permissions, share=None): Thread.__init__(self) self.server = connection self.tid = 0 self.fid = 0 self.share = share #self.port = transport.get_dport() self.pipe = pipe self.permissions = permissions self.daemon = True def connectPipe(self): try: lock.acquire() self.tid = self.server.connectTree('IPC$') self.server.waitNamedPipe(self.tid, self.pipe) self.fid = self.server.openFile(self.tid,self.pipe,self.permissions, creationOption = 0x40, fileAttributes = 0x80) self.server.setTimeout(1000000) except: import traceback traceback.print_exc() logging.error("Something wen't wrong connecting the pipes(%s), try again", self.__class__) class CommanderRemoteStdInPipe(CommanderPipes): def __init__(self, connection, pipe, permisssions, share=None): self.shell = None CommanderPipes.__init__(self, connection, pipe, permisssions, share) def run(self): self.connectPipe() #self.shell = RemoteShell(self.server, # self.port, # self.credentials, # self.tid, # self.fid, # self.share, # self.transport) #self.shell.cmdloop() class CommanderRemoteStdOutPipe(CommanderPipes): def __init__(self, connection, pipe, permisssions): CommanderPipes.__init__(self, connection, pipe, permisssions) def run(self): self.connectPipe() while True: try: ans = self.server.readFile(self.tid,self.fid, 0, 1024) except: pass else: try: global LastDataSent if ans != LastDataSent: sys.stdout.write(ans.decode('cp437')) sys.stdout.flush() else: LastDataSent = '' if LastDataSent > 10: LastDataSent = '' except: pass class CommanderRemoteStdErrPipe(CommanderPipes): def __init__(self, connection, pipe, permisssions): CommanderPipes.__init__(self, connection, pipe, permisssions) def run(self): self.connectPipe() while True: try: ans = self.server.readFile(self.tid,self.fid, 0, 1024) except: pass else: try: sys.stderr.write(str(ans)) sys.stderr.flush() except: pass

lib/psexec.py

import logging from threading import Thread, Lock from impacket.structure import Structure lock = Lock() class RemComMessage(Structure): structure = ( ('Command','4096s=""'), ('WorkingDir','260s=""'), ('Priority','<L=0x20'), ('ProcessID','<L=0x01'), ('Machine','260s=""'), ('NoWait','<L=0'), ) class RemComResponse(Structure): structure = ( ('ErrorCode','<L=0'), ('ReturnCode','<L=0'), ) RemComSTDOUT = "RemCom_stdout" RemComSTDIN = "RemCom_stdin" RemComSTDERR = "RemCom_stderr" class CommanderPipes(Thread): def __init__(self, connection, pipe, permissions, share=None): Thread.__init__(self) self.server = connection self.tid = 0 self.fid = 0 self.share = share #self.port = transport.get_dport() self.pipe = pipe self.permissions = permissions self.daemon = True def connectPipe(self): try: lock.acquire() self.tid = self.server.connectTree('IPC$') self.server.waitNamedPipe(self.tid, self.pipe) self.fid = self.server.openFile(self.tid,self.pipe,self.permissions, creationOption = 0x40, fileAttributes = 0x80) self.server.setTimeout(1000000) except: import traceback traceback.print_exc() logging.error("Something wen't wrong connecting the pipes(%s), try again", self.__class__) class CommanderRemoteStdInPipe(CommanderPipes): def __init__(self, connection, pipe, permisssions, share=None): self.shell = None CommanderPipes.__init__(self, connection, pipe, permisssions, share) def run(self): self.connectPipe() #self.shell = RemoteShell(self.server, # self.port, # self.credentials, # self.tid, # self.fid, # self.share, # self.transport) #self.shell.cmdloop() class CommanderRemoteStdOutPipe(CommanderPipes): def __init__(self, connection, pipe, permisssions): CommanderPipes.__init__(self, connection, pipe, permisssions) def run(self): self.connectPipe() while True: try: ans = self.server.readFile(self.tid,self.fid, 0, 1024) except: pass else: try: global LastDataSent if ans != LastDataSent: sys.stdout.write(ans.decode('cp437')) sys.stdout.flush() else: LastDataSent = '' if LastDataSent > 10: LastDataSent = '' except: pass class CommanderRemoteStdErrPipe(CommanderPipes): def __init__(self, connection, pipe, permisssions): CommanderPipes.__init__(self, connection, pipe, permisssions) def run(self): self.connectPipe() while True: try: ans = self.server.readFile(self.tid,self.fid, 0, 1024) except: pass else: try: sys.stderr.write(str(ans)) sys.stderr.flush() except: pass

0.361616

0.039862

import torch import math import numpy as np class LabelGuessor(object): def __init__(self, thresh): self.thresh = thresh def __call__(self, model, ims, balance, delT): org_state = { k: v.clone().detach() for k, v in model.state_dict().items() } is_train = model.training with torch.no_grad(): model.train() all_probs = [] logits = model(ims) probs = torch.softmax(logits, dim=1) scores, lbs = torch.max(probs, dim=1) # print("lbs ", lbs) # print("scores ", scores) mask = torch.ones_like(lbs,dtype=torch.float) labels, counts = np.unique(lbs.cpu(),return_counts=True) # print("labels", labels) # print("counts ", counts) # count_unlabels = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # for i in range(len(labels)): # count_unlabels[labels[i]] = counts[i] mxCount = max(counts) stdClass = np.std(counts) # print("stdClass ", stdClass) if balance > 0: if balance == 1 or balance == 4: idx = (mask == 0.0) else: idx = (scores > self.thresh) delT = 0 if mxCount > 0: ratios = [x/mxCount for x in counts] for i in range(len(labels)): tmp = (scores*(lbs==labels[i]).float()).ge(self.thresh - delT*(1-ratios[i])) # Which elements idx = idx | tmp if balance > 2: labels, counts = np.unique(lbs[idx].cpu(),return_counts=True) ratio = torch.zeros_like(mask,dtype=torch.float) for i in range(len(labels)): ratio += ((1/counts[i])*(lbs==labels[i]).float()) # Magnitude of mask elements Z = torch.sum(mask[idx]) # print("ratio ",ratio) mask = ratio[idx] if Z > 0: mask = Z * mask / torch.sum(mask) else: idx = (scores > self.thresh) mask[idx] = 1.0 else: idx = scores > self.thresh mask = mask[idx] lbs = lbs[idx] # print("1. lbs ", lbs) # print("2. mask ", mask) model.load_state_dict(org_state) if is_train: model.train() else: model.eval() return lbs.detach(), idx, mask, stdClass

PT-BOSS/label_guessor.py

import torch import math import numpy as np class LabelGuessor(object): def __init__(self, thresh): self.thresh = thresh def __call__(self, model, ims, balance, delT): org_state = { k: v.clone().detach() for k, v in model.state_dict().items() } is_train = model.training with torch.no_grad(): model.train() all_probs = [] logits = model(ims) probs = torch.softmax(logits, dim=1) scores, lbs = torch.max(probs, dim=1) # print("lbs ", lbs) # print("scores ", scores) mask = torch.ones_like(lbs,dtype=torch.float) labels, counts = np.unique(lbs.cpu(),return_counts=True) # print("labels", labels) # print("counts ", counts) # count_unlabels = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # for i in range(len(labels)): # count_unlabels[labels[i]] = counts[i] mxCount = max(counts) stdClass = np.std(counts) # print("stdClass ", stdClass) if balance > 0: if balance == 1 or balance == 4: idx = (mask == 0.0) else: idx = (scores > self.thresh) delT = 0 if mxCount > 0: ratios = [x/mxCount for x in counts] for i in range(len(labels)): tmp = (scores*(lbs==labels[i]).float()).ge(self.thresh - delT*(1-ratios[i])) # Which elements idx = idx | tmp if balance > 2: labels, counts = np.unique(lbs[idx].cpu(),return_counts=True) ratio = torch.zeros_like(mask,dtype=torch.float) for i in range(len(labels)): ratio += ((1/counts[i])*(lbs==labels[i]).float()) # Magnitude of mask elements Z = torch.sum(mask[idx]) # print("ratio ",ratio) mask = ratio[idx] if Z > 0: mask = Z * mask / torch.sum(mask) else: idx = (scores > self.thresh) mask[idx] = 1.0 else: idx = scores > self.thresh mask = mask[idx] lbs = lbs[idx] # print("1. lbs ", lbs) # print("2. mask ", mask) model.load_state_dict(org_state) if is_train: model.train() else: model.eval() return lbs.detach(), idx, mask, stdClass

0.247714

0.384768

import matplotlib.pyplot import seaborn import pandas import numpy class TfidfPlotter: color = seaborn.color_palette() def plot( self, dataset, tfidf_mat, tfidf_features, labels, top_n=10, ): print('calculating top tfidf scores per label') matplotlib.pyplot.figure(1) matplotlib.pyplot.suptitle( 'TF-IDF top words per label', fontsize=20, ) for label_index, label in enumerate(labels): print('\tlabel - {label}'.format( label=label, )) comments_label_index = dataset.index[dataset[label] == 1] top_tfidf_features = self.get_top_tfidf_features( tfidf_mat=tfidf_mat[comments_label_index, :], tfidf_features=tfidf_features, top_n=top_n, ) matplotlib.pyplot.subplot( 3, 2, label_index + 1, ) seaborn.barplot( top_tfidf_features.feature.iloc[0:top_n], top_tfidf_features.tfidf_score.iloc[0:top_n], color=self.color[label_index], alpha=0.8, ) matplotlib.pyplot.title( 'label : {label}'.format( label=label, ), fontsize=15, ) matplotlib.pyplot.xlabel('', fontsize=12) matplotlib.pyplot.show() def get_top_tfidf_features( self, tfidf_mat, tfidf_features, top_n, ): tfidf_mean_values = numpy.mean( tfidf_mat.toarray(), axis=0, ) top_n_ids = numpy.argsort(tfidf_mean_values)[::-1][:top_n] top_n_features = [ ( tfidf_features[index], tfidf_mean_values[index], ) for index in top_n_ids ] top_tfidf_features = pandas.DataFrame(top_n_features) top_tfidf_features.columns = [ 'feature', 'tfidf_score', ] return top_tfidf_features

plotting_utils/plot_tfidf.py

import matplotlib.pyplot import seaborn import pandas import numpy class TfidfPlotter: color = seaborn.color_palette() def plot( self, dataset, tfidf_mat, tfidf_features, labels, top_n=10, ): print('calculating top tfidf scores per label') matplotlib.pyplot.figure(1) matplotlib.pyplot.suptitle( 'TF-IDF top words per label', fontsize=20, ) for label_index, label in enumerate(labels): print('\tlabel - {label}'.format( label=label, )) comments_label_index = dataset.index[dataset[label] == 1] top_tfidf_features = self.get_top_tfidf_features( tfidf_mat=tfidf_mat[comments_label_index, :], tfidf_features=tfidf_features, top_n=top_n, ) matplotlib.pyplot.subplot( 3, 2, label_index + 1, ) seaborn.barplot( top_tfidf_features.feature.iloc[0:top_n], top_tfidf_features.tfidf_score.iloc[0:top_n], color=self.color[label_index], alpha=0.8, ) matplotlib.pyplot.title( 'label : {label}'.format( label=label, ), fontsize=15, ) matplotlib.pyplot.xlabel('', fontsize=12) matplotlib.pyplot.show() def get_top_tfidf_features( self, tfidf_mat, tfidf_features, top_n, ): tfidf_mean_values = numpy.mean( tfidf_mat.toarray(), axis=0, ) top_n_ids = numpy.argsort(tfidf_mean_values)[::-1][:top_n] top_n_features = [ ( tfidf_features[index], tfidf_mean_values[index], ) for index in top_n_ids ] top_tfidf_features = pandas.DataFrame(top_n_features) top_tfidf_features.columns = [ 'feature', 'tfidf_score', ] return top_tfidf_features

0.385375

0.333273

import json from Utils import RAR from Utils import Download from rarfile import RarFile from Utils import Json import os class ModelMan : def __init__(self ,googleDriveId:str , name:str , fileName:str): self.name = name self.fileName = fileName self.googleDriveId = googleDriveId def DownloadRAR(self): flag:bool = True try : flag = not RAR.Check(f'./Imports/{self.fileName}.rar') except: pass while flag: print(f'Download Model for {self.fileName}') Download.DownloadFromDrive(self.googleDriveId , f'./Imports/{self.fileName}.rar') flag = flag and not RAR.Check(f'./Imports/{self.fileName}.rar') def Extract(self): rarF = RarFile(f'Imports/{self.fileName}.rar') if len(rarF.namelist()) !=2 : warn = f'Error in rar file of model {self.name}' os.remove(f'Imports/{self.fileName}.rar') raise ValueError(warn) else : cnt =0 for inner in rarF.namelist(): ext = os.path.splitext(inner)[1] if ext.lower() == '.json' : cnt +=1 fileInBytes = rarF.read(inner) dic = Json.ConvertFromBinToJson(fileInBytes , True) self.shape = len(dic) with open(f"./Labels/{self.fileName}.json", "w") as f: json.dump(dic , f) elif ext.lower() == '.h5' : cnt+=1 fileInBytes = rarF.read(inner) with open(f'./Models/{self.fileName}.h5' ,"ab") as f : f.write(fileInBytes) if cnt !=2 : warn = f'Error in rar file (there are files not in same extension) on model {self.name}' os.remove(f'Imports/{self.fileName}.rar') raise ValueError(warn) def CreateRar(self): self.DownloadRAR() self.Extract()

Src/ModelM.py

import json from Utils import RAR from Utils import Download from rarfile import RarFile from Utils import Json import os class ModelMan : def __init__(self ,googleDriveId:str , name:str , fileName:str): self.name = name self.fileName = fileName self.googleDriveId = googleDriveId def DownloadRAR(self): flag:bool = True try : flag = not RAR.Check(f'./Imports/{self.fileName}.rar') except: pass while flag: print(f'Download Model for {self.fileName}') Download.DownloadFromDrive(self.googleDriveId , f'./Imports/{self.fileName}.rar') flag = flag and not RAR.Check(f'./Imports/{self.fileName}.rar') def Extract(self): rarF = RarFile(f'Imports/{self.fileName}.rar') if len(rarF.namelist()) !=2 : warn = f'Error in rar file of model {self.name}' os.remove(f'Imports/{self.fileName}.rar') raise ValueError(warn) else : cnt =0 for inner in rarF.namelist(): ext = os.path.splitext(inner)[1] if ext.lower() == '.json' : cnt +=1 fileInBytes = rarF.read(inner) dic = Json.ConvertFromBinToJson(fileInBytes , True) self.shape = len(dic) with open(f"./Labels/{self.fileName}.json", "w") as f: json.dump(dic , f) elif ext.lower() == '.h5' : cnt+=1 fileInBytes = rarF.read(inner) with open(f'./Models/{self.fileName}.h5' ,"ab") as f : f.write(fileInBytes) if cnt !=2 : warn = f'Error in rar file (there are files not in same extension) on model {self.name}' os.remove(f'Imports/{self.fileName}.rar') raise ValueError(warn) def CreateRar(self): self.DownloadRAR() self.Extract()

0.103942

0.078395

import os import sys import time import text_utils as tu from rfid_mifare_cloner import ( is_tag_reader_connected, create_dump_tag, write_new_tag, check_dependencies_installled, ) from exceptions import TagNotFoundException, NotClassifMifareTagException def welcome_screen(): """ Only shown when the user starts the CLI""" ascii_art = """ ____ ______________ __ ____ ____ ________ / __ \/ ____/ _/ __ \ / |/ (_) __/___ _________ / ____/ /___ ____ ___ _____ / /_/ / /_ / // / / / / /|_/ / / /_/ __ `/ ___/ _ \ / / / / __ \/ __ \/ _ \/ ___/ / _, _/ __/ _/ // /_/ / / / / / / __/ /_/ / / / __/ / /___/ / /_/ / / / / __/ / /_/ |_/_/ /___/_____/ /_/ /_/_/_/ \__,_/_/ \___/ \____/_/\____/_/ /_/\___/_/ """ print(tu.write_text_color(ascii_art, tu.bright_color(tu.YELLOW))) print( tu.write_text_color("#Sorasful " + "\n" * 2, tu.bright_color(tu.BLUE)) ) def first_screen(): """ Screen where the user should plug his reader or has already. """ if not is_tag_reader_connected(): sys.stdout.write( tu.write_text_color( "Please connect your tag reader", tu.bright_color(tu.RED) ) ) sys.stdout.flush() while not is_tag_reader_connected(): time.sleep(0.4) sys.stdout.write(u"\u001b[1000D") # move cursor to left to erase previous message sys.stdout.write( tu.write_text_color( "Tag reader detected and connected ! ", tu.bright_color(tu.GREEN) ) ) sys.stdout.write("\n" * 5) sys.stdout.flush() time.sleep(0.5) def dump_card_screen(tag_to_copy=True): """ The screen where the user will put his card he wants to duplicate on the reader. """ initial_text = ( "Put the tag you want to duplicate on the reader. " if tag_to_copy else "Put the destination tag to clone to. " ) sys.stdout.write(tu.write_text_color(initial_text, tu.bright_color(tu.RED))) sys.stdout.flush() valid_tag = False while not valid_tag: try: create_dump_tag( "to-copy" if tag_to_copy else "destination" ) # should success or raise something. valid_tag = True except TagNotFoundException: sys.stdout.write(u"\u001b[1000D") sys.stdout.write( " " * 80 ) # Clear the previous line which is longer that this one. sys.stdout.write(u"\u001b[1000D") sys.stdout.write(tu.write_text_color("No tag found :" + initial_text, tu.bright_color(tu.RED))) sys.stdout.flush() time.sleep(0.2) except NotClassifMifareTagException: sys.stdout.write(u"\u001b[1000D") sys.stdout.write( tu.write_text_color( "This tag is not a classic mifare tag, it won't work... Please try another", tu.bright_color(tu.RED), ) ) sys.stdout.flush() time.sleep(0.2) except KeyboardInterrupt: sys.exit() except: pass # Occasionnal errors when trying to access to the device too many times ... sys.stdout.write(u"\u001b[1000D") # move cursor to left to erase previous message sys.stdout.write( tu.write_text_color("Card successfully copied ! ", tu.bright_color(tu.GREEN)) ) if tag_to_copy: sys.stdout.write( tu.write_text_color( "Please, remove the tag from the reader within 10 seconds", tu.bright_color(tu.CYAN), ) ) else: sys.stdout.write( tu.write_text_color( "Don't remove the tag, we are processing to the copy of the tag. This shouldn't be long ", tu.bright_color(tu.CYAN), ) ) sys.stdout.write("\n" * 2) sys.stdout.flush() if tag_to_copy: time.sleep(10) def write_tag_screen(): """ The screen when the card is being duplicated to the destination file """ if write_new_tag(tag_to_copy="to-copy", destination="destination"): sys.stdout.write( tu.write_text_color( "Card succesfully duplicated ! You can now test it !\n Enjoy !", tu.bright_color(tu.GREEN), ) ) else: sys.stdout.write( tu.write_text_color( "An error has occured, please retry.", tu.bright_color(tu.RED) ) ) sys.stdout.flush() def command_line(): check_dependencies_installled() welcome_screen() first_screen() dump_card_screen(tag_to_copy=True) # We first copy data we want to duplicate dump_card_screen(tag_to_copy=False) # then we copy on the destination tag write_tag_screen() # clean files os.remove("to-copy.dmp") os.remove("destination.dmp") if __name__ == "__main__": command_line()

RFID_mifare_cloner/cli.py

import os import sys import time import text_utils as tu from rfid_mifare_cloner import ( is_tag_reader_connected, create_dump_tag, write_new_tag, check_dependencies_installled, ) from exceptions import TagNotFoundException, NotClassifMifareTagException def welcome_screen(): """ Only shown when the user starts the CLI""" ascii_art = """ ____ ______________ __ ____ ____ ________ / __ \/ ____/ _/ __ \ / |/ (_) __/___ _________ / ____/ /___ ____ ___ _____ / /_/ / /_ / // / / / / /|_/ / / /_/ __ `/ ___/ _ \ / / / / __ \/ __ \/ _ \/ ___/ / _, _/ __/ _/ // /_/ / / / / / / __/ /_/ / / / __/ / /___/ / /_/ / / / / __/ / /_/ |_/_/ /___/_____/ /_/ /_/_/_/ \__,_/_/ \___/ \____/_/\____/_/ /_/\___/_/ """ print(tu.write_text_color(ascii_art, tu.bright_color(tu.YELLOW))) print( tu.write_text_color("#Sorasful " + "\n" * 2, tu.bright_color(tu.BLUE)) ) def first_screen(): """ Screen where the user should plug his reader or has already. """ if not is_tag_reader_connected(): sys.stdout.write( tu.write_text_color( "Please connect your tag reader", tu.bright_color(tu.RED) ) ) sys.stdout.flush() while not is_tag_reader_connected(): time.sleep(0.4) sys.stdout.write(u"\u001b[1000D") # move cursor to left to erase previous message sys.stdout.write( tu.write_text_color( "Tag reader detected and connected ! ", tu.bright_color(tu.GREEN) ) ) sys.stdout.write("\n" * 5) sys.stdout.flush() time.sleep(0.5) def dump_card_screen(tag_to_copy=True): """ The screen where the user will put his card he wants to duplicate on the reader. """ initial_text = ( "Put the tag you want to duplicate on the reader. " if tag_to_copy else "Put the destination tag to clone to. " ) sys.stdout.write(tu.write_text_color(initial_text, tu.bright_color(tu.RED))) sys.stdout.flush() valid_tag = False while not valid_tag: try: create_dump_tag( "to-copy" if tag_to_copy else "destination" ) # should success or raise something. valid_tag = True except TagNotFoundException: sys.stdout.write(u"\u001b[1000D") sys.stdout.write( " " * 80 ) # Clear the previous line which is longer that this one. sys.stdout.write(u"\u001b[1000D") sys.stdout.write(tu.write_text_color("No tag found :" + initial_text, tu.bright_color(tu.RED))) sys.stdout.flush() time.sleep(0.2) except NotClassifMifareTagException: sys.stdout.write(u"\u001b[1000D") sys.stdout.write( tu.write_text_color( "This tag is not a classic mifare tag, it won't work... Please try another", tu.bright_color(tu.RED), ) ) sys.stdout.flush() time.sleep(0.2) except KeyboardInterrupt: sys.exit() except: pass # Occasionnal errors when trying to access to the device too many times ... sys.stdout.write(u"\u001b[1000D") # move cursor to left to erase previous message sys.stdout.write( tu.write_text_color("Card successfully copied ! ", tu.bright_color(tu.GREEN)) ) if tag_to_copy: sys.stdout.write( tu.write_text_color( "Please, remove the tag from the reader within 10 seconds", tu.bright_color(tu.CYAN), ) ) else: sys.stdout.write( tu.write_text_color( "Don't remove the tag, we are processing to the copy of the tag. This shouldn't be long ", tu.bright_color(tu.CYAN), ) ) sys.stdout.write("\n" * 2) sys.stdout.flush() if tag_to_copy: time.sleep(10) def write_tag_screen(): """ The screen when the card is being duplicated to the destination file """ if write_new_tag(tag_to_copy="to-copy", destination="destination"): sys.stdout.write( tu.write_text_color( "Card succesfully duplicated ! You can now test it !\n Enjoy !", tu.bright_color(tu.GREEN), ) ) else: sys.stdout.write( tu.write_text_color( "An error has occured, please retry.", tu.bright_color(tu.RED) ) ) sys.stdout.flush() def command_line(): check_dependencies_installled() welcome_screen() first_screen() dump_card_screen(tag_to_copy=True) # We first copy data we want to duplicate dump_card_screen(tag_to_copy=False) # then we copy on the destination tag write_tag_screen() # clean files os.remove("to-copy.dmp") os.remove("destination.dmp") if __name__ == "__main__": command_line()

0.268174

0.074703

from __future__ import unicode_literals import core.models from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('proposals', '0020_auto_20160202_1048'), ] operations = [ migrations.AlterField( model_name='talkproposal', name='abstract', field=core.models.EAWTextField(help_text='The overview of what the talk is about. If the talk assume some domain knowledge please state it here. If your talk is accepted, this will be displayed on both the website and the handbook. Should be one paragraph.', max_length=500, verbose_name='abstract'), ), migrations.AlterField( model_name='talkproposal', name='objective', field=core.models.EAWTextField(help_text='Who is the intended audience for your talk? (Be specific, "Python users" is not a good answer). And what will the attendees get out of your talk? When they leave the room, what will they learn that they didn\'t know before? This is NOT made public and for REVIEW ONLY.', max_length=500, verbose_name='objective'), ), migrations.AlterField( model_name='tutorialproposal', name='abstract', field=core.models.EAWTextField(help_text='The overview of what the talk is about. If the talk assume some domain knowledge please state it here. If your talk is accepted, this will be displayed on both the website and the handbook. Should be one paragraph.', max_length=500, verbose_name='abstract'), ), migrations.AlterField( model_name='tutorialproposal', name='objective', field=core.models.EAWTextField(help_text='Who is the intended audience for your talk? (Be specific, "Python users" is not a good answer). And what will the attendees get out of your talk? When they leave the room, what will they learn that they didn\'t know before? This is NOT made public and for REVIEW ONLY.', max_length=500, verbose_name='objective'), ), ]

src/proposals/migrations/0021_auto_20160222_0709.py

from __future__ import unicode_literals import core.models from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('proposals', '0020_auto_20160202_1048'), ] operations = [ migrations.AlterField( model_name='talkproposal', name='abstract', field=core.models.EAWTextField(help_text='The overview of what the talk is about. If the talk assume some domain knowledge please state it here. If your talk is accepted, this will be displayed on both the website and the handbook. Should be one paragraph.', max_length=500, verbose_name='abstract'), ), migrations.AlterField( model_name='talkproposal', name='objective', field=core.models.EAWTextField(help_text='Who is the intended audience for your talk? (Be specific, "Python users" is not a good answer). And what will the attendees get out of your talk? When they leave the room, what will they learn that they didn\'t know before? This is NOT made public and for REVIEW ONLY.', max_length=500, verbose_name='objective'), ), migrations.AlterField( model_name='tutorialproposal', name='abstract', field=core.models.EAWTextField(help_text='The overview of what the talk is about. If the talk assume some domain knowledge please state it here. If your talk is accepted, this will be displayed on both the website and the handbook. Should be one paragraph.', max_length=500, verbose_name='abstract'), ), migrations.AlterField( model_name='tutorialproposal', name='objective', field=core.models.EAWTextField(help_text='Who is the intended audience for your talk? (Be specific, "Python users" is not a good answer). And what will the attendees get out of your talk? When they leave the room, what will they learn that they didn\'t know before? This is NOT made public and for REVIEW ONLY.', max_length=500, verbose_name='objective'), ), ]

0.58522

0.17989

from discord import Guild from discord.errors import NotFound from contextlib import suppress from src.utils import channel_to_dict, member_to_dict, role_to_dict, user_to_dict, guild_to_dict class Pools: def __init__(self, bot): self.nonexistant = [] self.bot = bot async def get_all_emoji(self, guild: Guild): try: emoji_manager = self.bot.get_cog("Emoji Manager").managers[guild.id] except KeyError: return [] return [await e.as_dict_url() for e in emoji_manager.emojis] async def get_emoji(self, guild: Guild, emoji_id, fetch=False): if fetch: return {} try: emoji_manager = self.bot.get_cog("Emoji Manager").managers[guild.id] except KeyError: raise emoji = emoji_manager.find_emoji(a_id=emoji_id) return await emoji.as_dict_url() def get_all_channels(self, guild: Guild): return [channel_to_dict(ch) for ch in guild.channels] def get_all_roles(self, guild: Guild): return [role_to_dict(r) for r in guild.roles] def get_all_members(self, guild: Guild): return [member_to_dict(m) for m in guild.members] async def get_member(self, guild: Guild, member_id, fetch=False): if member_id in self.nonexistant: raise NotFound(f"unknown member {member_id}") member = guild.get_member(int(member_id)) if member is None and fetch: try: member = await guild.fetch_member(int(member_id)) except NotFound: self.nonexistant.append(member_id) if member is None: raise NotFound(f"unknown member {member_id}") return member_to_dict(member) async def get_user(self, user_id, fetch=False): if user_id in self.nonexistant: raise NotFound(f"unknown user {user_id}") user = self.bot.get_user(int(user_id)) if user is None and fetch: try: user = await self.bot.fetch_user(int(user_id)) except NotFound: self.nonexistant.append(user_id) if user is None: raise NotFound(f"unknown user {user_id}") return user_to_dict(user) def get_all_responses(self, guild: Guild): try: auto_responses = self.bot.get_cog("Auto Responses").responses[guild.id] except KeyError: return [] return [r.as_dict() for r in auto_responses.auto_responses] async def get_guild(self, member_id: int, guild_id: int, fetch=False): guild = self.bot.get_guild(int(guild_id)) if guild is None and fetch: with suppress(NotFound): guild = await self.bot.fetch_guild(int(guild_id)) if guild is None: raise NotFound(f"unknown guild {guild_id}, attempted fetch: {fetch}") member = guild.get_member(int(member_id)) settings = self.bot.settings[guild] g = guild_to_dict(guild) g.update({ 'owner': int(g['owner_id']) == int(member_id), 'has_architus': True, 'architus_admin': int(member_id) in settings.admins_ids, 'permissions': member.guild_permissions.value, }) return g

shard/src/api/pools.py

from discord import Guild from discord.errors import NotFound from contextlib import suppress from src.utils import channel_to_dict, member_to_dict, role_to_dict, user_to_dict, guild_to_dict class Pools: def __init__(self, bot): self.nonexistant = [] self.bot = bot async def get_all_emoji(self, guild: Guild): try: emoji_manager = self.bot.get_cog("Emoji Manager").managers[guild.id] except KeyError: return [] return [await e.as_dict_url() for e in emoji_manager.emojis] async def get_emoji(self, guild: Guild, emoji_id, fetch=False): if fetch: return {} try: emoji_manager = self.bot.get_cog("Emoji Manager").managers[guild.id] except KeyError: raise emoji = emoji_manager.find_emoji(a_id=emoji_id) return await emoji.as_dict_url() def get_all_channels(self, guild: Guild): return [channel_to_dict(ch) for ch in guild.channels] def get_all_roles(self, guild: Guild): return [role_to_dict(r) for r in guild.roles] def get_all_members(self, guild: Guild): return [member_to_dict(m) for m in guild.members] async def get_member(self, guild: Guild, member_id, fetch=False): if member_id in self.nonexistant: raise NotFound(f"unknown member {member_id}") member = guild.get_member(int(member_id)) if member is None and fetch: try: member = await guild.fetch_member(int(member_id)) except NotFound: self.nonexistant.append(member_id) if member is None: raise NotFound(f"unknown member {member_id}") return member_to_dict(member) async def get_user(self, user_id, fetch=False): if user_id in self.nonexistant: raise NotFound(f"unknown user {user_id}") user = self.bot.get_user(int(user_id)) if user is None and fetch: try: user = await self.bot.fetch_user(int(user_id)) except NotFound: self.nonexistant.append(user_id) if user is None: raise NotFound(f"unknown user {user_id}") return user_to_dict(user) def get_all_responses(self, guild: Guild): try: auto_responses = self.bot.get_cog("Auto Responses").responses[guild.id] except KeyError: return [] return [r.as_dict() for r in auto_responses.auto_responses] async def get_guild(self, member_id: int, guild_id: int, fetch=False): guild = self.bot.get_guild(int(guild_id)) if guild is None and fetch: with suppress(NotFound): guild = await self.bot.fetch_guild(int(guild_id)) if guild is None: raise NotFound(f"unknown guild {guild_id}, attempted fetch: {fetch}") member = guild.get_member(int(member_id)) settings = self.bot.settings[guild] g = guild_to_dict(guild) g.update({ 'owner': int(g['owner_id']) == int(member_id), 'has_architus': True, 'architus_admin': int(member_id) in settings.admins_ids, 'permissions': member.guild_permissions.value, }) return g

0.494629

0.097262

import sys import pandas as pd from sqlalchemy import create_engine def transform_categories(categories_df): """ Makes transformations in the categories data. Arguments: categories_df: the categories dataframe Output: categories_df_trans: the transformed categories dataframe """ categories_df_trans = categories_df.copy() #Store rows ids ids = categories_df_trans['id'] categories_df_trans = categories_df_trans['categories'].str.split(";",expand=True) row = categories_df_trans.iloc[0,:] category_colnames = row.apply(lambda x: x.split('-')[0]).values categories_df_trans.columns = category_colnames for column in categories_df_trans: # set each value to be the last character of the string categories_df_trans[column] = categories_df_trans[column].apply(lambda x: x.split('-')[1]) # convert column from string to numeric categories_df_trans[column] = categories_df_trans[column].apply(lambda x: int(x)) #Re-assign ids categories_df_trans['id'] = ids return categories_df_trans def transform_data(categories_df, messages_df): """ Performs all the necessary data transformations. Arguments: categories_df: categories dataframe messages_df: messages dataframe Output: df: the transformed dataframe """ categories_df_trans = transform_categories(categories_df) df = pd.merge(messages_df, categories_df_trans, how='inner', on='id') df = df[~df.duplicated()] return df def load_data(messages_filepath, categories_filepath): """ Reads the raw data saved as csv files and convert it into dataframes. Arguments: messages_filepath: path to the messages.csv file categories_filepath: path to the categories.csv file Output: messages_df: messages dataframe categories_df: categories dataframe """ messages_df = pd.read_csv(messages_filepath) categories_df = pd.read_csv(categories_filepath) return messages_df, categories_df def save_data(df, database_filename): """ Saves the clean modeling data into a SQLite database. Arguments: df: the transformed dataframe database_filename: the resulting database name Output: None """ engine = create_engine(f"sqlite:///{database_filename}") df.to_sql(name=database_filename.split('/')[1].split('.')[0], con=engine, if_exists = 'replace', index=False) def main(): if len(sys.argv) == 4: messages_filepath, categories_filepath, database_filepath = sys.argv[1:] print('Loading data...\n MESSAGES: {}\n CATEGORIES: {}' .format(messages_filepath, categories_filepath)) messages_df, categories_df = load_data(messages_filepath, categories_filepath) print('Cleaning data...') df = transform_data(categories_df, messages_df) print('Saving data...\n DATABASE: {}'.format(database_filepath)) save_data(df, database_filepath) print('Cleaned data saved to database!') else: print('Please provide the filepaths of the messages and categories '\ 'datasets as the first and second argument respectively, as '\ 'well as the filepath of the database to save the cleaned data '\ 'to as the third argument. \n\nExample: python process_data.py '\ 'disaster_messages.csv disaster_categories.csv '\ 'DisasterResponse.db') if __name__ == '__main__': main()

data/process_data.py

import sys import pandas as pd from sqlalchemy import create_engine def transform_categories(categories_df): """ Makes transformations in the categories data. Arguments: categories_df: the categories dataframe Output: categories_df_trans: the transformed categories dataframe """ categories_df_trans = categories_df.copy() #Store rows ids ids = categories_df_trans['id'] categories_df_trans = categories_df_trans['categories'].str.split(";",expand=True) row = categories_df_trans.iloc[0,:] category_colnames = row.apply(lambda x: x.split('-')[0]).values categories_df_trans.columns = category_colnames for column in categories_df_trans: # set each value to be the last character of the string categories_df_trans[column] = categories_df_trans[column].apply(lambda x: x.split('-')[1]) # convert column from string to numeric categories_df_trans[column] = categories_df_trans[column].apply(lambda x: int(x)) #Re-assign ids categories_df_trans['id'] = ids return categories_df_trans def transform_data(categories_df, messages_df): """ Performs all the necessary data transformations. Arguments: categories_df: categories dataframe messages_df: messages dataframe Output: df: the transformed dataframe """ categories_df_trans = transform_categories(categories_df) df = pd.merge(messages_df, categories_df_trans, how='inner', on='id') df = df[~df.duplicated()] return df def load_data(messages_filepath, categories_filepath): """ Reads the raw data saved as csv files and convert it into dataframes. Arguments: messages_filepath: path to the messages.csv file categories_filepath: path to the categories.csv file Output: messages_df: messages dataframe categories_df: categories dataframe """ messages_df = pd.read_csv(messages_filepath) categories_df = pd.read_csv(categories_filepath) return messages_df, categories_df def save_data(df, database_filename): """ Saves the clean modeling data into a SQLite database. Arguments: df: the transformed dataframe database_filename: the resulting database name Output: None """ engine = create_engine(f"sqlite:///{database_filename}") df.to_sql(name=database_filename.split('/')[1].split('.')[0], con=engine, if_exists = 'replace', index=False) def main(): if len(sys.argv) == 4: messages_filepath, categories_filepath, database_filepath = sys.argv[1:] print('Loading data...\n MESSAGES: {}\n CATEGORIES: {}' .format(messages_filepath, categories_filepath)) messages_df, categories_df = load_data(messages_filepath, categories_filepath) print('Cleaning data...') df = transform_data(categories_df, messages_df) print('Saving data...\n DATABASE: {}'.format(database_filepath)) save_data(df, database_filepath) print('Cleaned data saved to database!') else: print('Please provide the filepaths of the messages and categories '\ 'datasets as the first and second argument respectively, as '\ 'well as the filepath of the database to save the cleaned data '\ 'to as the third argument. \n\nExample: python process_data.py '\ 'disaster_messages.csv disaster_categories.csv '\ 'DisasterResponse.db') if __name__ == '__main__': main()

0.639624

0.51251

class Entry: def __init__(self, tagged, untagged): self.tagged = tagged self.untagged = untagged def get_tagged(self): return self.tagged def get_untagged(self): return self.untagged @staticmethod def decode(data): f_tagged = None if "tagged" in data: f_tagged = data["tagged"] if f_tagged is not None: f_tagged = Tagged.decode(f_tagged) f_untagged = None if "untagged" in data: f_untagged = data["untagged"] if f_untagged is not None: f_untagged = Untagged.decode(f_untagged) return Entry(f_tagged, f_untagged) def encode(self): data = dict() if self.tagged is not None: data["tagged"] = self.tagged.encode() if self.untagged is not None: data["untagged"] = self.untagged.encode() return data def __repr__(self): return "<Entry tagged:{!r}, untagged:{!r}>".format(self.tagged, self.untagged) class Tagged: @staticmethod def decode(data): f_tag = data["@type"] if f_tag == "foo": return Tagged_A.decode(data) if f_tag == "b": return Tagged_B.decode(data) if f_tag == "Bar": return Tagged_Bar.decode(data) if f_tag == "Baz": return Tagged_Baz.decode(data) raise Exception("bad type: " + f_tag) class Tagged_A: TYPE = "foo" def __init__(self, shared): self.shared = shared def get_shared(self): return self.shared @staticmethod def decode(data): f_shared = data["shared"] if not isinstance(f_shared, unicode): raise Exception("not a string") return Tagged_A(f_shared) def encode(self): data = dict() data["@type"] = "foo" if self.shared is None: raise Exception("shared: is a required field") data["shared"] = self.shared return data def __repr__(self): return "<Tagged_A shared:{!r}>".format(self.shared) class Tagged_B: TYPE = "b" def __init__(self, shared): self.shared = shared def get_shared(self): return self.shared @staticmethod def decode(data): f_shared = data["shared"] if not isinstance(f_shared, unicode): raise Exception("not a string") return Tagged_B(f_shared) def encode(self): data = dict() data["@type"] = "b" if self.shared is None: raise Exception("shared: is a required field") data["shared"] = self.shared return data def __repr__(self): return "<Tagged_B shared:{!r}>".format(self.shared) class Tagged_Bar: TYPE = "Bar" def __init__(self, shared): self.shared = shared def get_shared(self): return self.shared @staticmethod def decode(data): f_shared = data["shared"] if not isinstance(f_shared, unicode): raise Exception("not a string") return Tagged_Bar(f_shared) def encode(self): data = dict() data["@type"] = "Bar" if self.shared is None: raise Exception("shared: is a required field") data["shared"] = self.shared return data def __repr__(self): return "<Tagged_Bar shared:{!r}>".format(self.shared) class Tagged_Baz: TYPE = "Baz" def __init__(self, shared): self.shared = shared def get_shared(self): return self.shared @staticmethod def decode(data): f_shared = data["shared"] if not isinstance(f_shared, unicode): raise Exception("not a string") return Tagged_Baz(f_shared) def encode(self): data = dict() data["@type"] = "Baz" if self.shared is None: raise Exception("shared: is a required field") data["shared"] = self.shared return data def __repr__(self): return "<Tagged_Baz shared:{!r}>".format(self.shared) class Untagged: @staticmethod def decode(data): keys = set(data.keys()) if keys >= set(("a", "b")): return Untagged_A.decode(data) if keys >= set(("a",)): return Untagged_B.decode(data) if keys >= set(("b",)): return Untagged_C.decode(data) raise Exception("no sub type matching the given fields: " + repr(keys)) class Untagged_A: TYPE = "A" def __init__(self, shared, shared_ignore, a, b, ignore): self.shared = shared self.shared_ignore = shared_ignore self.a = a self.b = b self.ignore = ignore def get_shared(self): return self.shared def get_shared_ignore(self): return self.shared_ignore def get_a(self): return self.a def get_b(self): return self.b def get_ignore(self): return self.ignore @staticmethod def decode(data): f_shared = data["shared"] if not isinstance(f_shared, unicode): raise Exception("not a string") f_shared_ignore = None if "shared_ignore" in data: f_shared_ignore = data["shared_ignore"] if f_shared_ignore is not None: if not isinstance(f_shared_ignore, unicode): raise Exception("not a string") f_a = data["a"] if not isinstance(f_a, unicode): raise Exception("not a string") f_b = data["b"] if not isinstance(f_b, unicode): raise Exception("not a string") f_ignore = None if "ignore" in data: f_ignore = data["ignore"] if f_ignore is not None: if not isinstance(f_ignore, unicode): raise Exception("not a string") return Untagged_A(f_shared, f_shared_ignore, f_a, f_b, f_ignore) def encode(self): data = dict() if self.shared is None: raise Exception("shared: is a required field") data["shared"] = self.shared if self.shared_ignore is not None: data["shared_ignore"] = self.shared_ignore if self.a is None: raise Exception("a: is a required field") data["a"] = self.a if self.b is None: raise Exception("b: is a required field") data["b"] = self.b if self.ignore is not None: data["ignore"] = self.ignore return data def __repr__(self): return "<Untagged_A shared:{!r}, shared_ignore:{!r}, a:{!r}, b:{!r}, ignore:{!r}>".format(self.shared, self.shared_ignore, self.a, self.b, self.ignore) class Untagged_B: TYPE = "B" def __init__(self, shared, shared_ignore, a, ignore): self.shared = shared self.shared_ignore = shared_ignore self.a = a self.ignore = ignore def get_shared(self): return self.shared def get_shared_ignore(self): return self.shared_ignore def get_a(self): return self.a def get_ignore(self): return self.ignore @staticmethod def decode(data): f_shared = data["shared"] if not isinstance(f_shared, unicode): raise Exception("not a string") f_shared_ignore = None if "shared_ignore" in data: f_shared_ignore = data["shared_ignore"] if f_shared_ignore is not None: if not isinstance(f_shared_ignore, unicode): raise Exception("not a string") f_a = data["a"] if not isinstance(f_a, unicode): raise Exception("not a string") f_ignore = None if "ignore" in data: f_ignore = data["ignore"] if f_ignore is not None: if not isinstance(f_ignore, unicode): raise Exception("not a string") return Untagged_B(f_shared, f_shared_ignore, f_a, f_ignore) def encode(self): data = dict() if self.shared is None: raise Exception("shared: is a required field") data["shared"] = self.shared if self.shared_ignore is not None: data["shared_ignore"] = self.shared_ignore if self.a is None: raise Exception("a: is a required field") data["a"] = self.a if self.ignore is not None: data["ignore"] = self.ignore return data def __repr__(self): return "<Untagged_B shared:{!r}, shared_ignore:{!r}, a:{!r}, ignore:{!r}>".format(self.shared, self.shared_ignore, self.a, self.ignore) class Untagged_C: TYPE = "C" def __init__(self, shared, shared_ignore, b, ignore): self.shared = shared self.shared_ignore = shared_ignore self.b = b self.ignore = ignore def get_shared(self): return self.shared def get_shared_ignore(self): return self.shared_ignore def get_b(self): return self.b def get_ignore(self): return self.ignore @staticmethod def decode(data): f_shared = data["shared"] if not isinstance(f_shared, unicode): raise Exception("not a string") f_shared_ignore = None if "shared_ignore" in data: f_shared_ignore = data["shared_ignore"] if f_shared_ignore is not None: if not isinstance(f_shared_ignore, unicode): raise Exception("not a string") f_b = data["b"] if not isinstance(f_b, unicode): raise Exception("not a string") f_ignore = None if "ignore" in data: f_ignore = data["ignore"] if f_ignore is not None: if not isinstance(f_ignore, unicode): raise Exception("not a string") return Untagged_C(f_shared, f_shared_ignore, f_b, f_ignore) def encode(self): data = dict() if self.shared is None: raise Exception("shared: is a required field") data["shared"] = self.shared if self.shared_ignore is not None: data["shared_ignore"] = self.shared_ignore if self.b is None: raise Exception("b: is a required field") data["b"] = self.b if self.ignore is not None: data["ignore"] = self.ignore return data def __repr__(self): return "<Untagged_C shared:{!r}, shared_ignore:{!r}, b:{!r}, ignore:{!r}>".format(self.shared, self.shared_ignore, self.b, self.ignore)

it/interfaces/structures/default/python/test.py

class Entry: def __init__(self, tagged, untagged): self.tagged = tagged self.untagged = untagged def get_tagged(self): return self.tagged def get_untagged(self): return self.untagged @staticmethod def decode(data): f_tagged = None if "tagged" in data: f_tagged = data["tagged"] if f_tagged is not None: f_tagged = Tagged.decode(f_tagged) f_untagged = None if "untagged" in data: f_untagged = data["untagged"] if f_untagged is not None: f_untagged = Untagged.decode(f_untagged) return Entry(f_tagged, f_untagged) def encode(self): data = dict() if self.tagged is not None: data["tagged"] = self.tagged.encode() if self.untagged is not None: data["untagged"] = self.untagged.encode() return data def __repr__(self): return "<Entry tagged:{!r}, untagged:{!r}>".format(self.tagged, self.untagged) class Tagged: @staticmethod def decode(data): f_tag = data["@type"] if f_tag == "foo": return Tagged_A.decode(data) if f_tag == "b": return Tagged_B.decode(data) if f_tag == "Bar": return Tagged_Bar.decode(data) if f_tag == "Baz": return Tagged_Baz.decode(data) raise Exception("bad type: " + f_tag) class Tagged_A: TYPE = "foo" def __init__(self, shared): self.shared = shared def get_shared(self): return self.shared @staticmethod def decode(data): f_shared = data["shared"] if not isinstance(f_shared, unicode): raise Exception("not a string") return Tagged_A(f_shared) def encode(self): data = dict() data["@type"] = "foo" if self.shared is None: raise Exception("shared: is a required field") data["shared"] = self.shared return data def __repr__(self): return "<Tagged_A shared:{!r}>".format(self.shared) class Tagged_B: TYPE = "b" def __init__(self, shared): self.shared = shared def get_shared(self): return self.shared @staticmethod def decode(data): f_shared = data["shared"] if not isinstance(f_shared, unicode): raise Exception("not a string") return Tagged_B(f_shared) def encode(self): data = dict() data["@type"] = "b" if self.shared is None: raise Exception("shared: is a required field") data["shared"] = self.shared return data def __repr__(self): return "<Tagged_B shared:{!r}>".format(self.shared) class Tagged_Bar: TYPE = "Bar" def __init__(self, shared): self.shared = shared def get_shared(self): return self.shared @staticmethod def decode(data): f_shared = data["shared"] if not isinstance(f_shared, unicode): raise Exception("not a string") return Tagged_Bar(f_shared) def encode(self): data = dict() data["@type"] = "Bar" if self.shared is None: raise Exception("shared: is a required field") data["shared"] = self.shared return data def __repr__(self): return "<Tagged_Bar shared:{!r}>".format(self.shared) class Tagged_Baz: TYPE = "Baz" def __init__(self, shared): self.shared = shared def get_shared(self): return self.shared @staticmethod def decode(data): f_shared = data["shared"] if not isinstance(f_shared, unicode): raise Exception("not a string") return Tagged_Baz(f_shared) def encode(self): data = dict() data["@type"] = "Baz" if self.shared is None: raise Exception("shared: is a required field") data["shared"] = self.shared return data def __repr__(self): return "<Tagged_Baz shared:{!r}>".format(self.shared) class Untagged: @staticmethod def decode(data): keys = set(data.keys()) if keys >= set(("a", "b")): return Untagged_A.decode(data) if keys >= set(("a",)): return Untagged_B.decode(data) if keys >= set(("b",)): return Untagged_C.decode(data) raise Exception("no sub type matching the given fields: " + repr(keys)) class Untagged_A: TYPE = "A" def __init__(self, shared, shared_ignore, a, b, ignore): self.shared = shared self.shared_ignore = shared_ignore self.a = a self.b = b self.ignore = ignore def get_shared(self): return self.shared def get_shared_ignore(self): return self.shared_ignore def get_a(self): return self.a def get_b(self): return self.b def get_ignore(self): return self.ignore @staticmethod def decode(data): f_shared = data["shared"] if not isinstance(f_shared, unicode): raise Exception("not a string") f_shared_ignore = None if "shared_ignore" in data: f_shared_ignore = data["shared_ignore"] if f_shared_ignore is not None: if not isinstance(f_shared_ignore, unicode): raise Exception("not a string") f_a = data["a"] if not isinstance(f_a, unicode): raise Exception("not a string") f_b = data["b"] if not isinstance(f_b, unicode): raise Exception("not a string") f_ignore = None if "ignore" in data: f_ignore = data["ignore"] if f_ignore is not None: if not isinstance(f_ignore, unicode): raise Exception("not a string") return Untagged_A(f_shared, f_shared_ignore, f_a, f_b, f_ignore) def encode(self): data = dict() if self.shared is None: raise Exception("shared: is a required field") data["shared"] = self.shared if self.shared_ignore is not None: data["shared_ignore"] = self.shared_ignore if self.a is None: raise Exception("a: is a required field") data["a"] = self.a if self.b is None: raise Exception("b: is a required field") data["b"] = self.b if self.ignore is not None: data["ignore"] = self.ignore return data def __repr__(self): return "<Untagged_A shared:{!r}, shared_ignore:{!r}, a:{!r}, b:{!r}, ignore:{!r}>".format(self.shared, self.shared_ignore, self.a, self.b, self.ignore) class Untagged_B: TYPE = "B" def __init__(self, shared, shared_ignore, a, ignore): self.shared = shared self.shared_ignore = shared_ignore self.a = a self.ignore = ignore def get_shared(self): return self.shared def get_shared_ignore(self): return self.shared_ignore def get_a(self): return self.a def get_ignore(self): return self.ignore @staticmethod def decode(data): f_shared = data["shared"] if not isinstance(f_shared, unicode): raise Exception("not a string") f_shared_ignore = None if "shared_ignore" in data: f_shared_ignore = data["shared_ignore"] if f_shared_ignore is not None: if not isinstance(f_shared_ignore, unicode): raise Exception("not a string") f_a = data["a"] if not isinstance(f_a, unicode): raise Exception("not a string") f_ignore = None if "ignore" in data: f_ignore = data["ignore"] if f_ignore is not None: if not isinstance(f_ignore, unicode): raise Exception("not a string") return Untagged_B(f_shared, f_shared_ignore, f_a, f_ignore) def encode(self): data = dict() if self.shared is None: raise Exception("shared: is a required field") data["shared"] = self.shared if self.shared_ignore is not None: data["shared_ignore"] = self.shared_ignore if self.a is None: raise Exception("a: is a required field") data["a"] = self.a if self.ignore is not None: data["ignore"] = self.ignore return data def __repr__(self): return "<Untagged_B shared:{!r}, shared_ignore:{!r}, a:{!r}, ignore:{!r}>".format(self.shared, self.shared_ignore, self.a, self.ignore) class Untagged_C: TYPE = "C" def __init__(self, shared, shared_ignore, b, ignore): self.shared = shared self.shared_ignore = shared_ignore self.b = b self.ignore = ignore def get_shared(self): return self.shared def get_shared_ignore(self): return self.shared_ignore def get_b(self): return self.b def get_ignore(self): return self.ignore @staticmethod def decode(data): f_shared = data["shared"] if not isinstance(f_shared, unicode): raise Exception("not a string") f_shared_ignore = None if "shared_ignore" in data: f_shared_ignore = data["shared_ignore"] if f_shared_ignore is not None: if not isinstance(f_shared_ignore, unicode): raise Exception("not a string") f_b = data["b"] if not isinstance(f_b, unicode): raise Exception("not a string") f_ignore = None if "ignore" in data: f_ignore = data["ignore"] if f_ignore is not None: if not isinstance(f_ignore, unicode): raise Exception("not a string") return Untagged_C(f_shared, f_shared_ignore, f_b, f_ignore) def encode(self): data = dict() if self.shared is None: raise Exception("shared: is a required field") data["shared"] = self.shared if self.shared_ignore is not None: data["shared_ignore"] = self.shared_ignore if self.b is None: raise Exception("b: is a required field") data["b"] = self.b if self.ignore is not None: data["ignore"] = self.ignore return data def __repr__(self): return "<Untagged_C shared:{!r}, shared_ignore:{!r}, b:{!r}, ignore:{!r}>".format(self.shared, self.shared_ignore, self.b, self.ignore)

0.781247

0.221793

__author__ = '<NAME>' __date__ = '2019-11-17' __copyright__ = '(C) 2019, <NAME>' from PyQt5.QtCore import QCoreApplication, QVariant from qgis.core import (QgsProcessing, QgsFeatureSink, QgsProcessingException, QgsProcessingAlgorithm, QgsProcessingParameterFeatureSource, QgsProcessingParameterFeatureSink, QgsProcessingParameterString, QgsProcessingParameterPoint, QgsProcessingParameterNumber, QgsProcessingParameterCrs, QgsProcessingParameterFileDestination, QgsFields, QgsField, QgsWkbTypes, QgsFeature, QgsGeometry, QgsPointXY, QgsPoint, QgsApplication ) from numpy import radians, arctan, pi, sin, cos, matrix, sqrt, degrees, array, diag, ones, zeros, floor from numpy.linalg import norm, pinv, inv from lftools.geocapt.imgs import Imgs from lftools.geocapt.topogeo import * import os from qgis.PyQt.QtGui import QIcon class TraverseAdjustment(QgsProcessingAlgorithm): A = 'A' B = 'B' Y = 'Y' Z = 'Z' DIST = 'DIST' ANGS = 'ANGS' DIST_PREC = 'DIST_PREC' PPM = 'PPM' ANGS_PREC = 'ANGS_PREC' CRS = 'CRS' OUTPUT = 'OUTPUT' HTML = 'HTML' rho = 180*3600/pi LOC = QgsApplication.locale()[:2] def translate(self, string): return QCoreApplication.translate('Processing', string) def tr(self, *string): # Traduzir para o portugês: arg[0] - english (translate), arg[1] - português if self.LOC == 'pt': if len(string) == 2: return string[1] else: return self.translate(string[0]) else: return self.translate(string[0]) def createInstance(self): return TraverseAdjustment() def name(self): return 'traverseadjustment' def displayName(self): return self.tr('Traverse adjustment', 'Poligonal enquadrada') def group(self): return self.tr('Survey', 'Agrimensura') def groupId(self): return 'survey' def tags(self): return self.tr('survey,agrimensura,polygonal,adjustment,total,station,angle,least square').split(',') def icon(self): return QIcon(os.path.join(os.path.dirname(os.path.dirname(__file__)), 'images/total_station.png')) txt_en = 'This algorithm performs the traverse adjustments of a framed polygonal by least squares method, where the distances, angles, and directions observations are adjusted simultaneously, providing the most probable values for the given data set. Futhermore, the observations can be rigorously weighted based on their estimated errors and adjusted accordingly.' txt_pt = 'Este algoritmo realiza o ajustamento de poligonal enquadrada pelo método dos mínimos quadrados, onde as observações de distâncias, ângulos e direções são ajustadas simultaneamente, fornecendo os valores mais prováveis para o conjunto de dados. Além disso, as observações podem ser rigorosamente ponderadas considerando os erros estimados e ajustados.' figure = 'images/tutorial/survey_traverse.jpg' def shortHelpString(self): social_BW = Imgs().social_BW nota_en = '''Note: Sample data obtained from class notes of the Geodetic Survey discipline at UFPE. ''' nota_pt = '''Nota: Dados de exemplo obtidos das notas de aula da disciplina de Levantamento Geodésicos na UFPE. ''' footer = '''<div align="center"> <img src="'''+ os.path.join(os.path.dirname(os.path.dirname(__file__)), self.figure) +'''"> </div> <div align="right"> <div>''' + self.tr(nota_en, nota_pt) + ''' ''' +'</a><br><b>'+ self.tr('Author: <NAME>', 'Autor: <NAME>')+'''</b> </p>'''+ social_BW + '''</div> </div>''' return self.tr(self.txt_en, self.txt_pt) + footer def initAlgorithm(self, config=None): # INPUT self.addParameter( QgsProcessingParameterPoint( self.A, self.tr('A: first (E,N) coordinates','A: 1º ponto (E,N)'), defaultValue = QgsPointXY(150000, 250000) ) ) self.addParameter( QgsProcessingParameterPoint( self.B, self.tr('B: second (E,N) coordinates','B: 2º ponto (E,N)'), defaultValue = QgsPointXY(149922.119, 249875.269) ) ) self.addParameter( QgsProcessingParameterPoint( self.Y, self.tr('Y: penultimate (E,N) coordinates', 'Y: penúltimo ponto (E,N)'), defaultValue = QgsPointXY(150347.054, 249727.281) ) ) self.addParameter( QgsProcessingParameterPoint( self.Z, self.tr('Z: final (E,N) coordinates', 'Z: último ponto (E,N)'), defaultValue = QgsPointXY(150350.201, 249622.000) ) ) self.addParameter( QgsProcessingParameterString( self.DIST, self.tr('List of Horizontal Distances (m)', 'Lista de Distâncias Horizontais (m)'), defaultValue = '110.426, 72.375, 186.615, 125.153, 78.235, 130.679, 110.854', multiLine = True ) ) self.addParameter( QgsProcessingParameterString( self.ANGS, self.tr('List of Angles', 'Lista de Ângulos'), defaultValue = '''75°23'34", 202°4'36", 56°51'15", 283°31'32", 242°57'31", 185°5'12", 94°11'35", 266°13'20" ''', multiLine = True ) ) self.addParameter( QgsProcessingParameterNumber( self.DIST_PREC, self.tr('Initial distance precision (mm)', 'Precisão linear inicial (mm)'), type = 1, defaultValue = 3 ) ) self.addParameter( QgsProcessingParameterNumber( self.PPM, self.tr('PPM distance precision', 'Precisão linear em PPM'), type = 1, defaultValue = 3 ) ) self.addParameter( QgsProcessingParameterNumber( self.ANGS_PREC, self.tr('Angular precision (seconds)', 'Precisão angular (em segundos)'), type = 1, defaultValue = 10 ) ) self.addParameter( QgsProcessingParameterCrs( self.CRS, self.tr('CRS','SRC'), 'ProjectCrs')) # OUTPUT self.addParameter( QgsProcessingParameterFeatureSink( self.OUTPUT, self.tr('Adjusted Points', 'Pontos da Poligonal') ) ) self.addParameter( QgsProcessingParameterFileDestination( 'HTML', self.tr('Report of the closed traverse', 'Relatório de ajuste da Poligonal'), self.tr('HTML files (*.html)') ) ) # F(Xo) para distâncias: def F_X_d(self, pnts, B, Y): F_X = [[sqrt((B[0]-pnts[0][0])**2 + (B[1]-pnts[0][1])**2)]] for k in range(len(pnts)-1): x1 = pnts[k][0] y1 = pnts[k][1] x2 = pnts[k+1][0] y2 = pnts[k+1][1] F_X += [[sqrt((x1-x2)**2 + (y1-y2)**2)]] F_X += [[sqrt((Y[0]-pnts[-1][0])**2 + (Y[1]-pnts[-1][1])**2)]] return F_X # F(Xo) para ângulos: def F_X_a(self, pnts, A, B, Y, Z): pnts2 = [B] + pnts + [Y] # leitura do ângulo no sentido horário F_X = [[3600*degrees(DifAz(azimute(QgsPointXY(B[0], B[1]), QgsPointXY(A[0], A[1]))[0], azimute(QgsPointXY(B[0], B[1]),QgsPointXY(pnts[0][0], pnts[0][1]))[0]))]] for k in range(len(pnts2)-2): pnt0 = QgsPointXY(pnts2[k][0], pnts2[k][1]) pnt1 = QgsPointXY(pnts2[k+1][0], pnts2[k+1][1]) pnt2 = QgsPointXY(pnts2[k+2][0], pnts2[k+2][1]) F_X += [[3600*degrees(DifAz(azimute(pnt1,pnt0)[0], azimute(pnt1, pnt2)[0]))]] F_X += [[3600*degrees(DifAz(azimute(QgsPointXY(Y[0], Y[1]), QgsPointXY(pnts2[-2][0], pnts2[-2][1]))[0], azimute(QgsPointXY(Y[0], Y[1]), QgsPointXY(Z[0], Z[1]))[0]))]] return F_X def Jacobiana_d(self, pnts, B, Y, n_d, n_par): Jac = zeros([n_d, n_par]) pnts2 = [B] + pnts + [Y] for k in range(n_d): I = pnts2[k] J = pnts2[k+1] IJ = norm(array(J) - array(I)) linha = [(I[0]-J[0])/IJ, (I[1]-J[1])/IJ, (J[0]-I[0])/IJ, (J[1]-I[1])/IJ] if k == 0: Jac[k, 0:2] = linha[2:] elif k < (n_d-1): Jac[k, (2*k-2):(2*k-2 + 4)] = linha else: Jac[k, (2*k-2):(2*k-2 + 2)] = linha[:2] return list(Jac) def Jacobiana_a(self, pnts, A, B, Y, Z, n_angs, n_par): Jac = zeros([n_angs, n_par]) pnts2 = [A, B] + pnts + [Y, Z] for k in range(n_angs): B = pnts2[k] I = pnts2[k+1] F = pnts2[k+2] IB = norm(array(B) - array(I)) IF = norm(array(F) - array(I)) linha = [(I[1]-B[1])/IB**2, (B[0]-I[0])/IB**2, (B[1]-I[1])/IB**2 - (F[1]-I[1])/IF**2, (I[0]-B[0])/IB**2 - (I[0]-F[0])/IF**2, (F[1]-I[1])/IF**2, (I[0]-F[0])/IF**2] linha = list(self.rho*array(linha)) if n_par > 2: if k == 0: Jac[k, 0:2] = linha[4:] elif k==1: Jac[k, 0:4] = linha[2:] elif k < (n_angs-2): Jac[k, (2*k-4):(2*k-4 + 6)] = linha elif k == n_angs-2: Jac[k, (2*k-4):(2*k-4 + 4)] = linha[:4] else: Jac[k, (2*k-4):(2*k-4 + 2)] = linha[:2] else: if k == 0: Jac[0, 0:2] = linha[4:] elif k == 1: Jac[1, 0:2] = linha[2:4] elif k == 2: Jac[2, 0:2] = linha[:2] return list(Jac) def processAlgorithm(self, parameters, context, feedback): A = self.parameterAsPoint( parameters, self.A, context ) A = [A.x(), A.y()] B = self.parameterAsPoint( parameters, self.B, context ) B = [B.x(), B.y()] Y = self.parameterAsPoint( parameters, self.Y, context ) Y = [Y.x(), Y.y()] Z = self.parameterAsPoint( parameters, self.Z, context ) Z = [Z.x(), Z.y()] d = self.parameterAsString( parameters, self.DIST, context ) d = String2NumberList(d) #feedback.pushInfo('Distances list: ' + str(d)) angs = self.parameterAsString( parameters, self.ANGS, context ) angs = String2StringList(angs) #feedback.pushInfo('Angles list: ' + str(angs)) lista = [] for ang in angs: lista += [3600*float(dms2dd(ang))] angs = lista dist_precision = self.parameterAsDouble( parameters, self.DIST_PREC, context ) dist_precision *= 1e-3 # milimeter to meters ppm = self.parameterAsDouble( parameters, self.PPM, context ) ppm *= 1e-6 # ppm ang_precision = self.parameterAsDouble( parameters, self.ANGS_PREC, context ) CRS = self.parameterAsCrs( parameters, self.CRS, context ) if CRS.isGeographic(): raise QgsProcessingException(self.tr('The output CRS must be Projected!', 'O SRC da camada de saída deve ser Projetado!')) # OUTPUT Fields = QgsFields() Fields.append(QgsField('id', QVariant.Int)) GeomType = QgsWkbTypes.Point (sink, dest_id) = self.parameterAsSink( parameters, self.OUTPUT, context, Fields, GeomType, CRS ) if sink is None: raise QgsProcessingException(self.invalidSinkError(parameters, self.OUTPUT)) html_output = self.parameterAsFileOutput( parameters, self.HTML, context ) # Precisões sd_d = list(dist_precision + array(d)*ppm) sd_a = list(ang_precision*ones(len(angs))) # Observações Lb = matrix(d + angs).reshape([len(d)+len(angs),1]) # Cálculo de aproximações inicias Xo = [] pnts = [] Az0 = azimute(QgsPointXY(B[0], B[1]), QgsPointXY(A[0], A[1]))[0] p0 = B for k in range(len(d)-1): ang = pi/2 - Az0 - radians(angs[k]/3600) # leitura do ângulo no sentido horário x = p0[0] + d[k]*cos(ang) y = p0[1] + d[k]*sin(ang) Xo += [[x], [y]] pnts += [(x, y)] Az0 = -pi/2 - ang p0 = (x, y) pnts_ini = pnts # Cálculo do Erro de Fechamento Linear ang = pi/2 - Az0 - radians(angs[-2]/3600) x = p0[0] + d[-1]*cos(ang) y = p0[1] + d[-1]*sin(ang) Y_ = (x, y) Erro = array(Y_)-array(Y) feedback.pushInfo('Linear closure error: ' + str(round(norm(array(Y_)-array(Y)),4)) + ' m') feedback.pushInfo('E and N errors: ' + str((round(Erro[0],4),round(Erro[1],4))) + ' m') # Cálculo do Erro de Azimute Az0 = azimute(QgsPointXY(B[0], B[1]), QgsPointXY(A[0], A[1]))[0] for k in range(len(angs)): ang = pi/2 - Az0 - radians(angs[k]/3600) # leitura do ângulo no sentido horário Az = pi/2 - ang Az0 = Az -pi if Az<0 or Az>2*pi: if (Az<0): Az=Az+2*pi else: Az=Az-2*pi feedback.pushInfo('Angular closure error: ' + str(round(3600*(degrees(Az - azimute(QgsPointXY(Y[0], Y[1]), QgsPointXY(Z[0], Z[1]))[0])),2)) + ' sec') # Dados para matrix jacobiana n_par = len(pnts)*2 n_d = len(d) n_angs = len(angs) n_obs = n_d + n_angs # Matriz Peso P = matrix(diag(array(sd_d + sd_a)**(-2))) # Cálculo iterativo das Coordenadas (Parâmetros) cont = 0 cont_max = 10 tol = 1e-4 while cont < cont_max: F_Xo = self.F_X_d(pnts, B, Y) + self.F_X_a(pnts, A, B, Y, Z) J = matrix(list(self.Jacobiana_d(pnts, B, Y, n_d, n_par)) + list(self.Jacobiana_a(pnts, A, B, Y, Z, n_angs, n_par))) L = matrix(Lb - F_Xo) delta = pinv(J.T*P*J)*J.T*P*L Xa = array(Xo) + array(delta) cont += 1 #print('Iteração:', cont, '\ncoord:', Xa.T, '\ndelta:', delta.T) feedback.pushInfo('Iteração: ' + str( cont) + '\nCoord: ' + str(Xa.T) + '\nDelta:' + str(delta.T)) if max(abs(delta))[0,0] > tol: Xo = Xa pnts = [] for k in range(int(len(Xo)/2)): pnts += [(float(Xo[2*k][0]), float(Xo[2*k+1][0]))] else: break # Resíduos V = Lb - F_Xo # Sigma posteriori n = len(Lb) # número de observações u = len(Xa) # número de parâmetros sigma2 = V.T*P*V/(n-u) # Observações Ajustadas (La) La = Lb + V # MVC de Xa SigmaXa = sigma2[0,0]*pinv(J.T*P*J) # MVC de La SigmaLa = J*SigmaXa*J.T # MVC de Lb var_priori = 1.0 SigmaLb = var_priori*inv(P) # MVC dos resíduos SigmaV = SigmaLa + SigmaLb feature = QgsFeature() total = 100.0 /len(pnts) if len(pnts) else 0 for current, pnt in enumerate(pnts): geom = QgsGeometry(QgsPoint(float(pnt[0]), float(pnt[1]))) feature.setGeometry(geom) feature.setAttributes([current+1]) sink.addFeature(feature, QgsFeatureSink.FastInsert) if feedback.isCanceled(): break feedback.setProgress(int(current * total)) # Relatório tabela1 = '''<tr style=""> <td style="border: 1pt solid windowtext; padding: 0cm 5.4pt; width: 39.3pt;" valign="top" width="52"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><i>[EST]</i><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><i>[E]</i><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="font-style: italic;">[N]</span><o:p></o:p></p> </td> </tr> ''' tabela2 = '''<tr style=""> <td style="border: 1pt solid windowtext; padding: 0cm 5.4pt; width: 39.3pt;" valign="top" width="52"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><i>[EST]</i><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><i>[E]</i><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="font-style: italic;">[N]</span><o:p></o:p></p> </td> <td style="text-align: center;"><i> [S_E] </i></td> <td style="text-align: center;"><i> [S_N] </i></td> </tr> ''' tabela3 = '''<tr style=""> <td style="border-style: none solid solid; border-color: -moz-use-text-color windowtext windowtext; border-width: medium 1pt 1pt; padding: 0cm 5.4pt; width: 84.9pt;" valign="top" width="113"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >[obs]<o:p></o:p></span></p> </td> <td style="border-style: none solid solid none; border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 84.95pt;" valign="top" width="113"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >[r]<o:p></o:p></span></p> </td> <td style="border-style: none solid solid none; border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 117.6pt;" valign="top" width="157"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >[adj_obs]<o:p></o:p></span></p> </td> <td style="border-style: none solid solid none; border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 102.05pt;" valign="top" width="136"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >[sd]<o:p></o:p></span></p> </td> </tr> ''' texto = '''<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"> <html> <head> <meta content="text/html; charset=ISO-8859-1" http-equiv="content-type"> <title>'''+ self.tr('Traverse Adjustment Report', 'Relatório de Ajuste de Poligonal') + '''</title> <link rel = "icon" href = "https://github.com/LEOXINGU/lftools/blob/main/images/lftools.png?raw=true" type = "image/x-icon"> </head> <body style="color: rgb(0, 0, 0); background-color: rgb(255, 255, 204);" alink="#000099" link="#000099" vlink="#990099"> <p class="MsoNormal" style="text-align: center;" align="center"><b><span style="font-size: 12pt; line-height: 107%;">''' + self.tr('TRAVERSE ADJUSTMENT', 'POLIGONAL ENQUADRADA') + '''<o:p></o:p></span></b></p> <p class="MsoNormal" style="text-align: center;" align="center"><span style="font-style: italic;">''' + self.tr('Method of Least Squares', str2HTML('Método dos Mínimos Quadrados')) + '''</span></p> <p class="MsoNormal" style="text-align: center;" align="center"><b><u>''' + self.tr('REPORT', str2HTML('RELATÓRIO')) + '''<o:p></o:p></u></b></p> <div align="center"> <table style="text-align: center; width: 100%;" border="1" cellpadding="0" cellspacing="0"> <tbody> <tr> <td width="50%"><b><span style="" >'''+ self.tr('Initial approximation', str2HTML('Aproximação Incial')) + '''</span></b></td> <td width="50%"><b><span style="" >'''+ self.tr('Adjusted Coordinates', 'Coordenadas Ajustadas') + '''<o:p></o:p></span></b></td> </tr> <tr> <td style="text-align: center;"> <div align="center"> <table class="MsoTableGrid" style="border: medium none ; border-collapse: collapse;" border="1" cellpadding="0" cellspacing="0"> <tbody> <tr style=""> <td style="border: 1pt solid windowtext; padding: 0cm 5.4pt; width: 39.3pt;" valign="top" width="52"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><i>'''+self.tr('Station', str2HTML('Estação')) + '''</i><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="font-style: italic;">E</span><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="font-style: italic;">N</span><o:p></o:p></p> </td> </tr> [TABLE 1] </tbody> </table> </div> </td> <td style="text-align: center;"> <div align="center"></br> <table class="MsoTableGrid" style="border: medium none ; border-collapse: collapse;" border="1" cellpadding="0" cellspacing="0"> <tbody> <tr style=""> <td style="border: 1pt solid windowtext; padding: 0cm 5.4pt; width: 39.3pt;" valign="top" width="52"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><i>'''+self.tr('Station', str2HTML('Estação')) + '''</i><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="font-style: italic;">E</span><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="font-style: italic;">N</span><o:p></o:p></p> </td> <td style="text-align: center;">  σ<span style="font-style: italic;">E  </span></td> <td style="text-align: center;">  σ<span style="font-style: italic;">N  </span></td> </tr> [TABLE 2] </tbody> </table> <i><span style="" >''' + self.tr('Posteriori variance', str2HTML('Variância a posteriori')) + '''</span></i><span style="" >: </span><span style="" > <span style="color: red;">[sigma2]</span></span> </div> </td> </tr> <tr> <td colspan="2" rowspan="1"><b><span style="" >''' + self.tr('Observations', str2HTML('Observações')) + '''<o:p></o:p></span></b></td> </tr> <tr> <td colspan="2" rowspan="1" style="text-align: center;">   <div align="center"> <table class="MsoTableGrid" style="border: medium none ; width: 389.5pt; border-collapse: collapse;" border="1" cellpadding="0" cellspacing="0" width="519"> <tbody> <tr style=""> <td style="border: 1pt solid windowtext; padding: 0cm 5.4pt; width: 84.9pt;" valign="top" width="113"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >''' + self.tr('Observation', str2HTML('Observação')) + '''<o:p></o:p></span></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 84.95pt;" valign="top" width="113"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >''' + self.tr('Residual', str2HTML('Resíduo')) + '''<o:p></o:p></span></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 117.6pt;" valign="top" width="157"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >''' + self.tr('Adjusted Observation', str2HTML('Observação Ajustada')) + '''<o:p></o:p></span></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 102.05pt;" valign="top" width="136"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >''' + self.tr('Standard Deviation', str2HTML('Desvio Padrão')) + '''<o:p></o:p></span></p> </td> </tr> [TABLE 3] </tbody> </table></br> </div> </td> </tr> </tbody> </table> <p class="MsoNormal" style="text-align: left;" align="left"><i><span style="font-size: 10pt; line-height: 100%; color: rgb(127, 127, 127);">''' + self.tr(str2HTML('*The unit of measurement of the adjusted coordinates is the same as the input coordinates.'), str2HTML('*A unidade de medida das coordenadas ajustadas é a mesma da coordenadas de entrada.')) + '''<o:p></o:p></span></i></p> </div> <footer"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;" align="right"><b>''' + self.tr('<NAME>', str2HTML('<NAME>')) + ''' </br>''' + self.tr('Cartographic Engineer', 'Eng. Cartógrafo') + '''<o:p></o:p></b></p> </br> <div align="right">'''+ Imgs().social_table_color + ''' </div> <o:p></o:p></b></p> </footer> </body> </html> ''' # Aproximação inicial cont = 0 table1 = '' for k, pnt in enumerate(pnts_ini): X = pnt[0] Y = pnt[1] cont += 1 tableRowN = tabela1 itens = { '[EST]' : str(k+1), '[E]': self.tr('{:,.3f}'.format(X), '{:,.3f}'.format(X).replace(',', 'X').replace('.', ',').replace('X', '.')), '[N]': self.tr('{:,.3f}'.format(Y), '{:,.3f}'.format(Y).replace(',', 'X').replace('.', ',').replace('X', '.')), } for item in itens: tableRowN = tableRowN.replace(item, itens[item]) table1 += tableRowN # Ajustamento cont = 0 table2 = '' SD = SigmaXa.diagonal() for k in range(len(pnts_ini)): X = Xa[2*k, 0] Y = Xa[2*k+1, 0] sdx = sqrt(SD[0, 2*k]) sdy = sqrt(SD[0, 2*k+1]) cont += 1 tableRowN = tabela2 itens = { '[EST]' : str(k+1), '[E]': self.tr('{:,.3f}'.format(X), '{:,.3f}'.format(X).replace(',', 'X').replace('.', ',').replace('X', '.')), '[N]': self.tr('{:,.3f}'.format(Y), '{:,.3f}'.format(Y).replace(',', 'X').replace('.', ',').replace('X', '.')), '[S_E]': self.tr('{:,.3f}'.format(sdx), '{:,.3f}'.format(sdx).replace(',', 'X').replace('.', ',').replace('X', '.')), '[S_N]': self.tr('{:,.3f}'.format(sdy), '{:,.3f}'.format(sdy).replace(',', 'X').replace('.', ',').replace('X', '.')), } for item in itens: tableRowN = tableRowN.replace(item, itens[item]) table2 += tableRowN # Observações table3 = '' SD = SigmaLa.diagonal() for k in range(n_d): # Distâncias obs = Lb[k, 0] adj_obs = La[k, 0] sd = sqrt(SD[0, k]) r = V[k, 0] cont += 1 tableRowN = tabela3 itens = { '[obs]' : str(round(obs,3)), '[r]': str(round(r,4)), '[adj_obs]': str(round(adj_obs,3)), '[sd]': str(round(sd,3)) } for item in itens: tableRowN = tableRowN.replace(item, itens[item]) table3 += tableRowN for t in range(k+1, k+1+ n_angs): # Ângulos obs = Lb[t, 0] adj_obs = La[t, 0] sd = sqrt(SD[0, t]) r = V[t, 0] cont += 1 tableRowN = tabela3 itens = { '[obs]' : str2HTML(dd2dms(obs/3600,3)), '[r]': str(round(r,4)) + '"', '[adj_obs]': str2HTML(dd2dms(adj_obs/3600,3)), '[sd]': str(round(sd,3)) + '"' } for item in itens: tableRowN = tableRowN.replace(item, itens[item]) table3 += tableRowN # Documento prinicipal itens = { '[TABLE 1]': table1, '[TABLE 2]': table2, '[TABLE 3]': table3, '[sigma2]': str(round(sigma2[0,0],3)) } for item in itens: texto = texto.replace(item, itens[item]) # Exportar HTML arq = open(html_output, 'w') arq.write(texto) arq.close() feedback.pushInfo(self.tr('Operation completed successfully!', 'Operação finalizada com sucesso!')) feedback.pushInfo(self.tr('<NAME> - Cartographic Engineer', '<NAME> - Eng Cart')) return {self.OUTPUT: dest_id, self.HTML: html_output}

processing_provider/Survey_traverseAdjustment.py

__author__ = '<NAME>' __date__ = '2019-11-17' __copyright__ = '(C) 2019, <NAME>' from PyQt5.QtCore import QCoreApplication, QVariant from qgis.core import (QgsProcessing, QgsFeatureSink, QgsProcessingException, QgsProcessingAlgorithm, QgsProcessingParameterFeatureSource, QgsProcessingParameterFeatureSink, QgsProcessingParameterString, QgsProcessingParameterPoint, QgsProcessingParameterNumber, QgsProcessingParameterCrs, QgsProcessingParameterFileDestination, QgsFields, QgsField, QgsWkbTypes, QgsFeature, QgsGeometry, QgsPointXY, QgsPoint, QgsApplication ) from numpy import radians, arctan, pi, sin, cos, matrix, sqrt, degrees, array, diag, ones, zeros, floor from numpy.linalg import norm, pinv, inv from lftools.geocapt.imgs import Imgs from lftools.geocapt.topogeo import * import os from qgis.PyQt.QtGui import QIcon class TraverseAdjustment(QgsProcessingAlgorithm): A = 'A' B = 'B' Y = 'Y' Z = 'Z' DIST = 'DIST' ANGS = 'ANGS' DIST_PREC = 'DIST_PREC' PPM = 'PPM' ANGS_PREC = 'ANGS_PREC' CRS = 'CRS' OUTPUT = 'OUTPUT' HTML = 'HTML' rho = 180*3600/pi LOC = QgsApplication.locale()[:2] def translate(self, string): return QCoreApplication.translate('Processing', string) def tr(self, *string): # Traduzir para o portugês: arg[0] - english (translate), arg[1] - português if self.LOC == 'pt': if len(string) == 2: return string[1] else: return self.translate(string[0]) else: return self.translate(string[0]) def createInstance(self): return TraverseAdjustment() def name(self): return 'traverseadjustment' def displayName(self): return self.tr('Traverse adjustment', 'Poligonal enquadrada') def group(self): return self.tr('Survey', 'Agrimensura') def groupId(self): return 'survey' def tags(self): return self.tr('survey,agrimensura,polygonal,adjustment,total,station,angle,least square').split(',') def icon(self): return QIcon(os.path.join(os.path.dirname(os.path.dirname(__file__)), 'images/total_station.png')) txt_en = 'This algorithm performs the traverse adjustments of a framed polygonal by least squares method, where the distances, angles, and directions observations are adjusted simultaneously, providing the most probable values for the given data set. Futhermore, the observations can be rigorously weighted based on their estimated errors and adjusted accordingly.' txt_pt = 'Este algoritmo realiza o ajustamento de poligonal enquadrada pelo método dos mínimos quadrados, onde as observações de distâncias, ângulos e direções são ajustadas simultaneamente, fornecendo os valores mais prováveis para o conjunto de dados. Além disso, as observações podem ser rigorosamente ponderadas considerando os erros estimados e ajustados.' figure = 'images/tutorial/survey_traverse.jpg' def shortHelpString(self): social_BW = Imgs().social_BW nota_en = '''Note: Sample data obtained from class notes of the Geodetic Survey discipline at UFPE. ''' nota_pt = '''Nota: Dados de exemplo obtidos das notas de aula da disciplina de Levantamento Geodésicos na UFPE. ''' footer = '''<div align="center"> <img src="'''+ os.path.join(os.path.dirname(os.path.dirname(__file__)), self.figure) +'''"> </div> <div align="right"> <div>''' + self.tr(nota_en, nota_pt) + ''' ''' +'</a><br><b>'+ self.tr('Author: <NAME>', 'Autor: <NAME>')+'''</b> </p>'''+ social_BW + '''</div> </div>''' return self.tr(self.txt_en, self.txt_pt) + footer def initAlgorithm(self, config=None): # INPUT self.addParameter( QgsProcessingParameterPoint( self.A, self.tr('A: first (E,N) coordinates','A: 1º ponto (E,N)'), defaultValue = QgsPointXY(150000, 250000) ) ) self.addParameter( QgsProcessingParameterPoint( self.B, self.tr('B: second (E,N) coordinates','B: 2º ponto (E,N)'), defaultValue = QgsPointXY(149922.119, 249875.269) ) ) self.addParameter( QgsProcessingParameterPoint( self.Y, self.tr('Y: penultimate (E,N) coordinates', 'Y: penúltimo ponto (E,N)'), defaultValue = QgsPointXY(150347.054, 249727.281) ) ) self.addParameter( QgsProcessingParameterPoint( self.Z, self.tr('Z: final (E,N) coordinates', 'Z: último ponto (E,N)'), defaultValue = QgsPointXY(150350.201, 249622.000) ) ) self.addParameter( QgsProcessingParameterString( self.DIST, self.tr('List of Horizontal Distances (m)', 'Lista de Distâncias Horizontais (m)'), defaultValue = '110.426, 72.375, 186.615, 125.153, 78.235, 130.679, 110.854', multiLine = True ) ) self.addParameter( QgsProcessingParameterString( self.ANGS, self.tr('List of Angles', 'Lista de Ângulos'), defaultValue = '''75°23'34", 202°4'36", 56°51'15", 283°31'32", 242°57'31", 185°5'12", 94°11'35", 266°13'20" ''', multiLine = True ) ) self.addParameter( QgsProcessingParameterNumber( self.DIST_PREC, self.tr('Initial distance precision (mm)', 'Precisão linear inicial (mm)'), type = 1, defaultValue = 3 ) ) self.addParameter( QgsProcessingParameterNumber( self.PPM, self.tr('PPM distance precision', 'Precisão linear em PPM'), type = 1, defaultValue = 3 ) ) self.addParameter( QgsProcessingParameterNumber( self.ANGS_PREC, self.tr('Angular precision (seconds)', 'Precisão angular (em segundos)'), type = 1, defaultValue = 10 ) ) self.addParameter( QgsProcessingParameterCrs( self.CRS, self.tr('CRS','SRC'), 'ProjectCrs')) # OUTPUT self.addParameter( QgsProcessingParameterFeatureSink( self.OUTPUT, self.tr('Adjusted Points', 'Pontos da Poligonal') ) ) self.addParameter( QgsProcessingParameterFileDestination( 'HTML', self.tr('Report of the closed traverse', 'Relatório de ajuste da Poligonal'), self.tr('HTML files (*.html)') ) ) # F(Xo) para distâncias: def F_X_d(self, pnts, B, Y): F_X = [[sqrt((B[0]-pnts[0][0])**2 + (B[1]-pnts[0][1])**2)]] for k in range(len(pnts)-1): x1 = pnts[k][0] y1 = pnts[k][1] x2 = pnts[k+1][0] y2 = pnts[k+1][1] F_X += [[sqrt((x1-x2)**2 + (y1-y2)**2)]] F_X += [[sqrt((Y[0]-pnts[-1][0])**2 + (Y[1]-pnts[-1][1])**2)]] return F_X # F(Xo) para ângulos: def F_X_a(self, pnts, A, B, Y, Z): pnts2 = [B] + pnts + [Y] # leitura do ângulo no sentido horário F_X = [[3600*degrees(DifAz(azimute(QgsPointXY(B[0], B[1]), QgsPointXY(A[0], A[1]))[0], azimute(QgsPointXY(B[0], B[1]),QgsPointXY(pnts[0][0], pnts[0][1]))[0]))]] for k in range(len(pnts2)-2): pnt0 = QgsPointXY(pnts2[k][0], pnts2[k][1]) pnt1 = QgsPointXY(pnts2[k+1][0], pnts2[k+1][1]) pnt2 = QgsPointXY(pnts2[k+2][0], pnts2[k+2][1]) F_X += [[3600*degrees(DifAz(azimute(pnt1,pnt0)[0], azimute(pnt1, pnt2)[0]))]] F_X += [[3600*degrees(DifAz(azimute(QgsPointXY(Y[0], Y[1]), QgsPointXY(pnts2[-2][0], pnts2[-2][1]))[0], azimute(QgsPointXY(Y[0], Y[1]), QgsPointXY(Z[0], Z[1]))[0]))]] return F_X def Jacobiana_d(self, pnts, B, Y, n_d, n_par): Jac = zeros([n_d, n_par]) pnts2 = [B] + pnts + [Y] for k in range(n_d): I = pnts2[k] J = pnts2[k+1] IJ = norm(array(J) - array(I)) linha = [(I[0]-J[0])/IJ, (I[1]-J[1])/IJ, (J[0]-I[0])/IJ, (J[1]-I[1])/IJ] if k == 0: Jac[k, 0:2] = linha[2:] elif k < (n_d-1): Jac[k, (2*k-2):(2*k-2 + 4)] = linha else: Jac[k, (2*k-2):(2*k-2 + 2)] = linha[:2] return list(Jac) def Jacobiana_a(self, pnts, A, B, Y, Z, n_angs, n_par): Jac = zeros([n_angs, n_par]) pnts2 = [A, B] + pnts + [Y, Z] for k in range(n_angs): B = pnts2[k] I = pnts2[k+1] F = pnts2[k+2] IB = norm(array(B) - array(I)) IF = norm(array(F) - array(I)) linha = [(I[1]-B[1])/IB**2, (B[0]-I[0])/IB**2, (B[1]-I[1])/IB**2 - (F[1]-I[1])/IF**2, (I[0]-B[0])/IB**2 - (I[0]-F[0])/IF**2, (F[1]-I[1])/IF**2, (I[0]-F[0])/IF**2] linha = list(self.rho*array(linha)) if n_par > 2: if k == 0: Jac[k, 0:2] = linha[4:] elif k==1: Jac[k, 0:4] = linha[2:] elif k < (n_angs-2): Jac[k, (2*k-4):(2*k-4 + 6)] = linha elif k == n_angs-2: Jac[k, (2*k-4):(2*k-4 + 4)] = linha[:4] else: Jac[k, (2*k-4):(2*k-4 + 2)] = linha[:2] else: if k == 0: Jac[0, 0:2] = linha[4:] elif k == 1: Jac[1, 0:2] = linha[2:4] elif k == 2: Jac[2, 0:2] = linha[:2] return list(Jac) def processAlgorithm(self, parameters, context, feedback): A = self.parameterAsPoint( parameters, self.A, context ) A = [A.x(), A.y()] B = self.parameterAsPoint( parameters, self.B, context ) B = [B.x(), B.y()] Y = self.parameterAsPoint( parameters, self.Y, context ) Y = [Y.x(), Y.y()] Z = self.parameterAsPoint( parameters, self.Z, context ) Z = [Z.x(), Z.y()] d = self.parameterAsString( parameters, self.DIST, context ) d = String2NumberList(d) #feedback.pushInfo('Distances list: ' + str(d)) angs = self.parameterAsString( parameters, self.ANGS, context ) angs = String2StringList(angs) #feedback.pushInfo('Angles list: ' + str(angs)) lista = [] for ang in angs: lista += [3600*float(dms2dd(ang))] angs = lista dist_precision = self.parameterAsDouble( parameters, self.DIST_PREC, context ) dist_precision *= 1e-3 # milimeter to meters ppm = self.parameterAsDouble( parameters, self.PPM, context ) ppm *= 1e-6 # ppm ang_precision = self.parameterAsDouble( parameters, self.ANGS_PREC, context ) CRS = self.parameterAsCrs( parameters, self.CRS, context ) if CRS.isGeographic(): raise QgsProcessingException(self.tr('The output CRS must be Projected!', 'O SRC da camada de saída deve ser Projetado!')) # OUTPUT Fields = QgsFields() Fields.append(QgsField('id', QVariant.Int)) GeomType = QgsWkbTypes.Point (sink, dest_id) = self.parameterAsSink( parameters, self.OUTPUT, context, Fields, GeomType, CRS ) if sink is None: raise QgsProcessingException(self.invalidSinkError(parameters, self.OUTPUT)) html_output = self.parameterAsFileOutput( parameters, self.HTML, context ) # Precisões sd_d = list(dist_precision + array(d)*ppm) sd_a = list(ang_precision*ones(len(angs))) # Observações Lb = matrix(d + angs).reshape([len(d)+len(angs),1]) # Cálculo de aproximações inicias Xo = [] pnts = [] Az0 = azimute(QgsPointXY(B[0], B[1]), QgsPointXY(A[0], A[1]))[0] p0 = B for k in range(len(d)-1): ang = pi/2 - Az0 - radians(angs[k]/3600) # leitura do ângulo no sentido horário x = p0[0] + d[k]*cos(ang) y = p0[1] + d[k]*sin(ang) Xo += [[x], [y]] pnts += [(x, y)] Az0 = -pi/2 - ang p0 = (x, y) pnts_ini = pnts # Cálculo do Erro de Fechamento Linear ang = pi/2 - Az0 - radians(angs[-2]/3600) x = p0[0] + d[-1]*cos(ang) y = p0[1] + d[-1]*sin(ang) Y_ = (x, y) Erro = array(Y_)-array(Y) feedback.pushInfo('Linear closure error: ' + str(round(norm(array(Y_)-array(Y)),4)) + ' m') feedback.pushInfo('E and N errors: ' + str((round(Erro[0],4),round(Erro[1],4))) + ' m') # Cálculo do Erro de Azimute Az0 = azimute(QgsPointXY(B[0], B[1]), QgsPointXY(A[0], A[1]))[0] for k in range(len(angs)): ang = pi/2 - Az0 - radians(angs[k]/3600) # leitura do ângulo no sentido horário Az = pi/2 - ang Az0 = Az -pi if Az<0 or Az>2*pi: if (Az<0): Az=Az+2*pi else: Az=Az-2*pi feedback.pushInfo('Angular closure error: ' + str(round(3600*(degrees(Az - azimute(QgsPointXY(Y[0], Y[1]), QgsPointXY(Z[0], Z[1]))[0])),2)) + ' sec') # Dados para matrix jacobiana n_par = len(pnts)*2 n_d = len(d) n_angs = len(angs) n_obs = n_d + n_angs # Matriz Peso P = matrix(diag(array(sd_d + sd_a)**(-2))) # Cálculo iterativo das Coordenadas (Parâmetros) cont = 0 cont_max = 10 tol = 1e-4 while cont < cont_max: F_Xo = self.F_X_d(pnts, B, Y) + self.F_X_a(pnts, A, B, Y, Z) J = matrix(list(self.Jacobiana_d(pnts, B, Y, n_d, n_par)) + list(self.Jacobiana_a(pnts, A, B, Y, Z, n_angs, n_par))) L = matrix(Lb - F_Xo) delta = pinv(J.T*P*J)*J.T*P*L Xa = array(Xo) + array(delta) cont += 1 #print('Iteração:', cont, '\ncoord:', Xa.T, '\ndelta:', delta.T) feedback.pushInfo('Iteração: ' + str( cont) + '\nCoord: ' + str(Xa.T) + '\nDelta:' + str(delta.T)) if max(abs(delta))[0,0] > tol: Xo = Xa pnts = [] for k in range(int(len(Xo)/2)): pnts += [(float(Xo[2*k][0]), float(Xo[2*k+1][0]))] else: break # Resíduos V = Lb - F_Xo # Sigma posteriori n = len(Lb) # número de observações u = len(Xa) # número de parâmetros sigma2 = V.T*P*V/(n-u) # Observações Ajustadas (La) La = Lb + V # MVC de Xa SigmaXa = sigma2[0,0]*pinv(J.T*P*J) # MVC de La SigmaLa = J*SigmaXa*J.T # MVC de Lb var_priori = 1.0 SigmaLb = var_priori*inv(P) # MVC dos resíduos SigmaV = SigmaLa + SigmaLb feature = QgsFeature() total = 100.0 /len(pnts) if len(pnts) else 0 for current, pnt in enumerate(pnts): geom = QgsGeometry(QgsPoint(float(pnt[0]), float(pnt[1]))) feature.setGeometry(geom) feature.setAttributes([current+1]) sink.addFeature(feature, QgsFeatureSink.FastInsert) if feedback.isCanceled(): break feedback.setProgress(int(current * total)) # Relatório tabela1 = '''<tr style=""> <td style="border: 1pt solid windowtext; padding: 0cm 5.4pt; width: 39.3pt;" valign="top" width="52"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><i>[EST]</i><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><i>[E]</i><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="font-style: italic;">[N]</span><o:p></o:p></p> </td> </tr> ''' tabela2 = '''<tr style=""> <td style="border: 1pt solid windowtext; padding: 0cm 5.4pt; width: 39.3pt;" valign="top" width="52"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><i>[EST]</i><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><i>[E]</i><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="font-style: italic;">[N]</span><o:p></o:p></p> </td> <td style="text-align: center;"><i> [S_E] </i></td> <td style="text-align: center;"><i> [S_N] </i></td> </tr> ''' tabela3 = '''<tr style=""> <td style="border-style: none solid solid; border-color: -moz-use-text-color windowtext windowtext; border-width: medium 1pt 1pt; padding: 0cm 5.4pt; width: 84.9pt;" valign="top" width="113"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >[obs]<o:p></o:p></span></p> </td> <td style="border-style: none solid solid none; border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 84.95pt;" valign="top" width="113"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >[r]<o:p></o:p></span></p> </td> <td style="border-style: none solid solid none; border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 117.6pt;" valign="top" width="157"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >[adj_obs]<o:p></o:p></span></p> </td> <td style="border-style: none solid solid none; border-color: -moz-use-text-color windowtext windowtext -moz-use-text-color; border-width: medium 1pt 1pt medium; padding: 0cm 5.4pt; width: 102.05pt;" valign="top" width="136"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >[sd]<o:p></o:p></span></p> </td> </tr> ''' texto = '''<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"> <html> <head> <meta content="text/html; charset=ISO-8859-1" http-equiv="content-type"> <title>'''+ self.tr('Traverse Adjustment Report', 'Relatório de Ajuste de Poligonal') + '''</title> <link rel = "icon" href = "https://github.com/LEOXINGU/lftools/blob/main/images/lftools.png?raw=true" type = "image/x-icon"> </head> <body style="color: rgb(0, 0, 0); background-color: rgb(255, 255, 204);" alink="#000099" link="#000099" vlink="#990099"> <p class="MsoNormal" style="text-align: center;" align="center"><b><span style="font-size: 12pt; line-height: 107%;">''' + self.tr('TRAVERSE ADJUSTMENT', 'POLIGONAL ENQUADRADA') + '''<o:p></o:p></span></b></p> <p class="MsoNormal" style="text-align: center;" align="center"><span style="font-style: italic;">''' + self.tr('Method of Least Squares', str2HTML('Método dos Mínimos Quadrados')) + '''</span></p> <p class="MsoNormal" style="text-align: center;" align="center"><b><u>''' + self.tr('REPORT', str2HTML('RELATÓRIO')) + '''<o:p></o:p></u></b></p> <div align="center"> <table style="text-align: center; width: 100%;" border="1" cellpadding="0" cellspacing="0"> <tbody> <tr> <td width="50%"><b><span style="" >'''+ self.tr('Initial approximation', str2HTML('Aproximação Incial')) + '''</span></b></td> <td width="50%"><b><span style="" >'''+ self.tr('Adjusted Coordinates', 'Coordenadas Ajustadas') + '''<o:p></o:p></span></b></td> </tr> <tr> <td style="text-align: center;"> <div align="center"> <table class="MsoTableGrid" style="border: medium none ; border-collapse: collapse;" border="1" cellpadding="0" cellspacing="0"> <tbody> <tr style=""> <td style="border: 1pt solid windowtext; padding: 0cm 5.4pt; width: 39.3pt;" valign="top" width="52"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><i>'''+self.tr('Station', str2HTML('Estação')) + '''</i><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="font-style: italic;">E</span><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="font-style: italic;">N</span><o:p></o:p></p> </td> </tr> [TABLE 1] </tbody> </table> </div> </td> <td style="text-align: center;"> <div align="center"></br> <table class="MsoTableGrid" style="border: medium none ; border-collapse: collapse;" border="1" cellpadding="0" cellspacing="0"> <tbody> <tr style=""> <td style="border: 1pt solid windowtext; padding: 0cm 5.4pt; width: 39.3pt;" valign="top" width="52"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><i>'''+self.tr('Station', str2HTML('Estação')) + '''</i><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="font-style: italic;">E</span><o:p></o:p></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 46.05pt;" valign="top" width="61"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="font-style: italic;">N</span><o:p></o:p></p> </td> <td style="text-align: center;">  σ<span style="font-style: italic;">E  </span></td> <td style="text-align: center;">  σ<span style="font-style: italic;">N  </span></td> </tr> [TABLE 2] </tbody> </table> <i><span style="" >''' + self.tr('Posteriori variance', str2HTML('Variância a posteriori')) + '''</span></i><span style="" >: </span><span style="" > <span style="color: red;">[sigma2]</span></span> </div> </td> </tr> <tr> <td colspan="2" rowspan="1"><b><span style="" >''' + self.tr('Observations', str2HTML('Observações')) + '''<o:p></o:p></span></b></td> </tr> <tr> <td colspan="2" rowspan="1" style="text-align: center;">   <div align="center"> <table class="MsoTableGrid" style="border: medium none ; width: 389.5pt; border-collapse: collapse;" border="1" cellpadding="0" cellspacing="0" width="519"> <tbody> <tr style=""> <td style="border: 1pt solid windowtext; padding: 0cm 5.4pt; width: 84.9pt;" valign="top" width="113"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >''' + self.tr('Observation', str2HTML('Observação')) + '''<o:p></o:p></span></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 84.95pt;" valign="top" width="113"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >''' + self.tr('Residual', str2HTML('Resíduo')) + '''<o:p></o:p></span></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 117.6pt;" valign="top" width="157"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >''' + self.tr('Adjusted Observation', str2HTML('Observação Ajustada')) + '''<o:p></o:p></span></p> </td> <td style="border-style: solid solid solid none; border-color: windowtext windowtext windowtext -moz-use-text-color; border-width: 1pt 1pt 1pt medium; padding: 0cm 5.4pt; width: 102.05pt;" valign="top" width="136"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;" align="center"><span style="" >''' + self.tr('Standard Deviation', str2HTML('Desvio Padrão')) + '''<o:p></o:p></span></p> </td> </tr> [TABLE 3] </tbody> </table></br> </div> </td> </tr> </tbody> </table> <p class="MsoNormal" style="text-align: left;" align="left"><i><span style="font-size: 10pt; line-height: 100%; color: rgb(127, 127, 127);">''' + self.tr(str2HTML('*The unit of measurement of the adjusted coordinates is the same as the input coordinates.'), str2HTML('*A unidade de medida das coordenadas ajustadas é a mesma da coordenadas de entrada.')) + '''<o:p></o:p></span></i></p> </div> <footer"> <p class="MsoNormal" style="margin-bottom: 0.0001pt; text-align: right;" align="right"><b>''' + self.tr('<NAME>', str2HTML('<NAME>')) + ''' </br>''' + self.tr('Cartographic Engineer', 'Eng. Cartógrafo') + '''<o:p></o:p></b></p> </br> <div align="right">'''+ Imgs().social_table_color + ''' </div> <o:p></o:p></b></p> </footer> </body> </html> ''' # Aproximação inicial cont = 0 table1 = '' for k, pnt in enumerate(pnts_ini): X = pnt[0] Y = pnt[1] cont += 1 tableRowN = tabela1 itens = { '[EST]' : str(k+1), '[E]': self.tr('{:,.3f}'.format(X), '{:,.3f}'.format(X).replace(',', 'X').replace('.', ',').replace('X', '.')), '[N]': self.tr('{:,.3f}'.format(Y), '{:,.3f}'.format(Y).replace(',', 'X').replace('.', ',').replace('X', '.')), } for item in itens: tableRowN = tableRowN.replace(item, itens[item]) table1 += tableRowN # Ajustamento cont = 0 table2 = '' SD = SigmaXa.diagonal() for k in range(len(pnts_ini)): X = Xa[2*k, 0] Y = Xa[2*k+1, 0] sdx = sqrt(SD[0, 2*k]) sdy = sqrt(SD[0, 2*k+1]) cont += 1 tableRowN = tabela2 itens = { '[EST]' : str(k+1), '[E]': self.tr('{:,.3f}'.format(X), '{:,.3f}'.format(X).replace(',', 'X').replace('.', ',').replace('X', '.')), '[N]': self.tr('{:,.3f}'.format(Y), '{:,.3f}'.format(Y).replace(',', 'X').replace('.', ',').replace('X', '.')), '[S_E]': self.tr('{:,.3f}'.format(sdx), '{:,.3f}'.format(sdx).replace(',', 'X').replace('.', ',').replace('X', '.')), '[S_N]': self.tr('{:,.3f}'.format(sdy), '{:,.3f}'.format(sdy).replace(',', 'X').replace('.', ',').replace('X', '.')), } for item in itens: tableRowN = tableRowN.replace(item, itens[item]) table2 += tableRowN # Observações table3 = '' SD = SigmaLa.diagonal() for k in range(n_d): # Distâncias obs = Lb[k, 0] adj_obs = La[k, 0] sd = sqrt(SD[0, k]) r = V[k, 0] cont += 1 tableRowN = tabela3 itens = { '[obs]' : str(round(obs,3)), '[r]': str(round(r,4)), '[adj_obs]': str(round(adj_obs,3)), '[sd]': str(round(sd,3)) } for item in itens: tableRowN = tableRowN.replace(item, itens[item]) table3 += tableRowN for t in range(k+1, k+1+ n_angs): # Ângulos obs = Lb[t, 0] adj_obs = La[t, 0] sd = sqrt(SD[0, t]) r = V[t, 0] cont += 1 tableRowN = tabela3 itens = { '[obs]' : str2HTML(dd2dms(obs/3600,3)), '[r]': str(round(r,4)) + '"', '[adj_obs]': str2HTML(dd2dms(adj_obs/3600,3)), '[sd]': str(round(sd,3)) + '"' } for item in itens: tableRowN = tableRowN.replace(item, itens[item]) table3 += tableRowN # Documento prinicipal itens = { '[TABLE 1]': table1, '[TABLE 2]': table2, '[TABLE 3]': table3, '[sigma2]': str(round(sigma2[0,0],3)) } for item in itens: texto = texto.replace(item, itens[item]) # Exportar HTML arq = open(html_output, 'w') arq.write(texto) arq.close() feedback.pushInfo(self.tr('Operation completed successfully!', 'Operação finalizada com sucesso!')) feedback.pushInfo(self.tr('<NAME> - Cartographic Engineer', '<NAME> - Eng Cart')) return {self.OUTPUT: dest_id, self.HTML: html_output}

0.623721

0.207757

import unittest import jinja2 import app class OSTemplateViewTestCase(unittest.TestCase): def setUp(self): config = app.ConfigWrapper() self.app = app.app(config).test_client() def test_latest_defaults(self): obs = self.app.get('/') exp = jinja2.Template(TEMPLATE).render(tag='stable').encode('utf-8') assert obs.data == exp def test_latest_version(self): obs = self.app.get('/?version=latest') exp = jinja2.Template(TEMPLATE).render(tag='stable').encode('utf-8') assert obs.data == exp def test_404_version(self): obs = self.app.get('/?version=NON-EXISTANT') assert obs.status == '404 NOT FOUND' def test_latest_tag(self): obs = self.app.get('/?tag=testXX.YY.ZZ') exp = jinja2.Template(TEMPLATE).render(tag='testXX.YY.ZZ').encode('utf-8') assert obs.data == exp TEMPLATE = """kind: List apiVersion: v1 metadata: {} items: - apiVersion: v1 kind: ServiceAccount metadata: name: oshinko - apiVersion: v1 kind: RoleBinding metadata: name: oshinko-edit roleRef: name: edit subjects: - kind: ServiceAccount name: oshinko - apiVersion: v1 kind: Template labels: application: oshinko-pyspark createdBy: template-oshinko-pyspark-build-dc metadata: annotations: openshift.io/display-name: PySpark description: Create a buildconfig, imagestream and deploymentconfig using source-to-image and pyspark source hosted in git name: oshinko-pyspark-build-dc objects: - apiVersion: v1 kind: ImageStream metadata: name: ${APPLICATION_NAME} labels: app: ${APPLICATION_NAME} spec: dockerImageRepository: ${APPLICATION_NAME} tags: - name: latest - apiVersion: v1 kind: BuildConfig metadata: name: ${APPLICATION_NAME} labels: app: ${APPLICATION_NAME} spec: output: to: kind: ImageStreamTag name: ${APPLICATION_NAME}:latest source: git: ref: ${GIT_REF} uri: ${GIT_URI} type: Git strategy: sourceStrategy: type: Source env: - name: APP_FILE value: ${APP_FILE} forcePull: true from: kind: DockerImage name: radanalyticsio/radanalytics-pyspark:{{ tag }} triggers: - type: ConfigChange - type: ImageChange imageChange: {} - type: GitHub github: secret: ${APPLICATION_NAME} - type: Generic generic: secret: ${APPLICATION_NAME} - apiVersion: v1 kind: DeploymentConfig metadata: labels: app: ${APPLICATION_NAME} name: ${APPLICATION_NAME} spec: replicas: 1 selector: deploymentconfig: ${APPLICATION_NAME} strategy: type: Rolling template: metadata: labels: deploymentconfig: ${APPLICATION_NAME} app: ${APPLICATION_NAME} spec: containers: - name: ${APPLICATION_NAME} image: ${APPLICATION_NAME} imagePullPolicy: Always resources: {} terminationMessagePath: /dev/termination-log env: - name: OSHINKO_CLUSTER_NAME value: ${OSHINKO_CLUSTER_NAME} - name: APP_ARGS value: ${APP_ARGS} - name: SPARK_OPTIONS value: ${SPARK_OPTIONS} - name: OSHINKO_DEL_CLUSTER value: ${OSHINKO_DEL_CLUSTER} - name: APP_EXIT value: "true" - name: OSHINKO_NAMED_CONFIG value: ${OSHINKO_NAMED_CONFIG} - name: OSHINKO_SPARK_DRIVER_CONFIG value: ${OSHINKO_SPARK_DRIVER_CONFIG} - name: POD_NAME valueFrom: fieldRef: fieldPath: metadata.name volumeMounts: - mountPath: /etc/podinfo name: podinfo readOnly: false dnsPolicy: ClusterFirst restartPolicy: Always serviceAccount: oshinko volumes: - downwardAPI: items: - fieldRef: fieldPath: metadata.labels path: labels name: podinfo triggers: - type: ConfigChange - type: ImageChange imageChangeParams: automatic: true containerNames: - ${APPLICATION_NAME} from: kind: ImageStreamTag name: ${APPLICATION_NAME}:latest - apiVersion: v1 kind: Service metadata: name: ${APPLICATION_NAME} labels: app: ${APPLICATION_NAME} spec: ports: - name: 8080-tcp port: 8080 protocol: TCP targetPort: 8080 selector: deploymentconfig: ${APPLICATION_NAME} parameters: - name: APPLICATION_NAME displayName: Application Name description: The name to use for the BuildConfig, ImageStream and DeploymentConfig components from: pyspark-[a-z0-9]{4} generate: expression - name: GIT_URI displayName: Git Repository URL description: The URL of the repository with your application source code required: true - name: APP_ARGS displayName: Application Arguments description: Command line arguments to pass to the application - name: SPARK_OPTIONS displayName: spark-submit Options description: List of additional options to pass to spark-submit (for exmaple --conf property=value or --package ...). --master and --class are set by the launcher and should not be set here. - name: GIT_REF displayName: Git Reference description: Optional branch, tag or commit - name: OSHINKO_CLUSTER_NAME description: The name of the spark cluster to run against. The cluster will be created if it does not exist, and a random cluster name will be chosen if this value is left blank. - name: OSHINKO_NAMED_CONFIG description: The name of a stored cluster configuration to use if a cluster is created, default is 'default'. - name: OSHINKO_SPARK_DRIVER_CONFIG description: The name of a configmap to use for the spark configuration of the driver. If this configmap is empty the default spark configuration will be used. - name: OSHINKO_DEL_CLUSTER description: If a cluster is created on-demand, delete the cluster when the application finishes if this option is set to 'true' value: "true" required: true - name: APP_FILE description: The name of the main py file to run. If this is not specified and there is a single py file at top level of the git respository, that file will be chosen. - apiVersion: v1 kind: Template labels: application: oshinko-java-spark createdBy: template-oshinko-java-build-dc metadata: annotations: openshift.io/display-name: JavaSpark description: Create a buildconfig, imagestream and deploymentconfig using source-to-image and java spark source hosted in git name: oshinko-java-spark-build-dc objects: - apiVersion: v1 kind: ImageStream metadata: name: ${APPLICATION_NAME} labels: app: ${APPLICATION_NAME} spec: dockerImageRepository: ${APPLICATION_NAME} tags: - name: latest - apiVersion: v1 kind: BuildConfig metadata: name: ${APPLICATION_NAME} labels: app: ${APPLICATION_NAME} spec: output: to: kind: ImageStreamTag name: ${APPLICATION_NAME}:latest source: git: ref: ${GIT_REF} uri: ${GIT_URI} type: Git strategy: sourceStrategy: type: Source env: - name: APP_FILE value: ${APP_FILE} forcePull: true from: kind: DockerImage name: radanalyticsio/radanalytics-java-spark:{{ tag }} triggers: - type: ConfigChange - type: ImageChange imageChange: {} - type: GitHub github: secret: ${APPLICATION_NAME} - type: Generic generic: secret: ${APPLICATION_NAME} - apiVersion: v1 kind: DeploymentConfig metadata: labels: app: ${APPLICATION_NAME} name: ${APPLICATION_NAME} spec: replicas: 1 selector: deploymentconfig: ${APPLICATION_NAME} strategy: type: Rolling template: metadata: labels: deploymentconfig: ${APPLICATION_NAME} app: ${APPLICATION_NAME} spec: containers: - name: ${APPLICATION_NAME} image: ${APPLICATION_NAME} imagePullPolicy: Always resources: {} terminationMessagePath: /dev/termination-log env: - name: OSHINKO_CLUSTER_NAME value: ${OSHINKO_CLUSTER_NAME} - name: APP_ARGS value: ${APP_ARGS} - name: SPARK_OPTIONS value: ${SPARK_OPTIONS} - name: APP_MAIN_CLASS value: ${APP_MAIN_CLASS} - name: OSHINKO_DEL_CLUSTER value: ${OSHINKO_DEL_CLUSTER} - name: APP_EXIT value: "true" - name: OSHINKO_NAMED_CONFIG value: ${OSHINKO_NAMED_CONFIG} - name: OSHINKO_SPARK_DRIVER_CONFIG value: ${OSHINKO_SPARK_DRIVER_CONFIG} - name: POD_NAME valueFrom: fieldRef: fieldPath: metadata.name volumeMounts: - mountPath: /etc/podinfo name: podinfo readOnly: false dnsPolicy: ClusterFirst restartPolicy: Always serviceAccount: oshinko volumes: - downwardAPI: items: - fieldRef: fieldPath: metadata.labels path: labels name: podinfo triggers: - type: ConfigChange - type: ImageChange imageChangeParams: automatic: true containerNames: - ${APPLICATION_NAME} from: kind: ImageStreamTag name: ${APPLICATION_NAME}:latest - apiVersion: v1 kind: Service metadata: name: ${APPLICATION_NAME} labels: app: ${APPLICATION_NAME} spec: ports: - name: 8080-tcp port: 8080 protocol: TCP targetPort: 8080 selector: deploymentconfig: ${APPLICATION_NAME} parameters: - name: APPLICATION_NAME displayName: Application Name description: |- The name to use for the BuildConfig, ImageStream and DeploymentConfig components from: java-[a-z0-9]{4} generate: expression - name: GIT_URI displayName: Git Repository URL description: |- The URL of the repository with your application source code required: true - name: APP_MAIN_CLASS description: Application main class for jar-based applications - name: APP_ARGS displayName: Application Arguments description: Command line arguments to pass to the application - name: SPARK_OPTIONS displayName: spark-submit Options description: |- List of additional options to pass to spark-submit (for exmaple --conf property=value or --package ...). --master and --class are set by the launcher and should not be set here. - name: GIT_REF displayName: Git Reference description: Optional branch, tag or commit - name: OSHINKO_CLUSTER_NAME description: |- The name of the spark cluster to run against. The cluster will be created if it does not exist, and a random cluster name will be chosen if this value is left blank. - name: OSHINKO_NAMED_CONFIG description: |- The name of a stored cluster configuration to use if a cluster is created, default is 'default'. - name: OSHINKO_SPARK_DRIVER_CONFIG description: |- The name of a configmap to use for the spark configuration of the driver. If this configmap is empty the default spark configuration will be used. - name: OSHINKO_DEL_CLUSTER description: |- If a cluster is created on-demand, delete the cluster when the application finishes if this option is set to 'true' value: "true" required: true - name: APP_FILE description: |- The name of the main JAR file. If this is not specified and there is a single JAR produced by the build, that JAR will be chosen. - apiVersion: v1 kind: Template labels: application: oshinko-scala-spark createdBy: template-oshinko-scala-spark-build-dc metadata: annotations: description: Create a buildconfig, imagestream and deploymentconfig using source-to-image and Scala Spark source hosted in git name: oshinko-scala-spark-build-dc objects: - apiVersion: v1 kind: ImageStream metadata: name: ${APPLICATION_NAME} spec: dockerImageRepository: ${APPLICATION_NAME} tags: - name: latest - apiVersion: v1 kind: BuildConfig metadata: name: ${APPLICATION_NAME} spec: output: to: kind: ImageStreamTag name: ${APPLICATION_NAME}:latest source: git: ref: ${GIT_REF} uri: ${GIT_URI} type: Git strategy: sourceStrategy: type: Source env: - name: APP_FILE value: ${APP_FILE} forcePull: true from: kind: DockerImage name: radanalyticsio/radanalytics-scala-spark:{{ tag }} triggers: - type: ConfigChange - type: ImageChange imageChange: {} - type: GitHub github: secret: ${APPLICATION_NAME} - type: Generic generic: secret: ${APPLICATION_NAME} - apiVersion: v1 kind: DeploymentConfig metadata: labels: deploymentConfig: ${APPLICATION_NAME} name: ${APPLICATION_NAME} spec: replicas: 1 selector: deploymentConfig: ${APPLICATION_NAME} strategy: type: Rolling template: metadata: labels: deploymentConfig: ${APPLICATION_NAME} spec: containers: - name: ${APPLICATION_NAME} image: ${APPLICATION_NAME} imagePullPolicy: Always resources: {} terminationMessagePath: /dev/termination-log env: - name: OSHINKO_CLUSTER_NAME value: ${OSHINKO_CLUSTER_NAME} - name: APP_ARGS value: ${APP_ARGS} - name: SPARK_OPTIONS value: ${SPARK_OPTIONS} - name: APP_MAIN_CLASS value: ${APP_MAIN_CLASS} - name: OSHINKO_DEL_CLUSTER value: ${OSHINKO_DEL_CLUSTER} - name: APP_EXIT value: "true" - name: OSHINKO_NAMED_CONFIG value: ${OSHINKO_NAMED_CONFIG} - name: OSHINKO_SPARK_DRIVER_CONFIG value: ${OSHINKO_SPARK_DRIVER_CONFIG} - name: POD_NAME valueFrom: fieldRef: fieldPath: metadata.name volumeMounts: - mountPath: /etc/podinfo name: podinfo readOnly: false dnsPolicy: ClusterFirst restartPolicy: Always serviceAccount: oshinko volumes: - downwardAPI: items: - fieldRef: fieldPath: metadata.labels path: labels name: podinfo triggers: - type: ConfigChange - type: ImageChange imageChangeParams: automatic: true containerNames: - ${APPLICATION_NAME} from: kind: ImageStreamTag name: ${APPLICATION_NAME}:latest - apiVersion: v1 kind: Service metadata: name: ${APPLICATION_NAME} labels: app: ${APPLICATION_NAME} spec: ports: - name: 8080-tcp port: 8080 protocol: TCP targetPort: 8080 selector: deploymentconfig: ${APPLICATION_NAME} parameters: - name: APPLICATION_NAME displayName: Application Name description: |- The name to use for the BuildConfig, ImageStream and DeploymentConfig components from: scala-[a-z0-9]{4} generate: expression - name: GIT_URI displayName: Git Repository URL description: |- The URL of the repository with your application source code required: true - name: APP_MAIN_CLASS description: Application main class for jar-based applications - name: APP_ARGS displayName: Application Arguments description: Command line arguments to pass to the application - name: SPARK_OPTIONS displayName: spark-submit Options description: |- List of additional options to pass to spark-submit (for exmaple --conf property=value or --package ...). --master and --class are set by the launcher and should not be set here. - name: GIT_REF displayName: Git Reference description: Optional branch, tag or commit - name: OSHINKO_CLUSTER_NAME description: |- The name of the spark cluster to run against. The cluster will be created if it does not exist, and a random cluster name will be chosen if this value is left blank. - name: OSHINKO_NAMED_CONFIG description: |- The name of a stored cluster configuration to use if a cluster is created, default is 'default'. - name: OSHINKO_SPARK_DRIVER_CONFIG description: |- The name of a configmap to use for the spark configuration of the driver. If this configmap is empty the default spark configuration will be used. - name: OSHINKO_DEL_CLUSTER description: |- If a cluster is created on-demand, delete the cluster when the application finishes if this option is set to 'true' value: "true" required: true - name: APP_FILE description: |- The name of the main JAR file. If this is not specified and there is a single JAR produced by the build, that JAR will be chosen. - apiVersion: v1 kind: Template template: oshinko-webui metadata: name: oshinko-webui objects: - kind: Service apiVersion: v1 metadata: name: ${OSHINKO_WEB_NAME} labels: name: ${OSHINKO_WEB_NAME} spec: ports: - name: o-web-port protocol: TCP port: 8080 targetPort: 8080 selector: name: ${OSHINKO_WEB_NAME} - kind: DeploymentConfig apiVersion: v1 metadata: name: ${OSHINKO_DEPLOYMENT_NAME} spec: strategy: type: Rolling triggers: - type: ConfigChange replicas: 1 selector: name: ${OSHINKO_WEB_NAME} template: metadata: labels: name: ${OSHINKO_WEB_NAME} spec: containers: - name: ${OSHINKO_WEB_NAME} image: ${OSHINKO_WEB_IMAGE} ports: - name: o-web-port containerPort: 8080 protocol: TCP env: - name: OSHINKO_SPARK_IMAGE value: ${OSHINKO_CLUSTER_IMAGE} - name: OSHINKO_REFRESH_INTERVAL value: ${OSHINKO_REFRESH_INTERVAL} readinessProbe: failureThreshold: 3 httpGet: path: / port: 8080 scheme: HTTP periodSeconds: 10 successThreshold: 1 timeoutSeconds: 1 livenessProbe: failureThreshold: 3 httpGet: path: / port: 8080 scheme: HTTP periodSeconds: 10 successThreshold: 1 timeoutSeconds: 1 serviceAccount: oshinko - kind: Route apiVersion: v1 metadata: name: ${OSHINKO_WEB_NAME} spec: host: ${OSHINKO_WEB_ROUTE_HOSTNAME} to: kind: Service name: ${OSHINKO_WEB_NAME} parameters: - name: OSHINKO_CLUSTER_IMAGE description: Full name of the spark image to use when creating clusters required: false - name: OSHINKO_WEB_NAME description: Name of the oshinko web service value: "oshinko-web" - name: OSHINKO_WEB_IMAGE description: Full name of the oshinko web image required: true value: radanalyticsio/oshinko-webui:{{ tag }} - name: OSHINKO_WEB_ROUTE_HOSTNAME description: The hostname used to create the external route for the webui - name: OSHINKO_DEPLOYMENT_NAME description: Name of the oshinko deployment value: "oshinko" - name: OSHINKO_REFRESH_INTERVAL value: "5" description: Refresh interval for updating cluster list in seconds"""

tests.py

import unittest import jinja2 import app class OSTemplateViewTestCase(unittest.TestCase): def setUp(self): config = app.ConfigWrapper() self.app = app.app(config).test_client() def test_latest_defaults(self): obs = self.app.get('/') exp = jinja2.Template(TEMPLATE).render(tag='stable').encode('utf-8') assert obs.data == exp def test_latest_version(self): obs = self.app.get('/?version=latest') exp = jinja2.Template(TEMPLATE).render(tag='stable').encode('utf-8') assert obs.data == exp def test_404_version(self): obs = self.app.get('/?version=NON-EXISTANT') assert obs.status == '404 NOT FOUND' def test_latest_tag(self): obs = self.app.get('/?tag=testXX.YY.ZZ') exp = jinja2.Template(TEMPLATE).render(tag='testXX.YY.ZZ').encode('utf-8') assert obs.data == exp TEMPLATE = """kind: List apiVersion: v1 metadata: {} items: - apiVersion: v1 kind: ServiceAccount metadata: name: oshinko - apiVersion: v1 kind: RoleBinding metadata: name: oshinko-edit roleRef: name: edit subjects: - kind: ServiceAccount name: oshinko - apiVersion: v1 kind: Template labels: application: oshinko-pyspark createdBy: template-oshinko-pyspark-build-dc metadata: annotations: openshift.io/display-name: PySpark description: Create a buildconfig, imagestream and deploymentconfig using source-to-image and pyspark source hosted in git name: oshinko-pyspark-build-dc objects: - apiVersion: v1 kind: ImageStream metadata: name: ${APPLICATION_NAME} labels: app: ${APPLICATION_NAME} spec: dockerImageRepository: ${APPLICATION_NAME} tags: - name: latest - apiVersion: v1 kind: BuildConfig metadata: name: ${APPLICATION_NAME} labels: app: ${APPLICATION_NAME} spec: output: to: kind: ImageStreamTag name: ${APPLICATION_NAME}:latest source: git: ref: ${GIT_REF} uri: ${GIT_URI} type: Git strategy: sourceStrategy: type: Source env: - name: APP_FILE value: ${APP_FILE} forcePull: true from: kind: DockerImage name: radanalyticsio/radanalytics-pyspark:{{ tag }} triggers: - type: ConfigChange - type: ImageChange imageChange: {} - type: GitHub github: secret: ${APPLICATION_NAME} - type: Generic generic: secret: ${APPLICATION_NAME} - apiVersion: v1 kind: DeploymentConfig metadata: labels: app: ${APPLICATION_NAME} name: ${APPLICATION_NAME} spec: replicas: 1 selector: deploymentconfig: ${APPLICATION_NAME} strategy: type: Rolling template: metadata: labels: deploymentconfig: ${APPLICATION_NAME} app: ${APPLICATION_NAME} spec: containers: - name: ${APPLICATION_NAME} image: ${APPLICATION_NAME} imagePullPolicy: Always resources: {} terminationMessagePath: /dev/termination-log env: - name: OSHINKO_CLUSTER_NAME value: ${OSHINKO_CLUSTER_NAME} - name: APP_ARGS value: ${APP_ARGS} - name: SPARK_OPTIONS value: ${SPARK_OPTIONS} - name: OSHINKO_DEL_CLUSTER value: ${OSHINKO_DEL_CLUSTER} - name: APP_EXIT value: "true" - name: OSHINKO_NAMED_CONFIG value: ${OSHINKO_NAMED_CONFIG} - name: OSHINKO_SPARK_DRIVER_CONFIG value: ${OSHINKO_SPARK_DRIVER_CONFIG} - name: POD_NAME valueFrom: fieldRef: fieldPath: metadata.name volumeMounts: - mountPath: /etc/podinfo name: podinfo readOnly: false dnsPolicy: ClusterFirst restartPolicy: Always serviceAccount: oshinko volumes: - downwardAPI: items: - fieldRef: fieldPath: metadata.labels path: labels name: podinfo triggers: - type: ConfigChange - type: ImageChange imageChangeParams: automatic: true containerNames: - ${APPLICATION_NAME} from: kind: ImageStreamTag name: ${APPLICATION_NAME}:latest - apiVersion: v1 kind: Service metadata: name: ${APPLICATION_NAME} labels: app: ${APPLICATION_NAME} spec: ports: - name: 8080-tcp port: 8080 protocol: TCP targetPort: 8080 selector: deploymentconfig: ${APPLICATION_NAME} parameters: - name: APPLICATION_NAME displayName: Application Name description: The name to use for the BuildConfig, ImageStream and DeploymentConfig components from: pyspark-[a-z0-9]{4} generate: expression - name: GIT_URI displayName: Git Repository URL description: The URL of the repository with your application source code required: true - name: APP_ARGS displayName: Application Arguments description: Command line arguments to pass to the application - name: SPARK_OPTIONS displayName: spark-submit Options description: List of additional options to pass to spark-submit (for exmaple --conf property=value or --package ...). --master and --class are set by the launcher and should not be set here. - name: GIT_REF displayName: Git Reference description: Optional branch, tag or commit - name: OSHINKO_CLUSTER_NAME description: The name of the spark cluster to run against. The cluster will be created if it does not exist, and a random cluster name will be chosen if this value is left blank. - name: OSHINKO_NAMED_CONFIG description: The name of a stored cluster configuration to use if a cluster is created, default is 'default'. - name: OSHINKO_SPARK_DRIVER_CONFIG description: The name of a configmap to use for the spark configuration of the driver. If this configmap is empty the default spark configuration will be used. - name: OSHINKO_DEL_CLUSTER description: If a cluster is created on-demand, delete the cluster when the application finishes if this option is set to 'true' value: "true" required: true - name: APP_FILE description: The name of the main py file to run. If this is not specified and there is a single py file at top level of the git respository, that file will be chosen. - apiVersion: v1 kind: Template labels: application: oshinko-java-spark createdBy: template-oshinko-java-build-dc metadata: annotations: openshift.io/display-name: JavaSpark description: Create a buildconfig, imagestream and deploymentconfig using source-to-image and java spark source hosted in git name: oshinko-java-spark-build-dc objects: - apiVersion: v1 kind: ImageStream metadata: name: ${APPLICATION_NAME} labels: app: ${APPLICATION_NAME} spec: dockerImageRepository: ${APPLICATION_NAME} tags: - name: latest - apiVersion: v1 kind: BuildConfig metadata: name: ${APPLICATION_NAME} labels: app: ${APPLICATION_NAME} spec: output: to: kind: ImageStreamTag name: ${APPLICATION_NAME}:latest source: git: ref: ${GIT_REF} uri: ${GIT_URI} type: Git strategy: sourceStrategy: type: Source env: - name: APP_FILE value: ${APP_FILE} forcePull: true from: kind: DockerImage name: radanalyticsio/radanalytics-java-spark:{{ tag }} triggers: - type: ConfigChange - type: ImageChange imageChange: {} - type: GitHub github: secret: ${APPLICATION_NAME} - type: Generic generic: secret: ${APPLICATION_NAME} - apiVersion: v1 kind: DeploymentConfig metadata: labels: app: ${APPLICATION_NAME} name: ${APPLICATION_NAME} spec: replicas: 1 selector: deploymentconfig: ${APPLICATION_NAME} strategy: type: Rolling template: metadata: labels: deploymentconfig: ${APPLICATION_NAME} app: ${APPLICATION_NAME} spec: containers: - name: ${APPLICATION_NAME} image: ${APPLICATION_NAME} imagePullPolicy: Always resources: {} terminationMessagePath: /dev/termination-log env: - name: OSHINKO_CLUSTER_NAME value: ${OSHINKO_CLUSTER_NAME} - name: APP_ARGS value: ${APP_ARGS} - name: SPARK_OPTIONS value: ${SPARK_OPTIONS} - name: APP_MAIN_CLASS value: ${APP_MAIN_CLASS} - name: OSHINKO_DEL_CLUSTER value: ${OSHINKO_DEL_CLUSTER} - name: APP_EXIT value: "true" - name: OSHINKO_NAMED_CONFIG value: ${OSHINKO_NAMED_CONFIG} - name: OSHINKO_SPARK_DRIVER_CONFIG value: ${OSHINKO_SPARK_DRIVER_CONFIG} - name: POD_NAME valueFrom: fieldRef: fieldPath: metadata.name volumeMounts: - mountPath: /etc/podinfo name: podinfo readOnly: false dnsPolicy: ClusterFirst restartPolicy: Always serviceAccount: oshinko volumes: - downwardAPI: items: - fieldRef: fieldPath: metadata.labels path: labels name: podinfo triggers: - type: ConfigChange - type: ImageChange imageChangeParams: automatic: true containerNames: - ${APPLICATION_NAME} from: kind: ImageStreamTag name: ${APPLICATION_NAME}:latest - apiVersion: v1 kind: Service metadata: name: ${APPLICATION_NAME} labels: app: ${APPLICATION_NAME} spec: ports: - name: 8080-tcp port: 8080 protocol: TCP targetPort: 8080 selector: deploymentconfig: ${APPLICATION_NAME} parameters: - name: APPLICATION_NAME displayName: Application Name description: |- The name to use for the BuildConfig, ImageStream and DeploymentConfig components from: java-[a-z0-9]{4} generate: expression - name: GIT_URI displayName: Git Repository URL description: |- The URL of the repository with your application source code required: true - name: APP_MAIN_CLASS description: Application main class for jar-based applications - name: APP_ARGS displayName: Application Arguments description: Command line arguments to pass to the application - name: SPARK_OPTIONS displayName: spark-submit Options description: |- List of additional options to pass to spark-submit (for exmaple --conf property=value or --package ...). --master and --class are set by the launcher and should not be set here. - name: GIT_REF displayName: Git Reference description: Optional branch, tag or commit - name: OSHINKO_CLUSTER_NAME description: |- The name of the spark cluster to run against. The cluster will be created if it does not exist, and a random cluster name will be chosen if this value is left blank. - name: OSHINKO_NAMED_CONFIG description: |- The name of a stored cluster configuration to use if a cluster is created, default is 'default'. - name: OSHINKO_SPARK_DRIVER_CONFIG description: |- The name of a configmap to use for the spark configuration of the driver. If this configmap is empty the default spark configuration will be used. - name: OSHINKO_DEL_CLUSTER description: |- If a cluster is created on-demand, delete the cluster when the application finishes if this option is set to 'true' value: "true" required: true - name: APP_FILE description: |- The name of the main JAR file. If this is not specified and there is a single JAR produced by the build, that JAR will be chosen. - apiVersion: v1 kind: Template labels: application: oshinko-scala-spark createdBy: template-oshinko-scala-spark-build-dc metadata: annotations: description: Create a buildconfig, imagestream and deploymentconfig using source-to-image and Scala Spark source hosted in git name: oshinko-scala-spark-build-dc objects: - apiVersion: v1 kind: ImageStream metadata: name: ${APPLICATION_NAME} spec: dockerImageRepository: ${APPLICATION_NAME} tags: - name: latest - apiVersion: v1 kind: BuildConfig metadata: name: ${APPLICATION_NAME} spec: output: to: kind: ImageStreamTag name: ${APPLICATION_NAME}:latest source: git: ref: ${GIT_REF} uri: ${GIT_URI} type: Git strategy: sourceStrategy: type: Source env: - name: APP_FILE value: ${APP_FILE} forcePull: true from: kind: DockerImage name: radanalyticsio/radanalytics-scala-spark:{{ tag }} triggers: - type: ConfigChange - type: ImageChange imageChange: {} - type: GitHub github: secret: ${APPLICATION_NAME} - type: Generic generic: secret: ${APPLICATION_NAME} - apiVersion: v1 kind: DeploymentConfig metadata: labels: deploymentConfig: ${APPLICATION_NAME} name: ${APPLICATION_NAME} spec: replicas: 1 selector: deploymentConfig: ${APPLICATION_NAME} strategy: type: Rolling template: metadata: labels: deploymentConfig: ${APPLICATION_NAME} spec: containers: - name: ${APPLICATION_NAME} image: ${APPLICATION_NAME} imagePullPolicy: Always resources: {} terminationMessagePath: /dev/termination-log env: - name: OSHINKO_CLUSTER_NAME value: ${OSHINKO_CLUSTER_NAME} - name: APP_ARGS value: ${APP_ARGS} - name: SPARK_OPTIONS value: ${SPARK_OPTIONS} - name: APP_MAIN_CLASS value: ${APP_MAIN_CLASS} - name: OSHINKO_DEL_CLUSTER value: ${OSHINKO_DEL_CLUSTER} - name: APP_EXIT value: "true" - name: OSHINKO_NAMED_CONFIG value: ${OSHINKO_NAMED_CONFIG} - name: OSHINKO_SPARK_DRIVER_CONFIG value: ${OSHINKO_SPARK_DRIVER_CONFIG} - name: POD_NAME valueFrom: fieldRef: fieldPath: metadata.name volumeMounts: - mountPath: /etc/podinfo name: podinfo readOnly: false dnsPolicy: ClusterFirst restartPolicy: Always serviceAccount: oshinko volumes: - downwardAPI: items: - fieldRef: fieldPath: metadata.labels path: labels name: podinfo triggers: - type: ConfigChange - type: ImageChange imageChangeParams: automatic: true containerNames: - ${APPLICATION_NAME} from: kind: ImageStreamTag name: ${APPLICATION_NAME}:latest - apiVersion: v1 kind: Service metadata: name: ${APPLICATION_NAME} labels: app: ${APPLICATION_NAME} spec: ports: - name: 8080-tcp port: 8080 protocol: TCP targetPort: 8080 selector: deploymentconfig: ${APPLICATION_NAME} parameters: - name: APPLICATION_NAME displayName: Application Name description: |- The name to use for the BuildConfig, ImageStream and DeploymentConfig components from: scala-[a-z0-9]{4} generate: expression - name: GIT_URI displayName: Git Repository URL description: |- The URL of the repository with your application source code required: true - name: APP_MAIN_CLASS description: Application main class for jar-based applications - name: APP_ARGS displayName: Application Arguments description: Command line arguments to pass to the application - name: SPARK_OPTIONS displayName: spark-submit Options description: |- List of additional options to pass to spark-submit (for exmaple --conf property=value or --package ...). --master and --class are set by the launcher and should not be set here. - name: GIT_REF displayName: Git Reference description: Optional branch, tag or commit - name: OSHINKO_CLUSTER_NAME description: |- The name of the spark cluster to run against. The cluster will be created if it does not exist, and a random cluster name will be chosen if this value is left blank. - name: OSHINKO_NAMED_CONFIG description: |- The name of a stored cluster configuration to use if a cluster is created, default is 'default'. - name: OSHINKO_SPARK_DRIVER_CONFIG description: |- The name of a configmap to use for the spark configuration of the driver. If this configmap is empty the default spark configuration will be used. - name: OSHINKO_DEL_CLUSTER description: |- If a cluster is created on-demand, delete the cluster when the application finishes if this option is set to 'true' value: "true" required: true - name: APP_FILE description: |- The name of the main JAR file. If this is not specified and there is a single JAR produced by the build, that JAR will be chosen. - apiVersion: v1 kind: Template template: oshinko-webui metadata: name: oshinko-webui objects: - kind: Service apiVersion: v1 metadata: name: ${OSHINKO_WEB_NAME} labels: name: ${OSHINKO_WEB_NAME} spec: ports: - name: o-web-port protocol: TCP port: 8080 targetPort: 8080 selector: name: ${OSHINKO_WEB_NAME} - kind: DeploymentConfig apiVersion: v1 metadata: name: ${OSHINKO_DEPLOYMENT_NAME} spec: strategy: type: Rolling triggers: - type: ConfigChange replicas: 1 selector: name: ${OSHINKO_WEB_NAME} template: metadata: labels: name: ${OSHINKO_WEB_NAME} spec: containers: - name: ${OSHINKO_WEB_NAME} image: ${OSHINKO_WEB_IMAGE} ports: - name: o-web-port containerPort: 8080 protocol: TCP env: - name: OSHINKO_SPARK_IMAGE value: ${OSHINKO_CLUSTER_IMAGE} - name: OSHINKO_REFRESH_INTERVAL value: ${OSHINKO_REFRESH_INTERVAL} readinessProbe: failureThreshold: 3 httpGet: path: / port: 8080 scheme: HTTP periodSeconds: 10 successThreshold: 1 timeoutSeconds: 1 livenessProbe: failureThreshold: 3 httpGet: path: / port: 8080 scheme: HTTP periodSeconds: 10 successThreshold: 1 timeoutSeconds: 1 serviceAccount: oshinko - kind: Route apiVersion: v1 metadata: name: ${OSHINKO_WEB_NAME} spec: host: ${OSHINKO_WEB_ROUTE_HOSTNAME} to: kind: Service name: ${OSHINKO_WEB_NAME} parameters: - name: OSHINKO_CLUSTER_IMAGE description: Full name of the spark image to use when creating clusters required: false - name: OSHINKO_WEB_NAME description: Name of the oshinko web service value: "oshinko-web" - name: OSHINKO_WEB_IMAGE description: Full name of the oshinko web image required: true value: radanalyticsio/oshinko-webui:{{ tag }} - name: OSHINKO_WEB_ROUTE_HOSTNAME description: The hostname used to create the external route for the webui - name: OSHINKO_DEPLOYMENT_NAME description: Name of the oshinko deployment value: "oshinko" - name: OSHINKO_REFRESH_INTERVAL value: "5" description: Refresh interval for updating cluster list in seconds"""

0.54819

0.271783

from unittest import TestCase from unittest.mock import MagicMock, patch, Mock from samcli.lib.sync.sync_flow_factory import SyncFlowFactory from samcli.lib.utils.cloudformation import CloudFormationResourceSummary class TestSyncFlowFactory(TestCase): def create_factory(self, auto_dependency_layer: bool = False): stack_resource = MagicMock() stack_resource.resources = { "Resource1": { "Type": "TypeA", "Properties": {"Body1"}, }, "Resource2": { "Type": "TypeB", "Properties": {"Body2"}, "Metadata": { "SamResourceId": "CDKResource2", }, }, } factory = SyncFlowFactory( build_context=MagicMock(), deploy_context=MagicMock(), stacks=[stack_resource, MagicMock()], auto_dependency_layer=auto_dependency_layer, ) return factory @patch("samcli.lib.sync.sync_flow_factory.get_resource_summaries") @patch("samcli.lib.sync.sync_flow_factory.get_boto_resource_provider_with_config") @patch("samcli.lib.sync.sync_flow_factory.get_boto_client_provider_with_config") def test_load_physical_id_mapping( self, get_boto_client_provider_mock, get_boto_resource_provider_mock, get_resource_summaries_mock ): resource_summary_1 = CloudFormationResourceSummary( resource_type="", logical_resource_id="", physical_resource_id="PhysicalResource1" ) resource_summary_2 = CloudFormationResourceSummary( resource_type="", logical_resource_id="", physical_resource_id="PhysicalResource2" ) # get_resource_summaries_mock.return_value = {"Resource1": "PhysicalResource1", "Resource2": "PhysicalResource2"} get_resource_summaries_mock.return_value = {"Resource1": resource_summary_1, "Resource2": resource_summary_2} factory = self.create_factory() factory.load_physical_id_mapping() self.assertEqual(len(factory._physical_id_mapping), 2) self.assertEqual( factory._physical_id_mapping, {"Resource1": "PhysicalResource1", "Resource2": "PhysicalResource2"}, ) @patch("samcli.lib.sync.sync_flow_factory.ImageFunctionSyncFlow") @patch("samcli.lib.sync.sync_flow_factory.ZipFunctionSyncFlow") def test_create_lambda_flow_zip(self, zip_function_mock, image_function_mock): factory = self.create_factory() resource = {"Properties": {"PackageType": "Zip"}} result = factory._create_lambda_flow("Function1", resource) self.assertEqual(result, zip_function_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.ImageFunctionSyncFlow") @patch("samcli.lib.sync.sync_flow_factory.ZipFunctionSyncFlow") @patch("samcli.lib.sync.sync_flow_factory.AutoDependencyLayerParentSyncFlow") def test_create_lambda_flow_zip_with_auto_dependency_layer( self, auto_dependency_layer_mock, zip_function_mock, image_function_mock ): factory = self.create_factory(True) resource = {"Properties": {"PackageType": "Zip", "Runtime": "python3.8"}} result = factory._create_lambda_flow("Function1", resource) self.assertEqual(result, auto_dependency_layer_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.ImageFunctionSyncFlow") @patch("samcli.lib.sync.sync_flow_factory.ZipFunctionSyncFlow") @patch("samcli.lib.sync.sync_flow_factory.AutoDependencyLayerParentSyncFlow") def test_create_lambda_flow_zip_with_unsupported_runtime_auto_dependency_layer( self, auto_dependency_layer_mock, zip_function_mock, image_function_mock ): factory = self.create_factory(True) resource = {"Properties": {"PackageType": "Zip", "Runtime": "ruby2.7"}} result = factory._create_lambda_flow("Function1", resource) self.assertEqual(result, zip_function_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.ImageFunctionSyncFlow") @patch("samcli.lib.sync.sync_flow_factory.ZipFunctionSyncFlow") def test_create_lambda_flow_image(self, zip_function_mock, image_function_mock): factory = self.create_factory() resource = {"Properties": {"PackageType": "Image"}} result = factory._create_lambda_flow("Function1", resource) self.assertEqual(result, image_function_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.LayerSyncFlow") def test_create_layer_flow(self, layer_sync_mock): factory = self.create_factory() result = factory._create_layer_flow("Layer1", {}) self.assertEqual(result, layer_sync_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.ImageFunctionSyncFlow") @patch("samcli.lib.sync.sync_flow_factory.ZipFunctionSyncFlow") def test_create_lambda_flow_other(self, zip_function_mock, image_function_mock): factory = self.create_factory() resource = {"Properties": {"PackageType": "Other"}} result = factory._create_lambda_flow("Function1", resource) self.assertEqual(result, None) @patch("samcli.lib.sync.sync_flow_factory.RestApiSyncFlow") def test_create_rest_api_flow(self, rest_api_sync_mock): factory = self.create_factory() result = factory._create_rest_api_flow("API1", {}) self.assertEqual(result, rest_api_sync_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.HttpApiSyncFlow") def test_create_api_flow(self, http_api_sync_mock): factory = self.create_factory() result = factory._create_api_flow("API1", {}) self.assertEqual(result, http_api_sync_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.StepFunctionsSyncFlow") def test_create_stepfunctions_flow(self, stepfunctions_sync_mock): factory = self.create_factory() result = factory._create_stepfunctions_flow("StateMachine1", {}) self.assertEqual(result, stepfunctions_sync_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.get_resource_by_id") def test_create_sync_flow(self, get_resource_by_id_mock): factory = self.create_factory() sync_flow = MagicMock() resource_identifier = MagicMock() get_resource_by_id = MagicMock() get_resource_by_id_mock.return_value = get_resource_by_id generator_mock = MagicMock() generator_mock.return_value = sync_flow get_generator_function_mock = MagicMock() get_generator_function_mock.return_value = generator_mock factory._get_generator_function = get_generator_function_mock result = factory.create_sync_flow(resource_identifier) self.assertEqual(result, sync_flow) generator_mock.assert_called_once_with(factory, resource_identifier, get_resource_by_id) @patch("samcli.lib.sync.sync_flow_factory.get_resource_by_id") def test_create_unknown_resource_sync_flow(self, get_resource_by_id_mock): get_resource_by_id_mock.return_value = None factory = self.create_factory() self.assertIsNone(factory.create_sync_flow(MagicMock())) @patch("samcli.lib.sync.sync_flow_factory.get_resource_by_id") def test_create_none_generator_sync_flow(self, get_resource_by_id_mock): factory = self.create_factory() resource_identifier = MagicMock() get_resource_by_id = MagicMock() get_resource_by_id_mock.return_value = get_resource_by_id get_generator_function_mock = MagicMock() get_generator_function_mock.return_value = None factory._get_generator_function = get_generator_function_mock self.assertIsNone(factory.create_sync_flow(resource_identifier))

tests/unit/lib/sync/test_sync_flow_factory.py

from unittest import TestCase from unittest.mock import MagicMock, patch, Mock from samcli.lib.sync.sync_flow_factory import SyncFlowFactory from samcli.lib.utils.cloudformation import CloudFormationResourceSummary class TestSyncFlowFactory(TestCase): def create_factory(self, auto_dependency_layer: bool = False): stack_resource = MagicMock() stack_resource.resources = { "Resource1": { "Type": "TypeA", "Properties": {"Body1"}, }, "Resource2": { "Type": "TypeB", "Properties": {"Body2"}, "Metadata": { "SamResourceId": "CDKResource2", }, }, } factory = SyncFlowFactory( build_context=MagicMock(), deploy_context=MagicMock(), stacks=[stack_resource, MagicMock()], auto_dependency_layer=auto_dependency_layer, ) return factory @patch("samcli.lib.sync.sync_flow_factory.get_resource_summaries") @patch("samcli.lib.sync.sync_flow_factory.get_boto_resource_provider_with_config") @patch("samcli.lib.sync.sync_flow_factory.get_boto_client_provider_with_config") def test_load_physical_id_mapping( self, get_boto_client_provider_mock, get_boto_resource_provider_mock, get_resource_summaries_mock ): resource_summary_1 = CloudFormationResourceSummary( resource_type="", logical_resource_id="", physical_resource_id="PhysicalResource1" ) resource_summary_2 = CloudFormationResourceSummary( resource_type="", logical_resource_id="", physical_resource_id="PhysicalResource2" ) # get_resource_summaries_mock.return_value = {"Resource1": "PhysicalResource1", "Resource2": "PhysicalResource2"} get_resource_summaries_mock.return_value = {"Resource1": resource_summary_1, "Resource2": resource_summary_2} factory = self.create_factory() factory.load_physical_id_mapping() self.assertEqual(len(factory._physical_id_mapping), 2) self.assertEqual( factory._physical_id_mapping, {"Resource1": "PhysicalResource1", "Resource2": "PhysicalResource2"}, ) @patch("samcli.lib.sync.sync_flow_factory.ImageFunctionSyncFlow") @patch("samcli.lib.sync.sync_flow_factory.ZipFunctionSyncFlow") def test_create_lambda_flow_zip(self, zip_function_mock, image_function_mock): factory = self.create_factory() resource = {"Properties": {"PackageType": "Zip"}} result = factory._create_lambda_flow("Function1", resource) self.assertEqual(result, zip_function_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.ImageFunctionSyncFlow") @patch("samcli.lib.sync.sync_flow_factory.ZipFunctionSyncFlow") @patch("samcli.lib.sync.sync_flow_factory.AutoDependencyLayerParentSyncFlow") def test_create_lambda_flow_zip_with_auto_dependency_layer( self, auto_dependency_layer_mock, zip_function_mock, image_function_mock ): factory = self.create_factory(True) resource = {"Properties": {"PackageType": "Zip", "Runtime": "python3.8"}} result = factory._create_lambda_flow("Function1", resource) self.assertEqual(result, auto_dependency_layer_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.ImageFunctionSyncFlow") @patch("samcli.lib.sync.sync_flow_factory.ZipFunctionSyncFlow") @patch("samcli.lib.sync.sync_flow_factory.AutoDependencyLayerParentSyncFlow") def test_create_lambda_flow_zip_with_unsupported_runtime_auto_dependency_layer( self, auto_dependency_layer_mock, zip_function_mock, image_function_mock ): factory = self.create_factory(True) resource = {"Properties": {"PackageType": "Zip", "Runtime": "ruby2.7"}} result = factory._create_lambda_flow("Function1", resource) self.assertEqual(result, zip_function_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.ImageFunctionSyncFlow") @patch("samcli.lib.sync.sync_flow_factory.ZipFunctionSyncFlow") def test_create_lambda_flow_image(self, zip_function_mock, image_function_mock): factory = self.create_factory() resource = {"Properties": {"PackageType": "Image"}} result = factory._create_lambda_flow("Function1", resource) self.assertEqual(result, image_function_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.LayerSyncFlow") def test_create_layer_flow(self, layer_sync_mock): factory = self.create_factory() result = factory._create_layer_flow("Layer1", {}) self.assertEqual(result, layer_sync_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.ImageFunctionSyncFlow") @patch("samcli.lib.sync.sync_flow_factory.ZipFunctionSyncFlow") def test_create_lambda_flow_other(self, zip_function_mock, image_function_mock): factory = self.create_factory() resource = {"Properties": {"PackageType": "Other"}} result = factory._create_lambda_flow("Function1", resource) self.assertEqual(result, None) @patch("samcli.lib.sync.sync_flow_factory.RestApiSyncFlow") def test_create_rest_api_flow(self, rest_api_sync_mock): factory = self.create_factory() result = factory._create_rest_api_flow("API1", {}) self.assertEqual(result, rest_api_sync_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.HttpApiSyncFlow") def test_create_api_flow(self, http_api_sync_mock): factory = self.create_factory() result = factory._create_api_flow("API1", {}) self.assertEqual(result, http_api_sync_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.StepFunctionsSyncFlow") def test_create_stepfunctions_flow(self, stepfunctions_sync_mock): factory = self.create_factory() result = factory._create_stepfunctions_flow("StateMachine1", {}) self.assertEqual(result, stepfunctions_sync_mock.return_value) @patch("samcli.lib.sync.sync_flow_factory.get_resource_by_id") def test_create_sync_flow(self, get_resource_by_id_mock): factory = self.create_factory() sync_flow = MagicMock() resource_identifier = MagicMock() get_resource_by_id = MagicMock() get_resource_by_id_mock.return_value = get_resource_by_id generator_mock = MagicMock() generator_mock.return_value = sync_flow get_generator_function_mock = MagicMock() get_generator_function_mock.return_value = generator_mock factory._get_generator_function = get_generator_function_mock result = factory.create_sync_flow(resource_identifier) self.assertEqual(result, sync_flow) generator_mock.assert_called_once_with(factory, resource_identifier, get_resource_by_id) @patch("samcli.lib.sync.sync_flow_factory.get_resource_by_id") def test_create_unknown_resource_sync_flow(self, get_resource_by_id_mock): get_resource_by_id_mock.return_value = None factory = self.create_factory() self.assertIsNone(factory.create_sync_flow(MagicMock())) @patch("samcli.lib.sync.sync_flow_factory.get_resource_by_id") def test_create_none_generator_sync_flow(self, get_resource_by_id_mock): factory = self.create_factory() resource_identifier = MagicMock() get_resource_by_id = MagicMock() get_resource_by_id_mock.return_value = get_resource_by_id get_generator_function_mock = MagicMock() get_generator_function_mock.return_value = None factory._get_generator_function = get_generator_function_mock self.assertIsNone(factory.create_sync_flow(resource_identifier))

0.693992

0.308737

from __future__ import print_function import itertools from PhysicsTools.Heppy.analyzers.core.VertexHistograms import VertexHistograms from PhysicsTools.Heppy.analyzers.core.Analyzer import Analyzer from PhysicsTools.Heppy.analyzers.core.AutoHandle import AutoHandle from PhysicsTools.HeppyCore.statistics.average import Average from PhysicsTools.Heppy.physicsutils.PileUpSummaryInfo import PileUpSummaryInfo import PhysicsTools.HeppyCore.framework.config as cfg class VertexAnalyzer( Analyzer ): """selects a list of good primary vertices, and optionally add a pile-up weight to MC events. The list of good primary vertices is put in event.goodVertices. if no good vertex is found, the process function returns False. The weight is put in event.vertexWeight, and is multiplied to the global event weight, event.eventWeight. Example: vertexAna = cfg.Analyzer( 'VertexAnalyzer', goodVertices = 'goodPVFilter', vertexWeight = 'vertexWeightFall112011AB', # uncomment the following line if you want a vertex weight = 1 (no weighting) # fixedWeight = 1, verbose = False ) If fixedWeight is set to None, the vertex weight is read from the EDM collection with module name 'vertexWeightFall112011AB'. Otherwise, the weight is set to fixedWeight. The vertex weight collection was at some point produced in the PAT+CMG step, and could directly be accessed from the PAT or CMG tuple. In the most recent versions of the PAT+CMG tuple, this collection is not present anymore, and an additional full framework process must be ran to produce this collection, so that this analyzer can read it. An example cfg to do that can be found here: http://cmssw.cvs.cern.ch/cgi-bin/cmssw.cgi/UserCode/CMG/CMGTools/H2TauTau/prod/vertexWeight2011_cfg.py?view=markup """ def __init__(self, cfg_ana, cfg_comp, looperName): super(VertexAnalyzer, self).__init__(cfg_ana, cfg_comp, looperName) self.doHists=True if (hasattr(self.cfg_ana,'makeHists')) and (not self.cfg_ana.makeHists): self.doHists=False if self.doHists: self.pileup = VertexHistograms('/'.join([self.dirName, 'pileup.root'])) self.allVertices = self.cfg_ana.allVertices if (hasattr(self.cfg_ana,'allVertices')) else "_AUTO_" def declareHandles(self): super(VertexAnalyzer, self).declareHandles() if self.allVertices == '_AUTO_': self.handles['vertices'] = AutoHandle( "offlineSlimmedPrimaryVertices", 'std::vector<reco::Vertex>', fallbackLabel="offlinePrimaryVertices" ) else: self.handles['vertices'] = AutoHandle( self.allVertices, 'std::vector<reco::Vertex>' ) self.fixedWeight = None if self.cfg_comp.isMC: if hasattr( self.cfg_ana, 'fixedWeight'): self.fixedWeight = self.cfg_ana.fixedWeight else: self.mchandles['vertexWeight'] = AutoHandle( self.cfg_ana.vertexWeight, 'double' ) self.mchandles['pusi'] = AutoHandle( 'slimmedAddPileupInfo', 'std::vector<PileupSummaryInfo>', fallbackLabel='addPileupInfo', ) self.handles['rho'] = AutoHandle( ('fixedGridRhoFastjetAll',''), 'double' ) self.handles['rhoCN'] = AutoHandle( ('fixedGridRhoFastjetCentralNeutral',''), 'double' ) self.handles['sigma'] = AutoHandle( ('fixedGridSigmaFastjetAll',''), 'double', mayFail=True ) def beginLoop(self, setup): super(VertexAnalyzer,self).beginLoop(setup) self.averages.add('vertexWeight', Average('vertexWeight') ) self.counters.addCounter('GoodVertex') self.count = self.counters.counter('GoodVertex') self.count.register('All Events') self.count.register('Events With Good Vertex') def process(self, event): self.readCollections(event.input ) event.rho = self.handles['rho'].product()[0] event.rhoCN = self.handles['rhoCN'].product()[0] event.sigma = self.handles['sigma'].product()[0] if self.handles['sigma'].isValid() else -999 event.vertices = self.handles['vertices'].product() event.goodVertices = list(filter(self.testGoodVertex,event.vertices)) self.count.inc('All Events') event.vertexWeight = 1 if self.cfg_comp.isMC: event.pileUpInfo = map( PileUpSummaryInfo, self.mchandles['pusi'].product() ) if self.fixedWeight is None: event.vertexWeight = self.mchandles['vertexWeight'].product()[0] else: event.vertexWeight = self.fixedWeight event.eventWeight *= event.vertexWeight self.averages['vertexWeight'].add( event.vertexWeight ) if self.verbose: print('VertexAnalyzer: #vert = ', len(event.vertices), \ ', weight = ', event.vertexWeight) # Check if events needs to be skipped if no good vertex is found (useful for generator level studies) keepFailingEvents = False if hasattr( self.cfg_ana, 'keepFailingEvents'): keepFailingEvents = self.cfg_ana.keepFailingEvents if len(event.goodVertices)==0: event.passedVertexAnalyzer=False if not keepFailingEvents: return False else: event.passedVertexAnalyzer=True if self.doHists: self.pileup.hist.Fill( len(event.goodVertices) ) #A.R. mindist is one of the slowest functions, default commented # self.pileup.mindist.Fill( self.mindist(event.goodVertices) ) self.count.inc('Events With Good Vertex') return True def testGoodVertex(self,vertex): if vertex.isFake(): return False if vertex.ndof()<=4: return False if abs(vertex.z())>24: return False if vertex.position().Rho()>2: return False return True def mindist(self, vertices): mindist = 999999 for comb in itertools.combinations(vertices, 2): dist = abs(comb[0].z() - comb[1].z()) if dist<mindist: mindist = dist return mindist def write(self, setup): super(VertexAnalyzer, self).write(setup) if self.doHists: self.pileup.write() setattr(VertexAnalyzer,"defaultConfig",cfg.Analyzer( class_object=VertexAnalyzer, vertexWeight = None, fixedWeight = 1, verbose = False ) )

PhysicsTools/Heppy/python/analyzers/objects/VertexAnalyzer.py

from __future__ import print_function import itertools from PhysicsTools.Heppy.analyzers.core.VertexHistograms import VertexHistograms from PhysicsTools.Heppy.analyzers.core.Analyzer import Analyzer from PhysicsTools.Heppy.analyzers.core.AutoHandle import AutoHandle from PhysicsTools.HeppyCore.statistics.average import Average from PhysicsTools.Heppy.physicsutils.PileUpSummaryInfo import PileUpSummaryInfo import PhysicsTools.HeppyCore.framework.config as cfg class VertexAnalyzer( Analyzer ): """selects a list of good primary vertices, and optionally add a pile-up weight to MC events. The list of good primary vertices is put in event.goodVertices. if no good vertex is found, the process function returns False. The weight is put in event.vertexWeight, and is multiplied to the global event weight, event.eventWeight. Example: vertexAna = cfg.Analyzer( 'VertexAnalyzer', goodVertices = 'goodPVFilter', vertexWeight = 'vertexWeightFall112011AB', # uncomment the following line if you want a vertex weight = 1 (no weighting) # fixedWeight = 1, verbose = False ) If fixedWeight is set to None, the vertex weight is read from the EDM collection with module name 'vertexWeightFall112011AB'. Otherwise, the weight is set to fixedWeight. The vertex weight collection was at some point produced in the PAT+CMG step, and could directly be accessed from the PAT or CMG tuple. In the most recent versions of the PAT+CMG tuple, this collection is not present anymore, and an additional full framework process must be ran to produce this collection, so that this analyzer can read it. An example cfg to do that can be found here: http://cmssw.cvs.cern.ch/cgi-bin/cmssw.cgi/UserCode/CMG/CMGTools/H2TauTau/prod/vertexWeight2011_cfg.py?view=markup """ def __init__(self, cfg_ana, cfg_comp, looperName): super(VertexAnalyzer, self).__init__(cfg_ana, cfg_comp, looperName) self.doHists=True if (hasattr(self.cfg_ana,'makeHists')) and (not self.cfg_ana.makeHists): self.doHists=False if self.doHists: self.pileup = VertexHistograms('/'.join([self.dirName, 'pileup.root'])) self.allVertices = self.cfg_ana.allVertices if (hasattr(self.cfg_ana,'allVertices')) else "_AUTO_" def declareHandles(self): super(VertexAnalyzer, self).declareHandles() if self.allVertices == '_AUTO_': self.handles['vertices'] = AutoHandle( "offlineSlimmedPrimaryVertices", 'std::vector<reco::Vertex>', fallbackLabel="offlinePrimaryVertices" ) else: self.handles['vertices'] = AutoHandle( self.allVertices, 'std::vector<reco::Vertex>' ) self.fixedWeight = None if self.cfg_comp.isMC: if hasattr( self.cfg_ana, 'fixedWeight'): self.fixedWeight = self.cfg_ana.fixedWeight else: self.mchandles['vertexWeight'] = AutoHandle( self.cfg_ana.vertexWeight, 'double' ) self.mchandles['pusi'] = AutoHandle( 'slimmedAddPileupInfo', 'std::vector<PileupSummaryInfo>', fallbackLabel='addPileupInfo', ) self.handles['rho'] = AutoHandle( ('fixedGridRhoFastjetAll',''), 'double' ) self.handles['rhoCN'] = AutoHandle( ('fixedGridRhoFastjetCentralNeutral',''), 'double' ) self.handles['sigma'] = AutoHandle( ('fixedGridSigmaFastjetAll',''), 'double', mayFail=True ) def beginLoop(self, setup): super(VertexAnalyzer,self).beginLoop(setup) self.averages.add('vertexWeight', Average('vertexWeight') ) self.counters.addCounter('GoodVertex') self.count = self.counters.counter('GoodVertex') self.count.register('All Events') self.count.register('Events With Good Vertex') def process(self, event): self.readCollections(event.input ) event.rho = self.handles['rho'].product()[0] event.rhoCN = self.handles['rhoCN'].product()[0] event.sigma = self.handles['sigma'].product()[0] if self.handles['sigma'].isValid() else -999 event.vertices = self.handles['vertices'].product() event.goodVertices = list(filter(self.testGoodVertex,event.vertices)) self.count.inc('All Events') event.vertexWeight = 1 if self.cfg_comp.isMC: event.pileUpInfo = map( PileUpSummaryInfo, self.mchandles['pusi'].product() ) if self.fixedWeight is None: event.vertexWeight = self.mchandles['vertexWeight'].product()[0] else: event.vertexWeight = self.fixedWeight event.eventWeight *= event.vertexWeight self.averages['vertexWeight'].add( event.vertexWeight ) if self.verbose: print('VertexAnalyzer: #vert = ', len(event.vertices), \ ', weight = ', event.vertexWeight) # Check if events needs to be skipped if no good vertex is found (useful for generator level studies) keepFailingEvents = False if hasattr( self.cfg_ana, 'keepFailingEvents'): keepFailingEvents = self.cfg_ana.keepFailingEvents if len(event.goodVertices)==0: event.passedVertexAnalyzer=False if not keepFailingEvents: return False else: event.passedVertexAnalyzer=True if self.doHists: self.pileup.hist.Fill( len(event.goodVertices) ) #A.R. mindist is one of the slowest functions, default commented # self.pileup.mindist.Fill( self.mindist(event.goodVertices) ) self.count.inc('Events With Good Vertex') return True def testGoodVertex(self,vertex): if vertex.isFake(): return False if vertex.ndof()<=4: return False if abs(vertex.z())>24: return False if vertex.position().Rho()>2: return False return True def mindist(self, vertices): mindist = 999999 for comb in itertools.combinations(vertices, 2): dist = abs(comb[0].z() - comb[1].z()) if dist<mindist: mindist = dist return mindist def write(self, setup): super(VertexAnalyzer, self).write(setup) if self.doHists: self.pileup.write() setattr(VertexAnalyzer,"defaultConfig",cfg.Analyzer( class_object=VertexAnalyzer, vertexWeight = None, fixedWeight = 1, verbose = False ) )

0.658198

0.219923

# <codecell> from pandas import read_csv import os, os.path import csv import matplotlib.pyplot as plt import re import dateutil.parser os.chdir('/home/will/HIVReportGen/') # <codecell> def extract_YOB(inp): try: return float(inp.split('-')[0]) except AttributeError: return float(inp) except ValueError: #print('Bad YOB', inp) return np.nan def safe_float(m, default = np.nan): try: return float(m) except: return default def feet2meters(height): if (height == 'ND') or (height != height): return np.nan try: res = re.findall('(\d).\s{0,1}(\d{0,2})\D?', height) except TypeError: #print(height) raise TypeError try: ft = float(res[0][0]) inc = safe_float(res[0][1], default = 0.0) except IndexError: #print(height,res) raise IndexError except ValueError: #print(height, res) raise ValueError tot_inches = ft*12+inc meters = tot_inches*0.0254 if meters > 2: print(meters, height, res) return meters def checkbox_conv(inp): if inp != inp: return np.nan valdict = { 'checked':True, 'test positive':True, 'positive':True, 'yes':True, 'unchecked':False, 'test negative':False, 'negative':False, 'no':True} return valdict.get(inp.lower(), np.nan) def verbose_parser(inp): try: return dateutil.parser.parse(inp) except: return np.nan def fix_col_name(name): if "(choice='" in name: return name.split("(choice='",1)[1][:-2] else: return name # <codecell> from datetime import date, datetime from pandas import merge from copy import deepcopy class PatData(object): def __init__(self, redcap_file, config_file): if redcap_file is None: return self.config_data = read_csv(config_file, sep = '\t') self._generate_converter_dict() self._generate_agg_dict() with open(redcap_file) as handle: handle.read(1) self.redcap = read_csv(handle, converters=self.conv_dict) self.clip_dates() self.visit_redcap = None self.pat_redcap = None def CopyFromOtherData(self, OtherData): self.config_data = OtherData.config_data.copy() self.redcap = OtherData.redcap.copy() self.conv_dict = deepcopy(OtherData.conv_dict) self.date_clip_cols = deepcopy(OtherData.date_clip_cols) self.visit_agg = deepcopy(OtherData.visit_agg) self.pat_agg = deepcopy(OtherData.pat_agg) if OtherData.pat_redcap is not None: self.pat_redcap = OtherData.pat_redcap.copy() if OtherData.visit_redcap is not None: self.visit_redcap = OtherData.visit_redcap.copy() if OtherData.all_group is not None: self.all_group = OtherData.all_group.copy() def _generate_converter_dict(self): cdict = { 'DateParser':verbose_parser, 'extract_YOB':extract_YOB, 'checkbox_conv':checkbox_conv, 'safe_float':safe_float } conv_dict = {} date_clip_cols = set() for colname, convfun in zip(self.config_data['RawName'].values, self.config_data['ConvertFun'].values): cfun = cdict.get(convfun, None) if cfun: conv_dict[colname] = cfun if convfun == 'DateParser': date_clip_cols.add(colname) self.conv_dict = conv_dict self.date_clip_cols = date_clip_cols def _generate_agg_dict(self): self.visit_agg = {} self.pat_agg = {} for colname, aggfun in zip(self.config_data['RawName'].values, self.config_data['AggFunc'].values): if aggfun == aggfun: self.visit_agg[colname] = aggfun self.pat_agg[colname] = aggfun def clip_dates(self): maxday = datetime.today() minday = datetime(1900,1,1) for col in self.date_clip_cols: self.redcap[col] = self.redcap[col].clip(lower = minday, upper = maxday) def fix_visits(self): def fix_v(vis): if vis == 'first': return 0.0 try: return float(vis[1:]) except: return None self.redcap['VisitNum'] = self.redcap['Patient visit number'].apply(fix_v) def CalcAge(self): visit_years = self.redcap['Date of visit'].dropna().apply(lambda x:x.year) birth_years = self.redcap['Year of Birth'].dropna() self.redcap['CalcAge'] = visit_years-birth_years def CalcGender(self): self.redcap['GenotypicMale'] = (self.redcap['Gender'] == 'Male') | (self.redcap['Transgender designation'] == 'male to female') self.redcap['IdentifiesMale'] = (self.redcap['Gender'] == 'Male') & (self.redcap['Transgender designation'] != 'male to female') self.redcap['GenotypicFemale'] = (self.redcap['Gender'] == 'Female') | (self.redcap['Transgender designation'] == 'female to male') self.redcap['IdentifiesFemale'] = (self.redcap['Gender'] == 'Female') & (self.redcap['Transgender designation'] != 'female to male') def CalcBMI(self): self.redcap['Weight-kg'] = self.redcap['Weight'].apply(safe_float)/2.2 self.redcap['Height-m'] = self.redcap['Height'].apply(feet2meters) self.redcap['BMI'] = self.redcap['Weight-kg']/(self.redcap['Height-m']*self.redcap['Height-m']) def CalcYearsSero(self): visit_years = self.redcap['Date of visit'].dropna().apply(lambda x:x.year) seropos_years = self.redcap['HIV seropositive date'].dropna().apply(lambda x:x.year) self.redcap['Calc-Years-Seropositive'] = visit_years - seropos_years def CalcExposure(self): merge_cols = {'Exposure-MSM': ("Exposure Category (choice='Men who have sex with men (MSM)')", "Exposure Category (choice='MSM and IDU')"), 'Exposure-IDU': ("Exposure Category (choice='Injection drug use (IDU)')", "Exposure Category (choice='MSM and IDU')", "Exposure Category (choice='Heterosexual and IDU')"), 'Exposure-Heterosexual': ("Exposure Category (choice='Heterosexual and IDU')", "Exposure Category (choice='Heterosexual')"), 'Exposure-Hemophilia':("Exposure Category (choice='Hemophilia')",), 'Exposure-Transfusion':("Exposure Category (choice='Blood transfusion')",), 'Exposure-Perinatal':("Exposure Category (choice='Perinatal')",) } for merged_col, check_cols in merge_cols.items(): self.redcap[merged_col] = False for col in check_cols: self.redcap[merged_col] |= self.redcap[col] def AddGroupNames(self): self.groupnames = dict(zip([True, False], ['PosGroup', 'NegGroup'])) def CalcAll(self): self.AddGroupNames() self.fix_visits() self.CalcAge() self.CalcYearsSero() self.CalcGender() self.CalcBMI() self.CalcExposure() def ProcessVisits(self, visit_recap): """A method to subclass. Must return a DataFrame of the wanted visits.""" return visit_recap def ProcessPatients(self, pat_redcap): """A method to subclass. Must return a DataFrame of the wanted patients.""" return pat_redcap def ProcessRedcap(self): gkey = ['Patient ID', 'Patient visit number'] visit_redcap = self.redcap.groupby(gkey).agg(self.visit_agg) self.visit_redcap = self.ProcessVisits(visit_redcap) gkey = 'Patient ID' pat_redcap = self.visit_redcap.groupby(level=gkey).agg(self.pat_agg) self.pat_redcap = self.ProcessPatients(pat_redcap) def MakePatientGroups(self): self.SplitGroups(self.pat_redcap, self.pat_agg) def MakeVisitGroups(self): aligned_data, _ = self.visit_redcap.align(self.pat_redcap, level = 'Patient ID', join = 'inner') self.SplitGroups(aligned_data, self.visit_agg) def AssignGroups(self, aligned_data): raise NotImplementedError def SplitGroups(self, aligned_data, agg_dict): cur_levels = aligned_data.index.names aligned_data = aligned_data.reset_index() aligned_data['Grouping'] = self.AssignGroups(aligned_data) self.all_group = aligned_data.groupby(['Grouping'] + cur_levels).agg(agg_dict) def make_demo_figures(self): self.AgeHist() for key, group in self.config_data.groupby('PlotName'): if ((group['PlotType'] == 'BarChart').all()) & ((group['DemographicFunction'] == 'ChoiceCount').all()): try: self.plot_bar_chart(group['RawName'].values, key) except: print('bad on ', key) elif ((group['PlotType'] == 'BoxPlot').all()) & ((group['DemographicFunction'] == 'MeanFunc').all()): self.make_box_plot(group['RawName'].values, key) elif ((group['PlotType'] == 'LogBoxPlot').all()) & ((group['DemographicFunction'] == 'MeanFunc').all()): self.make_log_box_plot(group['RawName'].values, key) elif ((group['PlotType'] == 'BarChart').all()) & ((group['DemographicFunction'] == 'IntegerCount').all()): print(key, group['RawName'].values) self.make_integer_bar(group['RawName'].values[0], key) def AgeHist(self): bins = [20,30,40,50,60,70,80] fig = plt.figure() g1data = Series(np.histogram(self.all_group.ix[True]['CalcAge'].values, bins = bins)[0], index = bins[:-1]) g2data = Series(np.histogram(self.all_group.ix[False]['CalcAge'].values, bins = bins)[0], index = bins[:-1]) df = DataFrame({self.groupnames[True]:g1data, self.groupnames[False]:g2data}) df.plot(kind = 'bar', grid = True) plt.xlabel('Age at Visit') plt.ylabel('#') return fig, self.all_group['CalcAge'] def plot_bar_chart(self, items, title): g1sum = self.all_group.ix[True][items].mean()*100 g2sum = self.all_group.ix[False][items].mean()*100 allsum = self.all_group[items].mean()*100 df = DataFrame({self.groupnames[True]:g1sum, self.groupnames[False]:g2sum, 'All':allsum}) ncols = dict([(col, fix_col_name(col)) for col in df.index]) df = df.rename(index=ncols) fig = plt.figure() df.plot(kind = 'bar', ax = plt.gca(), grid = True) plt.title(title) plt.ylabel('%') return fig, self.all_group[items] def make_box_plot(self, items, title): g1items = self.all_group.ix[True][items].reset_index() g2items = self.all_group.ix[False][items].reset_index() allitems = self.all_group[items].reset_index() pltdata = [(allitems, 'All'), (g1items, self.groupnames[True]), (g2items, self.groupnames[False])] odict = {} for item, (data, name) in product(items, pltdata): odict[item + '--' + name] = data[item] fig = plt.figure() df = DataFrame(odict) df.boxplot(rot = 90, ax = plt.gca()) plt.title(title) plt.ylabel('Value') return fig, self.all_group[items] def make_log_box_plot(self, items, title): g1items = self.all_group.ix[True][items].reset_index() g2items = self.all_group.ix[False][items].reset_index() allitems = self.all_group[items].reset_index() pltdata = [(allitems, 'All'), (g1items, self.groupnames[True]), (g2items, self.groupnames[False])] odict = {} for item, (data, name) in product(items, pltdata): odict[item + '--' + name] = data[item] fig = plt.figure() df = DataFrame(odict) df.apply(np.log10).boxplot(rot = 90, ax = plt.gca()) plt.title(title) plt.ylabel('log10(Value)') return fig, self.all_group[items] def make_integer_bar(self, col, title): if len(self.all_group[col].unique()) < 2: return None, None bins = np.arange(0, self.all_group[col].max()+1) g1data = Series(np.histogram(self.all_group.ix[True][col].values, bins = bins)[0], index = bins[:-1])/len(self.all_group.ix[True]) g2data = Series(np.histogram(self.all_group.ix[False][col].values, bins = bins)[0], index = bins[:-1])/len(self.all_group.ix[False]) alldata = Series(np.histogram(self.all_group[col].values, bins = bins)[0], index = bins[:-1])//len(self.all_group) ndf = DataFrame({'All': alldata*100, self.groupnames[True]: g1data*100, self.groupnames[False]: g2data*100}) fig = plt.figure() ndf.plot(kind = 'bar', ax = plt.gca(), grid = True) plt.title(title) return fig, self.all_group[col] # <codecell> class GenderPatData(PatData): def AddGroupNames(self): self.groupnames = dict(zip([True, False], ['Male', 'Female'])) def AssignGroups(self, visit_redcap): return visit_redcap['IdentifiesMale'] # <codecell> config_file = 'Data/Config/ReportFile.csv' demo_file = 'Data/RedcapDumps/HIVAIDSGeneticAnalys_DATA_LABELS_2012-12-11_1720.csv' tmp = GenderPatData(demo_file, config_file) tmp.CalcAll() # <codecell> tmp.ProcessRedcap() tmp.MakePatientGroups() # <codecell> tmp.make_demo_figures() # <codecell> class NeuroPatData(PatData): def AddGroupNames(self): self.groupnames = dict(zip([True, False], ['No Neuro', 'With Neuro'])) def AssignGroups(self, aligned_data): return aligned_data["Mental Health Issues (choice='No neurological problems')"] ntmp = NeuroPatData(None, None) ntmp.CopyFromOtherData(tmp) ntmp.AddGroupNames() ntmp.MakePatientGroups() ntmp.make_demo_figures() # <codecell> from itertools import product items = ['Neurocognitive test', 'MSK Score', 'Psychomotor Speed Score', 'Memory Recall Score', 'Constructional Score', 'Total Modified Hopkins Dementia Score', ] col = items[-1] # <codecell> def safe_float(m): try: return float(m) except: return None demo_data['Weight'].apply(safe_float).hist() # <codecell> tmp.redcap # <codecell>

DemoGraphs.py

# <codecell> from pandas import read_csv import os, os.path import csv import matplotlib.pyplot as plt import re import dateutil.parser os.chdir('/home/will/HIVReportGen/') # <codecell> def extract_YOB(inp): try: return float(inp.split('-')[0]) except AttributeError: return float(inp) except ValueError: #print('Bad YOB', inp) return np.nan def safe_float(m, default = np.nan): try: return float(m) except: return default def feet2meters(height): if (height == 'ND') or (height != height): return np.nan try: res = re.findall('(\d).\s{0,1}(\d{0,2})\D?', height) except TypeError: #print(height) raise TypeError try: ft = float(res[0][0]) inc = safe_float(res[0][1], default = 0.0) except IndexError: #print(height,res) raise IndexError except ValueError: #print(height, res) raise ValueError tot_inches = ft*12+inc meters = tot_inches*0.0254 if meters > 2: print(meters, height, res) return meters def checkbox_conv(inp): if inp != inp: return np.nan valdict = { 'checked':True, 'test positive':True, 'positive':True, 'yes':True, 'unchecked':False, 'test negative':False, 'negative':False, 'no':True} return valdict.get(inp.lower(), np.nan) def verbose_parser(inp): try: return dateutil.parser.parse(inp) except: return np.nan def fix_col_name(name): if "(choice='" in name: return name.split("(choice='",1)[1][:-2] else: return name # <codecell> from datetime import date, datetime from pandas import merge from copy import deepcopy class PatData(object): def __init__(self, redcap_file, config_file): if redcap_file is None: return self.config_data = read_csv(config_file, sep = '\t') self._generate_converter_dict() self._generate_agg_dict() with open(redcap_file) as handle: handle.read(1) self.redcap = read_csv(handle, converters=self.conv_dict) self.clip_dates() self.visit_redcap = None self.pat_redcap = None def CopyFromOtherData(self, OtherData): self.config_data = OtherData.config_data.copy() self.redcap = OtherData.redcap.copy() self.conv_dict = deepcopy(OtherData.conv_dict) self.date_clip_cols = deepcopy(OtherData.date_clip_cols) self.visit_agg = deepcopy(OtherData.visit_agg) self.pat_agg = deepcopy(OtherData.pat_agg) if OtherData.pat_redcap is not None: self.pat_redcap = OtherData.pat_redcap.copy() if OtherData.visit_redcap is not None: self.visit_redcap = OtherData.visit_redcap.copy() if OtherData.all_group is not None: self.all_group = OtherData.all_group.copy() def _generate_converter_dict(self): cdict = { 'DateParser':verbose_parser, 'extract_YOB':extract_YOB, 'checkbox_conv':checkbox_conv, 'safe_float':safe_float } conv_dict = {} date_clip_cols = set() for colname, convfun in zip(self.config_data['RawName'].values, self.config_data['ConvertFun'].values): cfun = cdict.get(convfun, None) if cfun: conv_dict[colname] = cfun if convfun == 'DateParser': date_clip_cols.add(colname) self.conv_dict = conv_dict self.date_clip_cols = date_clip_cols def _generate_agg_dict(self): self.visit_agg = {} self.pat_agg = {} for colname, aggfun in zip(self.config_data['RawName'].values, self.config_data['AggFunc'].values): if aggfun == aggfun: self.visit_agg[colname] = aggfun self.pat_agg[colname] = aggfun def clip_dates(self): maxday = datetime.today() minday = datetime(1900,1,1) for col in self.date_clip_cols: self.redcap[col] = self.redcap[col].clip(lower = minday, upper = maxday) def fix_visits(self): def fix_v(vis): if vis == 'first': return 0.0 try: return float(vis[1:]) except: return None self.redcap['VisitNum'] = self.redcap['Patient visit number'].apply(fix_v) def CalcAge(self): visit_years = self.redcap['Date of visit'].dropna().apply(lambda x:x.year) birth_years = self.redcap['Year of Birth'].dropna() self.redcap['CalcAge'] = visit_years-birth_years def CalcGender(self): self.redcap['GenotypicMale'] = (self.redcap['Gender'] == 'Male') | (self.redcap['Transgender designation'] == 'male to female') self.redcap['IdentifiesMale'] = (self.redcap['Gender'] == 'Male') & (self.redcap['Transgender designation'] != 'male to female') self.redcap['GenotypicFemale'] = (self.redcap['Gender'] == 'Female') | (self.redcap['Transgender designation'] == 'female to male') self.redcap['IdentifiesFemale'] = (self.redcap['Gender'] == 'Female') & (self.redcap['Transgender designation'] != 'female to male') def CalcBMI(self): self.redcap['Weight-kg'] = self.redcap['Weight'].apply(safe_float)/2.2 self.redcap['Height-m'] = self.redcap['Height'].apply(feet2meters) self.redcap['BMI'] = self.redcap['Weight-kg']/(self.redcap['Height-m']*self.redcap['Height-m']) def CalcYearsSero(self): visit_years = self.redcap['Date of visit'].dropna().apply(lambda x:x.year) seropos_years = self.redcap['HIV seropositive date'].dropna().apply(lambda x:x.year) self.redcap['Calc-Years-Seropositive'] = visit_years - seropos_years def CalcExposure(self): merge_cols = {'Exposure-MSM': ("Exposure Category (choice='Men who have sex with men (MSM)')", "Exposure Category (choice='MSM and IDU')"), 'Exposure-IDU': ("Exposure Category (choice='Injection drug use (IDU)')", "Exposure Category (choice='MSM and IDU')", "Exposure Category (choice='Heterosexual and IDU')"), 'Exposure-Heterosexual': ("Exposure Category (choice='Heterosexual and IDU')", "Exposure Category (choice='Heterosexual')"), 'Exposure-Hemophilia':("Exposure Category (choice='Hemophilia')",), 'Exposure-Transfusion':("Exposure Category (choice='Blood transfusion')",), 'Exposure-Perinatal':("Exposure Category (choice='Perinatal')",) } for merged_col, check_cols in merge_cols.items(): self.redcap[merged_col] = False for col in check_cols: self.redcap[merged_col] |= self.redcap[col] def AddGroupNames(self): self.groupnames = dict(zip([True, False], ['PosGroup', 'NegGroup'])) def CalcAll(self): self.AddGroupNames() self.fix_visits() self.CalcAge() self.CalcYearsSero() self.CalcGender() self.CalcBMI() self.CalcExposure() def ProcessVisits(self, visit_recap): """A method to subclass. Must return a DataFrame of the wanted visits.""" return visit_recap def ProcessPatients(self, pat_redcap): """A method to subclass. Must return a DataFrame of the wanted patients.""" return pat_redcap def ProcessRedcap(self): gkey = ['Patient ID', 'Patient visit number'] visit_redcap = self.redcap.groupby(gkey).agg(self.visit_agg) self.visit_redcap = self.ProcessVisits(visit_redcap) gkey = 'Patient ID' pat_redcap = self.visit_redcap.groupby(level=gkey).agg(self.pat_agg) self.pat_redcap = self.ProcessPatients(pat_redcap) def MakePatientGroups(self): self.SplitGroups(self.pat_redcap, self.pat_agg) def MakeVisitGroups(self): aligned_data, _ = self.visit_redcap.align(self.pat_redcap, level = 'Patient ID', join = 'inner') self.SplitGroups(aligned_data, self.visit_agg) def AssignGroups(self, aligned_data): raise NotImplementedError def SplitGroups(self, aligned_data, agg_dict): cur_levels = aligned_data.index.names aligned_data = aligned_data.reset_index() aligned_data['Grouping'] = self.AssignGroups(aligned_data) self.all_group = aligned_data.groupby(['Grouping'] + cur_levels).agg(agg_dict) def make_demo_figures(self): self.AgeHist() for key, group in self.config_data.groupby('PlotName'): if ((group['PlotType'] == 'BarChart').all()) & ((group['DemographicFunction'] == 'ChoiceCount').all()): try: self.plot_bar_chart(group['RawName'].values, key) except: print('bad on ', key) elif ((group['PlotType'] == 'BoxPlot').all()) & ((group['DemographicFunction'] == 'MeanFunc').all()): self.make_box_plot(group['RawName'].values, key) elif ((group['PlotType'] == 'LogBoxPlot').all()) & ((group['DemographicFunction'] == 'MeanFunc').all()): self.make_log_box_plot(group['RawName'].values, key) elif ((group['PlotType'] == 'BarChart').all()) & ((group['DemographicFunction'] == 'IntegerCount').all()): print(key, group['RawName'].values) self.make_integer_bar(group['RawName'].values[0], key) def AgeHist(self): bins = [20,30,40,50,60,70,80] fig = plt.figure() g1data = Series(np.histogram(self.all_group.ix[True]['CalcAge'].values, bins = bins)[0], index = bins[:-1]) g2data = Series(np.histogram(self.all_group.ix[False]['CalcAge'].values, bins = bins)[0], index = bins[:-1]) df = DataFrame({self.groupnames[True]:g1data, self.groupnames[False]:g2data}) df.plot(kind = 'bar', grid = True) plt.xlabel('Age at Visit') plt.ylabel('#') return fig, self.all_group['CalcAge'] def plot_bar_chart(self, items, title): g1sum = self.all_group.ix[True][items].mean()*100 g2sum = self.all_group.ix[False][items].mean()*100 allsum = self.all_group[items].mean()*100 df = DataFrame({self.groupnames[True]:g1sum, self.groupnames[False]:g2sum, 'All':allsum}) ncols = dict([(col, fix_col_name(col)) for col in df.index]) df = df.rename(index=ncols) fig = plt.figure() df.plot(kind = 'bar', ax = plt.gca(), grid = True) plt.title(title) plt.ylabel('%') return fig, self.all_group[items] def make_box_plot(self, items, title): g1items = self.all_group.ix[True][items].reset_index() g2items = self.all_group.ix[False][items].reset_index() allitems = self.all_group[items].reset_index() pltdata = [(allitems, 'All'), (g1items, self.groupnames[True]), (g2items, self.groupnames[False])] odict = {} for item, (data, name) in product(items, pltdata): odict[item + '--' + name] = data[item] fig = plt.figure() df = DataFrame(odict) df.boxplot(rot = 90, ax = plt.gca()) plt.title(title) plt.ylabel('Value') return fig, self.all_group[items] def make_log_box_plot(self, items, title): g1items = self.all_group.ix[True][items].reset_index() g2items = self.all_group.ix[False][items].reset_index() allitems = self.all_group[items].reset_index() pltdata = [(allitems, 'All'), (g1items, self.groupnames[True]), (g2items, self.groupnames[False])] odict = {} for item, (data, name) in product(items, pltdata): odict[item + '--' + name] = data[item] fig = plt.figure() df = DataFrame(odict) df.apply(np.log10).boxplot(rot = 90, ax = plt.gca()) plt.title(title) plt.ylabel('log10(Value)') return fig, self.all_group[items] def make_integer_bar(self, col, title): if len(self.all_group[col].unique()) < 2: return None, None bins = np.arange(0, self.all_group[col].max()+1) g1data = Series(np.histogram(self.all_group.ix[True][col].values, bins = bins)[0], index = bins[:-1])/len(self.all_group.ix[True]) g2data = Series(np.histogram(self.all_group.ix[False][col].values, bins = bins)[0], index = bins[:-1])/len(self.all_group.ix[False]) alldata = Series(np.histogram(self.all_group[col].values, bins = bins)[0], index = bins[:-1])//len(self.all_group) ndf = DataFrame({'All': alldata*100, self.groupnames[True]: g1data*100, self.groupnames[False]: g2data*100}) fig = plt.figure() ndf.plot(kind = 'bar', ax = plt.gca(), grid = True) plt.title(title) return fig, self.all_group[col] # <codecell> class GenderPatData(PatData): def AddGroupNames(self): self.groupnames = dict(zip([True, False], ['Male', 'Female'])) def AssignGroups(self, visit_redcap): return visit_redcap['IdentifiesMale'] # <codecell> config_file = 'Data/Config/ReportFile.csv' demo_file = 'Data/RedcapDumps/HIVAIDSGeneticAnalys_DATA_LABELS_2012-12-11_1720.csv' tmp = GenderPatData(demo_file, config_file) tmp.CalcAll() # <codecell> tmp.ProcessRedcap() tmp.MakePatientGroups() # <codecell> tmp.make_demo_figures() # <codecell> class NeuroPatData(PatData): def AddGroupNames(self): self.groupnames = dict(zip([True, False], ['No Neuro', 'With Neuro'])) def AssignGroups(self, aligned_data): return aligned_data["Mental Health Issues (choice='No neurological problems')"] ntmp = NeuroPatData(None, None) ntmp.CopyFromOtherData(tmp) ntmp.AddGroupNames() ntmp.MakePatientGroups() ntmp.make_demo_figures() # <codecell> from itertools import product items = ['Neurocognitive test', 'MSK Score', 'Psychomotor Speed Score', 'Memory Recall Score', 'Constructional Score', 'Total Modified Hopkins Dementia Score', ] col = items[-1] # <codecell> def safe_float(m): try: return float(m) except: return None demo_data['Weight'].apply(safe_float).hist() # <codecell> tmp.redcap # <codecell>

0.248717

0.211702

import atexit import builtins import os import tempfile import webbrowser from types import TracebackType from typing import Any, Dict, Iterator, Optional, Union from urllib import parse import certifi import lomond import requests import simplejson import determined_common.requests from determined_common.api import authentication, errors # The path to a file containing an SSL certificate to trust specifically for the master, if any, or # False to disable cert verification entirely. If set to a path, it should always be a temporary # file that we own and can delete. _master_cert_bundle = None # type: Optional[Union[str, bool]] # The name we use to verify the master. _master_cert_name = None def set_master_cert_bundle(path: Optional[Union[str, bool]]) -> None: global _master_cert_bundle if path == "": path = None if path is None or isinstance(path, bool): _master_cert_bundle = path return # Don't use NamedTemporaryFile, since it would make the file inaccessible by path on Windows # after this (see https://docs.python.org/3/library/tempfile.html#tempfile.NamedTemporaryFile). fd, combined_path = tempfile.mkstemp(prefix="det-master-cert-") atexit.register(os.unlink, combined_path) with builtins.open(fd, "wb") as out: with builtins.open(certifi.where(), "rb") as base_certs: out.write(base_certs.read()) out.write(b"\n") with builtins.open(path, "rb") as custom_certs: out.write(custom_certs.read()) _master_cert_bundle = combined_path def set_master_cert_name(name: Optional[str]) -> None: if name == "": name = None global _master_cert_name _master_cert_name = name # Set the bundle if one is specified by the environment. This is done on import since we can't # always count on having an entry point we control (e.g., if someone is importing this code in a # notebook). f = os.environ.get("DET_MASTER_CERT_FILE") if f and f.lower() == "noverify": set_master_cert_bundle(False) else: set_master_cert_bundle(f) del f # Set the master servername from the environment. set_master_cert_name(os.environ.get("DET_MASTER_CERT_NAME")) def get_master_cert_bundle() -> Optional[Union[str, bool]]: return _master_cert_bundle def get_master_cert_name() -> Optional[str]: return _master_cert_name def parse_master_address(master_address: str) -> parse.ParseResult: if master_address.startswith("https://"): default_port = 443 elif master_address.startswith("http://"): default_port = 80 else: default_port = 8080 master_address = "http://{}".format(master_address) parsed = parse.urlparse(master_address) if not parsed.port: parsed = parsed._replace(netloc="{}:{}".format(parsed.netloc, default_port)) return parsed def make_url(master_address: str, suffix: str) -> str: parsed = parse_master_address(master_address) return parse.urljoin(parsed.geturl(), suffix) def maybe_upgrade_ws_scheme(master_address: str) -> str: parsed = parse.urlparse(master_address) if parsed.scheme == "https": return parsed._replace(scheme="wss").geturl() elif parsed.scheme == "http": return parsed._replace(scheme="ws").geturl() else: return master_address def add_token_to_headers(headers: Dict[str, str]) -> Dict[str, str]: task_token = authentication.Authentication.instance().get_task_token() if task_token: return {**headers, "Grpc-Metadata-x-task-token": "Bearer {}".format(task_token)} token = authentication.Authentication.instance().get_session_token() return {**headers, "Authorization": "Bearer {}".format(token)} def do_request( method: str, host: str, path: str, params: Optional[Dict[str, Any]] = None, body: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None, authenticated: bool = True, stream: bool = False, ) -> requests.Response: if headers is None: h = {} # type: Dict[str, str] else: h = headers if params is None: params = {} if authenticated: h = add_token_to_headers(h) try: r = determined_common.requests.request( method, make_url(host, path), params=params, json=body, headers=h, verify=_master_cert_bundle, stream=stream, server_hostname=_master_cert_name, ) except requests.exceptions.SSLError: raise except requests.exceptions.ConnectionError as e: raise errors.MasterNotFoundException(str(e)) except requests.exceptions.RequestException as e: raise errors.BadRequestException(str(e)) if r.status_code == 403: username = authentication.Authentication.instance().get_session_user() raise errors.UnauthenticatedException(username=username) if r.status_code >= 300: raise errors.APIException(r) return r def get( host: str, path: str, params: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None, authenticated: bool = True, stream: bool = False, ) -> requests.Response: """ Send a GET request to the remote API. """ return do_request( "GET", host, path, params=params, headers=headers, authenticated=authenticated, stream=stream, ) def delete( host: str, path: str, params: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None, authenticated: bool = True, ) -> requests.Response: """ Send a DELETE request to the remote API. """ return do_request( "DELETE", host, path, params=params, headers=headers, authenticated=authenticated ) def post( host: str, path: str, body: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None, authenticated: bool = True, ) -> requests.Response: """ Send a POST request to the remote API. """ return do_request("POST", host, path, body=body, headers=headers, authenticated=authenticated) def patch( host: str, path: str, body: Dict[str, Any], headers: Optional[Dict[str, str]] = None, authenticated: bool = True, ) -> requests.Response: """ Send a PATCH request to the remote API. """ return do_request("PATCH", host, path, body=body, headers=headers, authenticated=authenticated) def put( host: str, path: str, body: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None, authenticated: bool = True, ) -> requests.Response: """ Send a PUT request to the remote API. """ return do_request("PUT", host, path, body=body, headers=headers, authenticated=authenticated) def open(host: str, path: str) -> str: url = make_url(host, path) webbrowser.open(url) return url class WebSocket: def __init__(self, socket: lomond.WebSocket) -> None: self.socket = socket def __enter__(self) -> "WebSocket": return self def __iter__(self) -> Iterator[Any]: for event in self.socket.connect(ping_rate=0): if isinstance(event, lomond.events.Connected): # Ignore the initial connection event. pass elif isinstance(event, (lomond.events.Closing, lomond.events.Disconnected)): # The socket was successfully closed so we just return. return elif isinstance( event, (lomond.events.ConnectFail, lomond.events.Rejected, lomond.events.ProtocolError), ): # Any unexpected failures raise the standard API exception. raise errors.BadRequestException(message="WebSocket failure: {}".format(event)) elif isinstance(event, lomond.events.Text): # All web socket connections are expected to be in a JSON # format. yield simplejson.loads(event.text) def __exit__( self, exc_type: Optional[type], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType], ) -> None: if not self.socket.is_closed: self.socket.close() def ws(host: str, path: str) -> WebSocket: """ Connect to a web socket at the remote API. """ websocket = lomond.WebSocket(maybe_upgrade_ws_scheme(make_url(host, path))) token = authentication.Authentication.instance().get_session_token() websocket.add_header("Authorization".encode(), "Bearer {}".format(token).encode()) return WebSocket(websocket)

common/determined_common/api/request.py

import atexit import builtins import os import tempfile import webbrowser from types import TracebackType from typing import Any, Dict, Iterator, Optional, Union from urllib import parse import certifi import lomond import requests import simplejson import determined_common.requests from determined_common.api import authentication, errors # The path to a file containing an SSL certificate to trust specifically for the master, if any, or # False to disable cert verification entirely. If set to a path, it should always be a temporary # file that we own and can delete. _master_cert_bundle = None # type: Optional[Union[str, bool]] # The name we use to verify the master. _master_cert_name = None def set_master_cert_bundle(path: Optional[Union[str, bool]]) -> None: global _master_cert_bundle if path == "": path = None if path is None or isinstance(path, bool): _master_cert_bundle = path return # Don't use NamedTemporaryFile, since it would make the file inaccessible by path on Windows # after this (see https://docs.python.org/3/library/tempfile.html#tempfile.NamedTemporaryFile). fd, combined_path = tempfile.mkstemp(prefix="det-master-cert-") atexit.register(os.unlink, combined_path) with builtins.open(fd, "wb") as out: with builtins.open(certifi.where(), "rb") as base_certs: out.write(base_certs.read()) out.write(b"\n") with builtins.open(path, "rb") as custom_certs: out.write(custom_certs.read()) _master_cert_bundle = combined_path def set_master_cert_name(name: Optional[str]) -> None: if name == "": name = None global _master_cert_name _master_cert_name = name # Set the bundle if one is specified by the environment. This is done on import since we can't # always count on having an entry point we control (e.g., if someone is importing this code in a # notebook). f = os.environ.get("DET_MASTER_CERT_FILE") if f and f.lower() == "noverify": set_master_cert_bundle(False) else: set_master_cert_bundle(f) del f # Set the master servername from the environment. set_master_cert_name(os.environ.get("DET_MASTER_CERT_NAME")) def get_master_cert_bundle() -> Optional[Union[str, bool]]: return _master_cert_bundle def get_master_cert_name() -> Optional[str]: return _master_cert_name def parse_master_address(master_address: str) -> parse.ParseResult: if master_address.startswith("https://"): default_port = 443 elif master_address.startswith("http://"): default_port = 80 else: default_port = 8080 master_address = "http://{}".format(master_address) parsed = parse.urlparse(master_address) if not parsed.port: parsed = parsed._replace(netloc="{}:{}".format(parsed.netloc, default_port)) return parsed def make_url(master_address: str, suffix: str) -> str: parsed = parse_master_address(master_address) return parse.urljoin(parsed.geturl(), suffix) def maybe_upgrade_ws_scheme(master_address: str) -> str: parsed = parse.urlparse(master_address) if parsed.scheme == "https": return parsed._replace(scheme="wss").geturl() elif parsed.scheme == "http": return parsed._replace(scheme="ws").geturl() else: return master_address def add_token_to_headers(headers: Dict[str, str]) -> Dict[str, str]: task_token = authentication.Authentication.instance().get_task_token() if task_token: return {**headers, "Grpc-Metadata-x-task-token": "Bearer {}".format(task_token)} token = authentication.Authentication.instance().get_session_token() return {**headers, "Authorization": "Bearer {}".format(token)} def do_request( method: str, host: str, path: str, params: Optional[Dict[str, Any]] = None, body: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None, authenticated: bool = True, stream: bool = False, ) -> requests.Response: if headers is None: h = {} # type: Dict[str, str] else: h = headers if params is None: params = {} if authenticated: h = add_token_to_headers(h) try: r = determined_common.requests.request( method, make_url(host, path), params=params, json=body, headers=h, verify=_master_cert_bundle, stream=stream, server_hostname=_master_cert_name, ) except requests.exceptions.SSLError: raise except requests.exceptions.ConnectionError as e: raise errors.MasterNotFoundException(str(e)) except requests.exceptions.RequestException as e: raise errors.BadRequestException(str(e)) if r.status_code == 403: username = authentication.Authentication.instance().get_session_user() raise errors.UnauthenticatedException(username=username) if r.status_code >= 300: raise errors.APIException(r) return r def get( host: str, path: str, params: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None, authenticated: bool = True, stream: bool = False, ) -> requests.Response: """ Send a GET request to the remote API. """ return do_request( "GET", host, path, params=params, headers=headers, authenticated=authenticated, stream=stream, ) def delete( host: str, path: str, params: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None, authenticated: bool = True, ) -> requests.Response: """ Send a DELETE request to the remote API. """ return do_request( "DELETE", host, path, params=params, headers=headers, authenticated=authenticated ) def post( host: str, path: str, body: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None, authenticated: bool = True, ) -> requests.Response: """ Send a POST request to the remote API. """ return do_request("POST", host, path, body=body, headers=headers, authenticated=authenticated) def patch( host: str, path: str, body: Dict[str, Any], headers: Optional[Dict[str, str]] = None, authenticated: bool = True, ) -> requests.Response: """ Send a PATCH request to the remote API. """ return do_request("PATCH", host, path, body=body, headers=headers, authenticated=authenticated) def put( host: str, path: str, body: Optional[Dict[str, Any]] = None, headers: Optional[Dict[str, str]] = None, authenticated: bool = True, ) -> requests.Response: """ Send a PUT request to the remote API. """ return do_request("PUT", host, path, body=body, headers=headers, authenticated=authenticated) def open(host: str, path: str) -> str: url = make_url(host, path) webbrowser.open(url) return url class WebSocket: def __init__(self, socket: lomond.WebSocket) -> None: self.socket = socket def __enter__(self) -> "WebSocket": return self def __iter__(self) -> Iterator[Any]: for event in self.socket.connect(ping_rate=0): if isinstance(event, lomond.events.Connected): # Ignore the initial connection event. pass elif isinstance(event, (lomond.events.Closing, lomond.events.Disconnected)): # The socket was successfully closed so we just return. return elif isinstance( event, (lomond.events.ConnectFail, lomond.events.Rejected, lomond.events.ProtocolError), ): # Any unexpected failures raise the standard API exception. raise errors.BadRequestException(message="WebSocket failure: {}".format(event)) elif isinstance(event, lomond.events.Text): # All web socket connections are expected to be in a JSON # format. yield simplejson.loads(event.text) def __exit__( self, exc_type: Optional[type], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType], ) -> None: if not self.socket.is_closed: self.socket.close() def ws(host: str, path: str) -> WebSocket: """ Connect to a web socket at the remote API. """ websocket = lomond.WebSocket(maybe_upgrade_ws_scheme(make_url(host, path))) token = authentication.Authentication.instance().get_session_token() websocket.add_header("Authorization".encode(), "Bearer {}".format(token).encode()) return WebSocket(websocket)

0.68342

0.24979

import importlib import os from pathlib import Path from typing import Any import pytest from pydolphinscheduler.core import configuration from pydolphinscheduler.core.configuration import ( BUILD_IN_CONFIG_PATH, config_path, get_single_config, set_single_config, ) from pydolphinscheduler.exceptions import PyDSConfException from pydolphinscheduler.utils.yaml_parser import YamlParser from tests.testing.constants import DEV_MODE, ENV_PYDS_HOME from tests.testing.file import get_file_content @pytest.fixture def teardown_file_env(): """Util for deleting temp configuration file and pop env var after test finish.""" yield config_file_path = config_path() if config_file_path.exists(): config_file_path.unlink() os.environ.pop(ENV_PYDS_HOME, None) @pytest.mark.parametrize( "val, expect", [ ("1", 1), ("123", 123), ("4567", 4567), (b"1234", 1234), ], ) def test_get_int(val: Any, expect: int): """Test function :func:`configuration.get_int`.""" assert configuration.get_int(val) == expect @pytest.mark.parametrize( "val", [ "a", "1a", "1d2", "1723-", ], ) def test_get_int_error(val: Any): """Test function :func:`configuration.get_int`.""" with pytest.raises(ValueError): configuration.get_int(val) @pytest.mark.parametrize( "val, expect", [ ("t", True), ("true", True), (1, True), (True, True), ("f", False), ("false", False), (0, False), (123, False), ("abc", False), ("abc1", False), (False, False), ], ) def test_get_bool(val: Any, expect: bool): """Test function :func:`configuration.get_bool`.""" assert configuration.get_bool(val) == expect @pytest.mark.parametrize( "home, expect", [ (None, "~/pydolphinscheduler/config.yaml"), ("/tmp/pydolphinscheduler", "/tmp/pydolphinscheduler/config.yaml"), ("/tmp/test_abc", "/tmp/test_abc/config.yaml"), ], ) def test_config_path(home: Any, expect: str): """Test function :func:`config_path`.""" if home: os.environ[ENV_PYDS_HOME] = home assert Path(expect).expanduser() == configuration.config_path() @pytest.mark.parametrize( "home", [ None, "/tmp/pydolphinscheduler", "/tmp/test_abc", ], ) def test_init_config_file(teardown_file_env, home: Any): """Test init config file.""" if home: os.environ[ENV_PYDS_HOME] = home elif DEV_MODE: pytest.skip( "Avoid delete ~/pydolphinscheduler/config.yaml by accident when test locally." ) assert not config_path().exists() configuration.init_config_file() assert config_path().exists() assert get_file_content(config_path()) == get_file_content(BUILD_IN_CONFIG_PATH) @pytest.mark.parametrize( "home", [ None, "/tmp/pydolphinscheduler", "/tmp/test_abc", ], ) def test_init_config_file_duplicate(teardown_file_env, home: Any): """Test raise error with init config file which already exists.""" if home: os.environ[ENV_PYDS_HOME] = home elif DEV_MODE: pytest.skip( "Avoid delete ~/pydolphinscheduler/config.yaml by accident when test locally." ) assert not config_path().exists() configuration.init_config_file() assert config_path().exists() with pytest.raises(PyDSConfException, match=".*file already exists.*"): configuration.init_config_file() def test_get_configs_build_in(): """Test function :func:`get_configs` with build-in config file.""" content = get_file_content(BUILD_IN_CONFIG_PATH) assert YamlParser(content).src_parser == configuration.get_configs().src_parser assert YamlParser(content).dict_parser == configuration.get_configs().dict_parser @pytest.mark.parametrize( "key, val, new_val", [ ("java_gateway.address", "127.0.0.1", "127.1.1.1"), ("java_gateway.port", 25333, 25555), ("java_gateway.auto_convert", True, False), ("default.user.name", "userPythonGateway", "editUserPythonGateway"), ("default.user.password", "<PASSWORD>", "editUserPythonGateway"), ( "default.user.email", "<EMAIL>", "<EMAIL>", ), ("default.user.phone", 11111111111, 22222222222), ("default.user.state", 1, 0), ("default.workflow.project", "project-pydolphin", "eidt-project-pydolphin"), ("default.workflow.tenant", "tenant_pydolphin", "edit_tenant_pydolphin"), ("default.workflow.user", "userPythonGateway", "editUserPythonGateway"), ("default.workflow.queue", "queuePythonGateway", "editQueuePythonGateway"), ("default.workflow.worker_group", "default", "specific"), ("default.workflow.time_zone", "Asia/Shanghai", "Asia/Beijing"), ("default.workflow.warning_type", "NONE", "ALL"), ], ) def test_single_config_get_set(teardown_file_env, key: str, val: Any, new_val: Any): """Test function :func:`get_single_config` and :func:`set_single_config`.""" assert val == get_single_config(key) set_single_config(key, new_val) assert new_val == get_single_config(key) def test_single_config_get_set_not_exists_key(): """Test function :func:`get_single_config` and :func:`set_single_config` error while key not exists.""" not_exists_key = "i_am_not_exists_key" with pytest.raises(PyDSConfException, match=".*do not exists.*"): get_single_config(not_exists_key) with pytest.raises(PyDSConfException, match=".*do not exists.*"): set_single_config(not_exists_key, not_exists_key) @pytest.mark.parametrize( "config_name, expect", [ ("JAVA_GATEWAY_ADDRESS", "127.0.0.1"), ("JAVA_GATEWAY_PORT", 25333), ("JAVA_GATEWAY_AUTO_CONVERT", True), ("USER_NAME", "userPythonGateway"), ("USER_PASSWORD", "<PASSWORD>"), ("USER_EMAIL", "<EMAIL>"), ("USER_PHONE", "11111111111"), ("USER_STATE", 1), ("WORKFLOW_PROJECT", "project-pydolphin"), ("WORKFLOW_TENANT", "tenant_pydolphin"), ("WORKFLOW_USER", "userPythonGateway"), ("WORKFLOW_QUEUE", "queuePythonGateway"), ("WORKFLOW_WORKER_GROUP", "default"), ("WORKFLOW_TIME_ZONE", "Asia/Shanghai"), ("WORKFLOW_WARNING_TYPE", "NONE"), ], ) def test_get_configuration(config_name: str, expect: Any): """Test get exists attribute in :mod:`configuration`.""" assert expect == getattr(configuration, config_name) @pytest.mark.parametrize( "config_name, src, dest", [ ("JAVA_GATEWAY_ADDRESS", "127.0.0.1", "192.168.1.1"), ("JAVA_GATEWAY_PORT", 25333, 25334), ("JAVA_GATEWAY_AUTO_CONVERT", True, False), ("USER_NAME", "userPythonGateway", "envUserPythonGateway"), ("USER_PASSWORD", "<PASSWORD>", "envUserPythonGateway"), ( "USER_EMAIL", "<EMAIL>", "<EMAIL>", ), ("USER_PHONE", "11111111111", "22222222222"), ("USER_STATE", 1, 0), ("WORKFLOW_PROJECT", "project-pydolphin", "env-project-pydolphin"), ("WORKFLOW_TENANT", "tenant_pydolphin", "env-tenant_pydolphin"), ("WORKFLOW_USER", "userPythonGateway", "envUserPythonGateway"), ("WORKFLOW_QUEUE", "queuePythonGateway", "envQueuePythonGateway"), ("WORKFLOW_WORKER_GROUP", "default", "custom"), ("WORKFLOW_TIME_ZONE", "Asia/Shanghai", "America/Los_Angeles"), ("WORKFLOW_WARNING_TYPE", "NONE", "ALL"), ], ) def test_get_configuration_env(config_name: str, src: Any, dest: Any): """Test get exists attribute from environment variable in :mod:`configuration`.""" assert getattr(configuration, config_name) == src env_name = f"PYDS_{config_name}" os.environ[env_name] = str(dest) # reload module configuration to re-get config from environment. importlib.reload(configuration) assert getattr(configuration, config_name) == dest # pop and reload configuration to test whether this config equal to `src` value os.environ.pop(env_name, None) importlib.reload(configuration) assert getattr(configuration, config_name) == src assert env_name not in os.environ

dolphinscheduler-python/pydolphinscheduler/tests/core/test_configuration.py

import importlib import os from pathlib import Path from typing import Any import pytest from pydolphinscheduler.core import configuration from pydolphinscheduler.core.configuration import ( BUILD_IN_CONFIG_PATH, config_path, get_single_config, set_single_config, ) from pydolphinscheduler.exceptions import PyDSConfException from pydolphinscheduler.utils.yaml_parser import YamlParser from tests.testing.constants import DEV_MODE, ENV_PYDS_HOME from tests.testing.file import get_file_content @pytest.fixture def teardown_file_env(): """Util for deleting temp configuration file and pop env var after test finish.""" yield config_file_path = config_path() if config_file_path.exists(): config_file_path.unlink() os.environ.pop(ENV_PYDS_HOME, None) @pytest.mark.parametrize( "val, expect", [ ("1", 1), ("123", 123), ("4567", 4567), (b"1234", 1234), ], ) def test_get_int(val: Any, expect: int): """Test function :func:`configuration.get_int`.""" assert configuration.get_int(val) == expect @pytest.mark.parametrize( "val", [ "a", "1a", "1d2", "1723-", ], ) def test_get_int_error(val: Any): """Test function :func:`configuration.get_int`.""" with pytest.raises(ValueError): configuration.get_int(val) @pytest.mark.parametrize( "val, expect", [ ("t", True), ("true", True), (1, True), (True, True), ("f", False), ("false", False), (0, False), (123, False), ("abc", False), ("abc1", False), (False, False), ], ) def test_get_bool(val: Any, expect: bool): """Test function :func:`configuration.get_bool`.""" assert configuration.get_bool(val) == expect @pytest.mark.parametrize( "home, expect", [ (None, "~/pydolphinscheduler/config.yaml"), ("/tmp/pydolphinscheduler", "/tmp/pydolphinscheduler/config.yaml"), ("/tmp/test_abc", "/tmp/test_abc/config.yaml"), ], ) def test_config_path(home: Any, expect: str): """Test function :func:`config_path`.""" if home: os.environ[ENV_PYDS_HOME] = home assert Path(expect).expanduser() == configuration.config_path() @pytest.mark.parametrize( "home", [ None, "/tmp/pydolphinscheduler", "/tmp/test_abc", ], ) def test_init_config_file(teardown_file_env, home: Any): """Test init config file.""" if home: os.environ[ENV_PYDS_HOME] = home elif DEV_MODE: pytest.skip( "Avoid delete ~/pydolphinscheduler/config.yaml by accident when test locally." ) assert not config_path().exists() configuration.init_config_file() assert config_path().exists() assert get_file_content(config_path()) == get_file_content(BUILD_IN_CONFIG_PATH) @pytest.mark.parametrize( "home", [ None, "/tmp/pydolphinscheduler", "/tmp/test_abc", ], ) def test_init_config_file_duplicate(teardown_file_env, home: Any): """Test raise error with init config file which already exists.""" if home: os.environ[ENV_PYDS_HOME] = home elif DEV_MODE: pytest.skip( "Avoid delete ~/pydolphinscheduler/config.yaml by accident when test locally." ) assert not config_path().exists() configuration.init_config_file() assert config_path().exists() with pytest.raises(PyDSConfException, match=".*file already exists.*"): configuration.init_config_file() def test_get_configs_build_in(): """Test function :func:`get_configs` with build-in config file.""" content = get_file_content(BUILD_IN_CONFIG_PATH) assert YamlParser(content).src_parser == configuration.get_configs().src_parser assert YamlParser(content).dict_parser == configuration.get_configs().dict_parser @pytest.mark.parametrize( "key, val, new_val", [ ("java_gateway.address", "127.0.0.1", "127.1.1.1"), ("java_gateway.port", 25333, 25555), ("java_gateway.auto_convert", True, False), ("default.user.name", "userPythonGateway", "editUserPythonGateway"), ("default.user.password", "<PASSWORD>", "editUserPythonGateway"), ( "default.user.email", "<EMAIL>", "<EMAIL>", ), ("default.user.phone", 11111111111, 22222222222), ("default.user.state", 1, 0), ("default.workflow.project", "project-pydolphin", "eidt-project-pydolphin"), ("default.workflow.tenant", "tenant_pydolphin", "edit_tenant_pydolphin"), ("default.workflow.user", "userPythonGateway", "editUserPythonGateway"), ("default.workflow.queue", "queuePythonGateway", "editQueuePythonGateway"), ("default.workflow.worker_group", "default", "specific"), ("default.workflow.time_zone", "Asia/Shanghai", "Asia/Beijing"), ("default.workflow.warning_type", "NONE", "ALL"), ], ) def test_single_config_get_set(teardown_file_env, key: str, val: Any, new_val: Any): """Test function :func:`get_single_config` and :func:`set_single_config`.""" assert val == get_single_config(key) set_single_config(key, new_val) assert new_val == get_single_config(key) def test_single_config_get_set_not_exists_key(): """Test function :func:`get_single_config` and :func:`set_single_config` error while key not exists.""" not_exists_key = "i_am_not_exists_key" with pytest.raises(PyDSConfException, match=".*do not exists.*"): get_single_config(not_exists_key) with pytest.raises(PyDSConfException, match=".*do not exists.*"): set_single_config(not_exists_key, not_exists_key) @pytest.mark.parametrize( "config_name, expect", [ ("JAVA_GATEWAY_ADDRESS", "127.0.0.1"), ("JAVA_GATEWAY_PORT", 25333), ("JAVA_GATEWAY_AUTO_CONVERT", True), ("USER_NAME", "userPythonGateway"), ("USER_PASSWORD", "<PASSWORD>"), ("USER_EMAIL", "<EMAIL>"), ("USER_PHONE", "11111111111"), ("USER_STATE", 1), ("WORKFLOW_PROJECT", "project-pydolphin"), ("WORKFLOW_TENANT", "tenant_pydolphin"), ("WORKFLOW_USER", "userPythonGateway"), ("WORKFLOW_QUEUE", "queuePythonGateway"), ("WORKFLOW_WORKER_GROUP", "default"), ("WORKFLOW_TIME_ZONE", "Asia/Shanghai"), ("WORKFLOW_WARNING_TYPE", "NONE"), ], ) def test_get_configuration(config_name: str, expect: Any): """Test get exists attribute in :mod:`configuration`.""" assert expect == getattr(configuration, config_name) @pytest.mark.parametrize( "config_name, src, dest", [ ("JAVA_GATEWAY_ADDRESS", "127.0.0.1", "192.168.1.1"), ("JAVA_GATEWAY_PORT", 25333, 25334), ("JAVA_GATEWAY_AUTO_CONVERT", True, False), ("USER_NAME", "userPythonGateway", "envUserPythonGateway"), ("USER_PASSWORD", "<PASSWORD>", "envUserPythonGateway"), ( "USER_EMAIL", "<EMAIL>", "<EMAIL>", ), ("USER_PHONE", "11111111111", "22222222222"), ("USER_STATE", 1, 0), ("WORKFLOW_PROJECT", "project-pydolphin", "env-project-pydolphin"), ("WORKFLOW_TENANT", "tenant_pydolphin", "env-tenant_pydolphin"), ("WORKFLOW_USER", "userPythonGateway", "envUserPythonGateway"), ("WORKFLOW_QUEUE", "queuePythonGateway", "envQueuePythonGateway"), ("WORKFLOW_WORKER_GROUP", "default", "custom"), ("WORKFLOW_TIME_ZONE", "Asia/Shanghai", "America/Los_Angeles"), ("WORKFLOW_WARNING_TYPE", "NONE", "ALL"), ], ) def test_get_configuration_env(config_name: str, src: Any, dest: Any): """Test get exists attribute from environment variable in :mod:`configuration`.""" assert getattr(configuration, config_name) == src env_name = f"PYDS_{config_name}" os.environ[env_name] = str(dest) # reload module configuration to re-get config from environment. importlib.reload(configuration) assert getattr(configuration, config_name) == dest # pop and reload configuration to test whether this config equal to `src` value os.environ.pop(env_name, None) importlib.reload(configuration) assert getattr(configuration, config_name) == src assert env_name not in os.environ

0.615435

0.322993

from __future__ import with_statement from pybench import Test class WithFinally(Test): version = 2.0 operations = 20 rounds = 80000 class ContextManager(object): def __enter__(self): pass def __exit__(self, exc, val, tb): pass def test(self): cm = self.ContextManager() for i in xrange(self.rounds): with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass def calibrate(self): cm = self.ContextManager() for i in xrange(self.rounds): pass class TryFinally(Test): version = 2.0 operations = 20 rounds = 80000 class ContextManager(object): def __enter__(self): pass def __exit__(self): # "Context manager" objects used just for their cleanup # actions in finally blocks usually don't have parameters. pass def test(self): cm = self.ContextManager() for i in xrange(self.rounds): cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() def calibrate(self): cm = self.ContextManager() for i in xrange(self.rounds): pass class WithRaiseExcept(Test): version = 2.0 operations = 2 + 3 + 3 rounds = 100000 class BlockExceptions(object): def __enter__(self): pass def __exit__(self, exc, val, tb): return True def test(self): error = ValueError be = self.BlockExceptions() for i in xrange(self.rounds): with be: raise error with be: raise error with be: raise error,"something" with be: raise error,"something" with be: raise error,"something" with be: raise error("something") with be: raise error("something") with be: raise error("something") def calibrate(self): error = ValueError be = self.BlockExceptions() for i in xrange(self.rounds): pass

tests/benchmarks/pybench/With.py

from __future__ import with_statement from pybench import Test class WithFinally(Test): version = 2.0 operations = 20 rounds = 80000 class ContextManager(object): def __enter__(self): pass def __exit__(self, exc, val, tb): pass def test(self): cm = self.ContextManager() for i in xrange(self.rounds): with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass with cm: pass def calibrate(self): cm = self.ContextManager() for i in xrange(self.rounds): pass class TryFinally(Test): version = 2.0 operations = 20 rounds = 80000 class ContextManager(object): def __enter__(self): pass def __exit__(self): # "Context manager" objects used just for their cleanup # actions in finally blocks usually don't have parameters. pass def test(self): cm = self.ContextManager() for i in xrange(self.rounds): cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() cm.__enter__() try: pass finally: cm.__exit__() def calibrate(self): cm = self.ContextManager() for i in xrange(self.rounds): pass class WithRaiseExcept(Test): version = 2.0 operations = 2 + 3 + 3 rounds = 100000 class BlockExceptions(object): def __enter__(self): pass def __exit__(self, exc, val, tb): return True def test(self): error = ValueError be = self.BlockExceptions() for i in xrange(self.rounds): with be: raise error with be: raise error with be: raise error,"something" with be: raise error,"something" with be: raise error,"something" with be: raise error("something") with be: raise error("something") with be: raise error("something") def calibrate(self): error = ValueError be = self.BlockExceptions() for i in xrange(self.rounds): pass

0.493409

0.127979

import os import sys import json from PyQt5 import QtCore, QtWidgets from PyQt5.QtWidgets import * from PyQt5.QtGui import * class WindowClassificationTrainUpdateLossParam(QtWidgets.QWidget): forward_train = QtCore.pyqtSignal(); backward_scheduler_param = QtCore.pyqtSignal(); def __init__(self): super().__init__() self.cfg_setup() self.title = 'Experiment {} - Update Loss Params'.format(self.system["experiment"]) self.left = 10 self.top = 10 self.width = 900 self.height = 600 self.loss_ui_mxnet = []; self.loss_ui_keras = []; self.loss_ui_pytorch = []; self.current_loss_ = {}; self.current_loss_["name"] = ""; self.current_loss_["params"] = {}; self.initUI() def cfg_setup(self): with open('base_classification.json') as json_file: self.system = json.load(json_file) def initUI(self): self.setWindowTitle(self.title) self.setGeometry(self.left, self.top, self.width, self.height); # Backward self.b1 = QPushButton('Back', self) self.b1.move(600,550) self.b1.clicked.connect(self.backward) # Forward self.b2 = QPushButton('Next', self) self.b2.move(700,550) self.b2.clicked.connect(self.forward) # Quit self.b3 = QPushButton('Quit', self) self.b3.move(800,550) self.b3.clicked.connect(self.close) self.cb1 = QComboBox(self); self.cb1.move(20, 20); self.cb1.activated.connect(self.select_loss); self.cb2 = QComboBox(self); self.cb2.move(20, 20); self.cb2.activated.connect(self.select_loss); self.cb3 = QComboBox(self); self.cb3.move(20, 20); self.cb3.activated.connect(self.select_loss); self.mxnet_losses_list = ["select", "loss_l1", "loss_l2", "loss_softmax_crossentropy", "loss_crossentropy", "loss_sigmoid_binary_crossentropy", "loss_binary_crossentropy", "loss_kldiv", "loss_poisson_nll", "loss_huber", "loss_hinge", "loss_squared_hinge"]; self.keras_losses_list = ["select", "loss_l1", "loss_l2", "loss_crossentropy", "loss_binary_crossentropy", "loss_kldiv", "loss_hinge", "loss_squared_hinge"]; self.pytorch_losses_list = ["select", "loss_l1", "loss_l2", "loss_softmax_crossentropy", "loss_crossentropy", "loss_sigmoid_binary_crossentropy", "loss_binary_crossentropy", "loss_kldiv", "loss_poisson_nll", "loss_huber", "loss_hinge", "loss_squared_hinge", "loss_multimargin", "loss_squared_multimargin", "loss_multilabel_margin", "loss_multilabel_softmargin"]; if(self.system["backend"] == "Mxnet-1.5.1"): self.cb1.addItems(self.mxnet_losses_list); self.cb1.show(); self.cb2.hide(); self.cb3.hide(); elif(self.system["backend"] == "Keras-2.2.5_Tensorflow-1"): self.cb2.addItems(self.keras_losses_list); self.cb2.show(); self.cb1.hide(); self.cb3.hide(); elif(self.system["backend"] == "Pytorch-1.3.1"): self.cb3.addItems(self.pytorch_losses_list); self.cb3.show(); self.cb1.hide(); self.cb2.hide(); tmp = []; self.mx_lo1_l1 = QLabel(self); self.mx_lo1_l1.setText("1. Scalar Weight: "); self.mx_lo1_l1.move(20, 100); tmp.append(self.mx_lo1_l1); self.mx_lo1_e1 = QLineEdit(self) self.mx_lo1_e1.move(150, 100); self.mx_lo1_e1.setText("1.0"); tmp.append(self.mx_lo1_e1); self.mx_lo1_l2 = QLabel(self); self.mx_lo1_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo1_l2.move(20, 150); tmp.append(self.mx_lo1_l2); self.mx_lo1_e2 = QLineEdit(self) self.mx_lo1_e2.move(290, 150); self.mx_lo1_e2.setText("0"); tmp.append(self.mx_lo1_e2); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo2_l1 = QLabel(self); self.mx_lo2_l1.setText("1. Scalar Weight: "); self.mx_lo2_l1.move(20, 100); tmp.append(self.mx_lo2_l1); self.mx_lo2_e1 = QLineEdit(self) self.mx_lo2_e1.move(150, 100); self.mx_lo2_e1.setText("1.0"); tmp.append(self.mx_lo2_e1); self.mx_lo2_l2 = QLabel(self); self.mx_lo2_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo2_l2.move(20, 150); tmp.append(self.mx_lo2_l2); self.mx_lo2_e2 = QLineEdit(self) self.mx_lo2_e2.move(290, 150); self.mx_lo2_e2.setText("0"); tmp.append(self.mx_lo2_e2); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo3_l1 = QLabel(self); self.mx_lo3_l1.setText("1. Scalar Weight: "); self.mx_lo3_l1.move(20, 100); tmp.append(self.mx_lo3_l1); self.mx_lo3_e1 = QLineEdit(self) self.mx_lo3_e1.move(150, 100); self.mx_lo3_e1.setText("1.0"); tmp.append(self.mx_lo3_e1); self.mx_lo3_l2 = QLabel(self); self.mx_lo3_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo3_l2.move(20, 150); tmp.append(self.mx_lo3_l2); self.mx_lo3_e2 = QLineEdit(self) self.mx_lo3_e2.move(290, 150); self.mx_lo3_e2.setText("0"); tmp.append(self.mx_lo3_e2); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo4_l1 = QLabel(self); self.mx_lo4_l1.setText("1. Scalar Weight: "); self.mx_lo4_l1.move(20, 100); tmp.append(self.mx_lo4_l1); self.mx_lo4_e1 = QLineEdit(self) self.mx_lo4_e1.move(150, 100); self.mx_lo4_e1.setText("1.0"); tmp.append(self.mx_lo4_e1); self.mx_lo4_l2 = QLabel(self); self.mx_lo4_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo4_l2.move(20, 150); tmp.append(self.mx_lo4_l2); self.mx_lo4_e2 = QLineEdit(self) self.mx_lo4_e2.move(290, 150); self.mx_lo4_e2.setText("0"); tmp.append(self.mx_lo4_e2); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo5_l1 = QLabel(self); self.mx_lo5_l1.setText("1. Scalar Weight: "); self.mx_lo5_l1.move(20, 100); tmp.append(self.mx_lo5_l1); self.mx_lo5_e1 = QLineEdit(self) self.mx_lo5_e1.move(150, 100); self.mx_lo5_e1.setText("1.0"); tmp.append(self.mx_lo5_e1); self.mx_lo5_l2 = QLabel(self); self.mx_lo5_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo5_l2.move(20, 150); tmp.append(self.mx_lo5_l2); self.mx_lo5_e2 = QLineEdit(self) self.mx_lo5_e2.move(290, 150); self.mx_lo5_e2.setText("0"); tmp.append(self.mx_lo5_e2); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo6_l1 = QLabel(self); self.mx_lo6_l1.setText("1. Scalar Weight: "); self.mx_lo6_l1.move(20, 100); tmp.append(self.mx_lo6_l1); self.mx_lo6_e1 = QLineEdit(self) self.mx_lo6_e1.move(150, 100); self.mx_lo6_e1.setText("1.0"); tmp.append(self.mx_lo6_e1); self.mx_lo6_l2 = QLabel(self); self.mx_lo6_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo6_l2.move(20, 150); tmp.append(self.mx_lo6_l2); self.mx_lo6_e2 = QLineEdit(self) self.mx_lo6_e2.move(290, 150); self.mx_lo6_e2.setText("0"); tmp.append(self.mx_lo6_e2); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo7_l1 = QLabel(self); self.mx_lo7_l1.setText("1. Scalar Weight: "); self.mx_lo7_l1.move(20, 100); tmp.append(self.mx_lo7_l1); self.mx_lo7_e1 = QLineEdit(self) self.mx_lo7_e1.move(150, 100); self.mx_lo7_e1.setText("1.0"); tmp.append(self.mx_lo7_e1); self.mx_lo7_l2 = QLabel(self); self.mx_lo7_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo7_l2.move(20, 150); tmp.append(self.mx_lo7_l2); self.mx_lo7_e2 = QLineEdit(self) self.mx_lo7_e2.move(290, 150); self.mx_lo7_e2.setText("0"); tmp.append(self.mx_lo7_e2); self.mx_lo7_l3 = QLabel(self); self.mx_lo7_l3.setText("3. Input has log pre-applied: "); self.mx_lo7_l3.move(20, 200); tmp.append(self.mx_lo7_l3); self.mx_lo7_cb3 = QComboBox(self); self.mx_lo7_cb3.move(290, 200); self.mx_lo7_cb3.addItems(["No", "Yes"]); tmp.append(self.mx_lo7_cb3); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo8_l1 = QLabel(self); self.mx_lo8_l1.setText("1. Scalar Weight: "); self.mx_lo8_l1.move(20, 100); tmp.append(self.mx_lo8_l1); self.mx_lo8_e1 = QLineEdit(self) self.mx_lo8_e1.move(150, 100); self.mx_lo8_e1.setText("1.0"); tmp.append(self.mx_lo8_e1); self.mx_lo8_l2 = QLabel(self); self.mx_lo8_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo8_l2.move(20, 150); tmp.append(self.mx_lo8_l2); self.mx_lo8_e2 = QLineEdit(self) self.mx_lo8_e2.move(290, 150); self.mx_lo8_e2.setText("0"); tmp.append(self.mx_lo8_e2); self.mx_lo8_l3 = QLabel(self); self.mx_lo8_l3.setText("3. Input has log pre-applied: "); self.mx_lo8_l3.move(20, 200); tmp.append(self.mx_lo8_l3); self.mx_lo8_cb3 = QComboBox(self); self.mx_lo8_cb3.move(290, 200); self.mx_lo8_cb3.addItems(["No", "Yes"]); tmp.append(self.mx_lo8_cb3); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo9_l1 = QLabel(self); self.mx_lo9_l1.setText("1. Scalar Weight: "); self.mx_lo9_l1.move(20, 100); tmp.append(self.mx_lo9_l1); self.mx_lo9_e1 = QLineEdit(self) self.mx_lo9_e1.move(150, 100); self.mx_lo9_e1.setText("1.0"); tmp.append(self.mx_lo9_e1); self.mx_lo9_l2 = QLabel(self); self.mx_lo9_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo9_l2.move(20, 150); tmp.append(self.mx_lo9_l2); self.mx_lo9_e2 = QLineEdit(self) self.mx_lo9_e2.move(290, 150); self.mx_lo9_e2.setText("0"); tmp.append(self.mx_lo9_e2); self.mx_lo9_l3 = QLabel(self); self.mx_lo9_l3.setText("3. Threshold for mean estimator: "); self.mx_lo9_l3.move(20, 200); tmp.append(self.mx_lo9_l3); self.mx_lo9_e3 = QLineEdit(self) self.mx_lo9_e3.move(290, 200); self.mx_lo9_e3.setText("1.0"); tmp.append(self.mx_lo9_e3); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo10_l1 = QLabel(self); self.mx_lo10_l1.setText("1. Scalar Weight: "); self.mx_lo10_l1.move(20, 100); tmp.append(self.mx_lo10_l1); self.mx_lo10_e1 = QLineEdit(self) self.mx_lo10_e1.move(150, 100); self.mx_lo10_e1.setText("1.0"); tmp.append(self.mx_lo10_e1); self.mx_lo10_l2 = QLabel(self); self.mx_lo10_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo10_l2.move(20, 150); tmp.append(self.mx_lo10_l2); self.mx_lo10_e2 = QLineEdit(self) self.mx_lo10_e2.move(290, 150); self.mx_lo10_e2.setText("0"); tmp.append(self.mx_lo10_e2); self.mx_lo10_l3 = QLabel(self); self.mx_lo10_l3.setText("3. Margin: "); self.mx_lo10_l3.move(20, 200); tmp.append(self.mx_lo10_l3); self.mx_lo10_e3 = QLineEdit(self) self.mx_lo10_e3.move(150, 200); self.mx_lo10_e3.setText("1.0"); tmp.append(self.mx_lo10_e3); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo11_l1 = QLabel(self); self.mx_lo11_l1.setText("1. Scalar Weight: "); self.mx_lo11_l1.move(20, 100); tmp.append(self.mx_lo11_l1); self.mx_lo11_e1 = QLineEdit(self) self.mx_lo11_e1.move(150, 100); self.mx_lo11_e1.setText("1.0"); tmp.append(self.mx_lo11_e1); self.mx_lo11_l2 = QLabel(self); self.mx_lo11_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo11_l2.move(20, 150); tmp.append(self.mx_lo11_l2); self.mx_lo11_e2 = QLineEdit(self) self.mx_lo11_e2.move(290, 150); self.mx_lo11_e2.setText("0"); tmp.append(self.mx_lo11_e2); self.mx_lo11_l3 = QLabel(self); self.mx_lo11_l3.setText("3. Margin: "); self.mx_lo11_l3.move(20, 200); tmp.append(self.mx_lo11_l3); self.mx_lo11_e3 = QLineEdit(self) self.mx_lo11_e3.move(150, 200); self.mx_lo11_e3.setText("1.0"); tmp.append(self.mx_lo11_e3); self.loss_ui_mxnet.append(tmp) tmp = []; self.ke_lo1_l1 = QLabel(self); self.ke_lo1_l1.setText("1. Scalar Weight: "); self.ke_lo1_l1.move(20, 100); tmp.append(self.ke_lo1_l1); self.ke_lo1_e1 = QLineEdit(self) self.ke_lo1_e1.move(150, 100); self.ke_lo1_e1.setText("1.0"); tmp.append(self.ke_lo1_e1); self.ke_lo1_l2 = QLabel(self); self.ke_lo1_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.ke_lo1_l2.move(20, 150); tmp.append(self.ke_lo1_l2); self.ke_lo1_e2 = QLineEdit(self) self.ke_lo1_e2.move(290, 150); self.ke_lo1_e2.setText("0"); tmp.append(self.ke_lo1_e2); self.loss_ui_keras.append(tmp) tmp = []; self.ke_lo2_l1 = QLabel(self); self.ke_lo2_l1.setText("1. Scalar Weight: "); self.ke_lo2_l1.move(20, 100); tmp.append(self.ke_lo2_l1); self.ke_lo2_e1 = QLineEdit(self) self.ke_lo2_e1.move(150, 100); self.ke_lo2_e1.setText("1.0"); tmp.append(self.ke_lo2_e1); self.ke_lo2_l2 = QLabel(self); self.ke_lo2_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.ke_lo2_l2.move(20, 150); tmp.append(self.ke_lo2_l2); self.ke_lo2_e2 = QLineEdit(self) self.ke_lo2_e2.move(290, 150); self.ke_lo2_e2.setText("0"); tmp.append(self.ke_lo2_e2); self.loss_ui_keras.append(tmp) tmp = []; self.ke_lo3_l1 = QLabel(self); self.ke_lo3_l1.setText("1. Scalar Weight: "); self.ke_lo3_l1.move(20, 100); tmp.append(self.ke_lo3_l1); self.ke_lo3_e1 = QLineEdit(self) self.ke_lo3_e1.move(150, 100); self.ke_lo3_e1.setText("1.0"); tmp.append(self.ke_lo3_e1); self.ke_lo3_l2 = QLabel(self); self.ke_lo3_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.ke_lo3_l2.move(20, 150); tmp.append(self.ke_lo3_l2); self.ke_lo3_e2 = QLineEdit(self) self.ke_lo3_e2.move(290, 150); self.ke_lo3_e2.setText("0"); tmp.append(self.ke_lo3_e2); self.loss_ui_keras.append(tmp) tmp = []; self.ke_lo4_l1 = QLabel(self); self.ke_lo4_l1.setText("1. Scalar Weight: "); self.ke_lo4_l1.move(20, 100); tmp.append(self.ke_lo4_l1); self.ke_lo4_e1 = QLineEdit(self) self.ke_lo4_e1.move(150, 100); self.ke_lo4_e1.setText("1.0"); tmp.append(self.ke_lo4_e1); self.ke_lo4_l2 = QLabel(self); self.ke_lo4_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.ke_lo4_l2.move(20, 150); tmp.append(self.ke_lo4_l2); self.ke_lo4_e2 = QLineEdit(self) self.ke_lo4_e2.move(290, 150); self.ke_lo4_e2.setText("0"); tmp.append(self.ke_lo4_e2); self.loss_ui_keras.append(tmp) tmp = []; self.ke_lo5_l1 = QLabel(self); self.ke_lo5_l1.setText("1. Scalar Weight: "); self.ke_lo5_l1.move(20, 100); tmp.append(self.ke_lo5_l1); self.ke_lo5_e1 = QLineEdit(self) self.ke_lo5_e1.move(150, 100); self.ke_lo5_e1.setText("1.0"); tmp.append(self.ke_lo5_e1); self.ke_lo5_l2 = QLabel(self); self.ke_lo5_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.ke_lo5_l2.move(20, 150); tmp.append(self.ke_lo5_l2); self.ke_lo5_e2 = QLineEdit(self) self.ke_lo5_e2.move(290, 150); self.ke_lo5_e2.setText("0"); tmp.append(self.ke_lo5_e2); self.ke_lo5_l3 = QLabel(self); self.ke_lo5_l3.setText("3. Input has log pre-applied: "); self.ke_lo5_l3.move(20, 200); tmp.append(self.ke_lo5_l3); self.ke_lo5_cb3 = QComboBox(self); self.ke_lo5_cb3.move(290, 200); self.ke_lo5_cb3.addItems(["No", "Yes"]); tmp.append(self.ke_lo5_cb3); self.loss_ui_keras.append(tmp) tmp = []; self.ke_lo6_l1 = QLabel(self); self.ke_lo6_l1.setText("1. Scalar Weight: "); self.ke_lo6_l1.move(20, 100); tmp.append(self.ke_lo6_l1); self.ke_lo6_e1 = QLineEdit(self) self.ke_lo6_e1.move(150, 100); self.ke_lo6_e1.setText("1.0"); tmp.append(self.ke_lo6_e1); self.ke_lo6_l2 = QLabel(self); self.ke_lo6_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.ke_lo6_l2.move(20, 150); tmp.append(self.ke_lo6_l2); self.ke_lo6_e2 = QLineEdit(self) self.ke_lo6_e2.move(290, 150); self.ke_lo6_e2.setText("0"); tmp.append(self.ke_lo6_e2); self.ke_lo6_l3 = QLabel(self); self.ke_lo6_l3.setText("3. Margin: "); self.ke_lo6_l3.move(20, 200); tmp.append(self.ke_lo6_l3); self.ke_lo6_e3 = QLineEdit(self) self.ke_lo6_e3.move(150, 200); self.ke_lo6_e3.setText("1.0"); tmp.append(self.ke_lo6_e3); self.loss_ui_keras.append(tmp) tmp = []; self.ke_lo7_l1 = QLabel(self); self.ke_lo7_l1.setText("1. Scalar Weight: "); self.ke_lo7_l1.move(20, 100); tmp.append(self.ke_lo7_l1); self.ke_lo7_e1 = QLineEdit(self) self.ke_lo7_e1.move(150, 100); self.ke_lo7_e1.setText("1.0"); tmp.append(self.ke_lo7_e1); self.ke_lo7_l2 = QLabel(self); self.ke_lo7_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.ke_lo7_l2.move(20, 150); tmp.append(self.ke_lo7_l2); self.ke_lo7_e2 = QLineEdit(self) self.ke_lo7_e2.move(290, 150); self.ke_lo7_e2.setText("0"); tmp.append(self.ke_lo7_e2); self.ke_lo7_l3 = QLabel(self); self.ke_lo7_l3.setText("3. Margin: "); self.ke_lo7_l3.move(20, 200); tmp.append(self.ke_lo7_l3); self.ke_lo7_e3 = QLineEdit(self) self.ke_lo7_e3.move(150, 200); self.ke_lo7_e3.setText("1.0"); tmp.append(self.ke_lo7_e3); self.loss_ui_keras.append(tmp) tmp = []; self.py_lo1_l1 = QLabel(self); self.py_lo1_l1.setText("1. Scalar Weight: "); self.py_lo1_l1.move(20, 100); tmp.append(self.py_lo1_l1); self.py_lo1_e1 = QLineEdit(self) self.py_lo1_e1.move(150, 100); self.py_lo1_e1.setText("1.0"); tmp.append(self.py_lo1_e1); self.py_lo1_l2 = QLabel(self); self.py_lo1_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo1_l2.move(20, 150); tmp.append(self.py_lo1_l2); self.py_lo1_e2 = QLineEdit(self) self.py_lo1_e2.move(290, 150); self.py_lo1_e2.setText("0"); tmp.append(self.py_lo1_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo2_l1 = QLabel(self); self.py_lo2_l1.setText("1. Scalar Weight: "); self.py_lo2_l1.move(20, 100); tmp.append(self.py_lo2_l1); self.py_lo2_e1 = QLineEdit(self) self.py_lo2_e1.move(150, 100); self.py_lo2_e1.setText("1.0"); tmp.append(self.py_lo2_e1); self.py_lo2_l2 = QLabel(self); self.py_lo2_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo2_l2.move(20, 150); tmp.append(self.py_lo2_l2); self.py_lo2_e2 = QLineEdit(self) self.py_lo2_e2.move(290, 150); self.py_lo2_e2.setText("0"); tmp.append(self.py_lo2_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo3_l1 = QLabel(self); self.py_lo3_l1.setText("1. Scalar Weight: "); self.py_lo3_l1.move(20, 100); tmp.append(self.py_lo3_l1); self.py_lo3_e1 = QLineEdit(self) self.py_lo3_e1.move(150, 100); self.py_lo3_e1.setText("1.0"); tmp.append(self.py_lo3_e1); self.py_lo3_l2 = QLabel(self); self.py_lo3_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo3_l2.move(20, 150); tmp.append(self.py_lo3_l2); self.py_lo3_e2 = QLineEdit(self) self.py_lo3_e2.move(290, 150); self.py_lo3_e2.setText("0"); tmp.append(self.py_lo3_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo4_l1 = QLabel(self); self.py_lo4_l1.setText("1. Scalar Weight: "); self.py_lo4_l1.move(20, 100); tmp.append(self.py_lo4_l1); self.py_lo4_e1 = QLineEdit(self) self.py_lo4_e1.move(150, 100); self.py_lo4_e1.setText("1.0"); tmp.append(self.py_lo4_e1); self.py_lo4_l2 = QLabel(self); self.py_lo4_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo4_l2.move(20, 150); tmp.append(self.py_lo4_l2); self.py_lo4_e2 = QLineEdit(self) self.py_lo4_e2.move(290, 150); self.py_lo4_e2.setText("0"); tmp.append(self.py_lo4_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo5_l1 = QLabel(self); self.py_lo5_l1.setText("1. Scalar Weight: "); self.py_lo5_l1.move(20, 100); tmp.append(self.py_lo5_l1); self.py_lo5_e1 = QLineEdit(self) self.py_lo5_e1.move(150, 100); self.py_lo5_e1.setText("1.0"); tmp.append(self.py_lo5_e1); self.py_lo5_l2 = QLabel(self); self.py_lo5_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo5_l2.move(20, 150); tmp.append(self.py_lo5_l2); self.py_lo5_e2 = QLineEdit(self) self.py_lo5_e2.move(290, 150); self.py_lo5_e2.setText("0"); tmp.append(self.py_lo5_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo6_l1 = QLabel(self); self.py_lo6_l1.setText("1. Scalar Weight: "); self.py_lo6_l1.move(20, 100); tmp.append(self.py_lo6_l1); self.py_lo6_e1 = QLineEdit(self) self.py_lo6_e1.move(150, 100); self.py_lo6_e1.setText("1.0"); tmp.append(self.py_lo6_e1); self.py_lo6_l2 = QLabel(self); self.py_lo6_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo6_l2.move(20, 150); tmp.append(self.py_lo6_l2); self.py_lo6_e2 = QLineEdit(self) self.py_lo6_e2.move(290, 150); self.py_lo6_e2.setText("0"); tmp.append(self.py_lo6_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo7_l1 = QLabel(self); self.py_lo7_l1.setText("1. Scalar Weight: "); self.py_lo7_l1.move(20, 100); tmp.append(self.py_lo7_l1); self.py_lo7_e1 = QLineEdit(self) self.py_lo7_e1.move(150, 100); self.py_lo7_e1.setText("1.0"); tmp.append(self.py_lo7_e1); self.py_lo7_l2 = QLabel(self); self.py_lo7_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo7_l2.move(20, 150); tmp.append(self.py_lo7_l2); self.py_lo7_e2 = QLineEdit(self) self.py_lo7_e2.move(290, 150); self.py_lo7_e2.setText("0"); tmp.append(self.py_lo7_e2); self.py_lo7_l3 = QLabel(self); self.py_lo7_l3.setText("3. Input has log pre-applied: "); self.py_lo7_l3.move(20, 200); tmp.append(self.py_lo7_l3); self.py_lo7_cb3 = QComboBox(self); self.py_lo7_cb3.move(290, 200); self.py_lo7_cb3.addItems(["No", "Yes"]); tmp.append(self.py_lo7_cb3); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo8_l1 = QLabel(self); self.py_lo8_l1.setText("1. Scalar Weight: "); self.py_lo8_l1.move(20, 100); tmp.append(self.py_lo8_l1); self.py_lo8_e1 = QLineEdit(self) self.py_lo8_e1.move(150, 100); self.py_lo8_e1.setText("1.0"); tmp.append(self.py_lo8_e1); self.py_lo8_l2 = QLabel(self); self.py_lo8_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo8_l2.move(20, 150); tmp.append(self.py_lo8_l2); self.py_lo8_e2 = QLineEdit(self) self.py_lo8_e2.move(290, 150); self.py_lo8_e2.setText("0"); tmp.append(self.py_lo8_e2); self.py_lo8_l3 = QLabel(self); self.py_lo8_l3.setText("3. Input has log pre-applied: "); self.py_lo8_l3.move(20, 200); tmp.append(self.py_lo8_l3); self.py_lo8_cb3 = QComboBox(self); self.py_lo8_cb3.move(290, 200); self.py_lo8_cb3.addItems(["No", "Yes"]); tmp.append(self.py_lo8_cb3); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo9_l1 = QLabel(self); self.py_lo9_l1.setText("1. Scalar Weight: "); self.py_lo9_l1.move(20, 100); tmp.append(self.py_lo9_l1); self.py_lo9_e1 = QLineEdit(self) self.py_lo9_e1.move(150, 100); self.py_lo9_e1.setText("1.0"); tmp.append(self.py_lo9_e1); self.py_lo9_l2 = QLabel(self); self.py_lo9_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo9_l2.move(20, 150); tmp.append(self.py_lo9_l2); self.py_lo9_e2 = QLineEdit(self) self.py_lo9_e2.move(290, 150); self.py_lo9_e2.setText("0"); tmp.append(self.py_lo9_e2); self.py_lo9_l3 = QLabel(self); self.py_lo9_l3.setText("3. Threshold for mean estimator: "); self.py_lo9_l3.move(20, 200); tmp.append(self.py_lo9_l3); self.py_lo9_e3 = QLineEdit(self) self.py_lo9_e3.move(290, 200); self.py_lo9_e3.setText("1.0"); tmp.append(self.py_lo9_e3); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo10_l1 = QLabel(self); self.py_lo10_l1.setText("1. Scalar Weight: "); self.py_lo10_l1.move(20, 100); tmp.append(self.py_lo10_l1); self.py_lo10_e1 = QLineEdit(self) self.py_lo10_e1.move(150, 100); self.py_lo10_e1.setText("1.0"); tmp.append(self.py_lo10_e1); self.py_lo10_l2 = QLabel(self); self.py_lo10_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo10_l2.move(20, 150); tmp.append(self.py_lo10_l2); self.py_lo10_e2 = QLineEdit(self) self.py_lo10_e2.move(290, 150); self.py_lo10_e2.setText("0"); tmp.append(self.py_lo10_e2); self.py_lo10_l3 = QLabel(self); self.py_lo10_l3.setText("3. Margin: "); self.py_lo10_l3.move(20, 200); tmp.append(self.py_lo10_l3); self.py_lo10_e3 = QLineEdit(self) self.py_lo10_e3.move(150, 200); self.py_lo10_e3.setText("1.0"); tmp.append(self.py_lo10_e3); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo11_l1 = QLabel(self); self.py_lo11_l1.setText("1. Scalar Weight: "); self.py_lo11_l1.move(20, 100); tmp.append(self.py_lo11_l1); self.py_lo11_e1 = QLineEdit(self) self.py_lo11_e1.move(150, 100); self.py_lo11_e1.setText("1.0"); tmp.append(self.py_lo11_e1); self.py_lo11_l2 = QLabel(self); self.py_lo11_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo11_l2.move(20, 150); tmp.append(self.py_lo11_l2); self.py_lo11_e2 = QLineEdit(self) self.py_lo11_e2.move(290, 150); self.py_lo11_e2.setText("0"); tmp.append(self.py_lo11_e2); self.py_lo11_l3 = QLabel(self); self.py_lo11_l3.setText("3. Margin: "); self.py_lo11_l3.move(20, 200); tmp.append(self.py_lo11_l3); self.py_lo11_e3 = QLineEdit(self) self.py_lo11_e3.move(150, 200); self.py_lo11_e3.setText("1.0"); tmp.append(self.py_lo11_e3); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo12_l1 = QLabel(self); self.py_lo12_l1.setText("1. Scalar Weight: "); self.py_lo12_l1.move(20, 100); tmp.append(self.py_lo12_l1); self.py_lo12_e1 = QLineEdit(self) self.py_lo12_e1.move(150, 100); self.py_lo12_e1.setText("1.0"); tmp.append(self.py_lo12_e1); self.py_lo12_l2 = QLabel(self); self.py_lo12_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo12_l2.move(20, 150); tmp.append(self.py_lo12_l2); self.py_lo12_e2 = QLineEdit(self) self.py_lo12_e2.move(290, 150); self.py_lo12_e2.setText("0"); tmp.append(self.py_lo12_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo13_l1 = QLabel(self); self.py_lo13_l1.setText("1. Scalar Weight: "); self.py_lo13_l1.move(20, 100); tmp.append(self.py_lo13_l1); self.py_lo13_e1 = QLineEdit(self) self.py_lo13_e1.move(150, 100); self.py_lo13_e1.setText("1.0"); tmp.append(self.py_lo13_e1); self.py_lo13_l2 = QLabel(self); self.py_lo13_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo13_l2.move(20, 150); tmp.append(self.py_lo13_l2); self.py_lo13_e2 = QLineEdit(self) self.py_lo13_e2.move(290, 150); self.py_lo13_e2.setText("0"); tmp.append(self.py_lo13_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo14_l1 = QLabel(self); self.py_lo14_l1.setText("1. Scalar Weight: "); self.py_lo14_l1.move(20, 100); tmp.append(self.py_lo14_l1); self.py_lo14_e1 = QLineEdit(self) self.py_lo14_e1.move(150, 100); self.py_lo14_e1.setText("1.0"); tmp.append(self.py_lo14_e1); self.py_lo14_l2 = QLabel(self); self.py_lo14_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo14_l2.move(20, 150); tmp.append(self.py_lo14_l2); self.py_lo14_e2 = QLineEdit(self) self.py_lo14_e2.move(290, 150); self.py_lo14_e2.setText("0"); tmp.append(self.py_lo14_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo15_l1 = QLabel(self); self.py_lo15_l1.setText("1. Scalar Weight: "); self.py_lo15_l1.move(20, 100); tmp.append(self.py_lo15_l1); self.py_lo15_e1 = QLineEdit(self) self.py_lo15_e1.move(150, 100); self.py_lo15_e1.setText("1.0"); tmp.append(self.py_lo15_e1); self.py_lo15_l2 = QLabel(self); self.py_lo15_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo15_l2.move(20, 150); tmp.append(self.py_lo15_l2); self.py_lo15_e2 = QLineEdit(self) self.py_lo15_e2.move(290, 150); self.py_lo15_e2.setText("0"); tmp.append(self.py_lo15_e2); self.loss_ui_pytorch.append(tmp) self.select_loss(); self.tb1 = QTextEdit(self) self.tb1.move(550, 20) self.tb1.resize(300, 500) if(self.system["update"]["losses"]["active"]): wr = ""; wr = json.dumps(self.system["update"]["losses"]["value"], indent=4) self.tb1.setText(wr); else: self.tb1.setText("Using Default loss.") self.b4 = QPushButton('Select loss', self) self.b4.move(400,400) self.b4.clicked.connect(self.add_loss) self.b6 = QPushButton('Clear ', self) self.b6.move(400,500) self.b6.clicked.connect(self.clear_loss) def select_loss(self): self.current_loss = {}; self.current_loss["name"] = ""; self.current_loss["params"] = {}; if(self.system["backend"] == "Mxnet-1.5.1"): self.current_loss["name"] = self.cb1.currentText(); index = self.mxnet_losses_list.index(self.cb1.currentText()); for i in range(len(self.loss_ui_mxnet)): for j in range(len(self.loss_ui_mxnet[i])): if((index-1)==i): self.loss_ui_mxnet[i][j].show(); else: self.loss_ui_mxnet[i][j].hide(); for i in range(len(self.loss_ui_keras)): for j in range(len(self.loss_ui_keras[i])): self.loss_ui_keras[i][j].hide(); for i in range(len(self.loss_ui_pytorch)): for j in range(len(self.loss_ui_pytorch[i])): self.loss_ui_pytorch[i][j].hide(); elif(self.system["backend"] == "Keras-2.2.5_Tensorflow-1"): self.current_loss["name"] = self.cb2.currentText(); index = self.keras_losses_list.index(self.cb2.currentText()); for i in range(len(self.loss_ui_keras)): for j in range(len(self.loss_ui_keras[i])): if((index-1)==i): self.loss_ui_keras[i][j].show(); else: self.loss_ui_keras[i][j].hide(); for i in range(len(self.loss_ui_mxnet)): for j in range(len(self.loss_ui_mxnet[i])): self.loss_ui_mxnet[i][j].hide(); for i in range(len(self.loss_ui_pytorch)): for j in range(len(self.loss_ui_pytorch[i])): self.loss_ui_pytorch[i][j].hide(); elif(self.system["backend"] == "Pytorch-1.3.1"): self.current_loss["name"] = self.cb3.currentText(); index = self.pytorch_losses_list.index(self.cb3.currentText()); for i in range(len(self.loss_ui_pytorch)): for j in range(len(self.loss_ui_pytorch[i])): if((index-1)==i): self.loss_ui_pytorch[i][j].show(); else: self.loss_ui_pytorch[i][j].hide(); for i in range(len(self.loss_ui_keras)): for j in range(len(self.loss_ui_keras[i])): self.loss_ui_keras[i][j].hide(); for i in range(len(self.loss_ui_mxnet)): for j in range(len(self.loss_ui_mxnet[i])): self.loss_ui_mxnet[i][j].hide(); def add_loss(self): self.system["update"]["losses"]["active"] = True; if(self.system["backend"] == "Mxnet-1.5.1"): if(self.current_loss["name"] == self.mxnet_losses_list[1]): self.current_loss["params"]["weight"] = self.mx_lo1_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo1_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[2]): self.current_loss["params"]["weight"] = self.mx_lo2_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo2_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[3]): self.current_loss["params"]["weight"] = self.mx_lo3_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo3_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[4]): self.current_loss["params"]["weight"] = self.mx_lo4_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo4_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[5]): self.current_loss["params"]["weight"] = self.mx_lo5_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo5_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[6]): self.current_loss["params"]["weight"] = self.mx_lo6_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo6_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[7]): self.current_loss["params"]["weight"] = self.mx_lo7_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo7_e2.text(); self.current_loss["params"]["log_pre_applied"] = self.mx_lo7_cb3.currentText(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[8]): self.current_loss["params"]["weight"] = self.mx_lo8_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo8_e2.text(); self.current_loss["params"]["log_pre_applied"] = self.mx_lo8_cb3.currentText(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[9]): self.current_loss["params"]["weight"] = self.mx_lo9_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo9_e2.text(); self.current_loss["params"]["threshold_for_mean_estimator"] = self.mx_lo9_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[10]): self.current_loss["params"]["weight"] = self.mx_lo10_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo10_e2.text(); self.current_loss["params"]["margin"] = self.mx_lo10_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[11]): self.current_loss["params"]["weight"] = self.mx_lo11_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo11_e2.text(); self.current_loss["params"]["margin"] = self.mx_lo11_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.system["backend"] == "Keras-2.2.5_Tensorflow-1"): if(self.current_loss["name"] == self.keras_losses_list[1]): self.current_loss["params"]["weight"] = self.ke_lo1_e1.text(); self.current_loss["params"]["batch_axis"] = self.ke_lo1_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.keras_losses_list[2]): self.current_loss["params"]["weight"] = self.ke_lo2_e1.text(); self.current_loss["params"]["batch_axis"] = self.ke_lo2_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.keras_losses_list[3]): self.current_loss["params"]["weight"] = self.ke_lo3_e1.text(); self.current_loss["params"]["batch_axis"] = self.ke_lo3_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.keras_losses_list[4]): self.current_loss["params"]["weight"] = self.ke_lo4_e1.text(); self.current_loss["params"]["batch_axis"] = self.ke_lo4_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.keras_losses_list[5]): self.current_loss["params"]["weight"] = self.ke_lo5_e1.text(); self.current_loss["params"]["batch_axis"] = self.ke_lo5_e2.text(); self.current_loss["params"]["log_pre_applied"] = self.ke_lo5_cb3.currentText(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.keras_losses_list[6]): self.current_loss["params"]["weight"] = self.ke_lo6_e1.text(); self.current_loss["params"]["batch_axis"] = self.ke_lo6_e2.text(); self.current_loss["params"]["margin"] = self.ke_lo6_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.keras_losses_list[7]): self.current_loss["params"]["weight"] = self.ke_lo7_e1.text(); self.current_loss["params"]["batch_axis"] = self.ke_lo7_e2.text(); self.current_loss["params"]["margin"] = self.ke_lo7_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.system["backend"] == "Pytorch-1.3.1"): if(self.current_loss["name"] == self.pytorch_losses_list[1]): self.current_loss["params"]["weight"] = self.py_lo1_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo1_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[2]): self.current_loss["params"]["weight"] = self.py_lo2_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo2_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[3]): self.current_loss["params"]["weight"] = self.py_lo3_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo3_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[4]): self.current_loss["params"]["weight"] = self.py_lo4_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo4_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[5]): self.current_loss["params"]["weight"] = self.py_lo5_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo5_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[6]): self.current_loss["params"]["weight"] = self.py_lo6_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo6_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[7]): self.current_loss["params"]["weight"] = self.py_lo7_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo7_e2.text(); self.current_loss["params"]["log_pre_applied"] = self.py_lo7_cb3.currentText(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[8]): self.current_loss["params"]["weight"] = self.py_lo8_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo8_e2.text(); self.current_loss["params"]["log_pre_applied"] = self.py_lo8_cb3.currentText(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[9]): self.current_loss["params"]["weight"] = self.py_lo9_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo9_e2.text(); self.current_loss["params"]["threshold_for_mean_estimator"] = self.py_lo9_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[10]): self.current_loss["params"]["weight"] = self.py_lo10_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo10_e2.text(); self.current_loss["params"]["margin"] = self.py_lo10_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[11]): self.current_loss["params"]["weight"] = self.py_lo11_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo11_e2.text(); self.current_loss["params"]["margin"] = self.py_lo11_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[12]): self.current_loss["params"]["weight"] = self.py_lo12_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo12_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[13]): self.current_loss["params"]["weight"] = self.py_lo13_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo13_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[14]): self.current_loss["params"]["weight"] = self.py_lo14_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo14_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[15]): self.current_loss["params"]["weight"] = self.py_lo15_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo15_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; wr = ""; wr = json.dumps(self.system["update"]["losses"]["value"], indent=4) self.tb1.setText(wr); def clear_loss(self): self.system["update"]["losses"]["value"] = ""; self.system["update"]["losses"]["active"] = False; wr = ""; self.tb1.setText(wr); def forward(self): with open('base_classification.json', 'w') as outfile: json.dump(self.system, outfile) self.forward_train.emit(); def backward(self): with open('base_classification.json', 'w') as outfile: json.dump(self.system, outfile) self.backward_scheduler_param.emit(); ''' app = QApplication(sys.argv) screen = WindowClassificationTrainUpdateLossParam() screen.show() sys.exit(app.exec_()) '''

classification/training/update/WindowClassificationTrainUpdateLossParam.py

import os import sys import json from PyQt5 import QtCore, QtWidgets from PyQt5.QtWidgets import * from PyQt5.QtGui import * class WindowClassificationTrainUpdateLossParam(QtWidgets.QWidget): forward_train = QtCore.pyqtSignal(); backward_scheduler_param = QtCore.pyqtSignal(); def __init__(self): super().__init__() self.cfg_setup() self.title = 'Experiment {} - Update Loss Params'.format(self.system["experiment"]) self.left = 10 self.top = 10 self.width = 900 self.height = 600 self.loss_ui_mxnet = []; self.loss_ui_keras = []; self.loss_ui_pytorch = []; self.current_loss_ = {}; self.current_loss_["name"] = ""; self.current_loss_["params"] = {}; self.initUI() def cfg_setup(self): with open('base_classification.json') as json_file: self.system = json.load(json_file) def initUI(self): self.setWindowTitle(self.title) self.setGeometry(self.left, self.top, self.width, self.height); # Backward self.b1 = QPushButton('Back', self) self.b1.move(600,550) self.b1.clicked.connect(self.backward) # Forward self.b2 = QPushButton('Next', self) self.b2.move(700,550) self.b2.clicked.connect(self.forward) # Quit self.b3 = QPushButton('Quit', self) self.b3.move(800,550) self.b3.clicked.connect(self.close) self.cb1 = QComboBox(self); self.cb1.move(20, 20); self.cb1.activated.connect(self.select_loss); self.cb2 = QComboBox(self); self.cb2.move(20, 20); self.cb2.activated.connect(self.select_loss); self.cb3 = QComboBox(self); self.cb3.move(20, 20); self.cb3.activated.connect(self.select_loss); self.mxnet_losses_list = ["select", "loss_l1", "loss_l2", "loss_softmax_crossentropy", "loss_crossentropy", "loss_sigmoid_binary_crossentropy", "loss_binary_crossentropy", "loss_kldiv", "loss_poisson_nll", "loss_huber", "loss_hinge", "loss_squared_hinge"]; self.keras_losses_list = ["select", "loss_l1", "loss_l2", "loss_crossentropy", "loss_binary_crossentropy", "loss_kldiv", "loss_hinge", "loss_squared_hinge"]; self.pytorch_losses_list = ["select", "loss_l1", "loss_l2", "loss_softmax_crossentropy", "loss_crossentropy", "loss_sigmoid_binary_crossentropy", "loss_binary_crossentropy", "loss_kldiv", "loss_poisson_nll", "loss_huber", "loss_hinge", "loss_squared_hinge", "loss_multimargin", "loss_squared_multimargin", "loss_multilabel_margin", "loss_multilabel_softmargin"]; if(self.system["backend"] == "Mxnet-1.5.1"): self.cb1.addItems(self.mxnet_losses_list); self.cb1.show(); self.cb2.hide(); self.cb3.hide(); elif(self.system["backend"] == "Keras-2.2.5_Tensorflow-1"): self.cb2.addItems(self.keras_losses_list); self.cb2.show(); self.cb1.hide(); self.cb3.hide(); elif(self.system["backend"] == "Pytorch-1.3.1"): self.cb3.addItems(self.pytorch_losses_list); self.cb3.show(); self.cb1.hide(); self.cb2.hide(); tmp = []; self.mx_lo1_l1 = QLabel(self); self.mx_lo1_l1.setText("1. Scalar Weight: "); self.mx_lo1_l1.move(20, 100); tmp.append(self.mx_lo1_l1); self.mx_lo1_e1 = QLineEdit(self) self.mx_lo1_e1.move(150, 100); self.mx_lo1_e1.setText("1.0"); tmp.append(self.mx_lo1_e1); self.mx_lo1_l2 = QLabel(self); self.mx_lo1_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo1_l2.move(20, 150); tmp.append(self.mx_lo1_l2); self.mx_lo1_e2 = QLineEdit(self) self.mx_lo1_e2.move(290, 150); self.mx_lo1_e2.setText("0"); tmp.append(self.mx_lo1_e2); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo2_l1 = QLabel(self); self.mx_lo2_l1.setText("1. Scalar Weight: "); self.mx_lo2_l1.move(20, 100); tmp.append(self.mx_lo2_l1); self.mx_lo2_e1 = QLineEdit(self) self.mx_lo2_e1.move(150, 100); self.mx_lo2_e1.setText("1.0"); tmp.append(self.mx_lo2_e1); self.mx_lo2_l2 = QLabel(self); self.mx_lo2_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo2_l2.move(20, 150); tmp.append(self.mx_lo2_l2); self.mx_lo2_e2 = QLineEdit(self) self.mx_lo2_e2.move(290, 150); self.mx_lo2_e2.setText("0"); tmp.append(self.mx_lo2_e2); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo3_l1 = QLabel(self); self.mx_lo3_l1.setText("1. Scalar Weight: "); self.mx_lo3_l1.move(20, 100); tmp.append(self.mx_lo3_l1); self.mx_lo3_e1 = QLineEdit(self) self.mx_lo3_e1.move(150, 100); self.mx_lo3_e1.setText("1.0"); tmp.append(self.mx_lo3_e1); self.mx_lo3_l2 = QLabel(self); self.mx_lo3_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo3_l2.move(20, 150); tmp.append(self.mx_lo3_l2); self.mx_lo3_e2 = QLineEdit(self) self.mx_lo3_e2.move(290, 150); self.mx_lo3_e2.setText("0"); tmp.append(self.mx_lo3_e2); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo4_l1 = QLabel(self); self.mx_lo4_l1.setText("1. Scalar Weight: "); self.mx_lo4_l1.move(20, 100); tmp.append(self.mx_lo4_l1); self.mx_lo4_e1 = QLineEdit(self) self.mx_lo4_e1.move(150, 100); self.mx_lo4_e1.setText("1.0"); tmp.append(self.mx_lo4_e1); self.mx_lo4_l2 = QLabel(self); self.mx_lo4_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo4_l2.move(20, 150); tmp.append(self.mx_lo4_l2); self.mx_lo4_e2 = QLineEdit(self) self.mx_lo4_e2.move(290, 150); self.mx_lo4_e2.setText("0"); tmp.append(self.mx_lo4_e2); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo5_l1 = QLabel(self); self.mx_lo5_l1.setText("1. Scalar Weight: "); self.mx_lo5_l1.move(20, 100); tmp.append(self.mx_lo5_l1); self.mx_lo5_e1 = QLineEdit(self) self.mx_lo5_e1.move(150, 100); self.mx_lo5_e1.setText("1.0"); tmp.append(self.mx_lo5_e1); self.mx_lo5_l2 = QLabel(self); self.mx_lo5_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo5_l2.move(20, 150); tmp.append(self.mx_lo5_l2); self.mx_lo5_e2 = QLineEdit(self) self.mx_lo5_e2.move(290, 150); self.mx_lo5_e2.setText("0"); tmp.append(self.mx_lo5_e2); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo6_l1 = QLabel(self); self.mx_lo6_l1.setText("1. Scalar Weight: "); self.mx_lo6_l1.move(20, 100); tmp.append(self.mx_lo6_l1); self.mx_lo6_e1 = QLineEdit(self) self.mx_lo6_e1.move(150, 100); self.mx_lo6_e1.setText("1.0"); tmp.append(self.mx_lo6_e1); self.mx_lo6_l2 = QLabel(self); self.mx_lo6_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo6_l2.move(20, 150); tmp.append(self.mx_lo6_l2); self.mx_lo6_e2 = QLineEdit(self) self.mx_lo6_e2.move(290, 150); self.mx_lo6_e2.setText("0"); tmp.append(self.mx_lo6_e2); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo7_l1 = QLabel(self); self.mx_lo7_l1.setText("1. Scalar Weight: "); self.mx_lo7_l1.move(20, 100); tmp.append(self.mx_lo7_l1); self.mx_lo7_e1 = QLineEdit(self) self.mx_lo7_e1.move(150, 100); self.mx_lo7_e1.setText("1.0"); tmp.append(self.mx_lo7_e1); self.mx_lo7_l2 = QLabel(self); self.mx_lo7_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo7_l2.move(20, 150); tmp.append(self.mx_lo7_l2); self.mx_lo7_e2 = QLineEdit(self) self.mx_lo7_e2.move(290, 150); self.mx_lo7_e2.setText("0"); tmp.append(self.mx_lo7_e2); self.mx_lo7_l3 = QLabel(self); self.mx_lo7_l3.setText("3. Input has log pre-applied: "); self.mx_lo7_l3.move(20, 200); tmp.append(self.mx_lo7_l3); self.mx_lo7_cb3 = QComboBox(self); self.mx_lo7_cb3.move(290, 200); self.mx_lo7_cb3.addItems(["No", "Yes"]); tmp.append(self.mx_lo7_cb3); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo8_l1 = QLabel(self); self.mx_lo8_l1.setText("1. Scalar Weight: "); self.mx_lo8_l1.move(20, 100); tmp.append(self.mx_lo8_l1); self.mx_lo8_e1 = QLineEdit(self) self.mx_lo8_e1.move(150, 100); self.mx_lo8_e1.setText("1.0"); tmp.append(self.mx_lo8_e1); self.mx_lo8_l2 = QLabel(self); self.mx_lo8_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo8_l2.move(20, 150); tmp.append(self.mx_lo8_l2); self.mx_lo8_e2 = QLineEdit(self) self.mx_lo8_e2.move(290, 150); self.mx_lo8_e2.setText("0"); tmp.append(self.mx_lo8_e2); self.mx_lo8_l3 = QLabel(self); self.mx_lo8_l3.setText("3. Input has log pre-applied: "); self.mx_lo8_l3.move(20, 200); tmp.append(self.mx_lo8_l3); self.mx_lo8_cb3 = QComboBox(self); self.mx_lo8_cb3.move(290, 200); self.mx_lo8_cb3.addItems(["No", "Yes"]); tmp.append(self.mx_lo8_cb3); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo9_l1 = QLabel(self); self.mx_lo9_l1.setText("1. Scalar Weight: "); self.mx_lo9_l1.move(20, 100); tmp.append(self.mx_lo9_l1); self.mx_lo9_e1 = QLineEdit(self) self.mx_lo9_e1.move(150, 100); self.mx_lo9_e1.setText("1.0"); tmp.append(self.mx_lo9_e1); self.mx_lo9_l2 = QLabel(self); self.mx_lo9_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo9_l2.move(20, 150); tmp.append(self.mx_lo9_l2); self.mx_lo9_e2 = QLineEdit(self) self.mx_lo9_e2.move(290, 150); self.mx_lo9_e2.setText("0"); tmp.append(self.mx_lo9_e2); self.mx_lo9_l3 = QLabel(self); self.mx_lo9_l3.setText("3. Threshold for mean estimator: "); self.mx_lo9_l3.move(20, 200); tmp.append(self.mx_lo9_l3); self.mx_lo9_e3 = QLineEdit(self) self.mx_lo9_e3.move(290, 200); self.mx_lo9_e3.setText("1.0"); tmp.append(self.mx_lo9_e3); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo10_l1 = QLabel(self); self.mx_lo10_l1.setText("1. Scalar Weight: "); self.mx_lo10_l1.move(20, 100); tmp.append(self.mx_lo10_l1); self.mx_lo10_e1 = QLineEdit(self) self.mx_lo10_e1.move(150, 100); self.mx_lo10_e1.setText("1.0"); tmp.append(self.mx_lo10_e1); self.mx_lo10_l2 = QLabel(self); self.mx_lo10_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo10_l2.move(20, 150); tmp.append(self.mx_lo10_l2); self.mx_lo10_e2 = QLineEdit(self) self.mx_lo10_e2.move(290, 150); self.mx_lo10_e2.setText("0"); tmp.append(self.mx_lo10_e2); self.mx_lo10_l3 = QLabel(self); self.mx_lo10_l3.setText("3. Margin: "); self.mx_lo10_l3.move(20, 200); tmp.append(self.mx_lo10_l3); self.mx_lo10_e3 = QLineEdit(self) self.mx_lo10_e3.move(150, 200); self.mx_lo10_e3.setText("1.0"); tmp.append(self.mx_lo10_e3); self.loss_ui_mxnet.append(tmp) tmp = []; self.mx_lo11_l1 = QLabel(self); self.mx_lo11_l1.setText("1. Scalar Weight: "); self.mx_lo11_l1.move(20, 100); tmp.append(self.mx_lo11_l1); self.mx_lo11_e1 = QLineEdit(self) self.mx_lo11_e1.move(150, 100); self.mx_lo11_e1.setText("1.0"); tmp.append(self.mx_lo11_e1); self.mx_lo11_l2 = QLabel(self); self.mx_lo11_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.mx_lo11_l2.move(20, 150); tmp.append(self.mx_lo11_l2); self.mx_lo11_e2 = QLineEdit(self) self.mx_lo11_e2.move(290, 150); self.mx_lo11_e2.setText("0"); tmp.append(self.mx_lo11_e2); self.mx_lo11_l3 = QLabel(self); self.mx_lo11_l3.setText("3. Margin: "); self.mx_lo11_l3.move(20, 200); tmp.append(self.mx_lo11_l3); self.mx_lo11_e3 = QLineEdit(self) self.mx_lo11_e3.move(150, 200); self.mx_lo11_e3.setText("1.0"); tmp.append(self.mx_lo11_e3); self.loss_ui_mxnet.append(tmp) tmp = []; self.ke_lo1_l1 = QLabel(self); self.ke_lo1_l1.setText("1. Scalar Weight: "); self.ke_lo1_l1.move(20, 100); tmp.append(self.ke_lo1_l1); self.ke_lo1_e1 = QLineEdit(self) self.ke_lo1_e1.move(150, 100); self.ke_lo1_e1.setText("1.0"); tmp.append(self.ke_lo1_e1); self.ke_lo1_l2 = QLabel(self); self.ke_lo1_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.ke_lo1_l2.move(20, 150); tmp.append(self.ke_lo1_l2); self.ke_lo1_e2 = QLineEdit(self) self.ke_lo1_e2.move(290, 150); self.ke_lo1_e2.setText("0"); tmp.append(self.ke_lo1_e2); self.loss_ui_keras.append(tmp) tmp = []; self.ke_lo2_l1 = QLabel(self); self.ke_lo2_l1.setText("1. Scalar Weight: "); self.ke_lo2_l1.move(20, 100); tmp.append(self.ke_lo2_l1); self.ke_lo2_e1 = QLineEdit(self) self.ke_lo2_e1.move(150, 100); self.ke_lo2_e1.setText("1.0"); tmp.append(self.ke_lo2_e1); self.ke_lo2_l2 = QLabel(self); self.ke_lo2_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.ke_lo2_l2.move(20, 150); tmp.append(self.ke_lo2_l2); self.ke_lo2_e2 = QLineEdit(self) self.ke_lo2_e2.move(290, 150); self.ke_lo2_e2.setText("0"); tmp.append(self.ke_lo2_e2); self.loss_ui_keras.append(tmp) tmp = []; self.ke_lo3_l1 = QLabel(self); self.ke_lo3_l1.setText("1. Scalar Weight: "); self.ke_lo3_l1.move(20, 100); tmp.append(self.ke_lo3_l1); self.ke_lo3_e1 = QLineEdit(self) self.ke_lo3_e1.move(150, 100); self.ke_lo3_e1.setText("1.0"); tmp.append(self.ke_lo3_e1); self.ke_lo3_l2 = QLabel(self); self.ke_lo3_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.ke_lo3_l2.move(20, 150); tmp.append(self.ke_lo3_l2); self.ke_lo3_e2 = QLineEdit(self) self.ke_lo3_e2.move(290, 150); self.ke_lo3_e2.setText("0"); tmp.append(self.ke_lo3_e2); self.loss_ui_keras.append(tmp) tmp = []; self.ke_lo4_l1 = QLabel(self); self.ke_lo4_l1.setText("1. Scalar Weight: "); self.ke_lo4_l1.move(20, 100); tmp.append(self.ke_lo4_l1); self.ke_lo4_e1 = QLineEdit(self) self.ke_lo4_e1.move(150, 100); self.ke_lo4_e1.setText("1.0"); tmp.append(self.ke_lo4_e1); self.ke_lo4_l2 = QLabel(self); self.ke_lo4_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.ke_lo4_l2.move(20, 150); tmp.append(self.ke_lo4_l2); self.ke_lo4_e2 = QLineEdit(self) self.ke_lo4_e2.move(290, 150); self.ke_lo4_e2.setText("0"); tmp.append(self.ke_lo4_e2); self.loss_ui_keras.append(tmp) tmp = []; self.ke_lo5_l1 = QLabel(self); self.ke_lo5_l1.setText("1. Scalar Weight: "); self.ke_lo5_l1.move(20, 100); tmp.append(self.ke_lo5_l1); self.ke_lo5_e1 = QLineEdit(self) self.ke_lo5_e1.move(150, 100); self.ke_lo5_e1.setText("1.0"); tmp.append(self.ke_lo5_e1); self.ke_lo5_l2 = QLabel(self); self.ke_lo5_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.ke_lo5_l2.move(20, 150); tmp.append(self.ke_lo5_l2); self.ke_lo5_e2 = QLineEdit(self) self.ke_lo5_e2.move(290, 150); self.ke_lo5_e2.setText("0"); tmp.append(self.ke_lo5_e2); self.ke_lo5_l3 = QLabel(self); self.ke_lo5_l3.setText("3. Input has log pre-applied: "); self.ke_lo5_l3.move(20, 200); tmp.append(self.ke_lo5_l3); self.ke_lo5_cb3 = QComboBox(self); self.ke_lo5_cb3.move(290, 200); self.ke_lo5_cb3.addItems(["No", "Yes"]); tmp.append(self.ke_lo5_cb3); self.loss_ui_keras.append(tmp) tmp = []; self.ke_lo6_l1 = QLabel(self); self.ke_lo6_l1.setText("1. Scalar Weight: "); self.ke_lo6_l1.move(20, 100); tmp.append(self.ke_lo6_l1); self.ke_lo6_e1 = QLineEdit(self) self.ke_lo6_e1.move(150, 100); self.ke_lo6_e1.setText("1.0"); tmp.append(self.ke_lo6_e1); self.ke_lo6_l2 = QLabel(self); self.ke_lo6_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.ke_lo6_l2.move(20, 150); tmp.append(self.ke_lo6_l2); self.ke_lo6_e2 = QLineEdit(self) self.ke_lo6_e2.move(290, 150); self.ke_lo6_e2.setText("0"); tmp.append(self.ke_lo6_e2); self.ke_lo6_l3 = QLabel(self); self.ke_lo6_l3.setText("3. Margin: "); self.ke_lo6_l3.move(20, 200); tmp.append(self.ke_lo6_l3); self.ke_lo6_e3 = QLineEdit(self) self.ke_lo6_e3.move(150, 200); self.ke_lo6_e3.setText("1.0"); tmp.append(self.ke_lo6_e3); self.loss_ui_keras.append(tmp) tmp = []; self.ke_lo7_l1 = QLabel(self); self.ke_lo7_l1.setText("1. Scalar Weight: "); self.ke_lo7_l1.move(20, 100); tmp.append(self.ke_lo7_l1); self.ke_lo7_e1 = QLineEdit(self) self.ke_lo7_e1.move(150, 100); self.ke_lo7_e1.setText("1.0"); tmp.append(self.ke_lo7_e1); self.ke_lo7_l2 = QLabel(self); self.ke_lo7_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.ke_lo7_l2.move(20, 150); tmp.append(self.ke_lo7_l2); self.ke_lo7_e2 = QLineEdit(self) self.ke_lo7_e2.move(290, 150); self.ke_lo7_e2.setText("0"); tmp.append(self.ke_lo7_e2); self.ke_lo7_l3 = QLabel(self); self.ke_lo7_l3.setText("3. Margin: "); self.ke_lo7_l3.move(20, 200); tmp.append(self.ke_lo7_l3); self.ke_lo7_e3 = QLineEdit(self) self.ke_lo7_e3.move(150, 200); self.ke_lo7_e3.setText("1.0"); tmp.append(self.ke_lo7_e3); self.loss_ui_keras.append(tmp) tmp = []; self.py_lo1_l1 = QLabel(self); self.py_lo1_l1.setText("1. Scalar Weight: "); self.py_lo1_l1.move(20, 100); tmp.append(self.py_lo1_l1); self.py_lo1_e1 = QLineEdit(self) self.py_lo1_e1.move(150, 100); self.py_lo1_e1.setText("1.0"); tmp.append(self.py_lo1_e1); self.py_lo1_l2 = QLabel(self); self.py_lo1_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo1_l2.move(20, 150); tmp.append(self.py_lo1_l2); self.py_lo1_e2 = QLineEdit(self) self.py_lo1_e2.move(290, 150); self.py_lo1_e2.setText("0"); tmp.append(self.py_lo1_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo2_l1 = QLabel(self); self.py_lo2_l1.setText("1. Scalar Weight: "); self.py_lo2_l1.move(20, 100); tmp.append(self.py_lo2_l1); self.py_lo2_e1 = QLineEdit(self) self.py_lo2_e1.move(150, 100); self.py_lo2_e1.setText("1.0"); tmp.append(self.py_lo2_e1); self.py_lo2_l2 = QLabel(self); self.py_lo2_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo2_l2.move(20, 150); tmp.append(self.py_lo2_l2); self.py_lo2_e2 = QLineEdit(self) self.py_lo2_e2.move(290, 150); self.py_lo2_e2.setText("0"); tmp.append(self.py_lo2_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo3_l1 = QLabel(self); self.py_lo3_l1.setText("1. Scalar Weight: "); self.py_lo3_l1.move(20, 100); tmp.append(self.py_lo3_l1); self.py_lo3_e1 = QLineEdit(self) self.py_lo3_e1.move(150, 100); self.py_lo3_e1.setText("1.0"); tmp.append(self.py_lo3_e1); self.py_lo3_l2 = QLabel(self); self.py_lo3_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo3_l2.move(20, 150); tmp.append(self.py_lo3_l2); self.py_lo3_e2 = QLineEdit(self) self.py_lo3_e2.move(290, 150); self.py_lo3_e2.setText("0"); tmp.append(self.py_lo3_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo4_l1 = QLabel(self); self.py_lo4_l1.setText("1. Scalar Weight: "); self.py_lo4_l1.move(20, 100); tmp.append(self.py_lo4_l1); self.py_lo4_e1 = QLineEdit(self) self.py_lo4_e1.move(150, 100); self.py_lo4_e1.setText("1.0"); tmp.append(self.py_lo4_e1); self.py_lo4_l2 = QLabel(self); self.py_lo4_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo4_l2.move(20, 150); tmp.append(self.py_lo4_l2); self.py_lo4_e2 = QLineEdit(self) self.py_lo4_e2.move(290, 150); self.py_lo4_e2.setText("0"); tmp.append(self.py_lo4_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo5_l1 = QLabel(self); self.py_lo5_l1.setText("1. Scalar Weight: "); self.py_lo5_l1.move(20, 100); tmp.append(self.py_lo5_l1); self.py_lo5_e1 = QLineEdit(self) self.py_lo5_e1.move(150, 100); self.py_lo5_e1.setText("1.0"); tmp.append(self.py_lo5_e1); self.py_lo5_l2 = QLabel(self); self.py_lo5_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo5_l2.move(20, 150); tmp.append(self.py_lo5_l2); self.py_lo5_e2 = QLineEdit(self) self.py_lo5_e2.move(290, 150); self.py_lo5_e2.setText("0"); tmp.append(self.py_lo5_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo6_l1 = QLabel(self); self.py_lo6_l1.setText("1. Scalar Weight: "); self.py_lo6_l1.move(20, 100); tmp.append(self.py_lo6_l1); self.py_lo6_e1 = QLineEdit(self) self.py_lo6_e1.move(150, 100); self.py_lo6_e1.setText("1.0"); tmp.append(self.py_lo6_e1); self.py_lo6_l2 = QLabel(self); self.py_lo6_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo6_l2.move(20, 150); tmp.append(self.py_lo6_l2); self.py_lo6_e2 = QLineEdit(self) self.py_lo6_e2.move(290, 150); self.py_lo6_e2.setText("0"); tmp.append(self.py_lo6_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo7_l1 = QLabel(self); self.py_lo7_l1.setText("1. Scalar Weight: "); self.py_lo7_l1.move(20, 100); tmp.append(self.py_lo7_l1); self.py_lo7_e1 = QLineEdit(self) self.py_lo7_e1.move(150, 100); self.py_lo7_e1.setText("1.0"); tmp.append(self.py_lo7_e1); self.py_lo7_l2 = QLabel(self); self.py_lo7_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo7_l2.move(20, 150); tmp.append(self.py_lo7_l2); self.py_lo7_e2 = QLineEdit(self) self.py_lo7_e2.move(290, 150); self.py_lo7_e2.setText("0"); tmp.append(self.py_lo7_e2); self.py_lo7_l3 = QLabel(self); self.py_lo7_l3.setText("3. Input has log pre-applied: "); self.py_lo7_l3.move(20, 200); tmp.append(self.py_lo7_l3); self.py_lo7_cb3 = QComboBox(self); self.py_lo7_cb3.move(290, 200); self.py_lo7_cb3.addItems(["No", "Yes"]); tmp.append(self.py_lo7_cb3); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo8_l1 = QLabel(self); self.py_lo8_l1.setText("1. Scalar Weight: "); self.py_lo8_l1.move(20, 100); tmp.append(self.py_lo8_l1); self.py_lo8_e1 = QLineEdit(self) self.py_lo8_e1.move(150, 100); self.py_lo8_e1.setText("1.0"); tmp.append(self.py_lo8_e1); self.py_lo8_l2 = QLabel(self); self.py_lo8_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo8_l2.move(20, 150); tmp.append(self.py_lo8_l2); self.py_lo8_e2 = QLineEdit(self) self.py_lo8_e2.move(290, 150); self.py_lo8_e2.setText("0"); tmp.append(self.py_lo8_e2); self.py_lo8_l3 = QLabel(self); self.py_lo8_l3.setText("3. Input has log pre-applied: "); self.py_lo8_l3.move(20, 200); tmp.append(self.py_lo8_l3); self.py_lo8_cb3 = QComboBox(self); self.py_lo8_cb3.move(290, 200); self.py_lo8_cb3.addItems(["No", "Yes"]); tmp.append(self.py_lo8_cb3); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo9_l1 = QLabel(self); self.py_lo9_l1.setText("1. Scalar Weight: "); self.py_lo9_l1.move(20, 100); tmp.append(self.py_lo9_l1); self.py_lo9_e1 = QLineEdit(self) self.py_lo9_e1.move(150, 100); self.py_lo9_e1.setText("1.0"); tmp.append(self.py_lo9_e1); self.py_lo9_l2 = QLabel(self); self.py_lo9_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo9_l2.move(20, 150); tmp.append(self.py_lo9_l2); self.py_lo9_e2 = QLineEdit(self) self.py_lo9_e2.move(290, 150); self.py_lo9_e2.setText("0"); tmp.append(self.py_lo9_e2); self.py_lo9_l3 = QLabel(self); self.py_lo9_l3.setText("3. Threshold for mean estimator: "); self.py_lo9_l3.move(20, 200); tmp.append(self.py_lo9_l3); self.py_lo9_e3 = QLineEdit(self) self.py_lo9_e3.move(290, 200); self.py_lo9_e3.setText("1.0"); tmp.append(self.py_lo9_e3); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo10_l1 = QLabel(self); self.py_lo10_l1.setText("1. Scalar Weight: "); self.py_lo10_l1.move(20, 100); tmp.append(self.py_lo10_l1); self.py_lo10_e1 = QLineEdit(self) self.py_lo10_e1.move(150, 100); self.py_lo10_e1.setText("1.0"); tmp.append(self.py_lo10_e1); self.py_lo10_l2 = QLabel(self); self.py_lo10_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo10_l2.move(20, 150); tmp.append(self.py_lo10_l2); self.py_lo10_e2 = QLineEdit(self) self.py_lo10_e2.move(290, 150); self.py_lo10_e2.setText("0"); tmp.append(self.py_lo10_e2); self.py_lo10_l3 = QLabel(self); self.py_lo10_l3.setText("3. Margin: "); self.py_lo10_l3.move(20, 200); tmp.append(self.py_lo10_l3); self.py_lo10_e3 = QLineEdit(self) self.py_lo10_e3.move(150, 200); self.py_lo10_e3.setText("1.0"); tmp.append(self.py_lo10_e3); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo11_l1 = QLabel(self); self.py_lo11_l1.setText("1. Scalar Weight: "); self.py_lo11_l1.move(20, 100); tmp.append(self.py_lo11_l1); self.py_lo11_e1 = QLineEdit(self) self.py_lo11_e1.move(150, 100); self.py_lo11_e1.setText("1.0"); tmp.append(self.py_lo11_e1); self.py_lo11_l2 = QLabel(self); self.py_lo11_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo11_l2.move(20, 150); tmp.append(self.py_lo11_l2); self.py_lo11_e2 = QLineEdit(self) self.py_lo11_e2.move(290, 150); self.py_lo11_e2.setText("0"); tmp.append(self.py_lo11_e2); self.py_lo11_l3 = QLabel(self); self.py_lo11_l3.setText("3. Margin: "); self.py_lo11_l3.move(20, 200); tmp.append(self.py_lo11_l3); self.py_lo11_e3 = QLineEdit(self) self.py_lo11_e3.move(150, 200); self.py_lo11_e3.setText("1.0"); tmp.append(self.py_lo11_e3); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo12_l1 = QLabel(self); self.py_lo12_l1.setText("1. Scalar Weight: "); self.py_lo12_l1.move(20, 100); tmp.append(self.py_lo12_l1); self.py_lo12_e1 = QLineEdit(self) self.py_lo12_e1.move(150, 100); self.py_lo12_e1.setText("1.0"); tmp.append(self.py_lo12_e1); self.py_lo12_l2 = QLabel(self); self.py_lo12_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo12_l2.move(20, 150); tmp.append(self.py_lo12_l2); self.py_lo12_e2 = QLineEdit(self) self.py_lo12_e2.move(290, 150); self.py_lo12_e2.setText("0"); tmp.append(self.py_lo12_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo13_l1 = QLabel(self); self.py_lo13_l1.setText("1. Scalar Weight: "); self.py_lo13_l1.move(20, 100); tmp.append(self.py_lo13_l1); self.py_lo13_e1 = QLineEdit(self) self.py_lo13_e1.move(150, 100); self.py_lo13_e1.setText("1.0"); tmp.append(self.py_lo13_e1); self.py_lo13_l2 = QLabel(self); self.py_lo13_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo13_l2.move(20, 150); tmp.append(self.py_lo13_l2); self.py_lo13_e2 = QLineEdit(self) self.py_lo13_e2.move(290, 150); self.py_lo13_e2.setText("0"); tmp.append(self.py_lo13_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo14_l1 = QLabel(self); self.py_lo14_l1.setText("1. Scalar Weight: "); self.py_lo14_l1.move(20, 100); tmp.append(self.py_lo14_l1); self.py_lo14_e1 = QLineEdit(self) self.py_lo14_e1.move(150, 100); self.py_lo14_e1.setText("1.0"); tmp.append(self.py_lo14_e1); self.py_lo14_l2 = QLabel(self); self.py_lo14_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo14_l2.move(20, 150); tmp.append(self.py_lo14_l2); self.py_lo14_e2 = QLineEdit(self) self.py_lo14_e2.move(290, 150); self.py_lo14_e2.setText("0"); tmp.append(self.py_lo14_e2); self.loss_ui_pytorch.append(tmp) tmp = []; self.py_lo15_l1 = QLabel(self); self.py_lo15_l1.setText("1. Scalar Weight: "); self.py_lo15_l1.move(20, 100); tmp.append(self.py_lo15_l1); self.py_lo15_e1 = QLineEdit(self) self.py_lo15_e1.move(150, 100); self.py_lo15_e1.setText("1.0"); tmp.append(self.py_lo15_e1); self.py_lo15_l2 = QLabel(self); self.py_lo15_l2.setText("2. Batch Axis (0, 1, 2, 3): "); self.py_lo15_l2.move(20, 150); tmp.append(self.py_lo15_l2); self.py_lo15_e2 = QLineEdit(self) self.py_lo15_e2.move(290, 150); self.py_lo15_e2.setText("0"); tmp.append(self.py_lo15_e2); self.loss_ui_pytorch.append(tmp) self.select_loss(); self.tb1 = QTextEdit(self) self.tb1.move(550, 20) self.tb1.resize(300, 500) if(self.system["update"]["losses"]["active"]): wr = ""; wr = json.dumps(self.system["update"]["losses"]["value"], indent=4) self.tb1.setText(wr); else: self.tb1.setText("Using Default loss.") self.b4 = QPushButton('Select loss', self) self.b4.move(400,400) self.b4.clicked.connect(self.add_loss) self.b6 = QPushButton('Clear ', self) self.b6.move(400,500) self.b6.clicked.connect(self.clear_loss) def select_loss(self): self.current_loss = {}; self.current_loss["name"] = ""; self.current_loss["params"] = {}; if(self.system["backend"] == "Mxnet-1.5.1"): self.current_loss["name"] = self.cb1.currentText(); index = self.mxnet_losses_list.index(self.cb1.currentText()); for i in range(len(self.loss_ui_mxnet)): for j in range(len(self.loss_ui_mxnet[i])): if((index-1)==i): self.loss_ui_mxnet[i][j].show(); else: self.loss_ui_mxnet[i][j].hide(); for i in range(len(self.loss_ui_keras)): for j in range(len(self.loss_ui_keras[i])): self.loss_ui_keras[i][j].hide(); for i in range(len(self.loss_ui_pytorch)): for j in range(len(self.loss_ui_pytorch[i])): self.loss_ui_pytorch[i][j].hide(); elif(self.system["backend"] == "Keras-2.2.5_Tensorflow-1"): self.current_loss["name"] = self.cb2.currentText(); index = self.keras_losses_list.index(self.cb2.currentText()); for i in range(len(self.loss_ui_keras)): for j in range(len(self.loss_ui_keras[i])): if((index-1)==i): self.loss_ui_keras[i][j].show(); else: self.loss_ui_keras[i][j].hide(); for i in range(len(self.loss_ui_mxnet)): for j in range(len(self.loss_ui_mxnet[i])): self.loss_ui_mxnet[i][j].hide(); for i in range(len(self.loss_ui_pytorch)): for j in range(len(self.loss_ui_pytorch[i])): self.loss_ui_pytorch[i][j].hide(); elif(self.system["backend"] == "Pytorch-1.3.1"): self.current_loss["name"] = self.cb3.currentText(); index = self.pytorch_losses_list.index(self.cb3.currentText()); for i in range(len(self.loss_ui_pytorch)): for j in range(len(self.loss_ui_pytorch[i])): if((index-1)==i): self.loss_ui_pytorch[i][j].show(); else: self.loss_ui_pytorch[i][j].hide(); for i in range(len(self.loss_ui_keras)): for j in range(len(self.loss_ui_keras[i])): self.loss_ui_keras[i][j].hide(); for i in range(len(self.loss_ui_mxnet)): for j in range(len(self.loss_ui_mxnet[i])): self.loss_ui_mxnet[i][j].hide(); def add_loss(self): self.system["update"]["losses"]["active"] = True; if(self.system["backend"] == "Mxnet-1.5.1"): if(self.current_loss["name"] == self.mxnet_losses_list[1]): self.current_loss["params"]["weight"] = self.mx_lo1_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo1_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[2]): self.current_loss["params"]["weight"] = self.mx_lo2_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo2_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[3]): self.current_loss["params"]["weight"] = self.mx_lo3_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo3_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[4]): self.current_loss["params"]["weight"] = self.mx_lo4_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo4_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[5]): self.current_loss["params"]["weight"] = self.mx_lo5_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo5_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[6]): self.current_loss["params"]["weight"] = self.mx_lo6_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo6_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[7]): self.current_loss["params"]["weight"] = self.mx_lo7_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo7_e2.text(); self.current_loss["params"]["log_pre_applied"] = self.mx_lo7_cb3.currentText(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[8]): self.current_loss["params"]["weight"] = self.mx_lo8_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo8_e2.text(); self.current_loss["params"]["log_pre_applied"] = self.mx_lo8_cb3.currentText(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[9]): self.current_loss["params"]["weight"] = self.mx_lo9_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo9_e2.text(); self.current_loss["params"]["threshold_for_mean_estimator"] = self.mx_lo9_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[10]): self.current_loss["params"]["weight"] = self.mx_lo10_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo10_e2.text(); self.current_loss["params"]["margin"] = self.mx_lo10_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.mxnet_losses_list[11]): self.current_loss["params"]["weight"] = self.mx_lo11_e1.text(); self.current_loss["params"]["batch_axis"] = self.mx_lo11_e2.text(); self.current_loss["params"]["margin"] = self.mx_lo11_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.system["backend"] == "Keras-2.2.5_Tensorflow-1"): if(self.current_loss["name"] == self.keras_losses_list[1]): self.current_loss["params"]["weight"] = self.ke_lo1_e1.text(); self.current_loss["params"]["batch_axis"] = self.ke_lo1_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.keras_losses_list[2]): self.current_loss["params"]["weight"] = self.ke_lo2_e1.text(); self.current_loss["params"]["batch_axis"] = self.ke_lo2_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.keras_losses_list[3]): self.current_loss["params"]["weight"] = self.ke_lo3_e1.text(); self.current_loss["params"]["batch_axis"] = self.ke_lo3_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.keras_losses_list[4]): self.current_loss["params"]["weight"] = self.ke_lo4_e1.text(); self.current_loss["params"]["batch_axis"] = self.ke_lo4_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.keras_losses_list[5]): self.current_loss["params"]["weight"] = self.ke_lo5_e1.text(); self.current_loss["params"]["batch_axis"] = self.ke_lo5_e2.text(); self.current_loss["params"]["log_pre_applied"] = self.ke_lo5_cb3.currentText(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.keras_losses_list[6]): self.current_loss["params"]["weight"] = self.ke_lo6_e1.text(); self.current_loss["params"]["batch_axis"] = self.ke_lo6_e2.text(); self.current_loss["params"]["margin"] = self.ke_lo6_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.keras_losses_list[7]): self.current_loss["params"]["weight"] = self.ke_lo7_e1.text(); self.current_loss["params"]["batch_axis"] = self.ke_lo7_e2.text(); self.current_loss["params"]["margin"] = self.ke_lo7_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.system["backend"] == "Pytorch-1.3.1"): if(self.current_loss["name"] == self.pytorch_losses_list[1]): self.current_loss["params"]["weight"] = self.py_lo1_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo1_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[2]): self.current_loss["params"]["weight"] = self.py_lo2_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo2_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[3]): self.current_loss["params"]["weight"] = self.py_lo3_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo3_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[4]): self.current_loss["params"]["weight"] = self.py_lo4_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo4_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[5]): self.current_loss["params"]["weight"] = self.py_lo5_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo5_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[6]): self.current_loss["params"]["weight"] = self.py_lo6_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo6_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[7]): self.current_loss["params"]["weight"] = self.py_lo7_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo7_e2.text(); self.current_loss["params"]["log_pre_applied"] = self.py_lo7_cb3.currentText(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[8]): self.current_loss["params"]["weight"] = self.py_lo8_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo8_e2.text(); self.current_loss["params"]["log_pre_applied"] = self.py_lo8_cb3.currentText(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[9]): self.current_loss["params"]["weight"] = self.py_lo9_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo9_e2.text(); self.current_loss["params"]["threshold_for_mean_estimator"] = self.py_lo9_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[10]): self.current_loss["params"]["weight"] = self.py_lo10_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo10_e2.text(); self.current_loss["params"]["margin"] = self.py_lo10_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[11]): self.current_loss["params"]["weight"] = self.py_lo11_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo11_e2.text(); self.current_loss["params"]["margin"] = self.py_lo11_e3.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[12]): self.current_loss["params"]["weight"] = self.py_lo12_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo12_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[13]): self.current_loss["params"]["weight"] = self.py_lo13_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo13_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[14]): self.current_loss["params"]["weight"] = self.py_lo14_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo14_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; elif(self.current_loss["name"] == self.pytorch_losses_list[15]): self.current_loss["params"]["weight"] = self.py_lo15_e1.text(); self.current_loss["params"]["batch_axis"] = self.py_lo15_e2.text(); self.system["update"]["losses"]["value"] = self.current_loss; wr = ""; wr = json.dumps(self.system["update"]["losses"]["value"], indent=4) self.tb1.setText(wr); def clear_loss(self): self.system["update"]["losses"]["value"] = ""; self.system["update"]["losses"]["active"] = False; wr = ""; self.tb1.setText(wr); def forward(self): with open('base_classification.json', 'w') as outfile: json.dump(self.system, outfile) self.forward_train.emit(); def backward(self): with open('base_classification.json', 'w') as outfile: json.dump(self.system, outfile) self.backward_scheduler_param.emit(); ''' app = QApplication(sys.argv) screen = WindowClassificationTrainUpdateLossParam() screen.show() sys.exit(app.exec_()) '''

0.267217

0.122944

import math from collections import defaultdict import bisect import numpy as np from unittest.mock import MagicMock from mmdet.datasets import (ClassBalancedDataset, ConcatDataset, CustomDataset, MultiImageMixDataset, RepeatDataset) def test_dataset_wrapper(): CustomDataset.load_annotations = MagicMock() CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: idx) dataset_a = CustomDataset( ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='') len_a = 10 cat_ids_list_a = [ np.random.randint(0, 80, num).tolist() for num in np.random.randint(1, 20, len_a) ] dataset_a.data_infos = MagicMock() dataset_a.data_infos.__len__.return_value = len_a dataset_a.get_cat_ids = MagicMock( side_effect=lambda idx: cat_ids_list_a[idx]) dataset_b = CustomDataset( ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='') len_b = 20 cat_ids_list_b = [ np.random.randint(0, 80, num).tolist() for num in np.random.randint(1, 20, len_b) ] dataset_b.data_infos = MagicMock() dataset_b.data_infos.__len__.return_value = len_b dataset_b.get_cat_ids = MagicMock( side_effect=lambda idx: cat_ids_list_b[idx]) concat_dataset = ConcatDataset([dataset_a, dataset_b]) assert concat_dataset[5] == 5 assert concat_dataset[25] == 15 assert concat_dataset.get_cat_ids(5) == cat_ids_list_a[5] assert concat_dataset.get_cat_ids(25) == cat_ids_list_b[15] assert len(concat_dataset) == len(dataset_a) + len(dataset_b) repeat_dataset = RepeatDataset(dataset_a, 10) assert repeat_dataset[5] == 5 assert repeat_dataset[15] == 5 assert repeat_dataset[27] == 7 assert repeat_dataset.get_cat_ids(5) == cat_ids_list_a[5] assert repeat_dataset.get_cat_ids(15) == cat_ids_list_a[5] assert repeat_dataset.get_cat_ids(27) == cat_ids_list_a[7] assert len(repeat_dataset) == 10 * len(dataset_a) category_freq = defaultdict(int) for cat_ids in cat_ids_list_a: cat_ids = set(cat_ids) for cat_id in cat_ids: category_freq[cat_id] += 1 for k, v in category_freq.items(): category_freq[k] = v / len(cat_ids_list_a) mean_freq = np.mean(list(category_freq.values())) repeat_thr = mean_freq category_repeat = { cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq)) for cat_id, cat_freq in category_freq.items() } repeat_factors = [] for cat_ids in cat_ids_list_a: cat_ids = set(cat_ids) repeat_factor = max({category_repeat[cat_id] for cat_id in cat_ids}) repeat_factors.append(math.ceil(repeat_factor)) repeat_factors_cumsum = np.cumsum(repeat_factors) repeat_factor_dataset = ClassBalancedDataset(dataset_a, repeat_thr) assert len(repeat_factor_dataset) == repeat_factors_cumsum[-1] for idx in np.random.randint(0, len(repeat_factor_dataset), 3): assert repeat_factor_dataset[idx] == bisect.bisect_right( repeat_factors_cumsum, idx) img_scale = (60, 60) dynamic_scale = (80, 80) pipeline = [ dict(type='Mosaic', img_scale=img_scale, pad_val=114.0), dict( type='RandomAffine', scaling_ratio_range=(0.1, 2), border=(-img_scale[0] // 2, -img_scale[1] // 2)), dict( type='MixUp', img_scale=img_scale, ratio_range=(0.8, 1.6), pad_val=114.0), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Resize', keep_ratio=True), dict(type='Pad', pad_to_square=True, pad_val=114.0), ] CustomDataset.load_annotations = MagicMock() results = [] for _ in range(2): height = np.random.randint(10, 30) weight = np.random.randint(10, 30) img = np.ones((height, weight, 3)) gt_bbox = np.concatenate([ np.random.randint(1, 5, (2, 2)), np.random.randint(1, 5, (2, 2)) + 5 ], axis=1) gt_labels = np.random.randint(0, 80, 2) results.append(dict(gt_bboxes=gt_bbox, gt_labels=gt_labels, img=img)) CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: results[idx]) dataset_a = CustomDataset( ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='') len_a = 2 cat_ids_list_a = [ np.random.randint(0, 80, num).tolist() for num in np.random.randint(1, 20, len_a) ] dataset_a.data_infos = MagicMock() dataset_a.data_infos.__len__.return_value = len_a dataset_a.get_cat_ids = MagicMock( side_effect=lambda idx: cat_ids_list_a[idx]) multi_image_mix_dataset = MultiImageMixDataset(dataset_a, pipeline, dynamic_scale) for idx in range(len_a): results_ = multi_image_mix_dataset[idx] assert results_['img'].shape == (dynamic_scale[0], dynamic_scale[1], 3) # test skip_type_keys multi_image_mix_dataset = MultiImageMixDataset( dataset_a, pipeline, dynamic_scale, skip_type_keys=('MixUp', 'RandomFlip', 'Resize', 'Pad')) for idx in range(len_a): results_ = multi_image_mix_dataset[idx] assert results_['img'].shape == (img_scale[0], img_scale[1], 3)

tests/test_data/test_datasets/test_dataset_wrapper.py

import math from collections import defaultdict import bisect import numpy as np from unittest.mock import MagicMock from mmdet.datasets import (ClassBalancedDataset, ConcatDataset, CustomDataset, MultiImageMixDataset, RepeatDataset) def test_dataset_wrapper(): CustomDataset.load_annotations = MagicMock() CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: idx) dataset_a = CustomDataset( ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='') len_a = 10 cat_ids_list_a = [ np.random.randint(0, 80, num).tolist() for num in np.random.randint(1, 20, len_a) ] dataset_a.data_infos = MagicMock() dataset_a.data_infos.__len__.return_value = len_a dataset_a.get_cat_ids = MagicMock( side_effect=lambda idx: cat_ids_list_a[idx]) dataset_b = CustomDataset( ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='') len_b = 20 cat_ids_list_b = [ np.random.randint(0, 80, num).tolist() for num in np.random.randint(1, 20, len_b) ] dataset_b.data_infos = MagicMock() dataset_b.data_infos.__len__.return_value = len_b dataset_b.get_cat_ids = MagicMock( side_effect=lambda idx: cat_ids_list_b[idx]) concat_dataset = ConcatDataset([dataset_a, dataset_b]) assert concat_dataset[5] == 5 assert concat_dataset[25] == 15 assert concat_dataset.get_cat_ids(5) == cat_ids_list_a[5] assert concat_dataset.get_cat_ids(25) == cat_ids_list_b[15] assert len(concat_dataset) == len(dataset_a) + len(dataset_b) repeat_dataset = RepeatDataset(dataset_a, 10) assert repeat_dataset[5] == 5 assert repeat_dataset[15] == 5 assert repeat_dataset[27] == 7 assert repeat_dataset.get_cat_ids(5) == cat_ids_list_a[5] assert repeat_dataset.get_cat_ids(15) == cat_ids_list_a[5] assert repeat_dataset.get_cat_ids(27) == cat_ids_list_a[7] assert len(repeat_dataset) == 10 * len(dataset_a) category_freq = defaultdict(int) for cat_ids in cat_ids_list_a: cat_ids = set(cat_ids) for cat_id in cat_ids: category_freq[cat_id] += 1 for k, v in category_freq.items(): category_freq[k] = v / len(cat_ids_list_a) mean_freq = np.mean(list(category_freq.values())) repeat_thr = mean_freq category_repeat = { cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq)) for cat_id, cat_freq in category_freq.items() } repeat_factors = [] for cat_ids in cat_ids_list_a: cat_ids = set(cat_ids) repeat_factor = max({category_repeat[cat_id] for cat_id in cat_ids}) repeat_factors.append(math.ceil(repeat_factor)) repeat_factors_cumsum = np.cumsum(repeat_factors) repeat_factor_dataset = ClassBalancedDataset(dataset_a, repeat_thr) assert len(repeat_factor_dataset) == repeat_factors_cumsum[-1] for idx in np.random.randint(0, len(repeat_factor_dataset), 3): assert repeat_factor_dataset[idx] == bisect.bisect_right( repeat_factors_cumsum, idx) img_scale = (60, 60) dynamic_scale = (80, 80) pipeline = [ dict(type='Mosaic', img_scale=img_scale, pad_val=114.0), dict( type='RandomAffine', scaling_ratio_range=(0.1, 2), border=(-img_scale[0] // 2, -img_scale[1] // 2)), dict( type='MixUp', img_scale=img_scale, ratio_range=(0.8, 1.6), pad_val=114.0), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Resize', keep_ratio=True), dict(type='Pad', pad_to_square=True, pad_val=114.0), ] CustomDataset.load_annotations = MagicMock() results = [] for _ in range(2): height = np.random.randint(10, 30) weight = np.random.randint(10, 30) img = np.ones((height, weight, 3)) gt_bbox = np.concatenate([ np.random.randint(1, 5, (2, 2)), np.random.randint(1, 5, (2, 2)) + 5 ], axis=1) gt_labels = np.random.randint(0, 80, 2) results.append(dict(gt_bboxes=gt_bbox, gt_labels=gt_labels, img=img)) CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: results[idx]) dataset_a = CustomDataset( ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='') len_a = 2 cat_ids_list_a = [ np.random.randint(0, 80, num).tolist() for num in np.random.randint(1, 20, len_a) ] dataset_a.data_infos = MagicMock() dataset_a.data_infos.__len__.return_value = len_a dataset_a.get_cat_ids = MagicMock( side_effect=lambda idx: cat_ids_list_a[idx]) multi_image_mix_dataset = MultiImageMixDataset(dataset_a, pipeline, dynamic_scale) for idx in range(len_a): results_ = multi_image_mix_dataset[idx] assert results_['img'].shape == (dynamic_scale[0], dynamic_scale[1], 3) # test skip_type_keys multi_image_mix_dataset = MultiImageMixDataset( dataset_a, pipeline, dynamic_scale, skip_type_keys=('MixUp', 'RandomFlip', 'Resize', 'Pad')) for idx in range(len_a): results_ = multi_image_mix_dataset[idx] assert results_['img'].shape == (img_scale[0], img_scale[1], 3)

0.612657

0.519034

import logging import os import random import hydra from hydra.utils import instantiate import numpy as np import torch import wandb from allennlp.nn.util import get_text_field_mask from torch.distributions import Categorical from omegaconf import OmegaConf from viraal.config import (flatten_dict, get_key, pass_conf, register_interpolations, save_config, set_seeds) from viraal.train.text import batch_to_device, get_checkpoint from viraal.train.tag import TrainJoint from viraal.core.utils import destroy_trainer, apply logger = logging.getLogger(__name__) @hydra.main(config_path="../config/rerank.yaml", strict=False) def train_text(cfg): register_interpolations() pretrain_cfg = OmegaConf.load(os.path.join(cfg.rerank.pretrain, 'config.yaml')) cfg = OmegaConf.merge(pretrain_cfg, cfg) cfg_yaml = cfg.pretty(resolve=True) logger.info("====CONFIG====\n%s", cfg_yaml) save_config(cfg_yaml) set_seeds(cfg.misc.seed) if cfg.misc.wandb: pass_conf(wandb.init, cfg, 'wandb')(config=cfg.to_container(resolve=True)) tr = TrainJoint(cfg) tr.restore(get_checkpoint(os.path.join(cfg.rerank.pretrain, 'model'),cfg.rerank.iteration)) logger.info("Reranking training instances") rerank(tr, cfg) logger.info("Resetting model weights") tr.instantiate_model() logger.info("Training model") tr.train_loop() destroy_trainer(tr) def normalize(criter): c = criter.detach().cpu().numpy() return c/np.quantile(c, 0.99) def rerank(trainer, cfg): trainer.model.train() unlabeled = trainer.get_unlabeled_instances() nb_instances = len(unlabeled) to_select = int(cfg.rerank.part*len(trainer.train_instances)) iterator = trainer.iterator(unlabeled, num_epochs=1) if cfg.misc.tqdm: iterator = tqdm(iterator) criter_epoch = [] for batch in iterator: batch_to_device(batch, cfg.misc.device) embeddings = trainer.word_embeddings(batch["sentence"]) mask = get_text_field_mask(batch["sentence"]) sentence_lengths = mask.sum(dim=-1) logits_tag = trainer.model(embeddings=embeddings, mask=mask) labeled = np.array(batch["labeled"], dtype=bool) dist_tag = Categorical(logits=logits_tag.view(-1, logits_tag.size(-1))) criter = np.zeros(logits_tag.size(0)) if "ce" in cfg.rerank.criteria: tag_entropies = dist_tag.entropy().view(logits_tag.size(0), logits_tag.size(1))*mask.float() criter += normalize(tag_entropies.sum(dim=-1)/sentence_lengths.float()) if "vat" in cfg.rerank.criteria: model_forward = lambda embeddings: trainer.model( embeddings=embeddings, mask=mask ) vat_criter_tag = trainer.losses["vat"](logits_tag, model_forward, embeddings, mask) criter += normalize(vat_criter_tag.mean(dim=-1)) if "random" in cfg.rerank.criteria: criter += np.random.rand(logits_tag.size(0)) criter_epoch.append(criter) criter_epoch = np.concatenate(criter_epoch) selected = np.argsort(criter_epoch)[-to_select:] for idx in selected: unlabeled[idx]['labeled'].metadata = True if __name__ == "__main__": try: train_text() except Exception: logger.exception("Fatal error")

viraal/rerank/tag.py

import logging import os import random import hydra from hydra.utils import instantiate import numpy as np import torch import wandb from allennlp.nn.util import get_text_field_mask from torch.distributions import Categorical from omegaconf import OmegaConf from viraal.config import (flatten_dict, get_key, pass_conf, register_interpolations, save_config, set_seeds) from viraal.train.text import batch_to_device, get_checkpoint from viraal.train.tag import TrainJoint from viraal.core.utils import destroy_trainer, apply logger = logging.getLogger(__name__) @hydra.main(config_path="../config/rerank.yaml", strict=False) def train_text(cfg): register_interpolations() pretrain_cfg = OmegaConf.load(os.path.join(cfg.rerank.pretrain, 'config.yaml')) cfg = OmegaConf.merge(pretrain_cfg, cfg) cfg_yaml = cfg.pretty(resolve=True) logger.info("====CONFIG====\n%s", cfg_yaml) save_config(cfg_yaml) set_seeds(cfg.misc.seed) if cfg.misc.wandb: pass_conf(wandb.init, cfg, 'wandb')(config=cfg.to_container(resolve=True)) tr = TrainJoint(cfg) tr.restore(get_checkpoint(os.path.join(cfg.rerank.pretrain, 'model'),cfg.rerank.iteration)) logger.info("Reranking training instances") rerank(tr, cfg) logger.info("Resetting model weights") tr.instantiate_model() logger.info("Training model") tr.train_loop() destroy_trainer(tr) def normalize(criter): c = criter.detach().cpu().numpy() return c/np.quantile(c, 0.99) def rerank(trainer, cfg): trainer.model.train() unlabeled = trainer.get_unlabeled_instances() nb_instances = len(unlabeled) to_select = int(cfg.rerank.part*len(trainer.train_instances)) iterator = trainer.iterator(unlabeled, num_epochs=1) if cfg.misc.tqdm: iterator = tqdm(iterator) criter_epoch = [] for batch in iterator: batch_to_device(batch, cfg.misc.device) embeddings = trainer.word_embeddings(batch["sentence"]) mask = get_text_field_mask(batch["sentence"]) sentence_lengths = mask.sum(dim=-1) logits_tag = trainer.model(embeddings=embeddings, mask=mask) labeled = np.array(batch["labeled"], dtype=bool) dist_tag = Categorical(logits=logits_tag.view(-1, logits_tag.size(-1))) criter = np.zeros(logits_tag.size(0)) if "ce" in cfg.rerank.criteria: tag_entropies = dist_tag.entropy().view(logits_tag.size(0), logits_tag.size(1))*mask.float() criter += normalize(tag_entropies.sum(dim=-1)/sentence_lengths.float()) if "vat" in cfg.rerank.criteria: model_forward = lambda embeddings: trainer.model( embeddings=embeddings, mask=mask ) vat_criter_tag = trainer.losses["vat"](logits_tag, model_forward, embeddings, mask) criter += normalize(vat_criter_tag.mean(dim=-1)) if "random" in cfg.rerank.criteria: criter += np.random.rand(logits_tag.size(0)) criter_epoch.append(criter) criter_epoch = np.concatenate(criter_epoch) selected = np.argsort(criter_epoch)[-to_select:] for idx in selected: unlabeled[idx]['labeled'].metadata = True if __name__ == "__main__": try: train_text() except Exception: logger.exception("Fatal error")

0.535098

0.112893

import logging from typing import Dict, Optional, Sequence, Type, Union import tensorflow as tf from tensorflow.keras.layers import Layer from ..block.mlp import MLPBlock from ..ranking_metric import AvgPrecisionAt, NDCGAt, RecallAt from .base import PredictionTask def name_fn(name, inp): return "/".join([name, inp]) if name else None MetricOrMetricClass = Union[tf.keras.metrics.Metric, Type[tf.keras.metrics.Metric]] LOG = logging.getLogger("transformers4rec") @tf.keras.utils.register_keras_serializable(package="transformers4rec") class BinaryClassificationTask(PredictionTask): DEFAULT_LOSS = tf.keras.losses.BinaryCrossentropy() DEFAULT_METRICS = ( tf.keras.metrics.Precision, tf.keras.metrics.Recall, tf.keras.metrics.BinaryAccuracy, tf.keras.metrics.AUC, ) def __init__( self, target_name: Optional[str] = None, task_name: Optional[str] = None, task_block: Optional[Layer] = None, loss=DEFAULT_LOSS, metrics: Sequence[MetricOrMetricClass] = DEFAULT_METRICS, summary_type="first", **kwargs, ): super().__init__( loss=loss, metrics=list(metrics), target_name=target_name, task_name=task_name, summary_type=summary_type, task_block=task_block, **kwargs, ) self.pre = tf.keras.layers.Dense(1, activation="sigmoid", name=self.child_name("logit")) @tf.keras.utils.register_keras_serializable(package="transformers4rec") class RegressionTask(PredictionTask): DEFAULT_LOSS = tf.keras.losses.MeanSquaredError() DEFAULT_METRICS = (tf.keras.metrics.RootMeanSquaredError,) def __init__( self, target_name: Optional[str] = None, task_name: Optional[str] = None, task_block: Optional[Layer] = None, loss=DEFAULT_LOSS, metrics=DEFAULT_METRICS, summary_type="first", **kwargs, ): super().__init__( loss=loss, metrics=metrics, target_name=target_name, task_name=task_name, summary_type=summary_type, task_block=task_block, **kwargs, ) self.pre = tf.keras.layers.Dense(1, name=self.child_name("logit")) @tf.keras.utils.register_keras_serializable(package="transformers4rec") class NextItemPredictionTask(PredictionTask): """Next-item prediction task. Parameters ---------- loss: Loss function. SparseCategoricalCrossentropy() metrics: List of RankingMetrics to be evaluated. prediction_metrics: List of Keras metrics used to summarize the predictions. label_metrics: List of Keras metrics used to summarize the labels. loss_metrics: List of Keras metrics used to summarize the loss. name: Optional task name. target_dim: int Dimension of the target. weight_tying: bool The item id embedding table weights are shared with the prediction network layer. item_embedding_table: tf.Variable Variable of embedding table for the item. softmax_temperature: float Softmax temperature, used to reduce model overconfidence, so that softmax(logits / T). Value 1.0 reduces to regular softmax. """ DEFAULT_LOSS = tf.keras.losses.SparseCategoricalCrossentropy() DEFAULT_METRICS = ( # default metrics suppose labels are int encoded NDCGAt(top_ks=[10, 20], labels_onehot=True), AvgPrecisionAt(top_ks=[10, 20], labels_onehot=True), RecallAt(top_ks=[10, 20], labels_onehot=True), ) def __init__( self, loss=DEFAULT_LOSS, metrics=DEFAULT_METRICS, target_name: Optional[str] = None, task_name: Optional[str] = None, task_block: Optional[Layer] = None, weight_tying: bool = False, target_dim: int = None, softmax_temperature: float = 1, **kwargs, ): super().__init__( loss=loss, metrics=metrics, target_name=target_name, task_name=task_name, task_block=task_block, **kwargs, ) self.weight_tying = weight_tying self.target_dim = target_dim self.softmax_temperature = softmax_temperature def build(self, input_shape, body, inputs=None): # Retrieve the embedding module to get the name of itemid col and its related table if not len(input_shape) == 3 or isinstance(input_shape, dict): raise ValueError( "NextItemPredictionTask needs a 3-dim vector as input, found:" f"{input_shape}" ) if not inputs: inputs = body.inputs if not getattr(inputs, "item_id", None): raise ValueError( "For Item Prediction task a categorical_module " "including an item_id column is required." ) self.embeddings = inputs.categorical_layer if not self.target_dim: self.target_dim = self.embeddings.item_embedding_table.shape[0] if self.weight_tying: self.item_embedding_table = self.embeddings.item_embedding_table item_dim = self.item_embedding_table.shape[1] if input_shape[-1] != item_dim and not self.task_block: LOG.warning( f"Projecting inputs of NextItemPredictionTask to'{item_dim}' " f"As weight tying requires the input dimension '{input_shape[-1]}' " f"to be equal to the item-id embedding dimension '{item_dim}'" ) # project input tensors to same dimension as item-id embeddings self.task_block = MLPBlock([item_dim]) # Retrieve the masking if used in the model block self.masking = inputs.masking if self.masking: self.padding_idx = self.masking.padding_idx self.pre = _NextItemPredictionTask( target_dim=self.target_dim, weight_tying=self.weight_tying, item_embedding_table=self.item_embedding_table, softmax_temperature=self.softmax_temperature, ) return super().build(input_shape) def call(self, inputs, **kwargs): if isinstance(inputs, (tuple, list)): inputs = inputs[0] x = inputs if self.task_block: x = self.task_block(x) # retrieve labels from masking if self.masking: labels = self.masking.masked_targets else: labels = self.embeddings.item_seq # remove vectors of padded items trg_flat = tf.reshape(labels, -1) non_pad_mask = trg_flat != self.padding_idx x = self.remove_pad_3d(x, non_pad_mask) # compute predictions probs x = self.pre(x) return x def remove_pad_3d(self, inp_tensor, non_pad_mask): # inp_tensor: (n_batch x seqlen x emb_dim) inp_tensor = tf.reshape(inp_tensor, (-1, inp_tensor.shape[-1])) inp_tensor_fl = tf.boolean_mask( inp_tensor, tf.broadcast_to(tf.expand_dims(non_pad_mask, 1), inp_tensor.shape) ) out_tensor = tf.reshape(inp_tensor_fl, (-1, inp_tensor.shape[1])) return out_tensor def compute_loss( # type: ignore self, inputs, targets=None, compute_metrics=True, sample_weight: Optional[tf.Tensor] = None, **kwargs, ) -> tf.Tensor: if isinstance(targets, dict) and self.target_name: targets = targets[self.target_name] predictions = self(inputs) # retrieve labels from masking if self.masking: targets = self.masking.masked_targets # flatten labels and remove padding index targets = tf.reshape(targets, -1) non_pad_mask = targets != self.padding_idx targets = tf.boolean_mask(targets, non_pad_mask) loss = self.loss(y_true=targets, y_pred=predictions, sample_weight=sample_weight) if compute_metrics: update_ops = self.calculate_metrics(predictions, targets, forward=False, loss=loss) update_ops = [x for x in update_ops if x is not None] with tf.control_dependencies(update_ops): return tf.identity(loss) return loss def calculate_metrics( self, predictions, targets=None, sample_weight=None, forward=True, loss=None ): if isinstance(targets, dict) and self.target_name: targets = targets[self.target_name] if forward: predictions = self(predictions) # retrieve labels from masking if self.masking: targets = self.masking.masked_targets # flatten labels and remove padding index targets = tf.reshape(targets, -1) non_pad_mask = targets != self.padding_idx targets = tf.boolean_mask(targets, non_pad_mask) update_ops = [] for metric in self.eval_metrics: update_ops.append( metric.update_state(y_true=targets, y_pred=predictions, sample_weight=sample_weight) ) for metric in self.prediction_metrics: update_ops.append(metric.update_state(predictions, sample_weight=sample_weight)) for metric in self.label_metrics: update_ops.append(metric.update_state(targets, sample_weight=sample_weight)) for metric in self.loss_metrics: if not loss: loss = self.loss(y_true=targets, y_pred=predictions, sample_weight=sample_weight) update_ops.append(metric.update_state(loss, sample_weight=sample_weight)) return update_ops def metric_results(self, mode: str = None) -> Dict[str, tf.Tensor]: metrics = {metric.name: metric.result() for metric in self.eval_metrics} topks = {metric.name: metric.top_ks for metric in self.eval_metrics} # explode metrics for each cut-off in top_ks results = {} for name, metric in metrics.items(): for measure, k in zip(metric, topks[name]): results[f"{name}_{k}"] = measure return results class _NextItemPredictionTask(tf.keras.layers.Layer): """Predict the interacted item-id probabilities. - During inference, the task consists of predicting the next item. - During training, the class supports the following Language modeling tasks: Causal LM and Masked LM. p.s: we are planning to support Permutation LM and Replacement Token Detection in future release. Parameters: ----------- target_dim: int Dimension of the target. weight_tying: bool The item id embedding table weights are shared with the prediction network layer. item_embedding_table: tf.Variable Variable of embedding table for the item. softmax_temperature: float Softmax temperature, used to reduce model overconfidence, so that softmax(logits / T). Value 1.0 reduces to regular softmax. """ def __init__( self, target_dim: int, weight_tying: bool = False, item_embedding_table: Optional[tf.Variable] = None, softmax_temperature: float = 0, ): super().__init__() self.target_dim = target_dim self.weight_tying = weight_tying self.item_embedding_table = item_embedding_table self.softmax_temperature = softmax_temperature if self.weight_tying: if item_embedding_table is None: raise ValueError( "For Item Prediction task with weight tying " "the embedding table of item_id is required ." ) self.output_layer_bias = self.add_weight( name="output_layer_bias", shape=(self.target_dim,), initializer=tf.keras.initializers.Zeros(), ) else: self.output_layer = tf.keras.layers.Dense( units=self.target_dim, kernel_initializer="random_normal", bias_initializer="zeros", name="logits", ) def call(self, inputs: tf.Tensor, **kwargs): if self.weight_tying: logits = tf.matmul(inputs, tf.transpose(self.item_embedding_table)) logits = tf.nn.bias_add(logits, self.output_layer_bias) else: logits = self.output_layer(inputs) if self.softmax_temperature: # Softmax temperature to reduce model overconfidence # and better calibrate probs and accuracy logits = logits / self.softmax_temperature predictions = tf.nn.log_softmax(logits, axis=-1) return predictions def _get_name(self) -> str: return "NextItemPredictionTask"

transformers4rec/tf/model/prediction_task.py

import logging from typing import Dict, Optional, Sequence, Type, Union import tensorflow as tf from tensorflow.keras.layers import Layer from ..block.mlp import MLPBlock from ..ranking_metric import AvgPrecisionAt, NDCGAt, RecallAt from .base import PredictionTask def name_fn(name, inp): return "/".join([name, inp]) if name else None MetricOrMetricClass = Union[tf.keras.metrics.Metric, Type[tf.keras.metrics.Metric]] LOG = logging.getLogger("transformers4rec") @tf.keras.utils.register_keras_serializable(package="transformers4rec") class BinaryClassificationTask(PredictionTask): DEFAULT_LOSS = tf.keras.losses.BinaryCrossentropy() DEFAULT_METRICS = ( tf.keras.metrics.Precision, tf.keras.metrics.Recall, tf.keras.metrics.BinaryAccuracy, tf.keras.metrics.AUC, ) def __init__( self, target_name: Optional[str] = None, task_name: Optional[str] = None, task_block: Optional[Layer] = None, loss=DEFAULT_LOSS, metrics: Sequence[MetricOrMetricClass] = DEFAULT_METRICS, summary_type="first", **kwargs, ): super().__init__( loss=loss, metrics=list(metrics), target_name=target_name, task_name=task_name, summary_type=summary_type, task_block=task_block, **kwargs, ) self.pre = tf.keras.layers.Dense(1, activation="sigmoid", name=self.child_name("logit")) @tf.keras.utils.register_keras_serializable(package="transformers4rec") class RegressionTask(PredictionTask): DEFAULT_LOSS = tf.keras.losses.MeanSquaredError() DEFAULT_METRICS = (tf.keras.metrics.RootMeanSquaredError,) def __init__( self, target_name: Optional[str] = None, task_name: Optional[str] = None, task_block: Optional[Layer] = None, loss=DEFAULT_LOSS, metrics=DEFAULT_METRICS, summary_type="first", **kwargs, ): super().__init__( loss=loss, metrics=metrics, target_name=target_name, task_name=task_name, summary_type=summary_type, task_block=task_block, **kwargs, ) self.pre = tf.keras.layers.Dense(1, name=self.child_name("logit")) @tf.keras.utils.register_keras_serializable(package="transformers4rec") class NextItemPredictionTask(PredictionTask): """Next-item prediction task. Parameters ---------- loss: Loss function. SparseCategoricalCrossentropy() metrics: List of RankingMetrics to be evaluated. prediction_metrics: List of Keras metrics used to summarize the predictions. label_metrics: List of Keras metrics used to summarize the labels. loss_metrics: List of Keras metrics used to summarize the loss. name: Optional task name. target_dim: int Dimension of the target. weight_tying: bool The item id embedding table weights are shared with the prediction network layer. item_embedding_table: tf.Variable Variable of embedding table for the item. softmax_temperature: float Softmax temperature, used to reduce model overconfidence, so that softmax(logits / T). Value 1.0 reduces to regular softmax. """ DEFAULT_LOSS = tf.keras.losses.SparseCategoricalCrossentropy() DEFAULT_METRICS = ( # default metrics suppose labels are int encoded NDCGAt(top_ks=[10, 20], labels_onehot=True), AvgPrecisionAt(top_ks=[10, 20], labels_onehot=True), RecallAt(top_ks=[10, 20], labels_onehot=True), ) def __init__( self, loss=DEFAULT_LOSS, metrics=DEFAULT_METRICS, target_name: Optional[str] = None, task_name: Optional[str] = None, task_block: Optional[Layer] = None, weight_tying: bool = False, target_dim: int = None, softmax_temperature: float = 1, **kwargs, ): super().__init__( loss=loss, metrics=metrics, target_name=target_name, task_name=task_name, task_block=task_block, **kwargs, ) self.weight_tying = weight_tying self.target_dim = target_dim self.softmax_temperature = softmax_temperature def build(self, input_shape, body, inputs=None): # Retrieve the embedding module to get the name of itemid col and its related table if not len(input_shape) == 3 or isinstance(input_shape, dict): raise ValueError( "NextItemPredictionTask needs a 3-dim vector as input, found:" f"{input_shape}" ) if not inputs: inputs = body.inputs if not getattr(inputs, "item_id", None): raise ValueError( "For Item Prediction task a categorical_module " "including an item_id column is required." ) self.embeddings = inputs.categorical_layer if not self.target_dim: self.target_dim = self.embeddings.item_embedding_table.shape[0] if self.weight_tying: self.item_embedding_table = self.embeddings.item_embedding_table item_dim = self.item_embedding_table.shape[1] if input_shape[-1] != item_dim and not self.task_block: LOG.warning( f"Projecting inputs of NextItemPredictionTask to'{item_dim}' " f"As weight tying requires the input dimension '{input_shape[-1]}' " f"to be equal to the item-id embedding dimension '{item_dim}'" ) # project input tensors to same dimension as item-id embeddings self.task_block = MLPBlock([item_dim]) # Retrieve the masking if used in the model block self.masking = inputs.masking if self.masking: self.padding_idx = self.masking.padding_idx self.pre = _NextItemPredictionTask( target_dim=self.target_dim, weight_tying=self.weight_tying, item_embedding_table=self.item_embedding_table, softmax_temperature=self.softmax_temperature, ) return super().build(input_shape) def call(self, inputs, **kwargs): if isinstance(inputs, (tuple, list)): inputs = inputs[0] x = inputs if self.task_block: x = self.task_block(x) # retrieve labels from masking if self.masking: labels = self.masking.masked_targets else: labels = self.embeddings.item_seq # remove vectors of padded items trg_flat = tf.reshape(labels, -1) non_pad_mask = trg_flat != self.padding_idx x = self.remove_pad_3d(x, non_pad_mask) # compute predictions probs x = self.pre(x) return x def remove_pad_3d(self, inp_tensor, non_pad_mask): # inp_tensor: (n_batch x seqlen x emb_dim) inp_tensor = tf.reshape(inp_tensor, (-1, inp_tensor.shape[-1])) inp_tensor_fl = tf.boolean_mask( inp_tensor, tf.broadcast_to(tf.expand_dims(non_pad_mask, 1), inp_tensor.shape) ) out_tensor = tf.reshape(inp_tensor_fl, (-1, inp_tensor.shape[1])) return out_tensor def compute_loss( # type: ignore self, inputs, targets=None, compute_metrics=True, sample_weight: Optional[tf.Tensor] = None, **kwargs, ) -> tf.Tensor: if isinstance(targets, dict) and self.target_name: targets = targets[self.target_name] predictions = self(inputs) # retrieve labels from masking if self.masking: targets = self.masking.masked_targets # flatten labels and remove padding index targets = tf.reshape(targets, -1) non_pad_mask = targets != self.padding_idx targets = tf.boolean_mask(targets, non_pad_mask) loss = self.loss(y_true=targets, y_pred=predictions, sample_weight=sample_weight) if compute_metrics: update_ops = self.calculate_metrics(predictions, targets, forward=False, loss=loss) update_ops = [x for x in update_ops if x is not None] with tf.control_dependencies(update_ops): return tf.identity(loss) return loss def calculate_metrics( self, predictions, targets=None, sample_weight=None, forward=True, loss=None ): if isinstance(targets, dict) and self.target_name: targets = targets[self.target_name] if forward: predictions = self(predictions) # retrieve labels from masking if self.masking: targets = self.masking.masked_targets # flatten labels and remove padding index targets = tf.reshape(targets, -1) non_pad_mask = targets != self.padding_idx targets = tf.boolean_mask(targets, non_pad_mask) update_ops = [] for metric in self.eval_metrics: update_ops.append( metric.update_state(y_true=targets, y_pred=predictions, sample_weight=sample_weight) ) for metric in self.prediction_metrics: update_ops.append(metric.update_state(predictions, sample_weight=sample_weight)) for metric in self.label_metrics: update_ops.append(metric.update_state(targets, sample_weight=sample_weight)) for metric in self.loss_metrics: if not loss: loss = self.loss(y_true=targets, y_pred=predictions, sample_weight=sample_weight) update_ops.append(metric.update_state(loss, sample_weight=sample_weight)) return update_ops def metric_results(self, mode: str = None) -> Dict[str, tf.Tensor]: metrics = {metric.name: metric.result() for metric in self.eval_metrics} topks = {metric.name: metric.top_ks for metric in self.eval_metrics} # explode metrics for each cut-off in top_ks results = {} for name, metric in metrics.items(): for measure, k in zip(metric, topks[name]): results[f"{name}_{k}"] = measure return results class _NextItemPredictionTask(tf.keras.layers.Layer): """Predict the interacted item-id probabilities. - During inference, the task consists of predicting the next item. - During training, the class supports the following Language modeling tasks: Causal LM and Masked LM. p.s: we are planning to support Permutation LM and Replacement Token Detection in future release. Parameters: ----------- target_dim: int Dimension of the target. weight_tying: bool The item id embedding table weights are shared with the prediction network layer. item_embedding_table: tf.Variable Variable of embedding table for the item. softmax_temperature: float Softmax temperature, used to reduce model overconfidence, so that softmax(logits / T). Value 1.0 reduces to regular softmax. """ def __init__( self, target_dim: int, weight_tying: bool = False, item_embedding_table: Optional[tf.Variable] = None, softmax_temperature: float = 0, ): super().__init__() self.target_dim = target_dim self.weight_tying = weight_tying self.item_embedding_table = item_embedding_table self.softmax_temperature = softmax_temperature if self.weight_tying: if item_embedding_table is None: raise ValueError( "For Item Prediction task with weight tying " "the embedding table of item_id is required ." ) self.output_layer_bias = self.add_weight( name="output_layer_bias", shape=(self.target_dim,), initializer=tf.keras.initializers.Zeros(), ) else: self.output_layer = tf.keras.layers.Dense( units=self.target_dim, kernel_initializer="random_normal", bias_initializer="zeros", name="logits", ) def call(self, inputs: tf.Tensor, **kwargs): if self.weight_tying: logits = tf.matmul(inputs, tf.transpose(self.item_embedding_table)) logits = tf.nn.bias_add(logits, self.output_layer_bias) else: logits = self.output_layer(inputs) if self.softmax_temperature: # Softmax temperature to reduce model overconfidence # and better calibrate probs and accuracy logits = logits / self.softmax_temperature predictions = tf.nn.log_softmax(logits, axis=-1) return predictions def _get_name(self) -> str: return "NextItemPredictionTask"

0.949412

0.297467

import os import sys import inspect import meshlabxml as mlx import platform import glob import shutil import time from threading import Thread ''' Description: It takes via normal script "simplifybulk.py" 17 minutes to Decimate a 3M faces into 7 resolutions [100K, 200K, 300K, 400K, 500K, 600K, 750K]. With this version of the program, I have included threading so the time it takes now is only around 5 minutes (a considerable improvement). Usage: python3 simplifybulkthreaded.py You can of course include functionality to take arguments from command line/terminal like I did in simplify.py DO NOT FORGET to change: Decimations_List, originalMesh (name of original mesh), SimplifiedMesh & Textures to what suits you. Enjoy! ''' def simplify(originalMeshName, SimplifiedMeshName, NumberOfFaces, WithTexture): # File names FilterScript = 'SimplificationFilter.mlx' # script file original_mesh = originalMeshName # input file simplified_mesh = SimplifiedMeshName # output file Num_Of_Faces = int(NumberOfFaces) # Final Number of Faces #Check the input mesh number of faces (so that we do not decimate to a higher number of faces than original mesh) MetricsMeshDictionary = {} MetricsMeshDictionary = mlx.files.measure_topology(original_mesh) #print (MetricsMeshDictionary) print('\n Number of faces of original mesh is: ' + str(MetricsMeshDictionary['face_num'] )) if(MetricsMeshDictionary['face_num'] <= Num_Of_Faces): #exit the script and print a message about it print("\n SORRY your decimated mesh can not have higher number of faces that the input mesh.....") print("\n ......................................................................................") sys.exit() #Creating a folder named as the Number of faces: named '150000' print('\n Creating a folder to store the decimated model ...........') if not os.path.exists(str(Num_Of_Faces)): os.makedirs(str(Num_Of_Faces)) simplified_meshScript = mlx.FilterScript(file_in=original_mesh, file_out=str(Num_Of_Faces) + '/' + simplified_mesh, ml_version='2016.12') # Create FilterScript object mlx.remesh.simplify(simplified_meshScript, texture=WithTexture, faces=Num_Of_Faces, target_perc=0.0, quality_thr=1.0, preserve_boundary=True, boundary_weight=1.0, preserve_normal=True, optimal_placement=True, planar_quadric=True, selected=False, extra_tex_coord_weight=1.0) print('\n Beginning the process of Decimation ...........') simplified_meshScript.run_script() # Run the script os.chdir(str(Num_Of_Faces)) print('\n Process of Decimation Finished ...') print('\n Copying textures (PNG and JPEG) into the folder of decimated model....') #go back to parent directory so we can copy the textures to the 3D Model folder os.chdir('..') #Now checking for textures in the folder of the input mesh.... (plz change if needed) allfilelist= os.listdir('.') for Afile in allfilelist[:]: if not(Afile.endswith(".png") or Afile.endswith(".PNG") or Afile.endswith(".jpg") or Afile.endswith(".JPG")): allfilelist.remove(Afile) print('\n Found the LIST of images in PNG and JPEG (textures): ') print(allfilelist) for file in allfilelist: shutil.copy(file, str(Num_Of_Faces)) print('\n sleeping for 3 seconds.... ') time.sleep(3) Decimations_List = [100000, 200000, 300000, 400000, 500000, 600000, 750000] originalMesh = 'Lantern.1.obj' SimplifiedMesh = 'Lantern_Simplified.obj' Textures = True print("...........Starting timer for the decimation process............") initial_time = time.time() threads_list = [] for decimationResolution in Decimations_List: threads_list.append(Thread(target=simplify, args=(originalMesh, SimplifiedMesh, decimationResolution, Textures))) for thread in threads_list: try: thread.start() except KeyboardInterrupt: # std Python exception continue # moves to next thread iterable #Waiting for all threads to finish thread.join() print("I Finished Decimating in tandem.....") finish_time = time.time() print("---Decimation Process took: %s seconds ---" % (finish_time - initial_time)) print("\n Done.... have a good day!")

simplifybulkthreaded.py

import os import sys import inspect import meshlabxml as mlx import platform import glob import shutil import time from threading import Thread ''' Description: It takes via normal script "simplifybulk.py" 17 minutes to Decimate a 3M faces into 7 resolutions [100K, 200K, 300K, 400K, 500K, 600K, 750K]. With this version of the program, I have included threading so the time it takes now is only around 5 minutes (a considerable improvement). Usage: python3 simplifybulkthreaded.py You can of course include functionality to take arguments from command line/terminal like I did in simplify.py DO NOT FORGET to change: Decimations_List, originalMesh (name of original mesh), SimplifiedMesh & Textures to what suits you. Enjoy! ''' def simplify(originalMeshName, SimplifiedMeshName, NumberOfFaces, WithTexture): # File names FilterScript = 'SimplificationFilter.mlx' # script file original_mesh = originalMeshName # input file simplified_mesh = SimplifiedMeshName # output file Num_Of_Faces = int(NumberOfFaces) # Final Number of Faces #Check the input mesh number of faces (so that we do not decimate to a higher number of faces than original mesh) MetricsMeshDictionary = {} MetricsMeshDictionary = mlx.files.measure_topology(original_mesh) #print (MetricsMeshDictionary) print('\n Number of faces of original mesh is: ' + str(MetricsMeshDictionary['face_num'] )) if(MetricsMeshDictionary['face_num'] <= Num_Of_Faces): #exit the script and print a message about it print("\n SORRY your decimated mesh can not have higher number of faces that the input mesh.....") print("\n ......................................................................................") sys.exit() #Creating a folder named as the Number of faces: named '150000' print('\n Creating a folder to store the decimated model ...........') if not os.path.exists(str(Num_Of_Faces)): os.makedirs(str(Num_Of_Faces)) simplified_meshScript = mlx.FilterScript(file_in=original_mesh, file_out=str(Num_Of_Faces) + '/' + simplified_mesh, ml_version='2016.12') # Create FilterScript object mlx.remesh.simplify(simplified_meshScript, texture=WithTexture, faces=Num_Of_Faces, target_perc=0.0, quality_thr=1.0, preserve_boundary=True, boundary_weight=1.0, preserve_normal=True, optimal_placement=True, planar_quadric=True, selected=False, extra_tex_coord_weight=1.0) print('\n Beginning the process of Decimation ...........') simplified_meshScript.run_script() # Run the script os.chdir(str(Num_Of_Faces)) print('\n Process of Decimation Finished ...') print('\n Copying textures (PNG and JPEG) into the folder of decimated model....') #go back to parent directory so we can copy the textures to the 3D Model folder os.chdir('..') #Now checking for textures in the folder of the input mesh.... (plz change if needed) allfilelist= os.listdir('.') for Afile in allfilelist[:]: if not(Afile.endswith(".png") or Afile.endswith(".PNG") or Afile.endswith(".jpg") or Afile.endswith(".JPG")): allfilelist.remove(Afile) print('\n Found the LIST of images in PNG and JPEG (textures): ') print(allfilelist) for file in allfilelist: shutil.copy(file, str(Num_Of_Faces)) print('\n sleeping for 3 seconds.... ') time.sleep(3) Decimations_List = [100000, 200000, 300000, 400000, 500000, 600000, 750000] originalMesh = 'Lantern.1.obj' SimplifiedMesh = 'Lantern_Simplified.obj' Textures = True print("...........Starting timer for the decimation process............") initial_time = time.time() threads_list = [] for decimationResolution in Decimations_List: threads_list.append(Thread(target=simplify, args=(originalMesh, SimplifiedMesh, decimationResolution, Textures))) for thread in threads_list: try: thread.start() except KeyboardInterrupt: # std Python exception continue # moves to next thread iterable #Waiting for all threads to finish thread.join() print("I Finished Decimating in tandem.....") finish_time = time.time() print("---Decimation Process took: %s seconds ---" % (finish_time - initial_time)) print("\n Done.... have a good day!")

0.108012

0.174164

from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from dxm.lib.masking_api.api_client import ApiClient class ReidentificationJobApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def create_reidentification_job(self, body, **kwargs): # noqa: E501 """Create re-identification job # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_reidentification_job(body, async_req=True) >>> result = thread.get() :param async_req bool :param ReidentificationJob body: The re-identification job to create (required) :return: ReidentificationJob If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.create_reidentification_job_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.create_reidentification_job_with_http_info(body, **kwargs) # noqa: E501 return data def create_reidentification_job_with_http_info(self, body, **kwargs): # noqa: E501 """Create re-identification job # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_reidentification_job_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param ReidentificationJob body: The re-identification job to create (required) :return: ReidentificationJob If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method create_reidentification_job" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if self.api_client.client_side_validation and ('body' not in params or params['body'] is None): # noqa: E501 raise ValueError("Missing the required parameter `body` when calling `create_reidentification_job`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_key'] # noqa: E501 return self.api_client.call_api( '/reidentification-jobs', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ReidentificationJob', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def delete_reidentification_job(self, reidentification_job_id, **kwargs): # noqa: E501 """Delete re-identification job by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_reidentification_job(reidentification_job_id, async_req=True) >>> result = thread.get() :param async_req bool :param int reidentification_job_id: The ID of the re-identification job to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_reidentification_job_with_http_info(reidentification_job_id, **kwargs) # noqa: E501 else: (data) = self.delete_reidentification_job_with_http_info(reidentification_job_id, **kwargs) # noqa: E501 return data def delete_reidentification_job_with_http_info(self, reidentification_job_id, **kwargs): # noqa: E501 """Delete re-identification job by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_reidentification_job_with_http_info(reidentification_job_id, async_req=True) >>> result = thread.get() :param async_req bool :param int reidentification_job_id: The ID of the re-identification job to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['reidentification_job_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_reidentification_job" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'reidentification_job_id' is set if self.api_client.client_side_validation and ('reidentification_job_id' not in params or params['reidentification_job_id'] is None): # noqa: E501 raise ValueError("Missing the required parameter `reidentification_job_id` when calling `delete_reidentification_job`") # noqa: E501 collection_formats = {} path_params = {} if 'reidentification_job_id' in params: path_params['reidentificationJobId'] = params['reidentification_job_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_key'] # noqa: E501 return self.api_client.call_api( '/reidentification-jobs/{reidentificationJobId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_all_reidentification_jobs(self, **kwargs): # noqa: E501 """Get all re-identification jobs # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_all_reidentification_jobs(async_req=True) >>> result = thread.get() :param async_req bool :param int page_number: The page number for which to get re-identification jobs. This will default to the first page if excluded :param int page_size: The maximum number of objects to return. This will default to the DEFAULT_API_PAGE_SIZE property if not provided :param int environment_id: The ID of the environment to get all re-identification jobs from :return: ReidentificationJobList If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_all_reidentification_jobs_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_all_reidentification_jobs_with_http_info(**kwargs) # noqa: E501 return data def get_all_reidentification_jobs_with_http_info(self, **kwargs): # noqa: E501 """Get all re-identification jobs # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_all_reidentification_jobs_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :param int page_number: The page number for which to get re-identification jobs. This will default to the first page if excluded :param int page_size: The maximum number of objects to return. This will default to the DEFAULT_API_PAGE_SIZE property if not provided :param int environment_id: The ID of the environment to get all re-identification jobs from :return: ReidentificationJobList If the method is called asynchronously, returns the request thread. """ all_params = ['page_number', 'page_size', 'environment_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_all_reidentification_jobs" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'page_number' in params: query_params.append(('page_number', params['page_number'])) # noqa: E501 if 'page_size' in params: query_params.append(('page_size', params['page_size'])) # noqa: E501 if 'environment_id' in params: query_params.append(('environment_id', params['environment_id'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_key'] # noqa: E501 return self.api_client.call_api( '/reidentification-jobs', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ReidentificationJobList', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_reidentification_job_by_id(self, reidentification_job_id, **kwargs): # noqa: E501 """Get re-identification job by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_reidentification_job_by_id(reidentification_job_id, async_req=True) >>> result = thread.get() :param async_req bool :param int reidentification_job_id: The ID of the re-identification job to get (required) :return: ReidentificationJob If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_reidentification_job_by_id_with_http_info(reidentification_job_id, **kwargs) # noqa: E501 else: (data) = self.get_reidentification_job_by_id_with_http_info(reidentification_job_id, **kwargs) # noqa: E501 return data def get_reidentification_job_by_id_with_http_info(self, reidentification_job_id, **kwargs): # noqa: E501 """Get re-identification job by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_reidentification_job_by_id_with_http_info(reidentification_job_id, async_req=True) >>> result = thread.get() :param async_req bool :param int reidentification_job_id: The ID of the re-identification job to get (required) :return: ReidentificationJob If the method is called asynchronously, returns the request thread. """ all_params = ['reidentification_job_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_reidentification_job_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'reidentification_job_id' is set if self.api_client.client_side_validation and ('reidentification_job_id' not in params or params['reidentification_job_id'] is None): # noqa: E501 raise ValueError("Missing the required parameter `reidentification_job_id` when calling `get_reidentification_job_by_id`") # noqa: E501 collection_formats = {} path_params = {} if 'reidentification_job_id' in params: path_params['reidentificationJobId'] = params['reidentification_job_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_key'] # noqa: E501 return self.api_client.call_api( '/reidentification-jobs/{reidentificationJobId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ReidentificationJob', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def update_reidentification_job(self, reidentification_job_id, body, **kwargs): # noqa: E501 """Update re-identification job by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_reidentification_job(reidentification_job_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param int reidentification_job_id: The ID of the re-identification job to update (required) :param ReidentificationJob body: The updated re-identification job (required) :return: ReidentificationJob If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_reidentification_job_with_http_info(reidentification_job_id, body, **kwargs) # noqa: E501 else: (data) = self.update_reidentification_job_with_http_info(reidentification_job_id, body, **kwargs) # noqa: E501 return data def update_reidentification_job_with_http_info(self, reidentification_job_id, body, **kwargs): # noqa: E501 """Update re-identification job by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_reidentification_job_with_http_info(reidentification_job_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param int reidentification_job_id: The ID of the re-identification job to update (required) :param ReidentificationJob body: The updated re-identification job (required) :return: ReidentificationJob If the method is called asynchronously, returns the request thread. """ all_params = ['reidentification_job_id', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method update_reidentification_job" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'reidentification_job_id' is set if self.api_client.client_side_validation and ('reidentification_job_id' not in params or params['reidentification_job_id'] is None): # noqa: E501 raise ValueError("Missing the required parameter `reidentification_job_id` when calling `update_reidentification_job`") # noqa: E501 # verify the required parameter 'body' is set if self.api_client.client_side_validation and ('body' not in params or params['body'] is None): # noqa: E501 raise ValueError("Missing the required parameter `body` when calling `update_reidentification_job`") # noqa: E501 collection_formats = {} path_params = {} if 'reidentification_job_id' in params: path_params['reidentificationJobId'] = params['reidentification_job_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_key'] # noqa: E501 return self.api_client.call_api( '/reidentification-jobs/{reidentificationJobId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ReidentificationJob', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

dxm/lib/masking_api/api/reidentification_job_api.py

from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from dxm.lib.masking_api.api_client import ApiClient class ReidentificationJobApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def create_reidentification_job(self, body, **kwargs): # noqa: E501 """Create re-identification job # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_reidentification_job(body, async_req=True) >>> result = thread.get() :param async_req bool :param ReidentificationJob body: The re-identification job to create (required) :return: ReidentificationJob If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.create_reidentification_job_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.create_reidentification_job_with_http_info(body, **kwargs) # noqa: E501 return data def create_reidentification_job_with_http_info(self, body, **kwargs): # noqa: E501 """Create re-identification job # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_reidentification_job_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param ReidentificationJob body: The re-identification job to create (required) :return: ReidentificationJob If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method create_reidentification_job" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if self.api_client.client_side_validation and ('body' not in params or params['body'] is None): # noqa: E501 raise ValueError("Missing the required parameter `body` when calling `create_reidentification_job`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_key'] # noqa: E501 return self.api_client.call_api( '/reidentification-jobs', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ReidentificationJob', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def delete_reidentification_job(self, reidentification_job_id, **kwargs): # noqa: E501 """Delete re-identification job by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_reidentification_job(reidentification_job_id, async_req=True) >>> result = thread.get() :param async_req bool :param int reidentification_job_id: The ID of the re-identification job to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_reidentification_job_with_http_info(reidentification_job_id, **kwargs) # noqa: E501 else: (data) = self.delete_reidentification_job_with_http_info(reidentification_job_id, **kwargs) # noqa: E501 return data def delete_reidentification_job_with_http_info(self, reidentification_job_id, **kwargs): # noqa: E501 """Delete re-identification job by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_reidentification_job_with_http_info(reidentification_job_id, async_req=True) >>> result = thread.get() :param async_req bool :param int reidentification_job_id: The ID of the re-identification job to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['reidentification_job_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_reidentification_job" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'reidentification_job_id' is set if self.api_client.client_side_validation and ('reidentification_job_id' not in params or params['reidentification_job_id'] is None): # noqa: E501 raise ValueError("Missing the required parameter `reidentification_job_id` when calling `delete_reidentification_job`") # noqa: E501 collection_formats = {} path_params = {} if 'reidentification_job_id' in params: path_params['reidentificationJobId'] = params['reidentification_job_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_key'] # noqa: E501 return self.api_client.call_api( '/reidentification-jobs/{reidentificationJobId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_all_reidentification_jobs(self, **kwargs): # noqa: E501 """Get all re-identification jobs # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_all_reidentification_jobs(async_req=True) >>> result = thread.get() :param async_req bool :param int page_number: The page number for which to get re-identification jobs. This will default to the first page if excluded :param int page_size: The maximum number of objects to return. This will default to the DEFAULT_API_PAGE_SIZE property if not provided :param int environment_id: The ID of the environment to get all re-identification jobs from :return: ReidentificationJobList If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_all_reidentification_jobs_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_all_reidentification_jobs_with_http_info(**kwargs) # noqa: E501 return data def get_all_reidentification_jobs_with_http_info(self, **kwargs): # noqa: E501 """Get all re-identification jobs # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_all_reidentification_jobs_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :param int page_number: The page number for which to get re-identification jobs. This will default to the first page if excluded :param int page_size: The maximum number of objects to return. This will default to the DEFAULT_API_PAGE_SIZE property if not provided :param int environment_id: The ID of the environment to get all re-identification jobs from :return: ReidentificationJobList If the method is called asynchronously, returns the request thread. """ all_params = ['page_number', 'page_size', 'environment_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_all_reidentification_jobs" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'page_number' in params: query_params.append(('page_number', params['page_number'])) # noqa: E501 if 'page_size' in params: query_params.append(('page_size', params['page_size'])) # noqa: E501 if 'environment_id' in params: query_params.append(('environment_id', params['environment_id'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_key'] # noqa: E501 return self.api_client.call_api( '/reidentification-jobs', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ReidentificationJobList', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_reidentification_job_by_id(self, reidentification_job_id, **kwargs): # noqa: E501 """Get re-identification job by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_reidentification_job_by_id(reidentification_job_id, async_req=True) >>> result = thread.get() :param async_req bool :param int reidentification_job_id: The ID of the re-identification job to get (required) :return: ReidentificationJob If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_reidentification_job_by_id_with_http_info(reidentification_job_id, **kwargs) # noqa: E501 else: (data) = self.get_reidentification_job_by_id_with_http_info(reidentification_job_id, **kwargs) # noqa: E501 return data def get_reidentification_job_by_id_with_http_info(self, reidentification_job_id, **kwargs): # noqa: E501 """Get re-identification job by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_reidentification_job_by_id_with_http_info(reidentification_job_id, async_req=True) >>> result = thread.get() :param async_req bool :param int reidentification_job_id: The ID of the re-identification job to get (required) :return: ReidentificationJob If the method is called asynchronously, returns the request thread. """ all_params = ['reidentification_job_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_reidentification_job_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'reidentification_job_id' is set if self.api_client.client_side_validation and ('reidentification_job_id' not in params or params['reidentification_job_id'] is None): # noqa: E501 raise ValueError("Missing the required parameter `reidentification_job_id` when calling `get_reidentification_job_by_id`") # noqa: E501 collection_formats = {} path_params = {} if 'reidentification_job_id' in params: path_params['reidentificationJobId'] = params['reidentification_job_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_key'] # noqa: E501 return self.api_client.call_api( '/reidentification-jobs/{reidentificationJobId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ReidentificationJob', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def update_reidentification_job(self, reidentification_job_id, body, **kwargs): # noqa: E501 """Update re-identification job by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_reidentification_job(reidentification_job_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param int reidentification_job_id: The ID of the re-identification job to update (required) :param ReidentificationJob body: The updated re-identification job (required) :return: ReidentificationJob If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_reidentification_job_with_http_info(reidentification_job_id, body, **kwargs) # noqa: E501 else: (data) = self.update_reidentification_job_with_http_info(reidentification_job_id, body, **kwargs) # noqa: E501 return data def update_reidentification_job_with_http_info(self, reidentification_job_id, body, **kwargs): # noqa: E501 """Update re-identification job by ID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_reidentification_job_with_http_info(reidentification_job_id, body, async_req=True) >>> result = thread.get() :param async_req bool :param int reidentification_job_id: The ID of the re-identification job to update (required) :param ReidentificationJob body: The updated re-identification job (required) :return: ReidentificationJob If the method is called asynchronously, returns the request thread. """ all_params = ['reidentification_job_id', 'body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method update_reidentification_job" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'reidentification_job_id' is set if self.api_client.client_side_validation and ('reidentification_job_id' not in params or params['reidentification_job_id'] is None): # noqa: E501 raise ValueError("Missing the required parameter `reidentification_job_id` when calling `update_reidentification_job`") # noqa: E501 # verify the required parameter 'body' is set if self.api_client.client_side_validation and ('body' not in params or params['body'] is None): # noqa: E501 raise ValueError("Missing the required parameter `body` when calling `update_reidentification_job`") # noqa: E501 collection_formats = {} path_params = {} if 'reidentification_job_id' in params: path_params['reidentificationJobId'] = params['reidentification_job_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['api_key'] # noqa: E501 return self.api_client.call_api( '/reidentification-jobs/{reidentificationJobId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ReidentificationJob', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

0.712932

0.055926

from __future__ import division, unicode_literals, print_function, absolute_import import numpy as np import traitlets as tl from podpac.core.node import NodeException from podpac.core.units import UnitsDataArray from podpac.core.utils import common_doc from podpac.core.compositor.compositor import COMMON_COMPOSITOR_DOC, BaseCompositor @common_doc(COMMON_COMPOSITOR_DOC) class OrderedCompositor(BaseCompositor): """Compositor that combines sources based on their order in self.sources. The sources should generally be interpolated before being composited (i.e. not raw datasources). Attributes ---------- sources : list Source nodes, in order of preference. Later sources are only used where earlier sources do not provide data. source_coordinates : :class:`podpac.Coordinates` Coordinates that make each source unique. Must the same size as ``sources`` and single-dimensional. Optional. multithreading : bool, optional Default is True. If True, will always evaluate the compositor in serial, ignoring any MULTITHREADING settings """ multithreading = tl.Bool(False) @common_doc(COMMON_COMPOSITOR_DOC) def composite(self, coordinates, data_arrays, result=None): """Composites data_arrays in order that they appear. Once a request contains no nans, the result is returned. Parameters ---------- coordinates : :class:`podpac.Coordinates` {requested_coordinates} data_arrays : generator Evaluated source data, in the same order as the sources. result : podpac.UnitsDataArray, optional {eval_output} Returns ------- {eval_return} This composites the sources together until there are no nans or no more sources. """ if result is None: result = self.create_output_array(coordinates) else: result[:] = np.nan mask = UnitsDataArray.create(coordinates, outputs=self.outputs, data=0, dtype=bool) for data in data_arrays: if self.outputs is None: try: data = data.transpose(*result.dims) except ValueError: raise NodeException( "Cannot evaluate compositor with requested dims %s. " "The compositor source dims are %s. " "Specify the compositor 'dims' attribute to ignore extra requested dims." % (coordinates.dims, data.dims) ) self._composite(result, data, mask) else: for name in data["output"]: self._composite(result.sel(output=name), data.sel(output=name), mask.sel(output=name)) # stop if the results are full if np.all(mask): break return result @staticmethod def _composite(result, data, mask): source_mask = np.isfinite(data.data) b = ~mask & source_mask result.data[b.data] = data.data[b.data] mask |= source_mask

podpac/core/compositor/ordered_compositor.py

from __future__ import division, unicode_literals, print_function, absolute_import import numpy as np import traitlets as tl from podpac.core.node import NodeException from podpac.core.units import UnitsDataArray from podpac.core.utils import common_doc from podpac.core.compositor.compositor import COMMON_COMPOSITOR_DOC, BaseCompositor @common_doc(COMMON_COMPOSITOR_DOC) class OrderedCompositor(BaseCompositor): """Compositor that combines sources based on their order in self.sources. The sources should generally be interpolated before being composited (i.e. not raw datasources). Attributes ---------- sources : list Source nodes, in order of preference. Later sources are only used where earlier sources do not provide data. source_coordinates : :class:`podpac.Coordinates` Coordinates that make each source unique. Must the same size as ``sources`` and single-dimensional. Optional. multithreading : bool, optional Default is True. If True, will always evaluate the compositor in serial, ignoring any MULTITHREADING settings """ multithreading = tl.Bool(False) @common_doc(COMMON_COMPOSITOR_DOC) def composite(self, coordinates, data_arrays, result=None): """Composites data_arrays in order that they appear. Once a request contains no nans, the result is returned. Parameters ---------- coordinates : :class:`podpac.Coordinates` {requested_coordinates} data_arrays : generator Evaluated source data, in the same order as the sources. result : podpac.UnitsDataArray, optional {eval_output} Returns ------- {eval_return} This composites the sources together until there are no nans or no more sources. """ if result is None: result = self.create_output_array(coordinates) else: result[:] = np.nan mask = UnitsDataArray.create(coordinates, outputs=self.outputs, data=0, dtype=bool) for data in data_arrays: if self.outputs is None: try: data = data.transpose(*result.dims) except ValueError: raise NodeException( "Cannot evaluate compositor with requested dims %s. " "The compositor source dims are %s. " "Specify the compositor 'dims' attribute to ignore extra requested dims." % (coordinates.dims, data.dims) ) self._composite(result, data, mask) else: for name in data["output"]: self._composite(result.sel(output=name), data.sel(output=name), mask.sel(output=name)) # stop if the results are full if np.all(mask): break return result @staticmethod def _composite(result, data, mask): source_mask = np.isfinite(data.data) b = ~mask & source_mask result.data[b.data] = data.data[b.data] mask |= source_mask

0.82425

0.461623

import os import numpy as np import pandas as pd import time as tm import rpy2.robjects as robjects import tensorflow as tf import math import scipy.io as sio import optunity as opt from tensorflow.contrib.tensor_forest.python import tensor_forest from tensorflow.python.ops import resources import SparseMatrix as sm def run_LAmbDA(DataPath, LabelsPath, CV_RDataPath, OutputDir, GeneOrderPath = "", NumGenes = 0): ''' run LAmbDA classifier Wrapper script to run LAmbDA on a benchmark dataset with 5-fold cross validation, outputs lists of true and predicted cell labels as csv files, as well as computation time. Parameters ---------- DataPath : Data file path (.csv), cells-genes matrix with cell unique barcodes as row names and gene names as column names. LabelsPath : Cell population annotations file path (.csv). CV_RDataPath : Cross validation RData file path (.RData), obtained from Cross_Validation.R function. OutputDir : Output directory defining the path of the exported file. GeneOrderPath : Gene order file path (.csv) obtained from feature selection, defining the genes order for each cross validation fold, default is NULL. NumGenes : Number of genes used in case of feature selection (integer), default is 0. ''' # read the Rdata file robjects.r['load'](CV_RDataPath) nfolds = np.array(robjects.r['n_folds'], dtype = 'int') tokeep = np.array(robjects.r['Cells_to_Keep'], dtype = 'bool') col = np.array(robjects.r['col_Index'], dtype = 'int') col = col - 1 test_ind = np.array(robjects.r['Test_Idx']) train_ind = np.array(robjects.r['Train_Idx']) # read the data data = sm.importMM(DataPath) labels = pd.read_csv(LabelsPath, header=0,index_col=None, sep=',', usecols = col) labels = labels.iloc[tokeep] data = data.iloc[tokeep] data = data.fillna("0").astype(int) # read the feature file if (NumGenes > 0): features = pd.read_csv(GeneOrderPath,header=0,index_col=None, sep=',') # folder with results os.chdir(OutputDir) tr_time=[] ts_time=[] truelab = np.zeros([len(labels),1],dtype = int) predlab = np.zeros([len(labels),1],dtype = int) for i in range(np.squeeze(nfolds)): global X, Y, Gnp, Dnp, train, test, prt, cv test_ind_i = np.array(test_ind[i], dtype = 'int') - 1 train_ind_i = np.array(train_ind[i], dtype = 'int') - 1 X = np.array(data) if (NumGenes > 0): X = np.log2(X/10+1) feat_to_use = features.iloc[0:NumGenes,i] X = X[:,feat_to_use] else: X = np.log2(np.transpose(select_feats(np.transpose(X),0.5,80))/10+1) uniq = np.unique(labels) Y = np.zeros([len(labels),len(uniq)],int) for j in range(len(uniq)): Y[np.where(labels == uniq[j])[0],j] = 1 Y = np.array(Y) Gnp = np.zeros([len(uniq),len(uniq)],int) np.fill_diagonal(Gnp,1) Gnp = np.array(Gnp) Dnp = np.ones([len(uniq),1],int) Dnp = np.array(Dnp) train_samp = int(np.floor(0.75*len(train_ind_i))) test_samp = len(train_ind_i) - train_samp perm = np.random.permutation(len(train_ind_i)) train = perm[0:train_samp] test = perm[train_samp:test_samp+1] while(np.sum(np.sum(Y[train,:],0)<5)>0): perm = np.random.permutation(X.shape[0]) train = perm[0:train_samp+1] test = perm[train_samp+1:train_samp+test_samp+1] cv = i optunity_it = 0 prt = False opt_params = None start=tm.time() opt_params, _, _ = opt.minimize(run_LAmbDA2,solver_name='sobol', gamma=[0.8,1.2], delta=[0.05,0.95], tau=[10.0,11.0], prc_cut=[20,50], bs_prc=[0.2,0.6], num_trees=[10,200], max_nodes=[100,1000], num_evals=50) tr_time.append(tm.time()-start) print("Finished training!") prt = True train = train_ind_i test = test_ind_i start=tm.time() err = run_LAmbDA2(opt_params['gamma'], opt_params['delta'], opt_params['tau'], opt_params['prc_cut'], opt_params['bs_prc'], opt_params['num_trees'], opt_params['max_nodes']) ts_time.append(tm.time()-start) tf.reset_default_graph(); predfile = 'preds_cv' + str(cv) + '.mat' truefile = 'truth_cv' + str(cv) + '.mat' pred = sio.loadmat(predfile) truth = sio.loadmat(truefile) pred = pred['preds'] truth = truth['labels'] pred_ind = np.argmax(pred,axis=1) truth_ind = np.argmax(truth,axis=1) predlab[test_ind_i,0] = pred_ind truelab[test_ind_i,0] = truth_ind truelab = pd.DataFrame(truelab) predlab = pd.DataFrame(predlab) tr_time = pd.DataFrame(tr_time) ts_time = pd.DataFrame(ts_time) if (NumGenes == 0): truelab.to_csv("LAmbDA_True_Labels.csv", index = False) predlab.to_csv("LAmbDA_Pred_Labels.csv", index = False) tr_time.to_csv("LAmbDA_Training_Time.csv", index = False) ts_time.to_csv("LAmbDA_Testing_Time.csv", index = False) else: truelab.to_csv("LAmbDA_" + str(NumGenes) + "_True_Labels.csv", index = False) predlab.to_csv("LAmbDA_" + str(NumGenes) + "_Pred_Labels.csv", index = False) tr_time.to_csv("LAmbDA_" + str(NumGenes) + "_Training_Time.csv", index = False) ts_time.to_csv("LAmbDA_" + str(NumGenes) + "_Testing_Time.csv", index = False) ##### Functions copied from LAmbDA's Github def wt_cutoff(colnum,cutoff,Gtmp,gamma): rowsums = np.sum(Gtmp,axis=1); return(math.ceil(cutoff*(math.log((max(rowsums)/rowsums[colnum])+1,2)**gamma))) def resample(prc_cut,Y,Gtmp,train,gamma): add = list() rem = list() colsums = np.sum(Y[train,:],axis=0); cutoff = math.ceil(np.percentile(colsums,prc_cut)); for i in range(len(colsums)): if colsums[i] == 0: pass elif colsums[i] < wt_cutoff(i,cutoff,Gtmp,gamma): idx = np.squeeze(np.array(np.where(Y[train,i]>=1))); choice = np.random.choice(train[idx],int(wt_cutoff(i,cutoff,Gtmp,gamma)-colsums[i])) add = add + choice.tolist(); elif colsums[i] == wt_cutoff(i,cutoff,Gtmp,gamma): pass else: idx = np.squeeze(np.array(np.where(Y[train,i]>=1))); choice = np.random.choice(train[idx],int(colsums[i]-wt_cutoff(i,cutoff,Gtmp,gamma)),replace=False) rem = rem + choice.tolist() return np.concatenate((list([val for val in train if val not in rem]),add)); def select_feats(Xtmp,num_zero_prc_cut,var_prc_cut): #********************************************************************* # remove features with many zeros num_feat_zeros = np.sum(Xtmp==0,axis=1); Xtmp = Xtmp[num_feat_zeros<num_zero_prc_cut*Xtmp.shape[1],:] #********************************************************************* # remove features with low variance feat_vars = np.var(Xtmp,axis=1) Xtmp = Xtmp[feat_vars>np.percentile(feat_vars,var_prc_cut),:] return(Xtmp) def get_yn(predict,ys,delta,tau,output_feats): D = tf.cast(Dnp, tf.float32); G = tf.cast(Gnp, tf.float32); ys = tf.cast(ys, tf.float32); #print("start") Cm = tf.matmul(tf.transpose(tf.matmul(ys,D)),predict+0.1)/tf.reshape(tf.reduce_sum(tf.transpose(tf.matmul(ys,D)),1),(-1,1)); #print("1") mCm = tf.reshape(tf.reduce_mean(tf.cast(tf.matmul(tf.transpose(D),G)>0,tf.float32)*Cm,1),(-1,1)); #print("2") yw = tf.multiply(predict+0.1,tf.matmul(tf.matmul(ys,D),tf.pow(mCm/Cm,tau))); #print("3") ye = tf.multiply(tf.matmul(ys,G),yw); #print("4") yt = tf.matmul(ys,tf.matmul(tf.transpose(ys),ye)); #print("5") ya = (delta*yt)+((1-delta)*ye) #print("6") yn = tf.cast(tf.one_hot(tf.argmax(ya,axis=1),output_feats), dtype=tf.float32) #print("7") return(yn) def get_yi(rowsums,G2,ys): G2 = tf.cast(G2, tf.float32); ys = tf.cast(ys, tf.float32); yi = tf.cast(tf.matmul(ys,G2), dtype=tf.float32); return(yi) def run_LAmbDA2(gamma, delta, tau, prc_cut, bs_prc, num_trees, max_nodes): global X, Y, Gnp, Dnp, train, test, prt, cv D = tf.cast(Dnp, tf.float32); G = tf.cast(Gnp, tf.float32); #optunity_it = optunity_it+1; num_trees = int(num_trees); max_nodes = int(max_nodes); prc_cut = int(np.ceil(prc_cut)); print("gamma=%.4f, delta=%.4f, tau=%.4f, prc_cut=%i, bs_prc=%.4f, num_trees=%i, max_nodes=%i" % (gamma, delta, tau, prc_cut, bs_prc, num_trees, max_nodes)) input_feats = X.shape[1]; num_labls = G.shape.as_list(); output_feats = num_labls[1]; #print(output_feats) num_labls = num_labls[0]; rowsums = np.sum(Gnp,axis=1); train2 = resample(prc_cut, Y, Gnp, train, gamma); # Bug?? bs = int(np.ceil(bs_prc*train2.size)) xs = tf.placeholder(tf.float32, [None,input_feats]) #ys = tf.placeholder(tf.float32, [None,num_labls]) yin = tf.placeholder(tf.int32, [None]) print("Vars loaded xs and ys created") hparams = tensor_forest.ForestHParams(num_classes=output_feats, num_features=input_feats, num_trees=num_trees, max_nodes=max_nodes).fill() print("Tensor forest hparams created") forest_graph = tensor_forest.RandomForestGraphs(hparams) print("Tensor forest graph created") train_op = forest_graph.training_graph(xs, yin) loss_op = forest_graph.training_loss(xs, yin) print("Loss and train ops created") predict, _, _ = forest_graph.inference_graph(xs) print("Tensor forest variables created through predict") accuracy_op = tf.reduce_mean(tf.reduce_sum(tf.square(tf.one_hot(yin,output_feats)-predict),reduction_indices=[1])) print(tf.reduce_sum(tf.square(tf.one_hot(yin,output_feats)-predict),reduction_indices=[1])) #predict = tf.one_hot(pred); print("Lambda specific variables created") # Creating training and testing steps G2 = np.copy(Gnp); G2[rowsums>1,:] = 0; YI = np.matmul(Y,G2); YIrs = np.sum(YI,axis=1); trainI = train2[np.in1d(train2,np.where(YIrs==1))]; print("data type trainI,",trainI.dtype) testI = test[np.in1d(test,np.where(YIrs==1))]; print("trainI testI created") #init_vars=tf.global_variables_initializer() init_vars = tf.group(tf.global_variables_initializer(), resources.initialize_resources(resources.shared_resources())) sess = tf.Session() sess.run(init_vars) print("Session started") #beep = sess.run(predict,feed_dict={xs:X[1:100,:]}); #beep = sess.run(predict,feed_dict={xs:X[train2[0:bs],:]}); tensor_trainI = {xs: X[trainI, :], yin: sess.run(tf.argmax(get_yi(rowsums,G2,Y[trainI, :]),axis=1))} print("tensor_trainI made") tensor_testI = {xs: X[testI, :], yin: sess.run(tf.argmax(get_yi(rowsums,G2,Y[testI, :]),axis=1))} print("tensor_testI made") tensor_train = {xs: X[train2[0:bs], :], yin: sess.run(tf.argmax(get_yn(sess.run(predict,feed_dict={xs:X[train2[0:bs],:]}),Y[train2[0:bs], :],delta,tau,output_feats),axis=1))} print("tensor_train made") tensor_test = {xs: X[test, :], yin: sess.run(tf.argmax(get_yn(sess.run(predict,feed_dict={xs:X[test,:]}),Y[test, :],delta,tau,output_feats),axis=1))} print("tensor_test made") #********************************** #print("Loss and training steps created with sample tensors") # Setting params and initializing print("Beginning iterations") # Starting training iterations print(X.shape) for i in range(1,101): if i < 50: sess.run(train_op, feed_dict=tensor_trainI) #print("ran train op") if i % 10 == 0: print(str(sess.run(accuracy_op, feed_dict=tensor_trainI)) + ' ' + str(sess.run(accuracy_op, feed_dict=tensor_testI))) else: sess.run(train_op, feed_dict=tensor_train) if i % 10 == 0: print(str(sess.run(accuracy_op, feed_dict=tensor_train)) + ' ' + str(sess.run(accuracy_op, feed_dict=tensor_test))) elif i % 10 == 0: np.random_shuffle(train2); tensor_train = {xs: X[train2[0:bs], :], yin: sess.run(get_yn(sess.run(predict,feed_dict={xs:X[train2[0:bs],:]}),Y[train2[0:bs], :],delta,tau,output_feats))} if prt: blah = sess.run(predict, feed_dict=tensor_test); sio.savemat('preds_cv' + str(cv) + '.mat', {'preds': blah}); sio.savemat('truth_cv' + str(cv) + '.mat', {'labels': Y[test, :]}); acc = sess.run(accuracy_op, feed_dict=tensor_test) print("loss1=%.4f, gamma=%.4f, delta=%.4f, tau=%.4f, prc_cut=%i, bs_prc=%.4f, num_trees=%i, max_nodes=%i" % (acc, gamma, delta, tau, prc_cut, bs_prc, num_trees, max_nodes)) tf.reset_default_graph(); return(acc)

Scripts/run_LAmbDA.py

import os import numpy as np import pandas as pd import time as tm import rpy2.robjects as robjects import tensorflow as tf import math import scipy.io as sio import optunity as opt from tensorflow.contrib.tensor_forest.python import tensor_forest from tensorflow.python.ops import resources import SparseMatrix as sm def run_LAmbDA(DataPath, LabelsPath, CV_RDataPath, OutputDir, GeneOrderPath = "", NumGenes = 0): ''' run LAmbDA classifier Wrapper script to run LAmbDA on a benchmark dataset with 5-fold cross validation, outputs lists of true and predicted cell labels as csv files, as well as computation time. Parameters ---------- DataPath : Data file path (.csv), cells-genes matrix with cell unique barcodes as row names and gene names as column names. LabelsPath : Cell population annotations file path (.csv). CV_RDataPath : Cross validation RData file path (.RData), obtained from Cross_Validation.R function. OutputDir : Output directory defining the path of the exported file. GeneOrderPath : Gene order file path (.csv) obtained from feature selection, defining the genes order for each cross validation fold, default is NULL. NumGenes : Number of genes used in case of feature selection (integer), default is 0. ''' # read the Rdata file robjects.r['load'](CV_RDataPath) nfolds = np.array(robjects.r['n_folds'], dtype = 'int') tokeep = np.array(robjects.r['Cells_to_Keep'], dtype = 'bool') col = np.array(robjects.r['col_Index'], dtype = 'int') col = col - 1 test_ind = np.array(robjects.r['Test_Idx']) train_ind = np.array(robjects.r['Train_Idx']) # read the data data = sm.importMM(DataPath) labels = pd.read_csv(LabelsPath, header=0,index_col=None, sep=',', usecols = col) labels = labels.iloc[tokeep] data = data.iloc[tokeep] data = data.fillna("0").astype(int) # read the feature file if (NumGenes > 0): features = pd.read_csv(GeneOrderPath,header=0,index_col=None, sep=',') # folder with results os.chdir(OutputDir) tr_time=[] ts_time=[] truelab = np.zeros([len(labels),1],dtype = int) predlab = np.zeros([len(labels),1],dtype = int) for i in range(np.squeeze(nfolds)): global X, Y, Gnp, Dnp, train, test, prt, cv test_ind_i = np.array(test_ind[i], dtype = 'int') - 1 train_ind_i = np.array(train_ind[i], dtype = 'int') - 1 X = np.array(data) if (NumGenes > 0): X = np.log2(X/10+1) feat_to_use = features.iloc[0:NumGenes,i] X = X[:,feat_to_use] else: X = np.log2(np.transpose(select_feats(np.transpose(X),0.5,80))/10+1) uniq = np.unique(labels) Y = np.zeros([len(labels),len(uniq)],int) for j in range(len(uniq)): Y[np.where(labels == uniq[j])[0],j] = 1 Y = np.array(Y) Gnp = np.zeros([len(uniq),len(uniq)],int) np.fill_diagonal(Gnp,1) Gnp = np.array(Gnp) Dnp = np.ones([len(uniq),1],int) Dnp = np.array(Dnp) train_samp = int(np.floor(0.75*len(train_ind_i))) test_samp = len(train_ind_i) - train_samp perm = np.random.permutation(len(train_ind_i)) train = perm[0:train_samp] test = perm[train_samp:test_samp+1] while(np.sum(np.sum(Y[train,:],0)<5)>0): perm = np.random.permutation(X.shape[0]) train = perm[0:train_samp+1] test = perm[train_samp+1:train_samp+test_samp+1] cv = i optunity_it = 0 prt = False opt_params = None start=tm.time() opt_params, _, _ = opt.minimize(run_LAmbDA2,solver_name='sobol', gamma=[0.8,1.2], delta=[0.05,0.95], tau=[10.0,11.0], prc_cut=[20,50], bs_prc=[0.2,0.6], num_trees=[10,200], max_nodes=[100,1000], num_evals=50) tr_time.append(tm.time()-start) print("Finished training!") prt = True train = train_ind_i test = test_ind_i start=tm.time() err = run_LAmbDA2(opt_params['gamma'], opt_params['delta'], opt_params['tau'], opt_params['prc_cut'], opt_params['bs_prc'], opt_params['num_trees'], opt_params['max_nodes']) ts_time.append(tm.time()-start) tf.reset_default_graph(); predfile = 'preds_cv' + str(cv) + '.mat' truefile = 'truth_cv' + str(cv) + '.mat' pred = sio.loadmat(predfile) truth = sio.loadmat(truefile) pred = pred['preds'] truth = truth['labels'] pred_ind = np.argmax(pred,axis=1) truth_ind = np.argmax(truth,axis=1) predlab[test_ind_i,0] = pred_ind truelab[test_ind_i,0] = truth_ind truelab = pd.DataFrame(truelab) predlab = pd.DataFrame(predlab) tr_time = pd.DataFrame(tr_time) ts_time = pd.DataFrame(ts_time) if (NumGenes == 0): truelab.to_csv("LAmbDA_True_Labels.csv", index = False) predlab.to_csv("LAmbDA_Pred_Labels.csv", index = False) tr_time.to_csv("LAmbDA_Training_Time.csv", index = False) ts_time.to_csv("LAmbDA_Testing_Time.csv", index = False) else: truelab.to_csv("LAmbDA_" + str(NumGenes) + "_True_Labels.csv", index = False) predlab.to_csv("LAmbDA_" + str(NumGenes) + "_Pred_Labels.csv", index = False) tr_time.to_csv("LAmbDA_" + str(NumGenes) + "_Training_Time.csv", index = False) ts_time.to_csv("LAmbDA_" + str(NumGenes) + "_Testing_Time.csv", index = False) ##### Functions copied from LAmbDA's Github def wt_cutoff(colnum,cutoff,Gtmp,gamma): rowsums = np.sum(Gtmp,axis=1); return(math.ceil(cutoff*(math.log((max(rowsums)/rowsums[colnum])+1,2)**gamma))) def resample(prc_cut,Y,Gtmp,train,gamma): add = list() rem = list() colsums = np.sum(Y[train,:],axis=0); cutoff = math.ceil(np.percentile(colsums,prc_cut)); for i in range(len(colsums)): if colsums[i] == 0: pass elif colsums[i] < wt_cutoff(i,cutoff,Gtmp,gamma): idx = np.squeeze(np.array(np.where(Y[train,i]>=1))); choice = np.random.choice(train[idx],int(wt_cutoff(i,cutoff,Gtmp,gamma)-colsums[i])) add = add + choice.tolist(); elif colsums[i] == wt_cutoff(i,cutoff,Gtmp,gamma): pass else: idx = np.squeeze(np.array(np.where(Y[train,i]>=1))); choice = np.random.choice(train[idx],int(colsums[i]-wt_cutoff(i,cutoff,Gtmp,gamma)),replace=False) rem = rem + choice.tolist() return np.concatenate((list([val for val in train if val not in rem]),add)); def select_feats(Xtmp,num_zero_prc_cut,var_prc_cut): #********************************************************************* # remove features with many zeros num_feat_zeros = np.sum(Xtmp==0,axis=1); Xtmp = Xtmp[num_feat_zeros<num_zero_prc_cut*Xtmp.shape[1],:] #********************************************************************* # remove features with low variance feat_vars = np.var(Xtmp,axis=1) Xtmp = Xtmp[feat_vars>np.percentile(feat_vars,var_prc_cut),:] return(Xtmp) def get_yn(predict,ys,delta,tau,output_feats): D = tf.cast(Dnp, tf.float32); G = tf.cast(Gnp, tf.float32); ys = tf.cast(ys, tf.float32); #print("start") Cm = tf.matmul(tf.transpose(tf.matmul(ys,D)),predict+0.1)/tf.reshape(tf.reduce_sum(tf.transpose(tf.matmul(ys,D)),1),(-1,1)); #print("1") mCm = tf.reshape(tf.reduce_mean(tf.cast(tf.matmul(tf.transpose(D),G)>0,tf.float32)*Cm,1),(-1,1)); #print("2") yw = tf.multiply(predict+0.1,tf.matmul(tf.matmul(ys,D),tf.pow(mCm/Cm,tau))); #print("3") ye = tf.multiply(tf.matmul(ys,G),yw); #print("4") yt = tf.matmul(ys,tf.matmul(tf.transpose(ys),ye)); #print("5") ya = (delta*yt)+((1-delta)*ye) #print("6") yn = tf.cast(tf.one_hot(tf.argmax(ya,axis=1),output_feats), dtype=tf.float32) #print("7") return(yn) def get_yi(rowsums,G2,ys): G2 = tf.cast(G2, tf.float32); ys = tf.cast(ys, tf.float32); yi = tf.cast(tf.matmul(ys,G2), dtype=tf.float32); return(yi) def run_LAmbDA2(gamma, delta, tau, prc_cut, bs_prc, num_trees, max_nodes): global X, Y, Gnp, Dnp, train, test, prt, cv D = tf.cast(Dnp, tf.float32); G = tf.cast(Gnp, tf.float32); #optunity_it = optunity_it+1; num_trees = int(num_trees); max_nodes = int(max_nodes); prc_cut = int(np.ceil(prc_cut)); print("gamma=%.4f, delta=%.4f, tau=%.4f, prc_cut=%i, bs_prc=%.4f, num_trees=%i, max_nodes=%i" % (gamma, delta, tau, prc_cut, bs_prc, num_trees, max_nodes)) input_feats = X.shape[1]; num_labls = G.shape.as_list(); output_feats = num_labls[1]; #print(output_feats) num_labls = num_labls[0]; rowsums = np.sum(Gnp,axis=1); train2 = resample(prc_cut, Y, Gnp, train, gamma); # Bug?? bs = int(np.ceil(bs_prc*train2.size)) xs = tf.placeholder(tf.float32, [None,input_feats]) #ys = tf.placeholder(tf.float32, [None,num_labls]) yin = tf.placeholder(tf.int32, [None]) print("Vars loaded xs and ys created") hparams = tensor_forest.ForestHParams(num_classes=output_feats, num_features=input_feats, num_trees=num_trees, max_nodes=max_nodes).fill() print("Tensor forest hparams created") forest_graph = tensor_forest.RandomForestGraphs(hparams) print("Tensor forest graph created") train_op = forest_graph.training_graph(xs, yin) loss_op = forest_graph.training_loss(xs, yin) print("Loss and train ops created") predict, _, _ = forest_graph.inference_graph(xs) print("Tensor forest variables created through predict") accuracy_op = tf.reduce_mean(tf.reduce_sum(tf.square(tf.one_hot(yin,output_feats)-predict),reduction_indices=[1])) print(tf.reduce_sum(tf.square(tf.one_hot(yin,output_feats)-predict),reduction_indices=[1])) #predict = tf.one_hot(pred); print("Lambda specific variables created") # Creating training and testing steps G2 = np.copy(Gnp); G2[rowsums>1,:] = 0; YI = np.matmul(Y,G2); YIrs = np.sum(YI,axis=1); trainI = train2[np.in1d(train2,np.where(YIrs==1))]; print("data type trainI,",trainI.dtype) testI = test[np.in1d(test,np.where(YIrs==1))]; print("trainI testI created") #init_vars=tf.global_variables_initializer() init_vars = tf.group(tf.global_variables_initializer(), resources.initialize_resources(resources.shared_resources())) sess = tf.Session() sess.run(init_vars) print("Session started") #beep = sess.run(predict,feed_dict={xs:X[1:100,:]}); #beep = sess.run(predict,feed_dict={xs:X[train2[0:bs],:]}); tensor_trainI = {xs: X[trainI, :], yin: sess.run(tf.argmax(get_yi(rowsums,G2,Y[trainI, :]),axis=1))} print("tensor_trainI made") tensor_testI = {xs: X[testI, :], yin: sess.run(tf.argmax(get_yi(rowsums,G2,Y[testI, :]),axis=1))} print("tensor_testI made") tensor_train = {xs: X[train2[0:bs], :], yin: sess.run(tf.argmax(get_yn(sess.run(predict,feed_dict={xs:X[train2[0:bs],:]}),Y[train2[0:bs], :],delta,tau,output_feats),axis=1))} print("tensor_train made") tensor_test = {xs: X[test, :], yin: sess.run(tf.argmax(get_yn(sess.run(predict,feed_dict={xs:X[test,:]}),Y[test, :],delta,tau,output_feats),axis=1))} print("tensor_test made") #********************************** #print("Loss and training steps created with sample tensors") # Setting params and initializing print("Beginning iterations") # Starting training iterations print(X.shape) for i in range(1,101): if i < 50: sess.run(train_op, feed_dict=tensor_trainI) #print("ran train op") if i % 10 == 0: print(str(sess.run(accuracy_op, feed_dict=tensor_trainI)) + ' ' + str(sess.run(accuracy_op, feed_dict=tensor_testI))) else: sess.run(train_op, feed_dict=tensor_train) if i % 10 == 0: print(str(sess.run(accuracy_op, feed_dict=tensor_train)) + ' ' + str(sess.run(accuracy_op, feed_dict=tensor_test))) elif i % 10 == 0: np.random_shuffle(train2); tensor_train = {xs: X[train2[0:bs], :], yin: sess.run(get_yn(sess.run(predict,feed_dict={xs:X[train2[0:bs],:]}),Y[train2[0:bs], :],delta,tau,output_feats))} if prt: blah = sess.run(predict, feed_dict=tensor_test); sio.savemat('preds_cv' + str(cv) + '.mat', {'preds': blah}); sio.savemat('truth_cv' + str(cv) + '.mat', {'labels': Y[test, :]}); acc = sess.run(accuracy_op, feed_dict=tensor_test) print("loss1=%.4f, gamma=%.4f, delta=%.4f, tau=%.4f, prc_cut=%i, bs_prc=%.4f, num_trees=%i, max_nodes=%i" % (acc, gamma, delta, tau, prc_cut, bs_prc, num_trees, max_nodes)) tf.reset_default_graph(); return(acc)

0.287368

0.379091

from django.test import TransactionTestCase from django.conf import settings from django.test.client import Client from spotseeker_server.models import Spot, SpotExtendedInfo import simplejson as json import random from django.test.utils import override_settings from mock import patch from django.core import cache from spotseeker_server import models @override_settings(SPOTSEEKER_AUTH_MODULE='spotseeker_server.auth.all_ok') @override_settings(SPOTSEEKER_SPOT_FORM='spotseeker_server.org_forms.uw_spot.UWSpotForm') @override_settings(SPOTSEEKER_SPOTEXTENDEDINFO_FORM='spotseeker_server.org_forms.uw_spot.UWSpotExtendedInfoForm') @override_settings(SPOTSEEKER_AUTH_ADMINS=('demo_user',)) class UWSpotPUTTest(TransactionTestCase): """ Tests updating Spot information via PUT. """ def setUp(self): spot = Spot.objects.create(name="This is for testing PUT") SpotExtendedInfo.objects.create(spot=spot, key="aw_yisss", value="breadcrumbs") spot.save() self.spot = spot url = '/api/v1/spot/{0}'.format(self.spot.pk) self.url = url def test_bad_json(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): c = Client() response = c.put(self.url, 'this is just text', content_type="application/json", If_Match=self.spot.etag) self.assertEquals(response.status_code, 400, "Rejects non-json") def test_invalid_url(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): c = Client() response = c.put("/api/v1/spot/aa", '{}', content_type="application/json") self.assertEquals(response.status_code, 404, "Rejects a non-numeric url") def test_invalid_id_too_high(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): c = Client() test_id = self.spot.pk + 10000 test_url = '/api/v1/spot/{0}'.format(test_id) response = c.put(test_url, '{}', content_type="application/json") self.assertEquals(response.status_code, 404, "Rejects an id that doesn't exist yet (no PUT to create)") def test_empty_json(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): c = Client() response = c.put(self.url, '{}', content_type="application/json", If_Match=self.spot.etag) self.assertEquals(response.status_code, 400, "Rejects an empty body") def test_valid_json_no_etag(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): c = Client() new_name = "testing PUT name: {0}".format(random.random()) new_capacity = 10 response = c.put(self.url, '{{"name":"{0}","capacity":"{1}"}}'.format(new_name, new_capacity), content_type="application/json") self.assertEquals(response.status_code, 400, "Bad request w/o an etag") updated_spot = Spot.objects.get(pk=self.spot.pk) self.assertEquals(updated_spot.name, self.spot.name, "No etag - same name") self.assertEquals(updated_spot.capacity, self.spot.capacity, "no etag - same capacity") def test_valid_json_valid_etag(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): c = Client() new_name = "testing PUT name: {0}".format(random.random()) new_capacity = 20 response = c.get(self.url) etag = response["ETag"] json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"true","has_outlets":"true","manager":"Sam","organization":"UW"}}' % (new_name, new_capacity) response = c.put(self.url, json_string, content_type="application/json", If_Match=etag) self.assertEquals(response.status_code, 200, "Accepts a valid json string") updated_spot = Spot.objects.get(pk=self.spot.pk) self.assertEquals(updated_spot.name, new_name, "a valid PUT changes the name") self.assertEquals(updated_spot.capacity, new_capacity, "a valid PUT changes the capacity") def test_valid_json_outdated_etag(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): c = Client() new_name = "testing PUT name: {0}".format(random.random()) new_capacity = 30 response = c.get(self.url) etag = response["ETag"] intermediate_spot = Spot.objects.get(pk=self.spot.pk) intermediate_spot.name = "This interferes w/ the PUT" intermediate_spot.save() response = c.put(self.url, '{{"name":"{0}","capacity":"{1}"}}'.format(new_name, new_capacity), content_type="application/json", If_Match=etag) self.assertEquals(response.status_code, 409, "An outdated etag leads to a conflict") updated_spot = Spot.objects.get(pk=self.spot.pk) self.assertEquals(updated_spot.name, intermediate_spot.name, "keeps the intermediate name w/ an outdated etag") def test_valid_json_but_invalid_extended_info(self): with self.settings(SPOTSEEKER_AUTH_MODULE='spotseeker_server.auth.all_ok', SPOTSEEKER_SPOT_FORM='spotseeker_server.org_forms.uw_spot.UWSpotForm'): c = Client() new_name = "testing PUT name: {0}".format(random.random()) new_capacity = 20 response = c.get(self.url) etag = response["ETag"] json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"true","has_outlets":"true","has_computers":"true","num_computers":"10","manager":"Sam","organization":"UW"}}' % (new_name, new_capacity) response = c.put(self.url, json_string, content_type="application/json", If_Match=etag) self.assertEquals(response.status_code, 200, "Accepts a valid json string") # test: invalid extended info value response = c.get(self.url) etag = response["ETag"] updated_json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"true","has_outlets":"wub wub wub wu wu wuhhhh WUB WUB WUBBBBUB", "has_computers":"true", "num_computers":"10","manager":"Sam","organization":"UW"}}' % (new_name, new_capacity) response = c.put(self.url, updated_json_string, content_type="application/json", If_Match=etag) self.assertEquals(response.status_code, 400, "Doesn't update spot info with invalid extended info") response = c.get(self.url) self.assertEquals(json.loads(json_string)['extended_info'], json.loads(response.content)['extended_info'], "Doesn't update spot info with invalid extended info") # test: invalid int value invalid_int = "invalid_int" invalid_int_json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"true","has_outlets":"true", "has_computers":"true", "num_computers":"%s","manager":"Sam","organization":"UW"}}' % (new_name, new_capacity, invalid_int) response = c.put(self.url, invalid_int_json_string, content_type="application/json", If_Match=etag) self.assertEquals(response.status_code, 400, "Doesn't update spot info with invalid int value") response = c.get(self.url) self.assertEquals(json.loads(json_string)['extended_info'], json.loads(response.content)['extended_info'], "Doesn't update spot info with invalid int value")

test/uw_spot/spot_put.py

from django.test import TransactionTestCase from django.conf import settings from django.test.client import Client from spotseeker_server.models import Spot, SpotExtendedInfo import simplejson as json import random from django.test.utils import override_settings from mock import patch from django.core import cache from spotseeker_server import models @override_settings(SPOTSEEKER_AUTH_MODULE='spotseeker_server.auth.all_ok') @override_settings(SPOTSEEKER_SPOT_FORM='spotseeker_server.org_forms.uw_spot.UWSpotForm') @override_settings(SPOTSEEKER_SPOTEXTENDEDINFO_FORM='spotseeker_server.org_forms.uw_spot.UWSpotExtendedInfoForm') @override_settings(SPOTSEEKER_AUTH_ADMINS=('demo_user',)) class UWSpotPUTTest(TransactionTestCase): """ Tests updating Spot information via PUT. """ def setUp(self): spot = Spot.objects.create(name="This is for testing PUT") SpotExtendedInfo.objects.create(spot=spot, key="aw_yisss", value="breadcrumbs") spot.save() self.spot = spot url = '/api/v1/spot/{0}'.format(self.spot.pk) self.url = url def test_bad_json(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): c = Client() response = c.put(self.url, 'this is just text', content_type="application/json", If_Match=self.spot.etag) self.assertEquals(response.status_code, 400, "Rejects non-json") def test_invalid_url(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): c = Client() response = c.put("/api/v1/spot/aa", '{}', content_type="application/json") self.assertEquals(response.status_code, 404, "Rejects a non-numeric url") def test_invalid_id_too_high(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): c = Client() test_id = self.spot.pk + 10000 test_url = '/api/v1/spot/{0}'.format(test_id) response = c.put(test_url, '{}', content_type="application/json") self.assertEquals(response.status_code, 404, "Rejects an id that doesn't exist yet (no PUT to create)") def test_empty_json(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): c = Client() response = c.put(self.url, '{}', content_type="application/json", If_Match=self.spot.etag) self.assertEquals(response.status_code, 400, "Rejects an empty body") def test_valid_json_no_etag(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): c = Client() new_name = "testing PUT name: {0}".format(random.random()) new_capacity = 10 response = c.put(self.url, '{{"name":"{0}","capacity":"{1}"}}'.format(new_name, new_capacity), content_type="application/json") self.assertEquals(response.status_code, 400, "Bad request w/o an etag") updated_spot = Spot.objects.get(pk=self.spot.pk) self.assertEquals(updated_spot.name, self.spot.name, "No etag - same name") self.assertEquals(updated_spot.capacity, self.spot.capacity, "no etag - same capacity") def test_valid_json_valid_etag(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): c = Client() new_name = "testing PUT name: {0}".format(random.random()) new_capacity = 20 response = c.get(self.url) etag = response["ETag"] json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"true","has_outlets":"true","manager":"Sam","organization":"UW"}}' % (new_name, new_capacity) response = c.put(self.url, json_string, content_type="application/json", If_Match=etag) self.assertEquals(response.status_code, 200, "Accepts a valid json string") updated_spot = Spot.objects.get(pk=self.spot.pk) self.assertEquals(updated_spot.name, new_name, "a valid PUT changes the name") self.assertEquals(updated_spot.capacity, new_capacity, "a valid PUT changes the capacity") def test_valid_json_outdated_etag(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): c = Client() new_name = "testing PUT name: {0}".format(random.random()) new_capacity = 30 response = c.get(self.url) etag = response["ETag"] intermediate_spot = Spot.objects.get(pk=self.spot.pk) intermediate_spot.name = "This interferes w/ the PUT" intermediate_spot.save() response = c.put(self.url, '{{"name":"{0}","capacity":"{1}"}}'.format(new_name, new_capacity), content_type="application/json", If_Match=etag) self.assertEquals(response.status_code, 409, "An outdated etag leads to a conflict") updated_spot = Spot.objects.get(pk=self.spot.pk) self.assertEquals(updated_spot.name, intermediate_spot.name, "keeps the intermediate name w/ an outdated etag") def test_valid_json_but_invalid_extended_info(self): with self.settings(SPOTSEEKER_AUTH_MODULE='spotseeker_server.auth.all_ok', SPOTSEEKER_SPOT_FORM='spotseeker_server.org_forms.uw_spot.UWSpotForm'): c = Client() new_name = "testing PUT name: {0}".format(random.random()) new_capacity = 20 response = c.get(self.url) etag = response["ETag"] json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"true","has_outlets":"true","has_computers":"true","num_computers":"10","manager":"Sam","organization":"UW"}}' % (new_name, new_capacity) response = c.put(self.url, json_string, content_type="application/json", If_Match=etag) self.assertEquals(response.status_code, 200, "Accepts a valid json string") # test: invalid extended info value response = c.get(self.url) etag = response["ETag"] updated_json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"true","has_outlets":"wub wub wub wu wu wuhhhh WUB WUB WUBBBBUB", "has_computers":"true", "num_computers":"10","manager":"Sam","organization":"UW"}}' % (new_name, new_capacity) response = c.put(self.url, updated_json_string, content_type="application/json", If_Match=etag) self.assertEquals(response.status_code, 400, "Doesn't update spot info with invalid extended info") response = c.get(self.url) self.assertEquals(json.loads(json_string)['extended_info'], json.loads(response.content)['extended_info'], "Doesn't update spot info with invalid extended info") # test: invalid int value invalid_int = "invalid_int" invalid_int_json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"true","has_outlets":"true", "has_computers":"true", "num_computers":"%s","manager":"Sam","organization":"UW"}}' % (new_name, new_capacity, invalid_int) response = c.put(self.url, invalid_int_json_string, content_type="application/json", If_Match=etag) self.assertEquals(response.status_code, 400, "Doesn't update spot info with invalid int value") response = c.get(self.url) self.assertEquals(json.loads(json_string)['extended_info'], json.loads(response.content)['extended_info'], "Doesn't update spot info with invalid int value")

0.549399

0.089694

u""" Created at 2020.01.02 A python wrapper for running the netprophet to replace snakemake """ import os import sys import json import logging from argparse import ArgumentParser, ArgumentError from multiprocessing import Pool from shutil import rmtree from subprocess import check_call, CalledProcessError from tqdm import tqdm from CODE import prepare_resources from CODE import weighted_avg_similar_dbds from CODE import build_motif_network from CODE import combine_networks from CODE import convert_fire2meme from CODE import parse_network_scores from CODE import parse_motif_summary from CODE import parse_quantized_bins logging.basicConfig(level=logging.DEBUG, format="%(asctime)s %(name)-12s %(levelname)-8s %(message)s") def call(cmd): u""" :param cmd: :return: """ with open(os.devnull, "w+") as w: check_call(cmd, shell=True, stdout=w, stderr=w) class SnakeMakePipe(object): u""" """ def __init__(self, path: str, processes: int=1): u""" path to config file :param path: :param processes """ self.processes = processes # self.__root__ = os.path.abspath(os.path.dirname(__file__)) self.__root__ = "/opt/NetProphet_2.0" if not os.path.exists(path) or not os.path.isfile(path): raise FileNotFoundError("Cannot find config file at %s" % path) with open(path) as r: self.config = json.load(r) self.progress = os.path.join(self.config["NETPROPHET2_DIR"], "progress.json") if self.check_progress(11): logging.info("Please remove {} before re-run this pipeline".format(self.progress)) def check_progress(self, step): u""" :param step: :return: """ progress = [] if os.path.exists(self.progress): with open(self.progress) as r: progress = json.load(r) return step in progress def log_progress(self, step): progress = [] if os.path.exists(self.progress): with open(self.progress) as r: progress = json.load(r) if step not in progress: progress.append(step) with open(self.progress, "w+") as w: json.dump(progress, w, indent=4) def step1(self): u""" STEP 1 to create output dir or files :return: """ logging.info("STEP1: make_directories") if self.check_progress(1): logging.info("STEP1: skipped") return paths = [ os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp" ), os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/" ), os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/" ), os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/network_scores/" ), os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/network_bins/" ), os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/motifs_pfm/" ), os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/motifs_score/" ) ] for i in paths: if not os.path.exists(i): logging.info("Create %s" % i) os.makedirs(i) else: logging.info("%s exists" % i) self.log_progress(1) def step2(self): u""" :return: """ logging.info("STEP2: prepare_resources") if not self.check_progress(1): raise FileNotFoundError("Please run STEP1 before run STEP2") else: if not self.check_progress(2): prepare_resources.main([ "-g", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_GENES"] ), "-r", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "-e", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_EXPRESSION_DATA"] ), "-c", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_SAMPLE_CONDITIONS"] ), "-or", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/rdata.expr" ), "-of", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/data.fc.tsv" ), "-oa", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/allowed.adj" ), "-op1", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/data.pert.adj" ), "-op2", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/data.pert.tsv" ), ]) self.log_progress(2) def step3(self): u""" :return: """ logging.info("STEP3: map_np_network") if not self.check_progress(2): raise FileNotFoundError("Please run STEP2 before run STEP3") else: if not self.check_progress(3): check_call( "bash {program} -m -u {input_u} -t {input_t} -r {input_r} -a {input_a} -p {input_p} -d {input_d} -g {input_g} -f {input_f} -o {input_o} -n {output_n}".format(**{ "program": os.path.join(self.__root__, "SRC/NetProphet1/netprophet"), "input_u": os.path.join( self.config["NETPROPHET2_DIR"], "SRC/NetProphet1/"), "input_t": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_EXPRESSION_DATA"]), "input_r": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/rdata.expr" ), "input_a": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/allowed.adj" ), "input_p": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/data.pert.adj" ), "input_d": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_DE_ADJMTR"] ), "input_g": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_GENES"] ), "input_f": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "input_o": os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/" ), "output_n": os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/np.adjmtr" ) }), shell=True ) self.log_progress(3) def step4(self): u""" data_fc_expr=${1} pert_matrix=${2} tf_names=${3} output_adjmtr=${4} use_serial=${5} :return: """ logging.info("STEP4: map_bart_network") if not self.check_progress(3): raise FileNotFoundError("Please run STEP3 before run STEP4") else: if not self.check_progress(4): check_call("Rscript --vanilla {program} fcFile={data_fc_expr} isPerturbedFile={pert_matrix} tfNameFile={tf_names} saveTo={output_adjmtr}.tsv mpiBlockSize={processes}".format(**{ "program": os.path.join(self.__root__, "CODE/build_bart_network.r"), "data_fc_expr": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/data.fc.tsv" ), "pert_matrix": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/data.pert.adj" ), "tf_names": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "output_adjmtr": os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/bn.adjmtr" ), "processes": self.processes }), shell=True) # 推测，这里只是单纯的去除行名和列名 o = os.path.join(self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/bn.adjmtr") with open(o, "w+") as w: with open(o + ".tsv") as r: for idx, line in enumerate(r): if idx > 0: lines = line.split() if len(lines) > 0: w.write("\t".join(lines[1:]) + "\n") self.log_progress(4) def step5(self): u""" :return: """ logging.info("STEP5: weighted_average_np_network") if not self.check_progress(4): raise FileNotFoundError("Please run STEP4 before run STEP5") else: if not self.check_progress(5): weighted_avg_similar_dbds.main([ "-n", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/np.adjmtr" ), "-r", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "-a", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["DBD_PID_DIR"] ), "-d", "50", "-t", "single_dbds", "-o", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/npwa.adjmtr" ) ]) self.log_progress(5) def step6(self): u""" :return: """ logging.info("STEP6: weighted_average_bart_network") if not self.check_progress(5): raise FileNotFoundError("Please run STEP5 before run STEP6") else: if not self.check_progress(6): weighted_avg_similar_dbds.main([ "-n", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/bn.adjmtr" ), "-r", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "-a", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["DBD_PID_DIR"] ), "-d", "50", "-t", "single_dbds", "-o", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/bnwa.adjmtr" ) ]) self.log_progress(6) def step7(self): u""" :return: """ logging.info("STEP7: combine_npwa_bnwa") if not self.check_progress(6): raise FileNotFoundError("Please run STEP6 before run STEP7") else: if not self.check_progress(7): check_call( "Rscript {program} {input_n} {input_b} {output_o}".format(**{ "program": os.path.join(self.__root__, "CODE/quantile_combine_networks.r"), "input_n": os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/npwa.adjmtr" ), "input_b": os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/bnwa.adjmtr" ), "output_o": os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/npwa_bnwa.adjmtr" ) }), shell=True ) self.log_progress(7) def step8(self): u""" ## Check if all motifs are ready bash CODE/check_inference_status.sh ${OUTPUT_DIR}/motif_inference/motif_inference.log $REGULATORS $FLAG :return: """ logging.info("STEP8: infer_motifs") if not self.check_progress(7): raise FileNotFoundError("Please run STEP7 before run STEP8") else: if not self.check_progress(8): logging.info("Binning promoters based on network scores ... ") parse_network_scores.main([ "-a", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/npwa_bnwa.adjmtr" ), "-r", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "-t", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_GENES"] ), "-o", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/network_scores" ) ]) parse_quantized_bins.main([ "-n", "20", "-i", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/network_scores" ), "-o", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/network_bins" ), ]) logging.info("Done") promoter = os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_PROMOTERS"] ) out_dir = os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"] ) tasks = [] with open(os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"]) ) as r: for regulator in r: regulator = regulator.strip() tasks.append( "perl {FIREDIR}/fire.pl --expfiles={OUTDIR} --exptype=discrete --fastafile_dna={PROMOTER} --k=7 --jn=20 --jn_t=16 --nodups=1 --dorna=0 --dodnarna=0".format(**{ "FIREDIR": os.getenv("FIREDIR"), "OUTDIR": os.path.join(out_dir, "motif_inference/network_bins", regulator), "PROMOTER": promoter }) ) try: with Pool(self.processes) as p: list(tqdm(p.imap(call, tasks), total=len(tasks))) except CalledProcessError as err: logging.error(err) exit(1) self.log_progress(8) def step9(self): u""" :return: """ logging.info("STEP9: score_motifs") if not self.check_progress(8): raise FileNotFoundError("Please run STEP8 before run STEP9") else: if not self.check_progress(9): OUTPUT_DIR = os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"] ) MOTIFS_DIR = os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/network_bins/" ) REGULATORS = os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ) PROMOTERS = os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_PROMOTERS"] ) MOTIFS_LIST = os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/motifs.txt" ) logging.info("Parsing motif inference results ... ") parse_motif_summary.main([ "-a", "True", "-i", MOTIFS_DIR, "-o", MOTIFS_LIST ]) convert_fire2meme.main([ "-i", MOTIFS_LIST, "-o", os.path.join(OUTPUT_DIR, "motif_inference/motifs_pfm/") ]) logging.info("Done") # score_motifs $ $PROMOTERS $ ${OUTPUT_DIR}/motif_inference/motif_scoring.log FN_TF_PWM = os.path.join(OUTPUT_DIR, "motif_inference/motifs_pfm/") # directory of tf pwm FN_PROMOTERS = PROMOTERS # promoter sequence file OUT_FIMO = os.path.join(OUTPUT_DIR, "motif_inference/motifs_score") # directory of fimo alignment output tasks1, tasks2, tasks3 = [], [], [] with open(REGULATORS) as r: for regulator in r: regulator = regulator.strip() if not os.path.exists(os.path.join(FN_TF_PWM, regulator)): continue if os.path.exists(os.path.join(OUT_FIMO, regulator)): rmtree(os.path.join(OUT_FIMO, regulator)) os.makedirs(os.path.join(OUT_FIMO, regulator)) tasks1.append("{fimo} -o {OUT_FIMO}/{regulator} --thresh 5e-3 {FN_TF_PWM}/{regulator} {FN_PROMOTERS}".format(**{ "OUT_FIMO": OUT_FIMO, "regulator": regulator, "FN_TF_PWM": FN_TF_PWM, "FN_PROMOTERS": FN_PROMOTERS, "fimo": os.path.join(self.__root__, "SRC/meme/bin/fimo") })) tasks2.append("sed '1d' {OUT_FIMO}/{regulator}/fimo.txt | cut -f 1,2,7 > {OUT_FIMO}/{regulator}/temp.txt".format(**{ "OUT_FIMO": OUT_FIMO, "regulator": regulator, })) tasks3.append("ruby {program} -i {OUT_FIMO}/{regulator}/temp.txt > {OUT_FIMO}/{regulator}.summary".format(**{ "OUT_FIMO": OUT_FIMO, "regulator": regulator, "program": os.path.join(self.__root__, "CODE/estimate_affinity.rb") })) with Pool(self.processes) as p: try: list(tqdm(p.imap(call, tasks1), total=len(tasks1))) except CalledProcessError as err: pass try: list(tqdm(p.imap(call, tasks2), total=len(tasks2))) list(tqdm(p.imap(call, tasks3), total=len(tasks3))) except CalledProcessError as err: logging.error(err) exit(1) self.log_progress(9) def step10(self): u""" :return: """ logging.info("STEP10: build_motif_network") if not self.check_progress(9): raise FileNotFoundError("Please run STEP9 before run STEP10") else: if not self.check_progress(10): build_motif_network.main([ "-i", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/motifs.txt" ), "-r", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "-g", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_GENES"] ), "-f", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/motifs_score/" ), "-t", "robust", "-v", str(self.config["MOTIF_THRESHOLD"]), "-o", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/mn.adjmtr" ) ]) self.log_progress(10) def step11(self): u""" :return: """ logging.info("STEP11: assemble_final_network") if not self.check_progress(10): raise FileNotFoundError("Please run STEP10 before run STEP11") else: if not self.check_progress(11): combine_networks.main([ "-s", "resort", "-n", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/npwa_bnwa.adjmtr" ), "-b", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/mn.adjmtr" ), "-od", os.path.join(self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/"), "-om", "npwa_bnwa_mn.adjmtr" ]) weighted_avg_similar_dbds.main([ "-n", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/", "npwa_bnwa_mn.adjmtr" ), "-r", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "-a", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["DBD_PID_DIR"] ), "-d", "50", "-f", "single_dbds", "-o", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], self.config["FILENAME_NETPROPHET2_NETWORK"] ) ]) self.log_progress(11) if __name__ == '__main__': parser = ArgumentParser(description="NetProphet 2.0") parser.add_argument("-c", "--config", type=str, required=True, help="Path to config file") parser.add_argument("-p", "--processes", type=int, default=1, help="How many cpu to use") if len(sys.argv) <= 1: parser.print_help() else: try: args = parser.parse_args(sys.argv[1:]) if not os.path.exists(args.config) or not os.path.isfile(args.config): print("Please set the correct path to config file") exit(1) root_dir = os.path.abspath(os.path.dirname(__file__)) config = os.path.abspath(args.config) if args.processes <= 0: processes = 1 else: processes = args.processes runner = SnakeMakePipe(args.config, args.processes) runner.step1() runner.step2() runner.step3() runner.step4() runner.step5() runner.step6() runner.step7() runner.step8() runner.step9() runner.step10() runner.step11() except ArgumentError as err: print(err) parser.print_help()

main.py

u""" Created at 2020.01.02 A python wrapper for running the netprophet to replace snakemake """ import os import sys import json import logging from argparse import ArgumentParser, ArgumentError from multiprocessing import Pool from shutil import rmtree from subprocess import check_call, CalledProcessError from tqdm import tqdm from CODE import prepare_resources from CODE import weighted_avg_similar_dbds from CODE import build_motif_network from CODE import combine_networks from CODE import convert_fire2meme from CODE import parse_network_scores from CODE import parse_motif_summary from CODE import parse_quantized_bins logging.basicConfig(level=logging.DEBUG, format="%(asctime)s %(name)-12s %(levelname)-8s %(message)s") def call(cmd): u""" :param cmd: :return: """ with open(os.devnull, "w+") as w: check_call(cmd, shell=True, stdout=w, stderr=w) class SnakeMakePipe(object): u""" """ def __init__(self, path: str, processes: int=1): u""" path to config file :param path: :param processes """ self.processes = processes # self.__root__ = os.path.abspath(os.path.dirname(__file__)) self.__root__ = "/opt/NetProphet_2.0" if not os.path.exists(path) or not os.path.isfile(path): raise FileNotFoundError("Cannot find config file at %s" % path) with open(path) as r: self.config = json.load(r) self.progress = os.path.join(self.config["NETPROPHET2_DIR"], "progress.json") if self.check_progress(11): logging.info("Please remove {} before re-run this pipeline".format(self.progress)) def check_progress(self, step): u""" :param step: :return: """ progress = [] if os.path.exists(self.progress): with open(self.progress) as r: progress = json.load(r) return step in progress def log_progress(self, step): progress = [] if os.path.exists(self.progress): with open(self.progress) as r: progress = json.load(r) if step not in progress: progress.append(step) with open(self.progress, "w+") as w: json.dump(progress, w, indent=4) def step1(self): u""" STEP 1 to create output dir or files :return: """ logging.info("STEP1: make_directories") if self.check_progress(1): logging.info("STEP1: skipped") return paths = [ os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp" ), os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/" ), os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/" ), os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/network_scores/" ), os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/network_bins/" ), os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/motifs_pfm/" ), os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/motifs_score/" ) ] for i in paths: if not os.path.exists(i): logging.info("Create %s" % i) os.makedirs(i) else: logging.info("%s exists" % i) self.log_progress(1) def step2(self): u""" :return: """ logging.info("STEP2: prepare_resources") if not self.check_progress(1): raise FileNotFoundError("Please run STEP1 before run STEP2") else: if not self.check_progress(2): prepare_resources.main([ "-g", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_GENES"] ), "-r", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "-e", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_EXPRESSION_DATA"] ), "-c", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_SAMPLE_CONDITIONS"] ), "-or", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/rdata.expr" ), "-of", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/data.fc.tsv" ), "-oa", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/allowed.adj" ), "-op1", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/data.pert.adj" ), "-op2", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/data.pert.tsv" ), ]) self.log_progress(2) def step3(self): u""" :return: """ logging.info("STEP3: map_np_network") if not self.check_progress(2): raise FileNotFoundError("Please run STEP2 before run STEP3") else: if not self.check_progress(3): check_call( "bash {program} -m -u {input_u} -t {input_t} -r {input_r} -a {input_a} -p {input_p} -d {input_d} -g {input_g} -f {input_f} -o {input_o} -n {output_n}".format(**{ "program": os.path.join(self.__root__, "SRC/NetProphet1/netprophet"), "input_u": os.path.join( self.config["NETPROPHET2_DIR"], "SRC/NetProphet1/"), "input_t": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_EXPRESSION_DATA"]), "input_r": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/rdata.expr" ), "input_a": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/allowed.adj" ), "input_p": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/data.pert.adj" ), "input_d": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_DE_ADJMTR"] ), "input_g": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_GENES"] ), "input_f": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "input_o": os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/" ), "output_n": os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/np.adjmtr" ) }), shell=True ) self.log_progress(3) def step4(self): u""" data_fc_expr=${1} pert_matrix=${2} tf_names=${3} output_adjmtr=${4} use_serial=${5} :return: """ logging.info("STEP4: map_bart_network") if not self.check_progress(3): raise FileNotFoundError("Please run STEP3 before run STEP4") else: if not self.check_progress(4): check_call("Rscript --vanilla {program} fcFile={data_fc_expr} isPerturbedFile={pert_matrix} tfNameFile={tf_names} saveTo={output_adjmtr}.tsv mpiBlockSize={processes}".format(**{ "program": os.path.join(self.__root__, "CODE/build_bart_network.r"), "data_fc_expr": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/data.fc.tsv" ), "pert_matrix": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], "tmp/data.pert.adj" ), "tf_names": os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "output_adjmtr": os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/bn.adjmtr" ), "processes": self.processes }), shell=True) # 推测，这里只是单纯的去除行名和列名 o = os.path.join(self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/bn.adjmtr") with open(o, "w+") as w: with open(o + ".tsv") as r: for idx, line in enumerate(r): if idx > 0: lines = line.split() if len(lines) > 0: w.write("\t".join(lines[1:]) + "\n") self.log_progress(4) def step5(self): u""" :return: """ logging.info("STEP5: weighted_average_np_network") if not self.check_progress(4): raise FileNotFoundError("Please run STEP4 before run STEP5") else: if not self.check_progress(5): weighted_avg_similar_dbds.main([ "-n", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/np.adjmtr" ), "-r", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "-a", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["DBD_PID_DIR"] ), "-d", "50", "-t", "single_dbds", "-o", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/npwa.adjmtr" ) ]) self.log_progress(5) def step6(self): u""" :return: """ logging.info("STEP6: weighted_average_bart_network") if not self.check_progress(5): raise FileNotFoundError("Please run STEP5 before run STEP6") else: if not self.check_progress(6): weighted_avg_similar_dbds.main([ "-n", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/bn.adjmtr" ), "-r", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "-a", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["DBD_PID_DIR"] ), "-d", "50", "-t", "single_dbds", "-o", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/bnwa.adjmtr" ) ]) self.log_progress(6) def step7(self): u""" :return: """ logging.info("STEP7: combine_npwa_bnwa") if not self.check_progress(6): raise FileNotFoundError("Please run STEP6 before run STEP7") else: if not self.check_progress(7): check_call( "Rscript {program} {input_n} {input_b} {output_o}".format(**{ "program": os.path.join(self.__root__, "CODE/quantile_combine_networks.r"), "input_n": os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/npwa.adjmtr" ), "input_b": os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/bnwa.adjmtr" ), "output_o": os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/npwa_bnwa.adjmtr" ) }), shell=True ) self.log_progress(7) def step8(self): u""" ## Check if all motifs are ready bash CODE/check_inference_status.sh ${OUTPUT_DIR}/motif_inference/motif_inference.log $REGULATORS $FLAG :return: """ logging.info("STEP8: infer_motifs") if not self.check_progress(7): raise FileNotFoundError("Please run STEP7 before run STEP8") else: if not self.check_progress(8): logging.info("Binning promoters based on network scores ... ") parse_network_scores.main([ "-a", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/npwa_bnwa.adjmtr" ), "-r", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "-t", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_GENES"] ), "-o", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/network_scores" ) ]) parse_quantized_bins.main([ "-n", "20", "-i", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/network_scores" ), "-o", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/network_bins" ), ]) logging.info("Done") promoter = os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_PROMOTERS"] ) out_dir = os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"] ) tasks = [] with open(os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"]) ) as r: for regulator in r: regulator = regulator.strip() tasks.append( "perl {FIREDIR}/fire.pl --expfiles={OUTDIR} --exptype=discrete --fastafile_dna={PROMOTER} --k=7 --jn=20 --jn_t=16 --nodups=1 --dorna=0 --dodnarna=0".format(**{ "FIREDIR": os.getenv("FIREDIR"), "OUTDIR": os.path.join(out_dir, "motif_inference/network_bins", regulator), "PROMOTER": promoter }) ) try: with Pool(self.processes) as p: list(tqdm(p.imap(call, tasks), total=len(tasks))) except CalledProcessError as err: logging.error(err) exit(1) self.log_progress(8) def step9(self): u""" :return: """ logging.info("STEP9: score_motifs") if not self.check_progress(8): raise FileNotFoundError("Please run STEP8 before run STEP9") else: if not self.check_progress(9): OUTPUT_DIR = os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"] ) MOTIFS_DIR = os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/network_bins/" ) REGULATORS = os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ) PROMOTERS = os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_PROMOTERS"] ) MOTIFS_LIST = os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/motifs.txt" ) logging.info("Parsing motif inference results ... ") parse_motif_summary.main([ "-a", "True", "-i", MOTIFS_DIR, "-o", MOTIFS_LIST ]) convert_fire2meme.main([ "-i", MOTIFS_LIST, "-o", os.path.join(OUTPUT_DIR, "motif_inference/motifs_pfm/") ]) logging.info("Done") # score_motifs $ $PROMOTERS $ ${OUTPUT_DIR}/motif_inference/motif_scoring.log FN_TF_PWM = os.path.join(OUTPUT_DIR, "motif_inference/motifs_pfm/") # directory of tf pwm FN_PROMOTERS = PROMOTERS # promoter sequence file OUT_FIMO = os.path.join(OUTPUT_DIR, "motif_inference/motifs_score") # directory of fimo alignment output tasks1, tasks2, tasks3 = [], [], [] with open(REGULATORS) as r: for regulator in r: regulator = regulator.strip() if not os.path.exists(os.path.join(FN_TF_PWM, regulator)): continue if os.path.exists(os.path.join(OUT_FIMO, regulator)): rmtree(os.path.join(OUT_FIMO, regulator)) os.makedirs(os.path.join(OUT_FIMO, regulator)) tasks1.append("{fimo} -o {OUT_FIMO}/{regulator} --thresh 5e-3 {FN_TF_PWM}/{regulator} {FN_PROMOTERS}".format(**{ "OUT_FIMO": OUT_FIMO, "regulator": regulator, "FN_TF_PWM": FN_TF_PWM, "FN_PROMOTERS": FN_PROMOTERS, "fimo": os.path.join(self.__root__, "SRC/meme/bin/fimo") })) tasks2.append("sed '1d' {OUT_FIMO}/{regulator}/fimo.txt | cut -f 1,2,7 > {OUT_FIMO}/{regulator}/temp.txt".format(**{ "OUT_FIMO": OUT_FIMO, "regulator": regulator, })) tasks3.append("ruby {program} -i {OUT_FIMO}/{regulator}/temp.txt > {OUT_FIMO}/{regulator}.summary".format(**{ "OUT_FIMO": OUT_FIMO, "regulator": regulator, "program": os.path.join(self.__root__, "CODE/estimate_affinity.rb") })) with Pool(self.processes) as p: try: list(tqdm(p.imap(call, tasks1), total=len(tasks1))) except CalledProcessError as err: pass try: list(tqdm(p.imap(call, tasks2), total=len(tasks2))) list(tqdm(p.imap(call, tasks3), total=len(tasks3))) except CalledProcessError as err: logging.error(err) exit(1) self.log_progress(9) def step10(self): u""" :return: """ logging.info("STEP10: build_motif_network") if not self.check_progress(9): raise FileNotFoundError("Please run STEP9 before run STEP10") else: if not self.check_progress(10): build_motif_network.main([ "-i", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/motifs.txt" ), "-r", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "-g", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_GENES"] ), "-f", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "motif_inference/motifs_score/" ), "-t", "robust", "-v", str(self.config["MOTIF_THRESHOLD"]), "-o", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/mn.adjmtr" ) ]) self.log_progress(10) def step11(self): u""" :return: """ logging.info("STEP11: assemble_final_network") if not self.check_progress(10): raise FileNotFoundError("Please run STEP10 before run STEP11") else: if not self.check_progress(11): combine_networks.main([ "-s", "resort", "-n", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/npwa_bnwa.adjmtr" ), "-b", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/mn.adjmtr" ), "-od", os.path.join(self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/"), "-om", "npwa_bnwa_mn.adjmtr" ]) weighted_avg_similar_dbds.main([ "-n", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], "networks/", "npwa_bnwa_mn.adjmtr" ), "-r", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["FILENAME_REGULATORS"] ), "-a", os.path.join( self.config["NETPROPHET2_DIR"], self.config["RESOURCES_DIR"], self.config["DBD_PID_DIR"] ), "-d", "50", "-f", "single_dbds", "-o", os.path.join( self.config["NETPROPHET2_DIR"], self.config["OUTPUT_DIR"], self.config["FILENAME_NETPROPHET2_NETWORK"] ) ]) self.log_progress(11) if __name__ == '__main__': parser = ArgumentParser(description="NetProphet 2.0") parser.add_argument("-c", "--config", type=str, required=True, help="Path to config file") parser.add_argument("-p", "--processes", type=int, default=1, help="How many cpu to use") if len(sys.argv) <= 1: parser.print_help() else: try: args = parser.parse_args(sys.argv[1:]) if not os.path.exists(args.config) or not os.path.isfile(args.config): print("Please set the correct path to config file") exit(1) root_dir = os.path.abspath(os.path.dirname(__file__)) config = os.path.abspath(args.config) if args.processes <= 0: processes = 1 else: processes = args.processes runner = SnakeMakePipe(args.config, args.processes) runner.step1() runner.step2() runner.step3() runner.step4() runner.step5() runner.step6() runner.step7() runner.step8() runner.step9() runner.step10() runner.step11() except ArgumentError as err: print(err) parser.print_help()

0.35209

0.146667

import sys from functools import lru_cache, partial from itertools import combinations import generator_conf import jinja2 def all_combinations(l): return [x for n in range(len(l) + 1) for x in combinations(l, n)] def indent(n, s): return s.replace("\n", "\n" + " " * (n * 4)) def nested_statements(layers, *args, **kwargs): if layers: outer, *inners = layers def inner(depth, *args, **kwargs): s = nested_statements(inners, *args, **kwargs) return indent(depth, s) return outer(inner, *args, **kwargs) raise RuntimeError("The last layer must not call inner.") def run(root_dir, template_file, output_file): def generated_banner(): return "THIS FILE IS GENERATED FROM '{0}'. Make changes to that file instead of this one.".format(template_file) template_env = _get_jinja_environment(root_dir) template_env.globals.update( combinations=combinations, all_combinations=all_combinations, generated_banner=generated_banner, nested_statements=nested_statements, partial=partial, indent=indent, **generator_conf.exports, ) template = template_env.get_template(str(template_file)) output = template.render() if output_file: try: with open(output_file, "rt", encoding="UTF-8") as f: old_output = f.read() except IOError: old_output = None if output == old_output: # print(f"{output_file} does not need to be updated.") return False with open(output_file, "wt", encoding="UTF-8") as f: # print(f"Writing {output_file}.") print(template.render(), file=f, end="") return True else: print(output, end="") return None # Cache jinja environments to allow caching inside the environment. But don't cache many in case they get big. @lru_cache(2) def _get_jinja_environment(root_dir): template_loader = jinja2.FileSystemLoader(searchpath=[str(root_dir), str(f"{root_dir}/katana")]) template_env = jinja2.Environment(loader=template_loader) return template_env if __name__ == "__main__": DIR = sys.argv[1] TEMPLATE_FILE = sys.argv[2] run(DIR, TEMPLATE_FILE, None)

python/generate_from_jinja.py

import sys from functools import lru_cache, partial from itertools import combinations import generator_conf import jinja2 def all_combinations(l): return [x for n in range(len(l) + 1) for x in combinations(l, n)] def indent(n, s): return s.replace("\n", "\n" + " " * (n * 4)) def nested_statements(layers, *args, **kwargs): if layers: outer, *inners = layers def inner(depth, *args, **kwargs): s = nested_statements(inners, *args, **kwargs) return indent(depth, s) return outer(inner, *args, **kwargs) raise RuntimeError("The last layer must not call inner.") def run(root_dir, template_file, output_file): def generated_banner(): return "THIS FILE IS GENERATED FROM '{0}'. Make changes to that file instead of this one.".format(template_file) template_env = _get_jinja_environment(root_dir) template_env.globals.update( combinations=combinations, all_combinations=all_combinations, generated_banner=generated_banner, nested_statements=nested_statements, partial=partial, indent=indent, **generator_conf.exports, ) template = template_env.get_template(str(template_file)) output = template.render() if output_file: try: with open(output_file, "rt", encoding="UTF-8") as f: old_output = f.read() except IOError: old_output = None if output == old_output: # print(f"{output_file} does not need to be updated.") return False with open(output_file, "wt", encoding="UTF-8") as f: # print(f"Writing {output_file}.") print(template.render(), file=f, end="") return True else: print(output, end="") return None # Cache jinja environments to allow caching inside the environment. But don't cache many in case they get big. @lru_cache(2) def _get_jinja_environment(root_dir): template_loader = jinja2.FileSystemLoader(searchpath=[str(root_dir), str(f"{root_dir}/katana")]) template_env = jinja2.Environment(loader=template_loader) return template_env if __name__ == "__main__": DIR = sys.argv[1] TEMPLATE_FILE = sys.argv[2] run(DIR, TEMPLATE_FILE, None)

0.279632

0.126785

from RLTest import Env import random def aofTestCommon(env, reloadfn): # TODO: Change this attribute in rmtest env.cmd('ft.create', 'idx', 'schema', 'field1', 'text', 'field2', 'numeric') reloadfn() for x in range(1, 10): env.assertCmdOk('ft.add', 'idx', 'doc{}'.format(x), 1.0 / x, 'fields', 'field1', 'myText{}'.format(x), 'field2', 20 * x) exp = [9L, 'doc1', ['field1', 'myText1', 'field2', '20'], 'doc2', ['field1', 'myText2', 'field2', '40'], 'doc3', ['field1', 'myText3', 'field2', '60'], 'doc4', ['field1', 'myText4', 'field2', '80'], 'doc5', ['field1', 'myText5', 'field2', '100'], 'doc6', ['field1', 'myText6', 'field2', '120'], 'doc7', ['field1', 'myText7', 'field2', '140'], 'doc8', ['field1', 'myText8', 'field2', '160'], 'doc9', ['field1', 'myText9', 'field2', '180']] reloadfn() ret = env.cmd('ft.search', 'idx', 'myt*') env.assertEqual(exp, ret) def testAof(): env = Env(useAof=True) aofTestCommon(env, lambda: env.restart_and_reload()) def testRawAof(): env = Env(useAof=True) if env.env == 'existing-env': env.skip() aofTestCommon(env, lambda: env.broadcast('debug', 'loadaof')) def testRewriteAofSortables(): env = Env(useAof=True) env.cmd('FT.CREATE', 'idx', 'schema', 'field1', 'TEXT', 'SORTABLE', 'num1', 'NUMERIC', 'SORTABLE') env.cmd('FT.ADD', 'idx', 'doc', 1.0, 'FIELDS', 'field1', 'Hello World') env.restart_and_reload() env.broadcast('SAVE') # Load some documents for x in xrange(100): env.cmd('FT.ADD', 'idx', 'doc{}'.format(x), 1.0, 'FIELDS', 'field1', 'txt{}'.format(random.random()), 'num1', random.random()) for sspec in [('field1', 'asc'), ('num1', 'desc')]: cmd = ['FT.SEARCH', 'idx', 'txt', 'SORTBY', sspec[0], sspec[1]] res = env.cmd(*cmd) env.restart_and_reload() res2 = env.cmd(*cmd) env.assertEqual(res, res2) def testAofRewriteSortkeys(): env = Env(useAof=True) env.cmd('FT.CREATE', 'idx', 'SCHEMA', 'foo', 'TEXT', 'SORTABLE', 'bar', 'TAG') env.cmd('FT.ADD', 'idx', '1', '1', 'FIELDS', 'foo', 'A', 'bar', '1') env.cmd('FT.ADD', 'idx', '2', '1', 'fields', 'foo', 'B', 'bar', '1') res_exp = env.cmd('FT.SEARCH', 'idx', '@bar:{1}', 'SORTBY', 'foo', 'ASC', 'RETURN', '1', 'foo', 'WITHSORTKEYS') env.restart_and_reload() res_got = env.cmd('FT.SEARCH', 'idx', '@bar:{1}', 'SORTBY', 'foo', 'ASC', 'RETURN', '1', 'foo', 'WITHSORTKEYS') env.assertEqual(res_exp, res_got) def testAofRewriteTags(): env = Env(useAof=True) env.cmd('FT.CREATE', 'idx', 'SCHEMA', 'foo', 'TEXT', 'SORTABLE', 'bar', 'TAG') env.cmd('FT.ADD', 'idx', '1', '1', 'FIELDS', 'foo', 'A', 'bar', '1') env.cmd('FT.ADD', 'idx', '2', '1', 'fields', 'foo', 'B', 'bar', '1') info_a = to_dict(env.cmd('FT.INFO', 'idx')) env.restart_and_reload() info_b = to_dict(env.cmd('FT.INFO', 'idx')) env.assertEqual(info_a['fields'], info_b['fields']) # Try to drop the schema env.cmd('FT.DROP', 'idx') # Try to create it again - should work! env.cmd('FT.CREATE', 'idx', 'SCHEMA', 'foo', 'TEXT', 'SORTABLE', 'bar', 'TAG') env.cmd('FT.ADD', 'idx', '1', '1', 'FIELDS', 'foo', 'A', 'bar', '1') env.cmd('FT.ADD', 'idx', '2', '1', 'fields', 'foo', 'B', 'bar', '1') res = env.cmd('FT.SEARCH', 'idx', '@bar:{1}', 'SORTBY', 'foo', 'ASC', 'RETURN', '1', 'foo', 'WITHSORTKEYS') env.assertEqual([2L, '1', '$a', ['foo', 'A'], '2', '$b', ['foo', 'B']], res) def to_dict(r): return {r[i]: r[i + 1] for i in range(0, len(r), 2)}

src/pytest/test_aof.py

from RLTest import Env import random def aofTestCommon(env, reloadfn): # TODO: Change this attribute in rmtest env.cmd('ft.create', 'idx', 'schema', 'field1', 'text', 'field2', 'numeric') reloadfn() for x in range(1, 10): env.assertCmdOk('ft.add', 'idx', 'doc{}'.format(x), 1.0 / x, 'fields', 'field1', 'myText{}'.format(x), 'field2', 20 * x) exp = [9L, 'doc1', ['field1', 'myText1', 'field2', '20'], 'doc2', ['field1', 'myText2', 'field2', '40'], 'doc3', ['field1', 'myText3', 'field2', '60'], 'doc4', ['field1', 'myText4', 'field2', '80'], 'doc5', ['field1', 'myText5', 'field2', '100'], 'doc6', ['field1', 'myText6', 'field2', '120'], 'doc7', ['field1', 'myText7', 'field2', '140'], 'doc8', ['field1', 'myText8', 'field2', '160'], 'doc9', ['field1', 'myText9', 'field2', '180']] reloadfn() ret = env.cmd('ft.search', 'idx', 'myt*') env.assertEqual(exp, ret) def testAof(): env = Env(useAof=True) aofTestCommon(env, lambda: env.restart_and_reload()) def testRawAof(): env = Env(useAof=True) if env.env == 'existing-env': env.skip() aofTestCommon(env, lambda: env.broadcast('debug', 'loadaof')) def testRewriteAofSortables(): env = Env(useAof=True) env.cmd('FT.CREATE', 'idx', 'schema', 'field1', 'TEXT', 'SORTABLE', 'num1', 'NUMERIC', 'SORTABLE') env.cmd('FT.ADD', 'idx', 'doc', 1.0, 'FIELDS', 'field1', 'Hello World') env.restart_and_reload() env.broadcast('SAVE') # Load some documents for x in xrange(100): env.cmd('FT.ADD', 'idx', 'doc{}'.format(x), 1.0, 'FIELDS', 'field1', 'txt{}'.format(random.random()), 'num1', random.random()) for sspec in [('field1', 'asc'), ('num1', 'desc')]: cmd = ['FT.SEARCH', 'idx', 'txt', 'SORTBY', sspec[0], sspec[1]] res = env.cmd(*cmd) env.restart_and_reload() res2 = env.cmd(*cmd) env.assertEqual(res, res2) def testAofRewriteSortkeys(): env = Env(useAof=True) env.cmd('FT.CREATE', 'idx', 'SCHEMA', 'foo', 'TEXT', 'SORTABLE', 'bar', 'TAG') env.cmd('FT.ADD', 'idx', '1', '1', 'FIELDS', 'foo', 'A', 'bar', '1') env.cmd('FT.ADD', 'idx', '2', '1', 'fields', 'foo', 'B', 'bar', '1') res_exp = env.cmd('FT.SEARCH', 'idx', '@bar:{1}', 'SORTBY', 'foo', 'ASC', 'RETURN', '1', 'foo', 'WITHSORTKEYS') env.restart_and_reload() res_got = env.cmd('FT.SEARCH', 'idx', '@bar:{1}', 'SORTBY', 'foo', 'ASC', 'RETURN', '1', 'foo', 'WITHSORTKEYS') env.assertEqual(res_exp, res_got) def testAofRewriteTags(): env = Env(useAof=True) env.cmd('FT.CREATE', 'idx', 'SCHEMA', 'foo', 'TEXT', 'SORTABLE', 'bar', 'TAG') env.cmd('FT.ADD', 'idx', '1', '1', 'FIELDS', 'foo', 'A', 'bar', '1') env.cmd('FT.ADD', 'idx', '2', '1', 'fields', 'foo', 'B', 'bar', '1') info_a = to_dict(env.cmd('FT.INFO', 'idx')) env.restart_and_reload() info_b = to_dict(env.cmd('FT.INFO', 'idx')) env.assertEqual(info_a['fields'], info_b['fields']) # Try to drop the schema env.cmd('FT.DROP', 'idx') # Try to create it again - should work! env.cmd('FT.CREATE', 'idx', 'SCHEMA', 'foo', 'TEXT', 'SORTABLE', 'bar', 'TAG') env.cmd('FT.ADD', 'idx', '1', '1', 'FIELDS', 'foo', 'A', 'bar', '1') env.cmd('FT.ADD', 'idx', '2', '1', 'fields', 'foo', 'B', 'bar', '1') res = env.cmd('FT.SEARCH', 'idx', '@bar:{1}', 'SORTBY', 'foo', 'ASC', 'RETURN', '1', 'foo', 'WITHSORTKEYS') env.assertEqual([2L, '1', '$a', ['foo', 'A'], '2', '$b', ['foo', 'B']], res) def to_dict(r): return {r[i]: r[i + 1] for i in range(0, len(r), 2)}

0.303216

0.361446